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Intro to Biomedical Informatics 701

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Chirag Patel
Chirag Patel

Lecture for 12/1/15 lecture in HMS BMI 701

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Bioinformatics for discovery: Introduction to GWAS and EWAS BMI 701:Introduction to Biomedical Informatics 12/1/2015 chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org Chirag J Patel
P = G + EType 2 Diabetes Cancer Alzheimer’s Gene expression Phenotype Genome Variants Environment Infectious agents Nutrients Pollutants Drugs Complex traits are a function of genes and environment...
We are great at G investigation! over 2000 Genome-wide Association Studies (GWAS) https://www.ebi.ac.uk/gwas/ G
>2,000 traits/diseases >15,000 SNPs >16,000 SNP-trait associations https://www.ebi.ac.uk/gwas/
Dissecting G in P: What is a Genome-wide Association Study? Hypothesis-free β€œsearch engine” for genetic variants associated with a complex trait or disease in unrelated populations SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(Z) SNP(z) diseased non- diseasedgenome-wide
The road to GWAS...
A new paradigm of GWAS for discovery of G in P: Human Genome Project to GWAS Sequencing of the genome 2001 HapMap project: http://hapmap.ncbi.nlm.nih.gov/ Characterize common variation 2001-current day High-throughput variant assay < $99 for ~1M variants Measurement tools ~2003 (ongoing) ARTICLES Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls The Wellcome Trust Case Control Consortium* There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined ,2,000 individuals for each of 7 major diseases and a shared set of ,3,000 controls. Case-control comparisons identified 24 independent association signals at P , 5 3 1027 : 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn’s disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a 25 27 Vol 447|7 June 2007|doi:10.1038/nature05911 Nature 2008 Comprehensive, high-throughput analyses GWAS
Number of raw publications with subject of β€œGWAS” 0 1000 2000 3000 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year NumberofPublications'GWAS' pubmed MeSH terms: human + GWAS
Number of raw publications with subject of β€œGWAS” 0 1000 2000 3000 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year NumberofPublications'GWAS' pubmed MeSH terms: human + GWAS Risch + Merikangas linkage vs. association human genome sequenced GWAS age-related macular degeneration mega-meta-GWAS WTCCC GWAS is relevant today (even with NGS) around the corner
Why execute GWAS?
Geneticists have made substantial progress in identifying the genetic basis of many human diseases, at least those with conspicuous deter- minants.ThesesuccessesincludeHuntington's disease, Alzheimer's disease, and some forms of breast cancer. However, the detection of ge- netic factors for complex diseases-such as schizophrenia, bipolardisorder, anddiabetes- has been far more complicated. There have been numerous reports of genes or loci that might underlie these disorders, butfew ofthese findings have been replicated. The modest na- ture ofthe gene effectsforthese disorders likely explains the contradictory and inconclusive claims about their identification. Despite the small effects of such genes, the magnitude of theirattributable risk (theproportion ofpeople affectedduetothem) maybelargebecause they are quite frequent in the population, making them ofpublic health significance. Has the genetic study ofcomplex disorders reached its limits? The persistent lack of replicability of these reports of linkage be- tween various loci and complex diseases might imply that it has. We argue below that age analysis we have chosen for this argu- ment is a popular current paradigm in which pairs of siblings, both with the disease, are examined for sharing of alleles at multiple sites in the genome defined by genetic mark- ers. The more often the affected siblings share the same allele at a particular site, the more likely the site is close to the disease gene. Using the formulas in (1), we calculate the expected proportion Yofalleles shared by a pair ofaffected siblings for the best possible case-that is, a closely linked marker locus (recombination fraction 0 = 0) that is fully informative (heterozygosity = 1) (2)-as 1 +W wherew= pq(y-1)2 2+w (py+q)2 If there is no linkage of a marker at a particular site to the disease, the siblings would be expected to share alleles 50% ofthe time; that is, Y would equal 0.5. Values of Y for various values ofp and y are given in the third column of the table. For an allele of moderate frequency (p is 0.1 to 0.5) that con- linkage analysis for about 2 or less will ne because the numbe (more than -2500) able. Although testsof est effect are of low above example, direc a disease locus itself To illustrate this poi sion/disequilibrium t In this test, transmis at a locus from heter affected offspring is e lian inheritance, all a chance ofbeing tran eration. In contrast, associated with dise mitted more often th For this approach, with multiple affect just on single affect parents. For the same can calculate the pr parents as pq(y + 1 the probability for a transmit the high ris Association tests ca pairs of affected sibl associatedwithdiseas over 50% is the same the probability ofpar creased at lowvalues the probability ofpar creased. The formula The Future of Genetic Studies of Complex Human Diseases Neil Risch and Kathleen Merikangas onimm, 0In"a0,"a, Geneticists have made substantial progress in identifying the genetic basis of many human diseases, at least those with conspicuous deter- minants.ThesesuccessesincludeHuntington's disease, Alzheimer's disease, and some forms of breast cancer. However, the detection of ge- netic factors for complex diseases-such as schizophrenia, bipolardisorder, anddiabetes- has been far more complicated. There have been numerous reports of genes or loci that might underlie these disorders, butfew ofthese findings have been replicated. The modest na- ture ofthe gene effectsforthese disorders likely explains the contradictory and inconclusive claims about their identification. Despite the small effects of such genes, the magnitude of theirattributable risk (theproportion ofpeople affectedduetothem) maybelargebecause they are quite frequent in the population, making them ofpublic health significance. Has the genetic study ofcomplex disorders reached its limits? The persistent lack of replicability of these reports of linkage be- tween various loci and complex diseases might imply that it has. We argue below that age analysis we have chosen for this ar ment is a popular current paradigm in whi pairs of siblings, both with the disease, examined for sharing of alleles at multip sites in the genome defined by genetic mar ers. The more often the affected sibli share the same allele at a particular site, t more likely the site is close to the dise gene. Using the formulas in (1), we calcul the expected proportion Yofalleles shared a pair ofaffected siblings for the best possi case-that is, a closely linked marker lo (recombination fraction 0 = 0) that is fu informative (heterozygosity = 1) (2)-as 1 +W wherew= pq(y-1)2 2+w (py+q)2 If there is no linkage of a marker at particular site to the disease, the sibli would be expected to share alleles 50% oft time; that is, Y would equal 0.5. Values o for various values ofp and y are given in t third column of the table. For an allele moderate frequency (p is 0.1 to 0.5) that co The Future of Genetic Studies of Complex Human Diseases Neil Risch and Kathleen Merikangas Science, 1996 A new paradigm is needed for discovery!
How does a GWAS work?
Single nucleotide polymorphisms (SNPs): How many SNPs are in the human genome? >3,000,000,000 bases in human genome SNPs appear ~1000 bases ~3,000,000 SNPs 40-60% have minor allele frequency <5% GWAS focus on frequency >5% HapMap Consortium, 2010
Can’t measure everything: Tag SNPs and Linkage Disequilibrium (LD) LD = co-occurance of SNPs in a contiguous region Bush and Moore, 2012
The phenomenon of LD makes GWAS possible: How and why?: Indirect association additional studies to map the precise location of the influential SNP. Conceptually, the end result of GWAS under the common disease/common var- needed to capture the variation African genome. It is important to note that t ogy for measuring genomic Figure 3. Indirect Association. Genotyped SNPs often lie in a region of high linka will be statistically associated with disease as a surrogate for the disease SNP throu doi:10.1371/journal.pcbi.1002822.g003 Bush and Moore, 2012 LD blocks
Can’t measure everything: Tag SNPs and Linkage Disequilibrium Tag SNPs are common proxies for other SNPs 500K - 1M per chip tified significant associations for seven SNPs representing four new T2DM loci (Table 1). In all cases, the strongest association for the MAX statistic (see Methods) was obtained with the additive model. of this gene (Fig. 2a) solely in the secretory final stages of insulin * * * 0 2 4 –log10[P] –log10[P] * 4954642sr 2373971sr 3373971sr 445409sr 8012261sr 3349941sr 883429sr 2019462sr 0349941sr 90350501sr 036169sr 0415007sr 2225991sr 6136642sr 8136642sr 1869646sr 8798751sr 04928201sr 3926642sr 5926642sr 43666231sr 9926642sr 2954642sr 01350501sr 5769646sr 4577187sr 4769646sr 41350501sr 5784931sr 2173387sr 39250501sr 5050007sr 7492602sr 1255051sr 156868sr 4373387sr 4784931sr 7501107sr 2697402sr 91518711sr 6461001sr 29250501sr 5889103sr 8669646sr 0889103sr 4688392sr SLC30A8 IDE 0 2 4 7912381sr 3148707sr 0283856sr 52078111sr 5227373sr 0491242sr 2369412sr 2297881sr 662155sr 7790197sr 44068701sr 35075221sr 5826807sr 7851092sr 9409522sr –log10[P] –log10[P] EXT2 ALX4 0 2 4 *** * 0 2 4 a b c d LD block 2 alleles are correlated because they are inherited together Sladek et al, 2007
image: www.lifa-core.de/ Digitizing SNPs: e.g., Illumina Infinium Array image: illumina.com
Assessing Thousands of Factors Simultaneously: Data-driven search for differences in SNP frequencies ~100,000 - ~1,000,000 association tests disease cases healthy controls GCAGGTACATG...GGTA... GCAGGTACACG...GGTA... GCAGGTACATG...GGTA... GCAGGTACACG...GGTA... GCAGGTACATG...GGTA... GCAGGTACACG...GGTA... disease cases GCAGGTACATG...GGTA... GCAGGTACATG...GGTA... GCAGGTACATG...GGTA... GCAGGTACATG...GGTA... healthy controls
Associating One SNP with Disease Case-Control Study Design DiseaseSNP (A/a) ? A a diseased non- diseased cases controls
Associating One SNP with Disease What is an β€œOdds Ratio”? DiseaseSNP (A/a) ? A a diseased c d non- diseased x y cases controls Chi-squared test Odds Ratio a vs A: Odds of disease with allele a vs. Odds of disease with allele A 1: equal odds (no difference) >1: increased odds (increased risk) <1: decreased odds (decreased risk)
Associating One SNP with Disease Calculating the Odds Ratio DiseaseSNP (A/a) ? A a diseased c d non- diseased x y cases controls Chi-squared test Odds Ratio dx cy y/x d/c [d/(d+y)]/[y/(d+y)] Odds Ratio a vs A: [c/(x+y)]/[x/(c+x)] Odds with allele a Odds with allele A How would you interpret an OR of 2?
Associating One SNP with Disease Cohort Study Design DiseaseSNP (A/a) ? β€’Direct measure of risk vs. odds ratio β€’Need to wait! β€’If incidence is low, N needs to be large! Non-diseasedSNP (A/a) vs. Cox survival regression Relative Risk
Models to associate genotypes with disease Examples for a case-control study Aa AA AA aa Aa AaaaAa Disease Non-diseased ND=4 NC=4
Models to associate genotypes with disease Examples for a case-control study Aa AA AA aa Aa AaaaAa Disease Non-diseased ND=4 NC=4 A a diseased non- diseased 6 2 2 6 OR A (vs a) OR a (vs A)
AA Aa aa diseased non- diseased Models to associate genotypes with disease Genotypic Test (β€œ2 or 1 df test”) Aa AA AA aa Aa AaaaAa Diseased Non-diseased ND=4 NC=4 2 OR AA (vs. Aa) aa (vs. Aa) 2 0 220
Associating One SNP with Quantitative Trait (e.g., height, weight, cholesterol) 40 60 80 100 1 2 3 factor(SNP) trait GG GC CC height SNP rs1234 SNP rs123456 25 50 75 100 125 1 2 3 factor(SNP) trait height CC CT TT
Associating One SNP with Quantitative Trait Linear Regression and Additive Risk Model y=Ι‘+Ξ²x+Ξ΅ 25 50 75 100 125 1 2 3 factor(SNP) trait height CC (0) CT (1) TT (2) SNP rs123456 height = Ι‘+Ξ²x xCC=0 if individual is CC xCT=1 if individual is CT xTT=2 if individual is TT Ι‘ Ξ²: change in height for 1 risk allele T= risk allele Ξ²
Prototypical β€œManhattan plot” to visualize associations Science, 2007 ~100,000 - ~1,000,000 association tests evol part ease tase well biol T capt imp STR reve subs libri clea βˆ’log10(P) 0 5 10 15 Chromosome 22 X 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 80 60 40 100 rvedteststatistic a b NATURE|Vol 447|7 June 2007 AA Aa aa diseased non- diseased
ibility with schizophrenia, a psychotic disorder with many similar- ities to BD. In particular association findings have been reported with assium channel. Ion channelopathies are well-recognized as causes of episodic central nervous system disease, including seizures, ataxias βˆ’log10 (P) 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 Chromosome Type 2 diabetes 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Coronary artery disease Crohn’s disease Hypertension Rheumatoid arthritis Type 1 diabetes Bipolar disorder Figure 4 | Genome-wide scan for seven diseases. For each of seven diseases 2log10 of the trend test P value for quality-control-positive SNPs, excluding Chromosomes are shown in alternating colours for clarity, with P values ,1 3 1025 highlighted in green. All panels are truncated at
Type I Error: False Positives! what is a p-value? chance we attain the observed result if no difference (H0) Many tests: some can be significant (low p-value by chance)! 100 tests at a p-value of 0.05... how many would be significant per chance? Bonferroni β€œcorrection”: Correct the 0.05 significance level by number of tests e.g., 1M SNPs: 0.05/1x10-6 = 5x10-8
QQplot: Distribution of of observed p-values vs. Ho p- values Histogram of runif(10000) runif(10000) Frequency 0.0 0.2 0.4 0.6 0.8 1.0 0100200300400500 p-values under Ho Histogram of gwas$P.value gwas$P.value Frequency 0.0 0.2 0.4 0.6 0.8 1.0 050000100000150000 p-values of GWAS in Total Cholesterol Global Lipids Consortium, 2012random uniform distribution
QQplot: Distribution of of observed p-values vs. Ho p- values Histogram of gwas$P.value gwas$P.value Frequency 0.0 0.2 0.4 0.6 0.8 1.0 050000100000150000 p-values of GWAS in Total Cholesterol
Which diseases show evidence of association? Examining the QQplot of test statistics in WTCCC sent study cannot provideconclusive exclusion of any given gene. This is the consequence of several factors including: less-than-complete coverage of common variation genome-wide on the Affymetrix chip; poor coverage (by design) of rare variants, including many structural variants (thereby reducing power to detect rare, penetrant, alleles)25 ; difficultieswithdefining thefullgenomicextentofthegene ofinterest; and, despite the sample size, relatively low power to detect, at levels of already allow us, for selected diseases, to highlight pathways and mechanisms of particular interest. Naturally, extensive resequencing and fine-mapping work, followed by functional studies will be required before such inferences can be translated into robust state- ments about the molecular and physiological mechanisms involved. We turn now to a discussion of the main findings for each disease, focusing here only on the most significant and interesting results 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 BD Observedteststatistic Expected chi-squared value CAD CD HT RA T2D T1D Figure 3 | Quantile-quantile plots for seven genome-wide scans. For each of the seven disease collections, a quantile-quantile plot of the results of the trend test is shown in black for all SNPs that pass the standard project filters, have a minor allele frequency .1% and missing data rate ,1%. SNPs that 360,000 SNPs. SNPs at which the test statistic exceeds 30 are represented by triangles. Additional quantile-quantile plots, which also exclude all SNPs located in the regions of association listed in Table 3, are superimposed in blue (for BD, the exclusion of these SNPs has no visible effect on the plot, and
Observational associations do not equal causation...
Ice Cream $ Drowning Confounding bias What is a confounder? Summer! ? Confounder is correlated to both the β€œrisk” factor and disease, leading to invalid inference. Common source of bias in observational studies (e.g., case-control, cohort, etc)
SNP Disease Population Stratification: A source of possible confounding in GWAS race/ethnicity ? Ancestry correlated with allele frequency and disease GWAS are done on specific populations separately. (most have been done in populations of European ancestry)
FTO Diabetes Mediation SNPs indicative of a mediator factor? Example: FTO and Type 2 Diabetes Body Mass ? Association between FTO and Type 2 Diabetes via BMI? ... or does FTO have a independent role in Type 2 Diabetes...? FTO Body Mass
PLINK: (Standard) Whole Genome Analysis Software
PLINK: (Standard) Whole Genome Analysis Software http://pngu.mgh.harvard.edu/~purcell/plink/ β€’cited >9000 times since 2007 β€’allele frequency β€’linkage disequilibrium (LD) β€’data manipulation/filtering β€’association: allelic, genotypic models β€’chi-square β€’logistic β€’linear
Examples: GWASs in Type 2 Diabetes
Type 2 Diabetes Mellitus: A complex, multifactorial disease β€’Insulin production vs. use β€’beta-cell function β€’insulin sensitivity (BMI) β€’Moves glucose from blood into cells β€’Complications arise due to glucose in blood, hyperglycemia β€’diagnosed by blood glucose levels CDC, family history: 25% body weight, diet, lifestyle, age
ARTICLES A genome-wide association study identifies novel risk loci for type 2 diabetes Robert Sladek1,2,4 , Ghislain Rocheleau1 *, Johan Rung4 *, Christian Dina5 *, Lishuang Shen1 , David Serre1 , Philippe Boutin5 , Daniel Vincent4 , Alexandre Belisle4 , Samy Hadjadj6 , Beverley Balkau7 , Barbara Heude7 , Guillaume Charpentier8 , Thomas J. Hudson4,9 , Alexandre Montpetit4 , Alexey V. Pshezhetsky10 , Marc Prentki10,11 , Barry I. Posner2,12 , David J. Balding13 , David Meyre5 , Constantin Polychronakos1,3 & Philippe Froguel5,14 Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case–control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing b-cells, and two linkage disequilibrium blocks that contain genes potentially involved in b-cell development or function (IDE–KIF11–HHEX and EXT2–ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits. The rapidly increasing prevalence of type 2 diabetes mellitus (T2DM) is thought to be due to environmental factors, such as increased availabil- ity of food and decreased opportunity and motivation for physical activity, acting on genetically susceptible individuals. The heritability of T2DM is one of the best established among common diseases and, consequently, genetic risk factors for T2DM have been the subject of intense research1 . Although the genetic causes of many monogenic forms of diabetes (maturity onset diabetes in the young, neonatal mito- chondrial and other syndromic types of diabetes mellitus) have been elucidated, few variants leading to common T2DM have been clearly identified and individually confer only a small risk (odds ratio < 1.1– 1.25) of developing T2DM1 . Linkage studies have reported many T2DM-linked chromosomal regions and have identified putative, cau- sative genetic variants in CAPN10 (ref. 2), ENPP1 (ref. 3), HNF4A (refs 4, 5) and ACDC (also called ADIPOQ)6 . In parallel, candidate-gene studieshavereportedmanyT2DM-associatedloci,withcodingvariants in the nuclear receptor PPARG (P12A)7 and the potassium channel KCNJ11 (E23K)8 being among the very few that havebeen convincingly replicated. The strongest known (odds ratio < 1.7) T2DM association9 was recently mapped to the transcription factor TCF7L2 and has been consistently replicated in multiple populations10–20 . Subjects and study design The recent availability of high-density genotyping arrays, which com- bine the power of association studies with the systematic nature of a genome-wide search, led us to undertake a two-stage, genome-wide association study to identify additional T2DM susceptibility loci (Supplementary Fig. 1). In the first stage of this study, we obtained genotypes for 392,935 single-nucleotide polymorphisms (SNPs) in 1,363 T2DM cases and controls (Supplementary Table 1). In order to enrich for risk alleles21 , the diabetic subjects studied in stage 1 were selected to have at least one affected first degree relative and age at onset under 45 yr (excluding patients with maturity onset diabetes in the young). Furthermore, in order to decrease phenotypic hetero- geneity and to enrich for variants determining insulin resistance and b-cell dysfunction through mechanisms other than severe obesity, we initially studied diabetic patients with a body mass index (BMI) ,30 kg m22 . Control subjects were selected to have fasting blood glucose ,5.7 mmol l21 in DESIR, a large prospective cohort for the study of insulin resistance in French subjects22 . Genotypes for each study subject were obtained using two plat- forms: Illumina Infinium Human1 BeadArrays, which assay 109,365 SNPs chosen using a gene-centred design; and Human Hap300 BeadArrays, which assay 317,503 SNPs chosen to tag haplotype blocks identified by the Phase I HapMap23 . Of the 409,927 markers that passed quality control (Supplementary Tables 2 and 3), geno- types were obtained for an average of 99.2% (Human1) and 99.4% (Hap300) of markers for each subject with a reproducibility of .99.9% (both platforms). Forty-three subjects were removed from analysis because of evidence of intercontinental admixture (Sup- plementary Fig. 3) and an additional four because their genotype- determined gender disagreed with clinical records. In total, T2DM association was tested for 100,764 (Human1) and 309,163 (Hap300) SNPs representing 392,935 unique loci (Fig. 1). Because of unequal male/female ratios in our cases and controls, we analysed the 12,666 sex-chromosome SNPs separately for each gender. *These authors contributed equally to this work. 1 Departments of Human Genetics, 2 Medicine and 3 Pediatrics, Faculty of Medicine, McGill University, Montreal H3H 1P3, Canada. 4 McGill University and Genome Quebec Innovation Centre, Montreal H3A 1A4, Canada. 5 CNRS 8090-Institute of Biology, Pasteur Institute, Lille 59019 Cedex, France. 6 Endocrinology and Diabetology, University Hospital, Poitiers 86021 Cedex, France. 7 INSERM U780-IFR69, Villejuif 94807, France. 8 Endocrinology-Diabetology Unit, Corbeil-Essonnes Hospital, Corbeil-Essonnes 91100, France. 9 Ontario Institute for Cancer Research, Toronto M5G 1L7, Canada. 10 Montreal Diabetes Research Center, Montreal H2L 4M1, Canada. 11 Molecular Nutrition Unit and the Department of Nutrition, University of Montreal and the Centre Hospitalier de l’UniversiteΒ΄ de MontreΒ΄al, Montreal H3C 3J7, Canada. 12 Polypeptide Hormone Laboratory and Department of Anatomy and Cell Biology, Montreal H3A 2B2, Canada. 13 Department of Epidemiology & Public Health, Imperial College, St Mary’s Campus, Norfolk Place, London W2 1PG, UK. 14 Section of Genomic Medicine, Imperial College London W12 0NN, and Hammersmith Hospital, Du Cane Road, London W12 0HS, UK. 881 NatureΒ©2007 Publishing Group Nature, 2/2007 References and Notes 1. B. G. Richmond, D. S. Strait, Nature 404, 382 (2000). 2. J. Kingdon, Lowly Origins (Princeton Univ. Press, Princeton, NJ, 2003). 3. C. V. Ward, M. G. Leakey, A. Walker, Evol. Anthropol. 7, 197 (1999). 4. Y. Haile-Selassie, Nature 412, 178 (2001). 5. T. D. White et al., Nature 440, 883 (2006). 6. K. Kovarovic, P. Andrews, J. Hum. Evol., in press (available at http://dx.doi.org./doi:10.1016/j.jhevol.2007.01.001; doi: 10.1016/j.jhevol.2007.01.001). 7. N. Patterson, D. J. Richter, S. Gnerre, E. S. Lander, D. Reich, Nature 441, 1103 (2006). 8. K. D. Hunt et al., Primates 37, 363 (1996). 9. J. G. Fleagle et al., Symp. Zool. Soc. London 48, 359 (1981). 10. R. H. Crompton et al., Cour. Forsch-Inst. Senckenb. 243, 115 (2003). 11. J. T. Stern, Yrb. Phys. Anthropol. 19, 59 (1975). 12. S. K. S. Thorpe, R. H. Crompton, Am. J. Phys. Anthropol. 131, 384 (2006). 13. K. D. Hunt, J. Hum. Evol. 26, 183 (1994). 15. E. Larney, S. Larsen, Am. J. Phys. Anthropol. 125, 42 (2004). 16. S. K. S. Thorpe, R. H. Crompton, Am. J. Phys. Anthropol. 127, 58 (2005). 17. S. K. S. Thorpe, R. H. Crompton, M. M. Gunther, R. F. Ker, R. McN. Alexander, Am. J. Phys. Anthropol. 110, 179 (1999). 18. R. McN. Alexander, Principles of Animal Locomotion (Princeton Univ. Press, Princeton, NJ, 2003). 19. C. V. Ward, Yrbk. Phys. Anthropol. 45, 185 (2002). 20. R. W. Wrangham, N. L. Conklin-Brittain, K. D. Hunt, Int. J. Primatol. 19, 949 (1998). 21. H. Pontzer, R. W. Wrangham, J. Hum. Evol. 46, 317 (2004). 22. R. C. Payne et al., J. Anat. 208, 709 (2006). 23. M. Pickford, B. Senut, B. Gommery, in Late Cenozoic Environments and Hominid Evolution: a Tribute to Bill Bishop, P. Andrews, P. Banham, Eds. (Geological Society, London, 1999), pp. 27–38. 24. N. M. Young, L. MacLatchy, J. Hum. Evol. 46, 163 (2004). 25. D. Gommery, B. Senu, M. Pickford, E. Musiime, Ann. PalΓ©ontol. 88, 167 (2002). 26. C. V. Ward, in Handbook of Paleoanthropology Vol. 2: Primate Evolution and Human Origins, W. Henke, I. Tattersall, Eds. (Springer, Heidelberg, Germany, 2007), pp. 1011–1030. N. Ogihara, M. Nakatsukasa, Eds. (Springer, Heidelberg, Germany, 2006), pp. 199–208. 28. C. P. E. Zollikofer et al., Nature 434, 755 (2005). 29. M. Pickford, Anthropologie 69, 191 (2005). 30. We thank the Indonesian Institute of Science, Indonesian Nature Conservation Service, and Leuser Development Programme for granting permission and giving support for research in the Leuser Ecosystem. R. McN. Alexander, T. M. Blackburn, S. Burtles. J. Rees, N. Jeffery, E. E. Vereecke, A. Walker, A. Wilson, and B. Wood commented on the manuscript. R. Savage developed the animation (fig. S1). Studies of captive animals were hosted by the North of England Zoological Society. This research was supported by grants from the Leverhulme Trust, the Royal Society, the L.S.B. Leakey Foundation, and the Natural Environment Research Council. Supporting Online Material www.sciencemag.org/cgi/content/full/316/5829/1328/DC1 Table S1 Movies S1 to S3 5 February 2007; accepted 18 April 2007 10.1126/science.1140799 Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes for BioMedical Research*† New strategies for prevention and treatment of type 2 diabetes (T2D) require improved insight into disease etiology. We analyzed 386,731 common single-nucleotide polymorphisms (SNPs) in 1464 patients with T2D and 1467 matched controls, each characterized for measures of glucose metabolism, lipids, obesity, and blood pressure. With collaborators (FUSION and WTCCC/UKT2D), we identified and confirmed three loci associated with T2Dβ€”in a noncoding region near CDKN2A and CDKN2B, in an intron of IGF2BP2, and an intron of CDKAL1β€”and replicated associations near HHEX and in SLC30A8 found by a recent whole-genome association study. We identified and confirmed association of a SNP in an intron of glucokinase regulatory protein (GCKR) with serum triglycerides. The discovery of associated variants in unsuspected genes and outside coding regions illustrates the ability of genome-wide association studies to provide potentially important clues to the pathogenesis of common diseases. T ype 2 diabetes, obesity, and cardiovascular risk factors are caused by a combination of genetic susceptibility, environment, be- havior, and chance. Whole-genome association studies (WGAS) offer a new approach to gene discovery unbiased with regard to presumed functions or locations of causal variants. This approach is based on Fisher’s theory for additive effects at common alleles (1); human heterozy- to purifying selection, and has been made pos- sible by genomic advances such as the human genome sequence, SNP and HapMap databases, and genotyping arrays (3). We studied 1464 patients with T2D and 1467 controls from Finland and Sweden, each characterized for 18 clinical traits: anthropomet- ric measures, glucose tolerance and insulin se- cretion, lipids and apolipoproteins, and blood applying stringent quality-control filters, high- quality genotypes for 386,731 common SNPs were obtained (4). To extend the set of putative causal alleles tested for association, we devel- oped 284,968 additional multimarker (haplo- type) tests based on these SNP genotypes (5, 6). The 671,699 allelic tests capture (correlation co- efficient r2 β‰₯ 0.8) 78% of common SNPs in HapMap CEU (3). Each SNP and haplotype test was assessed for association to T2D and each of 18 traits with the software package PLINK (http://pngu.mgh. harvard.edu/purcell/plink/). For T2D, a weighted meta-analysis was used to combine results for the population-based and family-based subsam- ples (4). For quantitative traits, multivariable linear or logistic regression with or without co- variates was performed (4). Association results for each SNP, haplotype test, and phenotype are available (www.broad.mit.edu/diabetes/). In genome-wide analysis involving hundreds of thousands of statistical tests, modest levels of bias imposed on the null distribution can over- whelm a small number of true results. We used three strategies to search for evidence of sys- tematic bias from unrecognized population struc- ture, the analytical approach, and genotyping artifacts (7, 8). First, we examined the distribu- tion of P-values in the population-based sam- ple, observing a close match to that expected for a null distribution (genomic inflation factor lGC = 1.05 for T2D). Second, we calculated G. Brice,6 B. Bullman,7 J. Campbell,8 B. Castle,9 R. Cetnarsyj,8 C. Chapman,10 C. Chu,11 N. Coates,12 T. Cole,10 R. Davidson,4 A. Donaldson,13 H. Dorkins,3 F. Douglas,2 D. Eccles,9 R. Eeles,1 F. Elmslie,6 D. G. Evans,7 S. Goff,6 S. Goodman,5 D. Goudie,2 J. Gray,15 L. Greenhalgh,16 H. Gregory,17 S. V. Hodgson,6 T. Homfray,6 R. S. Houlston,1 L. Izatt,18 L. Jackson,18 L. Jeffers,19 V. Johnson-Roffey,12 F. Kavalier,18 C. Kirk,19 F. Lalloo,7 C. Langman,18 I. Locke,1 M. Longmuir,4 J. Mackay,20 A. Magee,19 S. Mansour,6 Z. Miedzybrodzka,17 J. Miller,11 P. Morrison,19 V. Murday,4 J. Paterson,21 G. Pichert,18 M. Porteous,8 N. Rahman,6 M. Rogers,15 S. Rowe,22 S. Shanley,1 A. Saggar,6 G. Scott,2 L. Side,23 L. Snadden,4 M. Steel,2 M. Thomas,5 S. Thomas,1 1 Clinical Genetics Service, Royal Marsden Hospital, Downs Road, Sutton, Surrey, SM2 5PT, UK. 2 Department of Clinical Genetics, Ninewells Hospital, Dundee, DD1 9SY, UK. 3 Medical and Community Genetics, Kennedy-Galton Centre, Level 8V, Northwick Park and St. Mark’s NHS Trust, Watford Rd, Harrow, HA1 3UJ, UK. 4 Institute of Medical Genetics, Yorkhill NHS Trust, Dalnair Street, Glasgow, G3 8SJ, UK. 5 Clinical Genetics Department, Royal Devon and Exeter Hospital (Heavitree), Gladstone Road, Exeter, EX1 2ED, UK. 6 Department of Clinical Genetics, St. George’s Hospital Medical School, Jenner Wing, Cranmer Terrace, London, SW17 0RE, UK. 7 Department of Medical Genetics, St. Mary’s Hospital, Hathersage Road, Manchester, M13 0JH, UK. 8 South East of Scotland Clinical Genetics Service, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK. 9 Department of Medical Genetics, The Princess Anne Hospital, Coxford Road, Southampton, S016 5YA, UK. 10 Clinical Genetics Unit, Birmingham Women’s Hospital, Metchley Park Road, Edgbaston, Birmingham, B15 2TG, UK. 11 Yorkshire Regional Genetic Service, Department of Clinical Genetics, Cancer Genetics Building, St. James University Hospital, Beckett Street, Leeds, LS9 7TF, UK. 12 Department of Clinical Genetics, Leicester Royal Infirm- ary, Leicester, LE1 5WW, UK. 13 Department of Clinical Genetics, St Michael’s Hospital, Southwell Street, Bristol, BS2 8EG, UK. 14 Institute of Human Genetics, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK. 15 Institute of Medical Genetics, University Hospital of Wales, Heath Park, Cardiff, CF14 4XW, UK. 16 Department of Clinical Genetics, Alder Hey Children’s Hospital, Eaton Road, Liverpool L12 2AP, UK. 17 Clinical Genetics Centre, Argyll House, Foresterhill, Aberdeen, AB25 2ZR, UK. 18 Clinical Genetics, 7th Floor New Guy’s House, Guy’s UK. 19 Clinical Belvoir Park H 20 Clinical and Health, 30 G 21 Department Trust, Box 13 22 Department of Chester Ho 23 Department Road, Headin Supporting www.sciencema Materials and Figs. S1 to S8 Tables S1 to S References 9 March 2007 Published onli 10.1126/scien Include this in A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants Laura J. Scott,1 Karen L. Mohlke,2 Lori L. Bonnycastle,3 Cristen J. Willer,1 Yun Li,1 William L. Duren,1 Michael R. Erdos,3 Heather M. Stringham,1 Peter S. Chines,3 Anne U. Jackson,1 Ludmila Prokunina-Olsson,3 Chia-Jen Ding,1 Amy J. Swift,3 Narisu Narisu,3 Tianle Hu,1 Randall Pruim,4 Rui Xiao,1 Xiao-Yi Li,1 Karen N. Conneely,1 Nancy L. Riebow,3 Andrew G. Sprau,3 Maurine Tong,3 Peggy P. White,1 Kurt N. Hetrick,5 Michael W. Barnhart,5 Craig W. Bark,5 Janet L. Goldstein,5 Lee Watkins,5 Fang Xiang,1 Jouko Saramies,6 Thomas A. Buchanan,7 Richard M. Watanabe,8,9 Timo T. Valle,10 Leena Kinnunen,10,11 GonΓ§alo R. Abecasis,1 Elizabeth W. Pugh,5 Kimberly F. Doheny,5 Richard N. Bergman,9 Jaakko Tuomilehto,10,11,12 Francis S. Collins,3 * Michael Boehnke1 * Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge. Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs. We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls. We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the ria (8). We ciation with the log-odd (8). We ob versus 31.6 P values < against the with a large consistent w SNPs that also sugges trols by birt successful; genomic co Analysi allowed us variation in portion, w (8, 13) that equilibrium Centre d’E (Utah resid 1 Department Genetics, Uni USA. 2 Depar Science, 6/2007 Study design: Richa Saxena1–6 and Valeriya Lyssenko7 (Team Leaders), Peter Almgren,7 Paul I. W. de Bakker,1–6 NoΓ«l P. Burtt,1 Jose C. Florez,1–6 Hong Chen,8 Joanne Meyer,8 Joel N. Hirschhorn,1,6,9–11 Mark J. Daly,1–3,5 Thomas E. Hughes,8 Leif Groop,7,12 David Altshuler1–6 (Chair) Clinical characterization and phenotypes: Valeriya Lyssenko7 and Richa Saxena1–6 (Team Leaders), Peter Almgren,7 Kristin Ardlie,1 Kristina Bengtsson BostrΓΆm,13 NoΓ«l P. Burtt,1 Hong Chen,8 Jose C. Florez,1–6 Bo Isomaa,14,15 Sekar Kathiresan,1,3,5 Guillaume Lettre,1,6,9–11 Ulf Lindblad,16 Helen N. Lyon,1,6,9–11 Olle Melander,7 Christopher Newton-Cheh,1–3,5 Peter Nilsson,17 Marju Orho- Melander,7 Lennart RΓ₯stam,16 Elizabeth K. Speliotes,1,3,6,9–11 Marja-Riitta Taskinen,12 Tiinamaija Tuomi,12,15 Benjamin F. Voight,1–3,5 David Altshuler,1–6 Joel N. Hirschhorn,1,6,9–11 Thomas E. Hughes,8 Leif Groop7,12 (Chair) DNA sample QC and diabetes replication genotyping: Candace Guiducci1 and Valeriya Lyssenko7 (Team Leaders), Anna Berglund,7 Joyce Carlson,18 Lauren Gianniny,1 Rachel Hackett,1 Liselotte Hall,18 Johan Holmkvist,7 Esa Laurila,7 Marju Orho-Melander,7 Marketa SjΓΆgren,7 Maria Sterner,18 Aarti Surti1 Margareta Svensson,7 Malin Svensson,7 Ryan Tewhey,1 NoΓ«l P. Burtt1 (Chair) Whole genome scan genotyping: Brendan Blumenstiel1 (Team Leader), Melissa Parkin,1 Matthew DeFelice,1 Candace Guiducci,1 Ryan Tewhey,1 Rachel Barry,1 Wendy Brodeur,1 NoΓ«l P. Burtt,1 Jody Camarata,1 Nancy Chia,1 Mary Fava,1 John Gibbons,1 Bob Handsaker,1 Claire Healy,1 Kieu Nguyen,1 Casey Gates,1 Carrie Sougnez,1 Diane Gage,1 Marcia Nizzari,1 David Altshuler,1–6 Stacey B. Gabriel1 (Chair) GCKR replication genotyping and analysis (MalmΓΆ Diet and Cancer Study): Sekar Kathiresan1,3,5 (Team Leader), Candace Guiducci,1 Aarti Surti,1 NoΓ«l P. Burtt,1 Olle Melander,7 Marju Orho-Melander7 (Chair) Statistical analysis: Benjamin F. Voight1–3,5 and Paul I. W. de Bakker1–6 (Team Leaders), Richa Saxena,1–6 Valeriya Lyssenko,7 Peter Almgren,7 NoΓ«l P. Burtt,1 Hong Chen,8 Gung-Wei Chirn,8 Qicheng Ma,8 Hemang Parikh,7 Delwood Richardson,8 Darrell Ricke,8 Jeffrey J. Roix,8 Leif Groop,7,12 Shaun Purcell,1,2 David Altshuler,1–6 Mark J. Daly1–3,5 (Chair) 1 Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA. 2 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA. 3 Department of Medicine, Mas- sachusetts General Hospital, Boston, MA 02114, USA. 4 Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA. 5 Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. 6 Depart- ment of Genetics, Harvard Medical School, Boston, MA 02115, USA. 7 Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 8 Diabetes and Metabolism Disease Area, Novartis Institutes for BioMedical Research, 100 Technology Square, Cambridge, MA 02139, USA. 9 Depart- ment of Pediatrics, Harvard Medical School, Boston, MA 02115, USA. 10 Division of Endocrinology, Children’s Hospital, Boston, MA 02115, USA. 11 Division of Genetics, Children’s Hospital, Boston, MA 02115, USA. 12 Department of Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland. 13 Skaraborg Institute, SkΓΆvde, Sweden. 14 Malmska Municipal Health Center and Hospital, Jakobstad, Finland. 15 FolkhΓ€lsan Research Center, Helsinki, Finland. 16 Depart- ment of Clinical Sciences, Community Medicine Research Unit, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 17 Department of Clinical Sciences, Medicine Research Unit, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 18 Clinical Chemistry, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 19 Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA. Supporting Online Material www.sciencemag.org/cgi/content/full/1142358/DC1 Materials and Methods Figs. S1 and S2 Tables S1 to S6 References 9 March 2007; accepted 20 April 2007 Published online 26 April 2007; 10.1126/science.1142358 Include this information when citing this paper. Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for Type 2 Diabetes Eleftheria Zeggini,1,2 * Michael N. Weedon,3,4 * Cecilia M. Lindgren,1,2 * Timothy M. Frayling,3,4 * Katherine S. Elliott,2 Hana Lango,3,4 Nicholas J. Timpson,2,5 John R. B. Perry,3,4 Nigel W. Rayner,1,2 Rachel M. Freathy,3,4 Jeffrey C. Barrett,2 Beverley Shields,4 Andrew P. Morris,2 Sian Ellard,4,6 Christopher J. Groves,1 Lorna W. Harries,4 Jonathan L. Marchini,7 Katharine R. Owen,1 Beatrice Knight,4 Lon R. Cardon,2 Mark Walker,8 Graham A. Hitman,9 Andrew D. Morris,10 Alex S. F. Doney,10 The Wellcome Trust Case Control Consortium (WTCCC),† Mark I. McCarthy,1,2 ‑§ Andrew T. Hattersley3,4 ‑ The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of Here, we describe how integration of data from the WTCCC scan and our own replication studies with similar information generated by the Diabetes Genetics Initiative (DGI) (6) and the Finland–United States Investigation of NIDDM Genetics (FUSION) (7) has identified several additional susceptibility variants for T2D. In the WTCCC study, analysis of 490,032 autosomal SNPs in 16,179 samples yielded 459,448 SNPs that passed initial quality control (5). We considered only the 393,453 autosomal SNPs with minor allele frequency (MAF) ex- ceeding 1% in both cases and controls and no extreme departure from Hardy-Weinberg equi- librium (P < 10βˆ’4 in cases or controls) (8). This T2D-specific data set shows no evidence of sub- stantial confounding from population substruc- ture and genotyping biases (8). To distinguish true associations from those reflecting fluctuations under the null or residual errors arising from aberrant allele calling, we first submitted putative signals from the WTCCC study to additional quality control, including cluster- plot visualization and validation genotyping on REPORTS onFebruary8,2010www.sciencemag.orgDownloadedfrom
ARTICLES A genome-wide association study identifies novel risk loci for type 2 diabetes Robert Sladek1,2,4 , Ghislain Rocheleau1 *, Johan Rung4 *, Christian Dina5 *, Lishuang Shen1 , David Serre1 , Philippe Boutin5 , Daniel Vincent4 , Alexandre Belisle4 , Samy Hadjadj6 , Beverley Balkau7 , Barbara Heude7 , Guillaume Charpentier8 , Thomas J. Hudson4,9 , Alexandre Montpetit4 , Alexey V. Pshezhetsky10 , Marc Prentki10,11 , Barry I. Posner2,12 , David J. Balding13 , David Meyre5 , Constantin Polychronakos1,3 & Philippe Froguel5,14 Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case–control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing b-cells, and two linkage disequilibrium blocks that contain genes potentially involved in b-cell development or function (IDE–KIF11–HHEX and EXT2–ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits. The rapidly increasing prevalence of type 2 diabetes mellitus (T2DM) is thought to be due to environmental factors, such as increased availabil- ity of food and decreased opportunity and motivation for physical activity, acting on genetically susceptible individuals. The heritability of T2DM is one of the best established among common diseases and, consequently, genetic risk factors for T2DM have been the subject of intense research1 . Although the genetic causes of many monogenic forms of diabetes (maturity onset diabetes in the young, neonatal mito- chondrial and other syndromic types of diabetes mellitus) have been elucidated, few variants leading to common T2DM have been clearly identified and individually confer only a small risk (odds ratio < 1.1– 1.25) of developing T2DM1 . Linkage studies have reported many T2DM-linked chromosomal regions and have identified putative, cau- sative genetic variants in CAPN10 (ref. 2), ENPP1 (ref. 3), HNF4A (refs genotypes for 392,935 single-nucleotide polymorphisms (SNPs) in 1,363 T2DM cases and controls (Supplementary Table 1). In order to enrich for risk alleles21 , the diabetic subjects studied in stage 1 were selected to have at least one affected first degree relative and age at onset under 45 yr (excluding patients with maturity onset diabetes in the young). Furthermore, in order to decrease phenotypic hetero- geneity and to enrich for variants determining insulin resistance and b-cell dysfunction through mechanisms other than severe obesity, we initially studied diabetic patients with a body mass index (BMI) ,30 kg m22 . Control subjects were selected to have fasting blood glucose ,5.7 mmol l21 in DESIR, a large prospective cohort for the study of insulin resistance in French subjects22 . Genotypes for each study subject were obtained using two plat- Sladek, 2007How many SNPs (p-value?) European-based; N ~ 1000 cases: high fasting blood glucose/non-obese controls: non-obese
Human Hap300 chip, showing no T2DM association in stage 1 (P . 0.01) and separated by at least 100 kb. Using the first principal component as a covariate for ancestry differences between cases and controls, we tested for association between rs932206 and disease status. Our result suggests that this apparent association is largely BMI on the association between marker and disease, as it is asymp- totically equivalent to the Armitage trend test used to detect asso- ciation in stages 1 and 2. None of the associations (Supplementary Table 7) was substantially changed by considering the effects of these covariates. 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 15 10 5 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19 20 21 22 X 18 Figure 1 | Graphical summary of stage 1 association results. T2DM association was determined for SNPs on the Human1 and Hap300 chips. The x axis represents the chromosome position from pter; the y axis shows 2log10[pMAX], the P-value obtained by the MAX statistic, for each SNP (Note the different scale on the y axis of the chromosome 10 plot.). SNPs that passed the cutoff for a fast-tracked second stage are highlighted in red. 882 NatureΒ©2007 Publishing Group Sladek, 2007
Identification of four novel T2DM loci Our fast-track stage 2 genotyping confirmed the reported association for rs7903146 (TCF7L2) on chromosome 10, and in addition iden- tified significant associations for seven SNPs representing four new T2DM loci (Table 1). In all cases, the strongest association for the MAX statistic (see Methods) was obtained with the additive model. The most significant of these corresponds to rs13266634, a non- synonymous SNP (R325W) in SLC30A8, located in a 33-kb linkage disequilibrium block on chromosome 8, containing only the 39 end of this gene (Fig. 2a). SLC30A8 encodes a zinc transporter expressed solely in the secretory vesicles of b-cells and is thus implicated in the final stages of insulin biosynthesis, which involve co-crystallization Table 1 | Confirmed association results SNP Chromosome Position (nucleotides) Risk allele Major allele MAF (case) MAF (ctrl) Odds ratio (het) Odds ratio (hom) PAR ls Stage 2 pMAX Stage 2 pMAX (perm) Stage 1 pMAX Stage 1 pMAX (perm) Nearest gene rs7903146 10 114,748,339 T C 0.406 0.293 1.65 6 0.19 2.77 6 0.50 0.28 1.0546 1.5 3 10234 ,1.0 3 1027 3.2 3 10217 ,3.3 3 10210 TCF7L2 rs13266634 8 118,253,964 C C 0.254 0.301 1.18 6 0.25 1.53 6 0.31 0.24 1.0089 6.1 3 1028 5.0 3 1027 2.1 3 1025 1.8 3 1025 SLC30A8 rs1111875 10 94,452,862 G G 0.358 0.402 1.19 6 0.19 1.44 6 0.24 0.19 1.0069 3.0 3 1026 7.4 3 1026 9.1 3 1026 7.3 3 1026 HHEX rs7923837 10 94,471,897 G G 0.335 0.377 1.22 6 0.21 1.45 6 0.25 0.20 1.0065 7.5 3 1026 2.2 3 1025 3.4 3 1026 2.5 3 1026 HHEX rs7480010 11 42,203,294 G A 0.336 0.301 1.14 6 0.13 1.40 6 0.25 0.08 1.0041 1.1 3 1024 2.9 3 1024 1.5 3 1025 1.2 3 1025 LOC387761 rs3740878 11 44,214,378 A A 0.240 0.272 1.26 6 0.29 1.46 6 0.33 0.24 1.0046 1.2 3 1024 2.8 3 1024 1.8 3 1025 1.3 3 1025 EXT2 rs11037909 11 44,212,190 T T 0.240 0.271 1.27 6 0.30 1.47 6 0.33 0.25 1.0045 1.8 3 1024 4.5 3 1024 1.8 3 1025 1.3 3 1025 EXT2 rs1113132 11 44,209,979 C C 0.237 0.267 1.15 6 0.27 1.36 6 0.31 0.19 1.0044 3.3 3 1024 8.1 3 1024 3.7 3 1025 2.9 3 1025 EXT2 Significant T2DM associations were confirmed for eight SNPs in five loci. Allele frequencies, odds ratios (with 95% confidence intervals) and PAR were calculated using only the stage 2 data. Allele frequencies in the controls were very close to those reported for the CEU set (European subjects genotyped in the HapMap project). Induced sibling recurrent risk ratios (ls) were estimated using stage 2 genotype counts for the control subjects and assuming a T2DM prevalence of 7% in the French population. hom, homozygous; het, heterozygous; major allele, the allele with the higher frequency in controls; pMAX, P-value of the MAX statistic from the x2 distribution; pMAX (perm), P-value of the MAX statistic from the permutation-derived empirical distribution (pMAX and pMAX (perm) are adjusted for variance inflation); risk allele, the allele with higher frequency in cases compared with controls. 0 2 4 –log10[P] –log10[P] SLC30A8 IDE HHEXKIF11 0 2 4 a b NATURE|Vol 445|22 February 2007 ARTICLES Sladek, 2007 5 3 1 5 3 1 15 10 5 1 1 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 3 4 5 8 9 10 13 14 15 19 20 X 18 DM 2log10[pMAX], the P-value obtained by the MAX statistic, for each SNP How would you interpret the p- values? Odds ratios? Confirmed 8 SNPs with N ~ 1000
Scaling up discovery by combining populations: meta-analyses
g the Diabetes Genetics nvestigation of NIDDM nd (iv) the Framingham omponent studies (n ΒΌ ry Table 1 online. aring, the four consortia n 10 and 20 SNPs promi- their individual, interim, mentary Table 2 online). oci with consistent effects dies. Two of these repre- 6PC2 and GCK. In addi- nerated evidence for an NPs around the MTNR1B rs1387153, P ΒΌ 2.2 Γ‚ 10Γ€11; DFS: rs10830963, 5.8 Γ‚ 10Γ€4, for the most ch analysis). The associa- d on formal meta-analysis r exclusion of individuals ΒΌ 1.1 Γ‚ 10Γ€57; rs4607517 NR1B), P ΒΌ 3.2 Γ‚ 10Γ€50; pplementary Table 3 and ent efforts to harmonize (including the additional data from the WTCCC, DGI and FUSION scans)10 (Supplementary Note). We found strong evidence that the minor G allele of rs10830963 was associated with increased risk of T2D (odds ratio ΒΌ 1.09 (1.05–1.12), P ΒΌ 3.3 Γ‚ 10Γ€7; Fig. 2 and Supplementary Table 6 online). The possibility that the fasting glucose association might DGI Study ID OR (95% CI) Weight (%) 1.12 (0.96, 1.30) 4.61 4.89 8.03 9.58 3.53 8.75 2.69 6.04 10.56 23.18 2.85 7.41 7.90 100.00 1.20 (1.03, 1.39) 1.07 (0.95, 1.20) 1.14 (1.03, 1.27) 1.00 (0.84, 1.19) 1.17 (1.04, 1.30) 1.07 (0.88, 1.31) 1.16 (1.02, 1.33) 1.00 (0.90, 1.10) 1.03 (0.96, 1.10) 0.91 (0.75, 1.10) 1.15 (1.02, 1.30) 1.16 (1.03, 1.30) 1.09 (1.05, 1.12) Meta-analysis P value = 3.3 Γ— 10 –7 FUSION WTCCC deCODE KORA Rotterdam CCC ADDITION/ELY Norfolk UKT2DGC OxGN/58BC FUSION Stage 2 METSIM .722 1 1.39 Overall (I 2 = 26.6%, P = 0.176) Figure 2 Association of rs10830963 with type 2 diabetes (T2D) in 13 case- control studies. VOLUME 41 [ NUMBER 1 [ JANUARY 2009 NATURE GENETICS Meta-analysis of SNP rs10830963: Combining findings from multiple cohorts Propenko, 2009
A RT I C L E S By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 Γ— 10βˆ’8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits. Type 2 diabetes (T2D) is characterized by insulin resistance and deficient beta-cell function1. The escalating prevalence of T2D and the limitations of currently available preventative and therapeutic options highlight the need for a more complete understanding of T2D pathogenesis. To date, approximately 25 genome-wide significant common variant associations with T2D have been described, mostly through genome-wide association (GWA) analyses2–13. The identities of the variants and genes mediating the susceptibility effects at most of these signals have yet to be established, and the known variants account for less than 10% of the overall estimated genetic contribution to T2D predisposition. Although some of the unexplained heritability will reflect variants poorly captured by existing GWA platforms, we reasoned that an expanded meta-analysis of existing GWA data would the inverse-variance method (Online Methods, Fig. 1, Supplementary Tables 1 and 2 and Supplementary Note). We observed only modest genomic control inflation ( gc = 1.07), suggesting that the observed results were not due to population stratification. After removing SNPs within established T2D loci (Supplementary Table 3), the result- ing quantile-quantile plot was consistent with a modest excess of disease associations of relatively small effect (Supplementary Note). Weak evidence for association at HLA variants strongly associated with autoimmune forms of diabetes (Supplementary Table 3 and Supplementary Note) suggested some case admixture involving subjects with type 1 diabetes or latent autoimmune diabetes of adult- hood; however, failure to detect T2D associations at other non-HLA type 1 diabetes susceptibility loci (for example, INS, PTPN22 and Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis Voight, 2010 Meta-analyses for T2D: N>40K and 90K identifies >30 loci among 2,400,000 SNPs
A RT I C L E S 13 autosomal loci exceeded the threshold for genome-wide significance (P ranging from 2.8 Γ— 10βˆ’8 to 1.4 Γ— 10βˆ’22) with allele-specific odds (r2 < 0.05), and conditional analyses (see below) establish these SNPs as independent (Fig. 2 and Supplementary Table 4). Further analysis 50 Locus established previously Locus identified by current study Locus not confirmed by current study BCL11A THADA NOTCH2 ADAMTS9 IRS1 IGF2BP2 WFS1 ZBED3 CDKAL1 HHEX/IDE KCNQ1 (2 signals*: ) TCF7L2 KCNJ11 CENTD2 MTNR1B HMGA2 ZFAND6 PRC1 FTO HNF1B DUSP9 Conditional analysis Unconditional analysis TSPAN8/LGR5 HNF1A CDC123/CAMK1D CHCHD9 CDKN2A/2B SLC30A8 TP53INP1 JAZF1 KLF14 PPAR 40 30 –log10(P)–log10(P) 20 10 10 1 2 3 4 5 6 7 8 Chromosome 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X 0 0 Suggestive statistical association (P < 1 10 –5 ) Association in identified or established region (P < 1 10 –4 ) Figure 1 Genome-wide Manhattan plots for the DIAGRAM+ stage 1 meta-analysis. Top panel summarizes the results of the unconditional meta- analysis. Previously established loci are denoted in red and loci identified by the current study are denoted in green. The ten signals in blue are those taken forward but not confirmed in stage 2 analyses. The genes used to name signals have been chosen on the basis of proximity to the index SNP and should not be presumed to indicate causality. The lower panel summarizes the results of equivalent meta-analysis after conditioning on 30 previously established and newly identified autosomal T2D-associated SNPs (denoted by the dotted lines below these loci in the upper panel). Newly discovered conditional signals (outside established loci) are denoted with an orange dot if they show suggestive levels of significance (P < 10βˆ’5), whereas secondary signals close to already confirmed T2D loci are shown in purple (P < 10βˆ’4). Meta-analyses for T2D: N>40K and 90K identifies >30 loci among 2,400,000 SNPs
0 20 40 60 80 100 recombinationrate(cM/Mb) ●●● ●● ●● ●●● ● ● ● ●●● ● ●●●●● ● ● ● ●●● ●● ●● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ●●●●● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ● ●●● ●●● ● ● ● ● ● ● ●●●●● ●●●● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ●●● ●● ●● ● ●● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ●● ● ●● ● ●●● ● ● ● ● ● ●●●● ● ● ●● ● ● ●●●●● ● ● 2 βˆ’> PGCP 98 SLC30A8 Region 0 2 4 6 8 10 βˆ’log10(Pβˆ’value) 0 20 40 60 80 100 recombinationrate(cM/Mb) rs3802177 ●●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ● ●●● ●● ● ●●●●●● ● ●●● ● ● ● ● ● ● ●● ●●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ● 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● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ●● ● ●● ● ● ●●● ● ● ● ● ● ●● rs10965250 stage 1 ● r^2: 0.8 βˆ’ 1.0 ● r^2: 0.6 βˆ’ 0.8 ● r^2: 0.4 βˆ’ 0.6 ● r^2: 0.2 βˆ’ 0.4 ● r^2: 0.0 βˆ’ 0.2 ● r^2 missing <βˆ’ MLLT3 KIAA1797 βˆ’> <βˆ’ PTPLAD2 <βˆ’ IFNB1 <βˆ’ IFNW1 <βˆ’ IFNA21 <βˆ’ IFNA4 <βˆ’ IFNA7 <βˆ’ IFNA13 MTAP βˆ’> <βˆ’ CDKN2A <βˆ’ CDKN2B DMRTA1 βˆ’> <βˆ’ ELAVL2 21 22 23 24 Position on chromosome 9 (Mb) 40 60 80 100 recombinationrate(c CDC123/CAMK1D Region 4 6 8 10 log10(Pβˆ’value) 40 60 80 100 recombinationrate(c rs12779790 ●●● ● ● ●● ● rs12779790 stage 1 ● r^2: 0.8 βˆ’ 1.0 ● r^2: 0.6 βˆ’ 0.8 ● r^2: 0.4 βˆ’ 0.6 ● r^2: 0.2 βˆ’ 0.4 ● r^2: 0.0 βˆ’ 0.2 ● r^2 missing HHEX/IDE Region 10 15 log10(Pβˆ’value) 40 60 80 100 recombinationrate(c rs5015480 ● ● ● ● ● ●● ● ● ● ●●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●● rs5015480 stage 1 ● r^2: 0.8 βˆ’ 1.0 ● r^2: 0.6 βˆ’ 0.8 ● r^2: 0.4 βˆ’ 0.6 ● r^2: 0.2 βˆ’ 0.4 ● r^2: 0.0 βˆ’ 0.2 ● r^2 missing .609 Not in a gene...In a gene... ~90% of GWAS hits are non-coding!
pporting!Figures! ! ! ~90% of GWAS hits are non-coding! Stamatoyannopoulos, Science 2012 Systematic Localization of Common Disease-Associated Variation in Regulatory DNA Matthew T. Maurano,1 * Richard Humbert,1 * Eric Rynes,1 * Robert E. Thurman,1 Eric Haugen,1 Hao Wang,1 Alex P. Reynolds,1 Richard Sandstrom,1 Hongzhu Qu,1,2 Jennifer Brody,3 Anthony Shafer,1 Fidencio Neri,1 Kristen Lee,1 Tanya Kutyavin,1 Sandra Stehling-Sun,1 Audra K. Johnson,1 Theresa K. Canfield,1 Erika Giste,1 Morgan Diegel,1 Daniel Bates,1 R. Scott Hansen,4 Shane Neph,1 Peter J. Sabo,1 Shelly Heimfeld,5 Antony Raubitschek,6 Steven Ziegler,6 Chris Cotsapas,7,8 Nona Sotoodehnia,3,9 Ian Glass,10 Shamil R. Sunyaev,11 Rajinder Kaul,4 John A. Stamatoyannopoulos1,12 † Genome-wide association studies have identified many noncoding variants associated with common diseases and traits. We show that these variants are concentrated in regulatory DNA marked by deoxyribonuclease I (DNase I) hypersensitive sites (DHSs). Eighty-eight percent of such DHSs are active during fetal development and are enriched in variants associated with gestational exposure–related phenotypes. We identified distant gene targets for hundreds of variant-containing DHSs that may explain phenotype associations. Disease-associated variants systematically perturb transcription factor recognition sequences, frequently alter allelic chromatin states, and form regulatory networks. We also demonstrated tissue-selective enrichment of more weakly disease-associated variants within DHSs and the de novo identification of pathogenic cell types for Crohn’s disease, multiple sclerosis, and an electrocardiogram trait, without prior knowledge of physiological mechanisms. Our results suggest pervasive involvement of regulatory DNA variation in common human disease and provide pathogenic insights into diverse disorders. D isease- and trait-associated genetic variants are rapidly being identified with genome- wide association studies (GWAS) and re- lated strategies (1). To date, hundreds of GWAS have been conducted, spanning diverse diseases and quantitative phenotypes (2) (fig. S1A). How- ever, the majority (~93%) of disease- and trait- associated variants emerging from these studies lie within noncoding sequence (fig. S1B), com- plicating their functional evaluation. Several lines of evidence suggest the involvement of a propor- tion of such variants in transcriptional regulatory mechanisms, including modulation of promoter and enhancer elements (3–6) and enrichment with- in expression quantitative trait loci (eQTL) (3, 7, 8). Human regulatory DNA encompasses a vari- ety of cis-regulatory elements within which the co- operative binding of transcription factors creates focal alterations in chromatin structure. Deoxy- ribonuclease I (DNase I) hypersensitive sites (DHSs) are sensitive and precise markers of this actuated regulatory DNA, and DNase I mapping has been instrumental in the discovery and census of hu- man cis-regulatory elements (9). We performed DNase I mapping genome-wide (10) in 349 cell and tissue samples, including 85 cell types studied under the ENCODE Project (10) and 264 sam- ples studied under the Roadmap Epigenomics Program (11). These encompass several classes nome. In total, we identified 3,899,693 distinct DHS positions along the genome (collectively spanning 42.2%), each of which was detected in one or more cell or tissue types (median = 5). Disease- and trait-associated variants are concentrated in regulatory DNA. We examined the distribution of 5654 noncoding genome-wide significant associations [5134 unique single- nucleotide polymorphisms (SNPs); fig. S1 and table S2] for 207 diseases and 447 quantitative traits (2) with the deep genome-scale maps of regulatory DNA marked by DHSs. This revealed a collective 40% enrichment of GWAS SNPs in DHSs (fig. S1C, P < 10βˆ’55 , binomial, compared to the distribution of HapMap SNPs). Fully 76.6% of all noncoding GWAS SNPs either lie within a DHS (57.1%, 2931 SNPs) or are in complete linkage disequilibrium (LD) with SNPs in a near- by DHS (19.5%, 999 SNPs) (Fig. 1A) (12). To con- firm this enrichment, we sampled variants from the 1000 Genomes Project (13) with the same ge- nomic feature localization (intronic versus inter- genic), distance from the nearest transcriptional start site, and allele frequency in individuals of European ancestry. We confirmed significant en- richment both for SNPs within DHSs (P < 10βˆ’59 , simulation) and also including variants in com- plete LD (r 2 = 1) with SNPs in DHSs (P < 10βˆ’37 , simulation) (fig. S2). In total, 47.5% of GWAS SNPs fall within gene bodies (fig. S1B); however, only 10.9% of intronic GWAS SNPs within DHSs are in strong LD (r2 β‰₯ 0.8) with a coding SNP, indicating that the vast majority of noncoding genic variants are not simply tagging coding sequence. Analo- gously, only 16.3% of GWAS variants within coding sequences are in strong LD with variants in DHSs. SNPs on widely used genotyping arrays (e.g., Affymetrix) were modestly enriched with- in DHSs (fig. S2), possibly due to selection of SNPs with robust experimental performance in genotyping assays. However, we found no evi- dence for sequence composition bias (table S3). To further examine the enrichment of GWAS SNPs in regulatory DNA, we systematically clas- sified all noncoding GWAS SNPs by the quality 1 Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. 2 Laboratory of Disease Genomics RESEARCH ARTICLE onSeptember12,2012www.sciencemag.orgDownloadedfrom
There have been few, if any, similar bursts of discovery in the history of medical research. David Hunter and Peter Kraft, NEJM, 2007
Common claims discussed in regards to GWAS: Despite issues, yielded many discoveries vs. cost to a doubling of the number of associated variants discov- ered. The proportion of genetic variation explained by significantly associated SNPs is usually low (typically less than 10%) for many complex traits, but for diseases such as CD and multiple sclerosis (MS [MIM 126200]), and for quantitative traits such as height and lipid traits, between Figure 1. GWAS Discoveries over Time Data obtained from the Published GWAS Catalog (see Web Resources). Only the top SNPs representing loci with association p values < 5 3 10Γ€8 are included, and so that multiple counting is avoided, SNPs identified for the same traits with LD r2 > 0.8 esti- mated from the entire HapMap samples are excluded. ~500,000 SNP chips x ~$500/chip = $250M Five years of GWAS Discovery (Visscher, 2012) $250M / ~2000 loci = $125K/locus Candidate genes: >$250M! 100 NIH R01s Fighter jet Hadron Collider: $9B
P = G + EType 2 Diabetes Cancer Alzheimer’s Gene expression Phenotype Genome Variants Environment Infectious agents Nutrients Pollutants Drugs Complex traits are a function of genes and environment...
Nothing comparable to elucidate E influence! We lack high-throughput methods and data to discover new E in P… E: ???
A similar paradigm for discovery should exist for E! Why?
Οƒ2 P = Οƒ2 G + Οƒ2 E
Οƒ2 G Οƒ2 P H2 = Heritability (H2) is the range of phenotypic variability attributed to genetic variability in a population Indicator of the proportion of phenotypic differences attributed to G.
Height is an example of a heritable trait: Francis Galton shows how its done (1887) β€œmid-height of 205 parents described 60% of variability of 928 offspring”
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Intro to Biomedical Informatics 701

  • 1. Bioinformatics for discovery: Introduction to GWAS and EWAS BMI 701:Introduction to Biomedical Informatics 12/1/2015 chirag@hms.harvard.edu @chiragjp www.chiragjpgroup.org Chirag J Patel
  • 2. P = G + EType 2 Diabetes Cancer Alzheimer’s Gene expression Phenotype Genome Variants Environment Infectious agents Nutrients Pollutants Drugs Complex traits are a function of genes and environment...
  • 3. We are great at G investigation! over 2000 Genome-wide Association Studies (GWAS) https://www.ebi.ac.uk/gwas/ G
  • 4. >2,000 traits/diseases >15,000 SNPs >16,000 SNP-trait associations https://www.ebi.ac.uk/gwas/
  • 5. Dissecting G in P: What is a Genome-wide Association Study? Hypothesis-free β€œsearch engine” for genetic variants associated with a complex trait or disease in unrelated populations SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(A) SNP(a) diseased non- diseased SNP(Z) SNP(z) diseased non- diseasedgenome-wide
  • 6. The road to GWAS...
  • 7. A new paradigm of GWAS for discovery of G in P: Human Genome Project to GWAS Sequencing of the genome 2001 HapMap project: http://hapmap.ncbi.nlm.nih.gov/ Characterize common variation 2001-current day High-throughput variant assay < $99 for ~1M variants Measurement tools ~2003 (ongoing) ARTICLES Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls The Wellcome Trust Case Control Consortium* There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined ,2,000 individuals for each of 7 major diseases and a shared set of ,3,000 controls. Case-control comparisons identified 24 independent association signals at P , 5 3 1027 : 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn’s disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a 25 27 Vol 447|7 June 2007|doi:10.1038/nature05911 Nature 2008 Comprehensive, high-throughput analyses GWAS
  • 8. Number of raw publications with subject of β€œGWAS” 0 1000 2000 3000 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year NumberofPublications'GWAS' pubmed MeSH terms: human + GWAS
  • 9. Number of raw publications with subject of β€œGWAS” 0 1000 2000 3000 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year NumberofPublications'GWAS' pubmed MeSH terms: human + GWAS Risch + Merikangas linkage vs. association human genome sequenced GWAS age-related macular degeneration mega-meta-GWAS WTCCC GWAS is relevant today (even with NGS) around the corner
  • 11. Geneticists have made substantial progress in identifying the genetic basis of many human diseases, at least those with conspicuous deter- minants.ThesesuccessesincludeHuntington's disease, Alzheimer's disease, and some forms of breast cancer. However, the detection of ge- netic factors for complex diseases-such as schizophrenia, bipolardisorder, anddiabetes- has been far more complicated. There have been numerous reports of genes or loci that might underlie these disorders, butfew ofthese findings have been replicated. The modest na- ture ofthe gene effectsforthese disorders likely explains the contradictory and inconclusive claims about their identification. Despite the small effects of such genes, the magnitude of theirattributable risk (theproportion ofpeople affectedduetothem) maybelargebecause they are quite frequent in the population, making them ofpublic health significance. Has the genetic study ofcomplex disorders reached its limits? The persistent lack of replicability of these reports of linkage be- tween various loci and complex diseases might imply that it has. We argue below that age analysis we have chosen for this argu- ment is a popular current paradigm in which pairs of siblings, both with the disease, are examined for sharing of alleles at multiple sites in the genome defined by genetic mark- ers. The more often the affected siblings share the same allele at a particular site, the more likely the site is close to the disease gene. Using the formulas in (1), we calculate the expected proportion Yofalleles shared by a pair ofaffected siblings for the best possible case-that is, a closely linked marker locus (recombination fraction 0 = 0) that is fully informative (heterozygosity = 1) (2)-as 1 +W wherew= pq(y-1)2 2+w (py+q)2 If there is no linkage of a marker at a particular site to the disease, the siblings would be expected to share alleles 50% ofthe time; that is, Y would equal 0.5. Values of Y for various values ofp and y are given in the third column of the table. For an allele of moderate frequency (p is 0.1 to 0.5) that con- linkage analysis for about 2 or less will ne because the numbe (more than -2500) able. Although testsof est effect are of low above example, direc a disease locus itself To illustrate this poi sion/disequilibrium t In this test, transmis at a locus from heter affected offspring is e lian inheritance, all a chance ofbeing tran eration. In contrast, associated with dise mitted more often th For this approach, with multiple affect just on single affect parents. For the same can calculate the pr parents as pq(y + 1 the probability for a transmit the high ris Association tests ca pairs of affected sibl associatedwithdiseas over 50% is the same the probability ofpar creased at lowvalues the probability ofpar creased. The formula The Future of Genetic Studies of Complex Human Diseases Neil Risch and Kathleen Merikangas onimm, 0In"a0,"a, Geneticists have made substantial progress in identifying the genetic basis of many human diseases, at least those with conspicuous deter- minants.ThesesuccessesincludeHuntington's disease, Alzheimer's disease, and some forms of breast cancer. However, the detection of ge- netic factors for complex diseases-such as schizophrenia, bipolardisorder, anddiabetes- has been far more complicated. There have been numerous reports of genes or loci that might underlie these disorders, butfew ofthese findings have been replicated. The modest na- ture ofthe gene effectsforthese disorders likely explains the contradictory and inconclusive claims about their identification. Despite the small effects of such genes, the magnitude of theirattributable risk (theproportion ofpeople affectedduetothem) maybelargebecause they are quite frequent in the population, making them ofpublic health significance. Has the genetic study ofcomplex disorders reached its limits? The persistent lack of replicability of these reports of linkage be- tween various loci and complex diseases might imply that it has. We argue below that age analysis we have chosen for this ar ment is a popular current paradigm in whi pairs of siblings, both with the disease, examined for sharing of alleles at multip sites in the genome defined by genetic mar ers. The more often the affected sibli share the same allele at a particular site, t more likely the site is close to the dise gene. Using the formulas in (1), we calcul the expected proportion Yofalleles shared a pair ofaffected siblings for the best possi case-that is, a closely linked marker lo (recombination fraction 0 = 0) that is fu informative (heterozygosity = 1) (2)-as 1 +W wherew= pq(y-1)2 2+w (py+q)2 If there is no linkage of a marker at particular site to the disease, the sibli would be expected to share alleles 50% oft time; that is, Y would equal 0.5. Values o for various values ofp and y are given in t third column of the table. For an allele moderate frequency (p is 0.1 to 0.5) that co The Future of Genetic Studies of Complex Human Diseases Neil Risch and Kathleen Merikangas Science, 1996 A new paradigm is needed for discovery!
  • 12. How does a GWAS work?
  • 13. Single nucleotide polymorphisms (SNPs): How many SNPs are in the human genome? >3,000,000,000 bases in human genome SNPs appear ~1000 bases ~3,000,000 SNPs 40-60% have minor allele frequency <5% GWAS focus on frequency >5% HapMap Consortium, 2010
  • 14. Can’t measure everything: Tag SNPs and Linkage Disequilibrium (LD) LD = co-occurance of SNPs in a contiguous region Bush and Moore, 2012
  • 15. The phenomenon of LD makes GWAS possible: How and why?: Indirect association additional studies to map the precise location of the influential SNP. Conceptually, the end result of GWAS under the common disease/common var- needed to capture the variation African genome. It is important to note that t ogy for measuring genomic Figure 3. Indirect Association. Genotyped SNPs often lie in a region of high linka will be statistically associated with disease as a surrogate for the disease SNP throu doi:10.1371/journal.pcbi.1002822.g003 Bush and Moore, 2012 LD blocks
  • 16. Can’t measure everything: Tag SNPs and Linkage Disequilibrium Tag SNPs are common proxies for other SNPs 500K - 1M per chip tified significant associations for seven SNPs representing four new T2DM loci (Table 1). In all cases, the strongest association for the MAX statistic (see Methods) was obtained with the additive model. of this gene (Fig. 2a) solely in the secretory final stages of insulin * * * 0 2 4 –log10[P] –log10[P] * 4954642sr 2373971sr 3373971sr 445409sr 8012261sr 3349941sr 883429sr 2019462sr 0349941sr 90350501sr 036169sr 0415007sr 2225991sr 6136642sr 8136642sr 1869646sr 8798751sr 04928201sr 3926642sr 5926642sr 43666231sr 9926642sr 2954642sr 01350501sr 5769646sr 4577187sr 4769646sr 41350501sr 5784931sr 2173387sr 39250501sr 5050007sr 7492602sr 1255051sr 156868sr 4373387sr 4784931sr 7501107sr 2697402sr 91518711sr 6461001sr 29250501sr 5889103sr 8669646sr 0889103sr 4688392sr SLC30A8 IDE 0 2 4 7912381sr 3148707sr 0283856sr 52078111sr 5227373sr 0491242sr 2369412sr 2297881sr 662155sr 7790197sr 44068701sr 35075221sr 5826807sr 7851092sr 9409522sr –log10[P] –log10[P] EXT2 ALX4 0 2 4 *** * 0 2 4 a b c d LD block 2 alleles are correlated because they are inherited together Sladek et al, 2007
  • 17. image: www.lifa-core.de/ Digitizing SNPs: e.g., Illumina Infinium Array image: illumina.com
  • 18. Assessing Thousands of Factors Simultaneously: Data-driven search for differences in SNP frequencies ~100,000 - ~1,000,000 association tests disease cases healthy controls GCAGGTACATG...GGTA... GCAGGTACACG...GGTA... GCAGGTACATG...GGTA... GCAGGTACACG...GGTA... GCAGGTACATG...GGTA... GCAGGTACACG...GGTA... disease cases GCAGGTACATG...GGTA... GCAGGTACATG...GGTA... GCAGGTACATG...GGTA... GCAGGTACATG...GGTA... healthy controls
  • 19. Associating One SNP with Disease Case-Control Study Design DiseaseSNP (A/a) ? A a diseased non- diseased cases controls
  • 20. Associating One SNP with Disease What is an β€œOdds Ratio”? DiseaseSNP (A/a) ? A a diseased c d non- diseased x y cases controls Chi-squared test Odds Ratio a vs A: Odds of disease with allele a vs. Odds of disease with allele A 1: equal odds (no difference) >1: increased odds (increased risk) <1: decreased odds (decreased risk)
  • 21. Associating One SNP with Disease Calculating the Odds Ratio DiseaseSNP (A/a) ? A a diseased c d non- diseased x y cases controls Chi-squared test Odds Ratio dx cy y/x d/c [d/(d+y)]/[y/(d+y)] Odds Ratio a vs A: [c/(x+y)]/[x/(c+x)] Odds with allele a Odds with allele A How would you interpret an OR of 2?
  • 22. Associating One SNP with Disease Cohort Study Design DiseaseSNP (A/a) ? β€’Direct measure of risk vs. odds ratio β€’Need to wait! β€’If incidence is low, N needs to be large! Non-diseasedSNP (A/a) vs. Cox survival regression Relative Risk
  • 23. Models to associate genotypes with disease Examples for a case-control study Aa AA AA aa Aa AaaaAa Disease Non-diseased ND=4 NC=4
  • 24. Models to associate genotypes with disease Examples for a case-control study Aa AA AA aa Aa AaaaAa Disease Non-diseased ND=4 NC=4 A a diseased non- diseased 6 2 2 6 OR A (vs a) OR a (vs A)
  • 25. AA Aa aa diseased non- diseased Models to associate genotypes with disease Genotypic Test (β€œ2 or 1 df test”) Aa AA AA aa Aa AaaaAa Diseased Non-diseased ND=4 NC=4 2 OR AA (vs. Aa) aa (vs. Aa) 2 0 220
  • 26. Associating One SNP with Quantitative Trait (e.g., height, weight, cholesterol) 40 60 80 100 1 2 3 factor(SNP) trait GG GC CC height SNP rs1234 SNP rs123456 25 50 75 100 125 1 2 3 factor(SNP) trait height CC CT TT
  • 27. Associating One SNP with Quantitative Trait Linear Regression and Additive Risk Model y=Ι‘+Ξ²x+Ξ΅ 25 50 75 100 125 1 2 3 factor(SNP) trait height CC (0) CT (1) TT (2) SNP rs123456 height = Ι‘+Ξ²x xCC=0 if individual is CC xCT=1 if individual is CT xTT=2 if individual is TT Ι‘ Ξ²: change in height for 1 risk allele T= risk allele Ξ²
  • 28. Prototypical β€œManhattan plot” to visualize associations Science, 2007 ~100,000 - ~1,000,000 association tests evol part ease tase well biol T capt imp STR reve subs libri clea βˆ’log10(P) 0 5 10 15 Chromosome 22 X 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 80 60 40 100 rvedteststatistic a b NATURE|Vol 447|7 June 2007 AA Aa aa diseased non- diseased
  • 29. ibility with schizophrenia, a psychotic disorder with many similar- ities to BD. In particular association findings have been reported with assium channel. Ion channelopathies are well-recognized as causes of episodic central nervous system disease, including seizures, ataxias βˆ’log10 (P) 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15 Chromosome Type 2 diabetes 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 22 XX 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Coronary artery disease Crohn’s disease Hypertension Rheumatoid arthritis Type 1 diabetes Bipolar disorder Figure 4 | Genome-wide scan for seven diseases. For each of seven diseases 2log10 of the trend test P value for quality-control-positive SNPs, excluding Chromosomes are shown in alternating colours for clarity, with P values ,1 3 1025 highlighted in green. All panels are truncated at
  • 30. Type I Error: False Positives! what is a p-value? chance we attain the observed result if no difference (H0) Many tests: some can be significant (low p-value by chance)! 100 tests at a p-value of 0.05... how many would be significant per chance? Bonferroni β€œcorrection”: Correct the 0.05 significance level by number of tests e.g., 1M SNPs: 0.05/1x10-6 = 5x10-8
  • 31. QQplot: Distribution of of observed p-values vs. Ho p- values Histogram of runif(10000) runif(10000) Frequency 0.0 0.2 0.4 0.6 0.8 1.0 0100200300400500 p-values under Ho Histogram of gwas$P.value gwas$P.value Frequency 0.0 0.2 0.4 0.6 0.8 1.0 050000100000150000 p-values of GWAS in Total Cholesterol Global Lipids Consortium, 2012random uniform distribution
  • 32. QQplot: Distribution of of observed p-values vs. Ho p- values Histogram of gwas$P.value gwas$P.value Frequency 0.0 0.2 0.4 0.6 0.8 1.0 050000100000150000 p-values of GWAS in Total Cholesterol
  • 33. Which diseases show evidence of association? Examining the QQplot of test statistics in WTCCC sent study cannot provideconclusive exclusion of any given gene. This is the consequence of several factors including: less-than-complete coverage of common variation genome-wide on the Affymetrix chip; poor coverage (by design) of rare variants, including many structural variants (thereby reducing power to detect rare, penetrant, alleles)25 ; difficultieswithdefining thefullgenomicextentofthegene ofinterest; and, despite the sample size, relatively low power to detect, at levels of already allow us, for selected diseases, to highlight pathways and mechanisms of particular interest. Naturally, extensive resequencing and fine-mapping work, followed by functional studies will be required before such inferences can be translated into robust state- ments about the molecular and physiological mechanisms involved. We turn now to a discussion of the main findings for each disease, focusing here only on the most significant and interesting results 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 25 20 20 15 15 10 10 5 5 30 0 0 BD Observedteststatistic Expected chi-squared value CAD CD HT RA T2D T1D Figure 3 | Quantile-quantile plots for seven genome-wide scans. For each of the seven disease collections, a quantile-quantile plot of the results of the trend test is shown in black for all SNPs that pass the standard project filters, have a minor allele frequency .1% and missing data rate ,1%. SNPs that 360,000 SNPs. SNPs at which the test statistic exceeds 30 are represented by triangles. Additional quantile-quantile plots, which also exclude all SNPs located in the regions of association listed in Table 3, are superimposed in blue (for BD, the exclusion of these SNPs has no visible effect on the plot, and
  • 34. Observational associations do not equal causation...
  • 35. Ice Cream $ Drowning Confounding bias What is a confounder? Summer! ? Confounder is correlated to both the β€œrisk” factor and disease, leading to invalid inference. Common source of bias in observational studies (e.g., case-control, cohort, etc)
  • 36. SNP Disease Population Stratification: A source of possible confounding in GWAS race/ethnicity ? Ancestry correlated with allele frequency and disease GWAS are done on specific populations separately. (most have been done in populations of European ancestry)
  • 37. FTO Diabetes Mediation SNPs indicative of a mediator factor? Example: FTO and Type 2 Diabetes Body Mass ? Association between FTO and Type 2 Diabetes via BMI? ... or does FTO have a independent role in Type 2 Diabetes...? FTO Body Mass
  • 38. PLINK: (Standard) Whole Genome Analysis Software
  • 39. PLINK: (Standard) Whole Genome Analysis Software http://pngu.mgh.harvard.edu/~purcell/plink/ β€’cited >9000 times since 2007 β€’allele frequency β€’linkage disequilibrium (LD) β€’data manipulation/filtering β€’association: allelic, genotypic models β€’chi-square β€’logistic β€’linear
  • 40. Examples: GWASs in Type 2 Diabetes
  • 41. Type 2 Diabetes Mellitus: A complex, multifactorial disease β€’Insulin production vs. use β€’beta-cell function β€’insulin sensitivity (BMI) β€’Moves glucose from blood into cells β€’Complications arise due to glucose in blood, hyperglycemia β€’diagnosed by blood glucose levels CDC, family history: 25% body weight, diet, lifestyle, age
  • 42. ARTICLES A genome-wide association study identifies novel risk loci for type 2 diabetes Robert Sladek1,2,4 , Ghislain Rocheleau1 *, Johan Rung4 *, Christian Dina5 *, Lishuang Shen1 , David Serre1 , Philippe Boutin5 , Daniel Vincent4 , Alexandre Belisle4 , Samy Hadjadj6 , Beverley Balkau7 , Barbara Heude7 , Guillaume Charpentier8 , Thomas J. Hudson4,9 , Alexandre Montpetit4 , Alexey V. Pshezhetsky10 , Marc Prentki10,11 , Barry I. Posner2,12 , David J. Balding13 , David Meyre5 , Constantin Polychronakos1,3 & Philippe Froguel5,14 Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case–control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing b-cells, and two linkage disequilibrium blocks that contain genes potentially involved in b-cell development or function (IDE–KIF11–HHEX and EXT2–ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits. The rapidly increasing prevalence of type 2 diabetes mellitus (T2DM) is thought to be due to environmental factors, such as increased availabil- ity of food and decreased opportunity and motivation for physical activity, acting on genetically susceptible individuals. The heritability of T2DM is one of the best established among common diseases and, consequently, genetic risk factors for T2DM have been the subject of intense research1 . Although the genetic causes of many monogenic forms of diabetes (maturity onset diabetes in the young, neonatal mito- chondrial and other syndromic types of diabetes mellitus) have been elucidated, few variants leading to common T2DM have been clearly identified and individually confer only a small risk (odds ratio < 1.1– 1.25) of developing T2DM1 . Linkage studies have reported many T2DM-linked chromosomal regions and have identified putative, cau- sative genetic variants in CAPN10 (ref. 2), ENPP1 (ref. 3), HNF4A (refs 4, 5) and ACDC (also called ADIPOQ)6 . In parallel, candidate-gene studieshavereportedmanyT2DM-associatedloci,withcodingvariants in the nuclear receptor PPARG (P12A)7 and the potassium channel KCNJ11 (E23K)8 being among the very few that havebeen convincingly replicated. The strongest known (odds ratio < 1.7) T2DM association9 was recently mapped to the transcription factor TCF7L2 and has been consistently replicated in multiple populations10–20 . Subjects and study design The recent availability of high-density genotyping arrays, which com- bine the power of association studies with the systematic nature of a genome-wide search, led us to undertake a two-stage, genome-wide association study to identify additional T2DM susceptibility loci (Supplementary Fig. 1). In the first stage of this study, we obtained genotypes for 392,935 single-nucleotide polymorphisms (SNPs) in 1,363 T2DM cases and controls (Supplementary Table 1). In order to enrich for risk alleles21 , the diabetic subjects studied in stage 1 were selected to have at least one affected first degree relative and age at onset under 45 yr (excluding patients with maturity onset diabetes in the young). Furthermore, in order to decrease phenotypic hetero- geneity and to enrich for variants determining insulin resistance and b-cell dysfunction through mechanisms other than severe obesity, we initially studied diabetic patients with a body mass index (BMI) ,30 kg m22 . Control subjects were selected to have fasting blood glucose ,5.7 mmol l21 in DESIR, a large prospective cohort for the study of insulin resistance in French subjects22 . Genotypes for each study subject were obtained using two plat- forms: Illumina Infinium Human1 BeadArrays, which assay 109,365 SNPs chosen using a gene-centred design; and Human Hap300 BeadArrays, which assay 317,503 SNPs chosen to tag haplotype blocks identified by the Phase I HapMap23 . Of the 409,927 markers that passed quality control (Supplementary Tables 2 and 3), geno- types were obtained for an average of 99.2% (Human1) and 99.4% (Hap300) of markers for each subject with a reproducibility of .99.9% (both platforms). Forty-three subjects were removed from analysis because of evidence of intercontinental admixture (Sup- plementary Fig. 3) and an additional four because their genotype- determined gender disagreed with clinical records. In total, T2DM association was tested for 100,764 (Human1) and 309,163 (Hap300) SNPs representing 392,935 unique loci (Fig. 1). Because of unequal male/female ratios in our cases and controls, we analysed the 12,666 sex-chromosome SNPs separately for each gender. *These authors contributed equally to this work. 1 Departments of Human Genetics, 2 Medicine and 3 Pediatrics, Faculty of Medicine, McGill University, Montreal H3H 1P3, Canada. 4 McGill University and Genome Quebec Innovation Centre, Montreal H3A 1A4, Canada. 5 CNRS 8090-Institute of Biology, Pasteur Institute, Lille 59019 Cedex, France. 6 Endocrinology and Diabetology, University Hospital, Poitiers 86021 Cedex, France. 7 INSERM U780-IFR69, Villejuif 94807, France. 8 Endocrinology-Diabetology Unit, Corbeil-Essonnes Hospital, Corbeil-Essonnes 91100, France. 9 Ontario Institute for Cancer Research, Toronto M5G 1L7, Canada. 10 Montreal Diabetes Research Center, Montreal H2L 4M1, Canada. 11 Molecular Nutrition Unit and the Department of Nutrition, University of Montreal and the Centre Hospitalier de l’UniversiteΒ΄ de MontreΒ΄al, Montreal H3C 3J7, Canada. 12 Polypeptide Hormone Laboratory and Department of Anatomy and Cell Biology, Montreal H3A 2B2, Canada. 13 Department of Epidemiology & Public Health, Imperial College, St Mary’s Campus, Norfolk Place, London W2 1PG, UK. 14 Section of Genomic Medicine, Imperial College London W12 0NN, and Hammersmith Hospital, Du Cane Road, London W12 0HS, UK. 881 NatureΒ©2007 Publishing Group Nature, 2/2007 References and Notes 1. B. G. Richmond, D. S. Strait, Nature 404, 382 (2000). 2. J. Kingdon, Lowly Origins (Princeton Univ. Press, Princeton, NJ, 2003). 3. C. V. Ward, M. G. Leakey, A. Walker, Evol. Anthropol. 7, 197 (1999). 4. Y. Haile-Selassie, Nature 412, 178 (2001). 5. T. D. White et al., Nature 440, 883 (2006). 6. K. Kovarovic, P. Andrews, J. Hum. Evol., in press (available at http://dx.doi.org./doi:10.1016/j.jhevol.2007.01.001; doi: 10.1016/j.jhevol.2007.01.001). 7. N. Patterson, D. J. Richter, S. Gnerre, E. S. Lander, D. Reich, Nature 441, 1103 (2006). 8. K. D. Hunt et al., Primates 37, 363 (1996). 9. J. G. Fleagle et al., Symp. Zool. Soc. London 48, 359 (1981). 10. R. H. Crompton et al., Cour. Forsch-Inst. Senckenb. 243, 115 (2003). 11. J. T. Stern, Yrb. Phys. Anthropol. 19, 59 (1975). 12. S. K. S. Thorpe, R. H. Crompton, Am. J. Phys. Anthropol. 131, 384 (2006). 13. K. D. Hunt, J. Hum. Evol. 26, 183 (1994). 15. E. Larney, S. Larsen, Am. J. Phys. Anthropol. 125, 42 (2004). 16. S. K. S. Thorpe, R. H. Crompton, Am. J. Phys. Anthropol. 127, 58 (2005). 17. S. K. S. Thorpe, R. H. Crompton, M. M. Gunther, R. F. Ker, R. McN. Alexander, Am. J. Phys. Anthropol. 110, 179 (1999). 18. R. McN. Alexander, Principles of Animal Locomotion (Princeton Univ. Press, Princeton, NJ, 2003). 19. C. V. Ward, Yrbk. Phys. Anthropol. 45, 185 (2002). 20. R. W. Wrangham, N. L. Conklin-Brittain, K. D. Hunt, Int. J. Primatol. 19, 949 (1998). 21. H. Pontzer, R. W. Wrangham, J. Hum. Evol. 46, 317 (2004). 22. R. C. Payne et al., J. Anat. 208, 709 (2006). 23. M. Pickford, B. Senut, B. Gommery, in Late Cenozoic Environments and Hominid Evolution: a Tribute to Bill Bishop, P. Andrews, P. Banham, Eds. (Geological Society, London, 1999), pp. 27–38. 24. N. M. Young, L. MacLatchy, J. Hum. Evol. 46, 163 (2004). 25. D. Gommery, B. Senu, M. Pickford, E. Musiime, Ann. PalΓ©ontol. 88, 167 (2002). 26. C. V. Ward, in Handbook of Paleoanthropology Vol. 2: Primate Evolution and Human Origins, W. Henke, I. Tattersall, Eds. (Springer, Heidelberg, Germany, 2007), pp. 1011–1030. N. Ogihara, M. Nakatsukasa, Eds. (Springer, Heidelberg, Germany, 2006), pp. 199–208. 28. C. P. E. Zollikofer et al., Nature 434, 755 (2005). 29. M. Pickford, Anthropologie 69, 191 (2005). 30. We thank the Indonesian Institute of Science, Indonesian Nature Conservation Service, and Leuser Development Programme for granting permission and giving support for research in the Leuser Ecosystem. R. McN. Alexander, T. M. Blackburn, S. Burtles. J. Rees, N. Jeffery, E. E. Vereecke, A. Walker, A. Wilson, and B. Wood commented on the manuscript. R. Savage developed the animation (fig. S1). Studies of captive animals were hosted by the North of England Zoological Society. This research was supported by grants from the Leverhulme Trust, the Royal Society, the L.S.B. Leakey Foundation, and the Natural Environment Research Council. Supporting Online Material www.sciencemag.org/cgi/content/full/316/5829/1328/DC1 Table S1 Movies S1 to S3 5 February 2007; accepted 18 April 2007 10.1126/science.1140799 Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes for BioMedical Research*† New strategies for prevention and treatment of type 2 diabetes (T2D) require improved insight into disease etiology. We analyzed 386,731 common single-nucleotide polymorphisms (SNPs) in 1464 patients with T2D and 1467 matched controls, each characterized for measures of glucose metabolism, lipids, obesity, and blood pressure. With collaborators (FUSION and WTCCC/UKT2D), we identified and confirmed three loci associated with T2Dβ€”in a noncoding region near CDKN2A and CDKN2B, in an intron of IGF2BP2, and an intron of CDKAL1β€”and replicated associations near HHEX and in SLC30A8 found by a recent whole-genome association study. We identified and confirmed association of a SNP in an intron of glucokinase regulatory protein (GCKR) with serum triglycerides. The discovery of associated variants in unsuspected genes and outside coding regions illustrates the ability of genome-wide association studies to provide potentially important clues to the pathogenesis of common diseases. T ype 2 diabetes, obesity, and cardiovascular risk factors are caused by a combination of genetic susceptibility, environment, be- havior, and chance. Whole-genome association studies (WGAS) offer a new approach to gene discovery unbiased with regard to presumed functions or locations of causal variants. This approach is based on Fisher’s theory for additive effects at common alleles (1); human heterozy- to purifying selection, and has been made pos- sible by genomic advances such as the human genome sequence, SNP and HapMap databases, and genotyping arrays (3). We studied 1464 patients with T2D and 1467 controls from Finland and Sweden, each characterized for 18 clinical traits: anthropomet- ric measures, glucose tolerance and insulin se- cretion, lipids and apolipoproteins, and blood applying stringent quality-control filters, high- quality genotypes for 386,731 common SNPs were obtained (4). To extend the set of putative causal alleles tested for association, we devel- oped 284,968 additional multimarker (haplo- type) tests based on these SNP genotypes (5, 6). The 671,699 allelic tests capture (correlation co- efficient r2 β‰₯ 0.8) 78% of common SNPs in HapMap CEU (3). Each SNP and haplotype test was assessed for association to T2D and each of 18 traits with the software package PLINK (http://pngu.mgh. harvard.edu/purcell/plink/). For T2D, a weighted meta-analysis was used to combine results for the population-based and family-based subsam- ples (4). For quantitative traits, multivariable linear or logistic regression with or without co- variates was performed (4). Association results for each SNP, haplotype test, and phenotype are available (www.broad.mit.edu/diabetes/). In genome-wide analysis involving hundreds of thousands of statistical tests, modest levels of bias imposed on the null distribution can over- whelm a small number of true results. We used three strategies to search for evidence of sys- tematic bias from unrecognized population struc- ture, the analytical approach, and genotyping artifacts (7, 8). First, we examined the distribu- tion of P-values in the population-based sam- ple, observing a close match to that expected for a null distribution (genomic inflation factor lGC = 1.05 for T2D). Second, we calculated G. Brice,6 B. Bullman,7 J. Campbell,8 B. Castle,9 R. Cetnarsyj,8 C. Chapman,10 C. Chu,11 N. Coates,12 T. Cole,10 R. Davidson,4 A. Donaldson,13 H. Dorkins,3 F. Douglas,2 D. Eccles,9 R. Eeles,1 F. Elmslie,6 D. G. Evans,7 S. Goff,6 S. Goodman,5 D. Goudie,2 J. Gray,15 L. Greenhalgh,16 H. Gregory,17 S. V. Hodgson,6 T. Homfray,6 R. S. Houlston,1 L. Izatt,18 L. Jackson,18 L. Jeffers,19 V. Johnson-Roffey,12 F. Kavalier,18 C. Kirk,19 F. Lalloo,7 C. Langman,18 I. Locke,1 M. Longmuir,4 J. Mackay,20 A. Magee,19 S. Mansour,6 Z. Miedzybrodzka,17 J. Miller,11 P. Morrison,19 V. Murday,4 J. Paterson,21 G. Pichert,18 M. Porteous,8 N. Rahman,6 M. Rogers,15 S. Rowe,22 S. Shanley,1 A. Saggar,6 G. Scott,2 L. Side,23 L. Snadden,4 M. Steel,2 M. Thomas,5 S. Thomas,1 1 Clinical Genetics Service, Royal Marsden Hospital, Downs Road, Sutton, Surrey, SM2 5PT, UK. 2 Department of Clinical Genetics, Ninewells Hospital, Dundee, DD1 9SY, UK. 3 Medical and Community Genetics, Kennedy-Galton Centre, Level 8V, Northwick Park and St. Mark’s NHS Trust, Watford Rd, Harrow, HA1 3UJ, UK. 4 Institute of Medical Genetics, Yorkhill NHS Trust, Dalnair Street, Glasgow, G3 8SJ, UK. 5 Clinical Genetics Department, Royal Devon and Exeter Hospital (Heavitree), Gladstone Road, Exeter, EX1 2ED, UK. 6 Department of Clinical Genetics, St. George’s Hospital Medical School, Jenner Wing, Cranmer Terrace, London, SW17 0RE, UK. 7 Department of Medical Genetics, St. Mary’s Hospital, Hathersage Road, Manchester, M13 0JH, UK. 8 South East of Scotland Clinical Genetics Service, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK. 9 Department of Medical Genetics, The Princess Anne Hospital, Coxford Road, Southampton, S016 5YA, UK. 10 Clinical Genetics Unit, Birmingham Women’s Hospital, Metchley Park Road, Edgbaston, Birmingham, B15 2TG, UK. 11 Yorkshire Regional Genetic Service, Department of Clinical Genetics, Cancer Genetics Building, St. James University Hospital, Beckett Street, Leeds, LS9 7TF, UK. 12 Department of Clinical Genetics, Leicester Royal Infirm- ary, Leicester, LE1 5WW, UK. 13 Department of Clinical Genetics, St Michael’s Hospital, Southwell Street, Bristol, BS2 8EG, UK. 14 Institute of Human Genetics, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK. 15 Institute of Medical Genetics, University Hospital of Wales, Heath Park, Cardiff, CF14 4XW, UK. 16 Department of Clinical Genetics, Alder Hey Children’s Hospital, Eaton Road, Liverpool L12 2AP, UK. 17 Clinical Genetics Centre, Argyll House, Foresterhill, Aberdeen, AB25 2ZR, UK. 18 Clinical Genetics, 7th Floor New Guy’s House, Guy’s UK. 19 Clinical Belvoir Park H 20 Clinical and Health, 30 G 21 Department Trust, Box 13 22 Department of Chester Ho 23 Department Road, Headin Supporting www.sciencema Materials and Figs. S1 to S8 Tables S1 to S References 9 March 2007 Published onli 10.1126/scien Include this in A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants Laura J. Scott,1 Karen L. Mohlke,2 Lori L. Bonnycastle,3 Cristen J. Willer,1 Yun Li,1 William L. Duren,1 Michael R. Erdos,3 Heather M. Stringham,1 Peter S. Chines,3 Anne U. Jackson,1 Ludmila Prokunina-Olsson,3 Chia-Jen Ding,1 Amy J. Swift,3 Narisu Narisu,3 Tianle Hu,1 Randall Pruim,4 Rui Xiao,1 Xiao-Yi Li,1 Karen N. Conneely,1 Nancy L. Riebow,3 Andrew G. Sprau,3 Maurine Tong,3 Peggy P. White,1 Kurt N. Hetrick,5 Michael W. Barnhart,5 Craig W. Bark,5 Janet L. Goldstein,5 Lee Watkins,5 Fang Xiang,1 Jouko Saramies,6 Thomas A. Buchanan,7 Richard M. Watanabe,8,9 Timo T. Valle,10 Leena Kinnunen,10,11 GonΓ§alo R. Abecasis,1 Elizabeth W. Pugh,5 Kimberly F. Doheny,5 Richard N. Bergman,9 Jaakko Tuomilehto,10,11,12 Francis S. Collins,3 * Michael Boehnke1 * Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge. Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs. We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls. We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the ria (8). We ciation with the log-odd (8). We ob versus 31.6 P values < against the with a large consistent w SNPs that also sugges trols by birt successful; genomic co Analysi allowed us variation in portion, w (8, 13) that equilibrium Centre d’E (Utah resid 1 Department Genetics, Uni USA. 2 Depar Science, 6/2007 Study design: Richa Saxena1–6 and Valeriya Lyssenko7 (Team Leaders), Peter Almgren,7 Paul I. W. de Bakker,1–6 NoΓ«l P. Burtt,1 Jose C. Florez,1–6 Hong Chen,8 Joanne Meyer,8 Joel N. Hirschhorn,1,6,9–11 Mark J. Daly,1–3,5 Thomas E. Hughes,8 Leif Groop,7,12 David Altshuler1–6 (Chair) Clinical characterization and phenotypes: Valeriya Lyssenko7 and Richa Saxena1–6 (Team Leaders), Peter Almgren,7 Kristin Ardlie,1 Kristina Bengtsson BostrΓΆm,13 NoΓ«l P. Burtt,1 Hong Chen,8 Jose C. Florez,1–6 Bo Isomaa,14,15 Sekar Kathiresan,1,3,5 Guillaume Lettre,1,6,9–11 Ulf Lindblad,16 Helen N. Lyon,1,6,9–11 Olle Melander,7 Christopher Newton-Cheh,1–3,5 Peter Nilsson,17 Marju Orho- Melander,7 Lennart RΓ₯stam,16 Elizabeth K. Speliotes,1,3,6,9–11 Marja-Riitta Taskinen,12 Tiinamaija Tuomi,12,15 Benjamin F. Voight,1–3,5 David Altshuler,1–6 Joel N. Hirschhorn,1,6,9–11 Thomas E. Hughes,8 Leif Groop7,12 (Chair) DNA sample QC and diabetes replication genotyping: Candace Guiducci1 and Valeriya Lyssenko7 (Team Leaders), Anna Berglund,7 Joyce Carlson,18 Lauren Gianniny,1 Rachel Hackett,1 Liselotte Hall,18 Johan Holmkvist,7 Esa Laurila,7 Marju Orho-Melander,7 Marketa SjΓΆgren,7 Maria Sterner,18 Aarti Surti1 Margareta Svensson,7 Malin Svensson,7 Ryan Tewhey,1 NoΓ«l P. Burtt1 (Chair) Whole genome scan genotyping: Brendan Blumenstiel1 (Team Leader), Melissa Parkin,1 Matthew DeFelice,1 Candace Guiducci,1 Ryan Tewhey,1 Rachel Barry,1 Wendy Brodeur,1 NoΓ«l P. Burtt,1 Jody Camarata,1 Nancy Chia,1 Mary Fava,1 John Gibbons,1 Bob Handsaker,1 Claire Healy,1 Kieu Nguyen,1 Casey Gates,1 Carrie Sougnez,1 Diane Gage,1 Marcia Nizzari,1 David Altshuler,1–6 Stacey B. Gabriel1 (Chair) GCKR replication genotyping and analysis (MalmΓΆ Diet and Cancer Study): Sekar Kathiresan1,3,5 (Team Leader), Candace Guiducci,1 Aarti Surti,1 NoΓ«l P. Burtt,1 Olle Melander,7 Marju Orho-Melander7 (Chair) Statistical analysis: Benjamin F. Voight1–3,5 and Paul I. W. de Bakker1–6 (Team Leaders), Richa Saxena,1–6 Valeriya Lyssenko,7 Peter Almgren,7 NoΓ«l P. Burtt,1 Hong Chen,8 Gung-Wei Chirn,8 Qicheng Ma,8 Hemang Parikh,7 Delwood Richardson,8 Darrell Ricke,8 Jeffrey J. Roix,8 Leif Groop,7,12 Shaun Purcell,1,2 David Altshuler,1–6 Mark J. Daly1–3,5 (Chair) 1 Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA. 2 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA. 3 Department of Medicine, Mas- sachusetts General Hospital, Boston, MA 02114, USA. 4 Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA. 5 Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. 6 Depart- ment of Genetics, Harvard Medical School, Boston, MA 02115, USA. 7 Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 8 Diabetes and Metabolism Disease Area, Novartis Institutes for BioMedical Research, 100 Technology Square, Cambridge, MA 02139, USA. 9 Depart- ment of Pediatrics, Harvard Medical School, Boston, MA 02115, USA. 10 Division of Endocrinology, Children’s Hospital, Boston, MA 02115, USA. 11 Division of Genetics, Children’s Hospital, Boston, MA 02115, USA. 12 Department of Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland. 13 Skaraborg Institute, SkΓΆvde, Sweden. 14 Malmska Municipal Health Center and Hospital, Jakobstad, Finland. 15 FolkhΓ€lsan Research Center, Helsinki, Finland. 16 Depart- ment of Clinical Sciences, Community Medicine Research Unit, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 17 Department of Clinical Sciences, Medicine Research Unit, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 18 Clinical Chemistry, University Hospital MalmΓΆ, Lund University, MalmΓΆ, Sweden. 19 Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA. Supporting Online Material www.sciencemag.org/cgi/content/full/1142358/DC1 Materials and Methods Figs. S1 and S2 Tables S1 to S6 References 9 March 2007; accepted 20 April 2007 Published online 26 April 2007; 10.1126/science.1142358 Include this information when citing this paper. Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for Type 2 Diabetes Eleftheria Zeggini,1,2 * Michael N. Weedon,3,4 * Cecilia M. Lindgren,1,2 * Timothy M. Frayling,3,4 * Katherine S. Elliott,2 Hana Lango,3,4 Nicholas J. Timpson,2,5 John R. B. Perry,3,4 Nigel W. Rayner,1,2 Rachel M. Freathy,3,4 Jeffrey C. Barrett,2 Beverley Shields,4 Andrew P. Morris,2 Sian Ellard,4,6 Christopher J. Groves,1 Lorna W. Harries,4 Jonathan L. Marchini,7 Katharine R. Owen,1 Beatrice Knight,4 Lon R. Cardon,2 Mark Walker,8 Graham A. Hitman,9 Andrew D. Morris,10 Alex S. F. Doney,10 The Wellcome Trust Case Control Consortium (WTCCC),† Mark I. McCarthy,1,2 ‑§ Andrew T. Hattersley3,4 ‑ The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of Here, we describe how integration of data from the WTCCC scan and our own replication studies with similar information generated by the Diabetes Genetics Initiative (DGI) (6) and the Finland–United States Investigation of NIDDM Genetics (FUSION) (7) has identified several additional susceptibility variants for T2D. In the WTCCC study, analysis of 490,032 autosomal SNPs in 16,179 samples yielded 459,448 SNPs that passed initial quality control (5). We considered only the 393,453 autosomal SNPs with minor allele frequency (MAF) ex- ceeding 1% in both cases and controls and no extreme departure from Hardy-Weinberg equi- librium (P < 10βˆ’4 in cases or controls) (8). This T2D-specific data set shows no evidence of sub- stantial confounding from population substruc- ture and genotyping biases (8). To distinguish true associations from those reflecting fluctuations under the null or residual errors arising from aberrant allele calling, we first submitted putative signals from the WTCCC study to additional quality control, including cluster- plot visualization and validation genotyping on REPORTS onFebruary8,2010www.sciencemag.orgDownloadedfrom
  • 43. ARTICLES A genome-wide association study identifies novel risk loci for type 2 diabetes Robert Sladek1,2,4 , Ghislain Rocheleau1 *, Johan Rung4 *, Christian Dina5 *, Lishuang Shen1 , David Serre1 , Philippe Boutin5 , Daniel Vincent4 , Alexandre Belisle4 , Samy Hadjadj6 , Beverley Balkau7 , Barbara Heude7 , Guillaume Charpentier8 , Thomas J. Hudson4,9 , Alexandre Montpetit4 , Alexey V. Pshezhetsky10 , Marc Prentki10,11 , Barry I. Posner2,12 , David J. Balding13 , David Meyre5 , Constantin Polychronakos1,3 & Philippe Froguel5,14 Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case–control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing b-cells, and two linkage disequilibrium blocks that contain genes potentially involved in b-cell development or function (IDE–KIF11–HHEX and EXT2–ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits. The rapidly increasing prevalence of type 2 diabetes mellitus (T2DM) is thought to be due to environmental factors, such as increased availabil- ity of food and decreased opportunity and motivation for physical activity, acting on genetically susceptible individuals. The heritability of T2DM is one of the best established among common diseases and, consequently, genetic risk factors for T2DM have been the subject of intense research1 . Although the genetic causes of many monogenic forms of diabetes (maturity onset diabetes in the young, neonatal mito- chondrial and other syndromic types of diabetes mellitus) have been elucidated, few variants leading to common T2DM have been clearly identified and individually confer only a small risk (odds ratio < 1.1– 1.25) of developing T2DM1 . Linkage studies have reported many T2DM-linked chromosomal regions and have identified putative, cau- sative genetic variants in CAPN10 (ref. 2), ENPP1 (ref. 3), HNF4A (refs genotypes for 392,935 single-nucleotide polymorphisms (SNPs) in 1,363 T2DM cases and controls (Supplementary Table 1). In order to enrich for risk alleles21 , the diabetic subjects studied in stage 1 were selected to have at least one affected first degree relative and age at onset under 45 yr (excluding patients with maturity onset diabetes in the young). Furthermore, in order to decrease phenotypic hetero- geneity and to enrich for variants determining insulin resistance and b-cell dysfunction through mechanisms other than severe obesity, we initially studied diabetic patients with a body mass index (BMI) ,30 kg m22 . Control subjects were selected to have fasting blood glucose ,5.7 mmol l21 in DESIR, a large prospective cohort for the study of insulin resistance in French subjects22 . Genotypes for each study subject were obtained using two plat- Sladek, 2007How many SNPs (p-value?) European-based; N ~ 1000 cases: high fasting blood glucose/non-obese controls: non-obese
  • 44. Human Hap300 chip, showing no T2DM association in stage 1 (P . 0.01) and separated by at least 100 kb. Using the first principal component as a covariate for ancestry differences between cases and controls, we tested for association between rs932206 and disease status. Our result suggests that this apparent association is largely BMI on the association between marker and disease, as it is asymp- totically equivalent to the Armitage trend test used to detect asso- ciation in stages 1 and 2. None of the associations (Supplementary Table 7) was substantially changed by considering the effects of these covariates. 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 15 10 5 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19 20 21 22 X 18 Figure 1 | Graphical summary of stage 1 association results. T2DM association was determined for SNPs on the Human1 and Hap300 chips. The x axis represents the chromosome position from pter; the y axis shows 2log10[pMAX], the P-value obtained by the MAX statistic, for each SNP (Note the different scale on the y axis of the chromosome 10 plot.). SNPs that passed the cutoff for a fast-tracked second stage are highlighted in red. 882 NatureΒ©2007 Publishing Group Sladek, 2007
  • 45. Identification of four novel T2DM loci Our fast-track stage 2 genotyping confirmed the reported association for rs7903146 (TCF7L2) on chromosome 10, and in addition iden- tified significant associations for seven SNPs representing four new T2DM loci (Table 1). In all cases, the strongest association for the MAX statistic (see Methods) was obtained with the additive model. The most significant of these corresponds to rs13266634, a non- synonymous SNP (R325W) in SLC30A8, located in a 33-kb linkage disequilibrium block on chromosome 8, containing only the 39 end of this gene (Fig. 2a). SLC30A8 encodes a zinc transporter expressed solely in the secretory vesicles of b-cells and is thus implicated in the final stages of insulin biosynthesis, which involve co-crystallization Table 1 | Confirmed association results SNP Chromosome Position (nucleotides) Risk allele Major allele MAF (case) MAF (ctrl) Odds ratio (het) Odds ratio (hom) PAR ls Stage 2 pMAX Stage 2 pMAX (perm) Stage 1 pMAX Stage 1 pMAX (perm) Nearest gene rs7903146 10 114,748,339 T C 0.406 0.293 1.65 6 0.19 2.77 6 0.50 0.28 1.0546 1.5 3 10234 ,1.0 3 1027 3.2 3 10217 ,3.3 3 10210 TCF7L2 rs13266634 8 118,253,964 C C 0.254 0.301 1.18 6 0.25 1.53 6 0.31 0.24 1.0089 6.1 3 1028 5.0 3 1027 2.1 3 1025 1.8 3 1025 SLC30A8 rs1111875 10 94,452,862 G G 0.358 0.402 1.19 6 0.19 1.44 6 0.24 0.19 1.0069 3.0 3 1026 7.4 3 1026 9.1 3 1026 7.3 3 1026 HHEX rs7923837 10 94,471,897 G G 0.335 0.377 1.22 6 0.21 1.45 6 0.25 0.20 1.0065 7.5 3 1026 2.2 3 1025 3.4 3 1026 2.5 3 1026 HHEX rs7480010 11 42,203,294 G A 0.336 0.301 1.14 6 0.13 1.40 6 0.25 0.08 1.0041 1.1 3 1024 2.9 3 1024 1.5 3 1025 1.2 3 1025 LOC387761 rs3740878 11 44,214,378 A A 0.240 0.272 1.26 6 0.29 1.46 6 0.33 0.24 1.0046 1.2 3 1024 2.8 3 1024 1.8 3 1025 1.3 3 1025 EXT2 rs11037909 11 44,212,190 T T 0.240 0.271 1.27 6 0.30 1.47 6 0.33 0.25 1.0045 1.8 3 1024 4.5 3 1024 1.8 3 1025 1.3 3 1025 EXT2 rs1113132 11 44,209,979 C C 0.237 0.267 1.15 6 0.27 1.36 6 0.31 0.19 1.0044 3.3 3 1024 8.1 3 1024 3.7 3 1025 2.9 3 1025 EXT2 Significant T2DM associations were confirmed for eight SNPs in five loci. Allele frequencies, odds ratios (with 95% confidence intervals) and PAR were calculated using only the stage 2 data. Allele frequencies in the controls were very close to those reported for the CEU set (European subjects genotyped in the HapMap project). Induced sibling recurrent risk ratios (ls) were estimated using stage 2 genotype counts for the control subjects and assuming a T2DM prevalence of 7% in the French population. hom, homozygous; het, heterozygous; major allele, the allele with the higher frequency in controls; pMAX, P-value of the MAX statistic from the x2 distribution; pMAX (perm), P-value of the MAX statistic from the permutation-derived empirical distribution (pMAX and pMAX (perm) are adjusted for variance inflation); risk allele, the allele with higher frequency in cases compared with controls. 0 2 4 –log10[P] –log10[P] SLC30A8 IDE HHEXKIF11 0 2 4 a b NATURE|Vol 445|22 February 2007 ARTICLES Sladek, 2007 5 3 1 5 3 1 15 10 5 1 1 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 5 3 1 3 4 5 8 9 10 13 14 15 19 20 X 18 DM 2log10[pMAX], the P-value obtained by the MAX statistic, for each SNP How would you interpret the p- values? Odds ratios? Confirmed 8 SNPs with N ~ 1000
  • 46. Scaling up discovery by combining populations: meta-analyses
  • 47. g the Diabetes Genetics nvestigation of NIDDM nd (iv) the Framingham omponent studies (n ΒΌ ry Table 1 online. aring, the four consortia n 10 and 20 SNPs promi- their individual, interim, mentary Table 2 online). oci with consistent effects dies. Two of these repre- 6PC2 and GCK. In addi- nerated evidence for an NPs around the MTNR1B rs1387153, P ΒΌ 2.2 Γ‚ 10Γ€11; DFS: rs10830963, 5.8 Γ‚ 10Γ€4, for the most ch analysis). The associa- d on formal meta-analysis r exclusion of individuals ΒΌ 1.1 Γ‚ 10Γ€57; rs4607517 NR1B), P ΒΌ 3.2 Γ‚ 10Γ€50; pplementary Table 3 and ent efforts to harmonize (including the additional data from the WTCCC, DGI and FUSION scans)10 (Supplementary Note). We found strong evidence that the minor G allele of rs10830963 was associated with increased risk of T2D (odds ratio ΒΌ 1.09 (1.05–1.12), P ΒΌ 3.3 Γ‚ 10Γ€7; Fig. 2 and Supplementary Table 6 online). The possibility that the fasting glucose association might DGI Study ID OR (95% CI) Weight (%) 1.12 (0.96, 1.30) 4.61 4.89 8.03 9.58 3.53 8.75 2.69 6.04 10.56 23.18 2.85 7.41 7.90 100.00 1.20 (1.03, 1.39) 1.07 (0.95, 1.20) 1.14 (1.03, 1.27) 1.00 (0.84, 1.19) 1.17 (1.04, 1.30) 1.07 (0.88, 1.31) 1.16 (1.02, 1.33) 1.00 (0.90, 1.10) 1.03 (0.96, 1.10) 0.91 (0.75, 1.10) 1.15 (1.02, 1.30) 1.16 (1.03, 1.30) 1.09 (1.05, 1.12) Meta-analysis P value = 3.3 Γ— 10 –7 FUSION WTCCC deCODE KORA Rotterdam CCC ADDITION/ELY Norfolk UKT2DGC OxGN/58BC FUSION Stage 2 METSIM .722 1 1.39 Overall (I 2 = 26.6%, P = 0.176) Figure 2 Association of rs10830963 with type 2 diabetes (T2D) in 13 case- control studies. VOLUME 41 [ NUMBER 1 [ JANUARY 2009 NATURE GENETICS Meta-analysis of SNP rs10830963: Combining findings from multiple cohorts Propenko, 2009
  • 48. A RT I C L E S By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 Γ— 10βˆ’8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits. Type 2 diabetes (T2D) is characterized by insulin resistance and deficient beta-cell function1. The escalating prevalence of T2D and the limitations of currently available preventative and therapeutic options highlight the need for a more complete understanding of T2D pathogenesis. To date, approximately 25 genome-wide significant common variant associations with T2D have been described, mostly through genome-wide association (GWA) analyses2–13. The identities of the variants and genes mediating the susceptibility effects at most of these signals have yet to be established, and the known variants account for less than 10% of the overall estimated genetic contribution to T2D predisposition. Although some of the unexplained heritability will reflect variants poorly captured by existing GWA platforms, we reasoned that an expanded meta-analysis of existing GWA data would the inverse-variance method (Online Methods, Fig. 1, Supplementary Tables 1 and 2 and Supplementary Note). We observed only modest genomic control inflation ( gc = 1.07), suggesting that the observed results were not due to population stratification. After removing SNPs within established T2D loci (Supplementary Table 3), the result- ing quantile-quantile plot was consistent with a modest excess of disease associations of relatively small effect (Supplementary Note). Weak evidence for association at HLA variants strongly associated with autoimmune forms of diabetes (Supplementary Table 3 and Supplementary Note) suggested some case admixture involving subjects with type 1 diabetes or latent autoimmune diabetes of adult- hood; however, failure to detect T2D associations at other non-HLA type 1 diabetes susceptibility loci (for example, INS, PTPN22 and Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis Voight, 2010 Meta-analyses for T2D: N>40K and 90K identifies >30 loci among 2,400,000 SNPs
  • 49. A RT I C L E S 13 autosomal loci exceeded the threshold for genome-wide significance (P ranging from 2.8 Γ— 10βˆ’8 to 1.4 Γ— 10βˆ’22) with allele-specific odds (r2 < 0.05), and conditional analyses (see below) establish these SNPs as independent (Fig. 2 and Supplementary Table 4). Further analysis 50 Locus established previously Locus identified by current study Locus not confirmed by current study BCL11A THADA NOTCH2 ADAMTS9 IRS1 IGF2BP2 WFS1 ZBED3 CDKAL1 HHEX/IDE KCNQ1 (2 signals*: ) TCF7L2 KCNJ11 CENTD2 MTNR1B HMGA2 ZFAND6 PRC1 FTO HNF1B DUSP9 Conditional analysis Unconditional analysis TSPAN8/LGR5 HNF1A CDC123/CAMK1D CHCHD9 CDKN2A/2B SLC30A8 TP53INP1 JAZF1 KLF14 PPAR 40 30 –log10(P)–log10(P) 20 10 10 1 2 3 4 5 6 7 8 Chromosome 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X 0 0 Suggestive statistical association (P < 1 10 –5 ) Association in identified or established region (P < 1 10 –4 ) Figure 1 Genome-wide Manhattan plots for the DIAGRAM+ stage 1 meta-analysis. Top panel summarizes the results of the unconditional meta- analysis. Previously established loci are denoted in red and loci identified by the current study are denoted in green. The ten signals in blue are those taken forward but not confirmed in stage 2 analyses. The genes used to name signals have been chosen on the basis of proximity to the index SNP and should not be presumed to indicate causality. The lower panel summarizes the results of equivalent meta-analysis after conditioning on 30 previously established and newly identified autosomal T2D-associated SNPs (denoted by the dotted lines below these loci in the upper panel). Newly discovered conditional signals (outside established loci) are denoted with an orange dot if they show suggestive levels of significance (P < 10βˆ’5), whereas secondary signals close to already confirmed T2D loci are shown in purple (P < 10βˆ’4). Meta-analyses for T2D: N>40K and 90K identifies >30 loci among 2,400,000 SNPs
  • 50. 0 20 40 60 80 100 recombinationrate(cM/Mb) ●●● ●● ●● ●●● ● ● ● ●●● ● ●●●●● ● ● ● ●●● ●● ●● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ●●●●● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ● ●●● ●●● ● ● ● ● ● ● ●●●●● ●●●● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ●●● ●● ●● ● ●● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ●● ● ●● ● ●●● ● ● ● ● ● ●●●● ● ● ●● ● ● ●●●●● ● ● 2 βˆ’> PGCP 98 SLC30A8 Region 0 2 4 6 8 10 βˆ’log10(Pβˆ’value) 0 20 40 60 80 100 recombinationrate(cM/Mb) rs3802177 ●●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ● ● ● ●●● ●● ● ●●●●●● ● ●●● ● ● ● ● ● ● ●● ●●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● 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●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ●● ● ●● ● ● ●●● ● ● ● ● ● ●● rs10965250 stage 1 ● r^2: 0.8 βˆ’ 1.0 ● r^2: 0.6 βˆ’ 0.8 ● r^2: 0.4 βˆ’ 0.6 ● r^2: 0.2 βˆ’ 0.4 ● r^2: 0.0 βˆ’ 0.2 ● r^2 missing <βˆ’ MLLT3 KIAA1797 βˆ’> <βˆ’ PTPLAD2 <βˆ’ IFNB1 <βˆ’ IFNW1 <βˆ’ IFNA21 <βˆ’ IFNA4 <βˆ’ IFNA7 <βˆ’ IFNA13 MTAP βˆ’> <βˆ’ CDKN2A <βˆ’ CDKN2B DMRTA1 βˆ’> <βˆ’ ELAVL2 21 22 23 24 Position on chromosome 9 (Mb) 40 60 80 100 recombinationrate(c CDC123/CAMK1D Region 4 6 8 10 log10(Pβˆ’value) 40 60 80 100 recombinationrate(c rs12779790 ●●● ● ● ●● ● rs12779790 stage 1 ● r^2: 0.8 βˆ’ 1.0 ● r^2: 0.6 βˆ’ 0.8 ● r^2: 0.4 βˆ’ 0.6 ● r^2: 0.2 βˆ’ 0.4 ● r^2: 0.0 βˆ’ 0.2 ● r^2 missing HHEX/IDE Region 10 15 log10(Pβˆ’value) 40 60 80 100 recombinationrate(c rs5015480 ● ● ● ● ● ●● ● ● ● ●●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●● rs5015480 stage 1 ● r^2: 0.8 βˆ’ 1.0 ● r^2: 0.6 βˆ’ 0.8 ● r^2: 0.4 βˆ’ 0.6 ● r^2: 0.2 βˆ’ 0.4 ● r^2: 0.0 βˆ’ 0.2 ● r^2 missing .609 Not in a gene...In a gene... ~90% of GWAS hits are non-coding!
  • 51. pporting!Figures! ! ! ~90% of GWAS hits are non-coding! Stamatoyannopoulos, Science 2012 Systematic Localization of Common Disease-Associated Variation in Regulatory DNA Matthew T. Maurano,1 * Richard Humbert,1 * Eric Rynes,1 * Robert E. Thurman,1 Eric Haugen,1 Hao Wang,1 Alex P. Reynolds,1 Richard Sandstrom,1 Hongzhu Qu,1,2 Jennifer Brody,3 Anthony Shafer,1 Fidencio Neri,1 Kristen Lee,1 Tanya Kutyavin,1 Sandra Stehling-Sun,1 Audra K. Johnson,1 Theresa K. Canfield,1 Erika Giste,1 Morgan Diegel,1 Daniel Bates,1 R. Scott Hansen,4 Shane Neph,1 Peter J. Sabo,1 Shelly Heimfeld,5 Antony Raubitschek,6 Steven Ziegler,6 Chris Cotsapas,7,8 Nona Sotoodehnia,3,9 Ian Glass,10 Shamil R. Sunyaev,11 Rajinder Kaul,4 John A. Stamatoyannopoulos1,12 † Genome-wide association studies have identified many noncoding variants associated with common diseases and traits. We show that these variants are concentrated in regulatory DNA marked by deoxyribonuclease I (DNase I) hypersensitive sites (DHSs). Eighty-eight percent of such DHSs are active during fetal development and are enriched in variants associated with gestational exposure–related phenotypes. We identified distant gene targets for hundreds of variant-containing DHSs that may explain phenotype associations. Disease-associated variants systematically perturb transcription factor recognition sequences, frequently alter allelic chromatin states, and form regulatory networks. We also demonstrated tissue-selective enrichment of more weakly disease-associated variants within DHSs and the de novo identification of pathogenic cell types for Crohn’s disease, multiple sclerosis, and an electrocardiogram trait, without prior knowledge of physiological mechanisms. Our results suggest pervasive involvement of regulatory DNA variation in common human disease and provide pathogenic insights into diverse disorders. D isease- and trait-associated genetic variants are rapidly being identified with genome- wide association studies (GWAS) and re- lated strategies (1). To date, hundreds of GWAS have been conducted, spanning diverse diseases and quantitative phenotypes (2) (fig. S1A). How- ever, the majority (~93%) of disease- and trait- associated variants emerging from these studies lie within noncoding sequence (fig. S1B), com- plicating their functional evaluation. Several lines of evidence suggest the involvement of a propor- tion of such variants in transcriptional regulatory mechanisms, including modulation of promoter and enhancer elements (3–6) and enrichment with- in expression quantitative trait loci (eQTL) (3, 7, 8). Human regulatory DNA encompasses a vari- ety of cis-regulatory elements within which the co- operative binding of transcription factors creates focal alterations in chromatin structure. Deoxy- ribonuclease I (DNase I) hypersensitive sites (DHSs) are sensitive and precise markers of this actuated regulatory DNA, and DNase I mapping has been instrumental in the discovery and census of hu- man cis-regulatory elements (9). We performed DNase I mapping genome-wide (10) in 349 cell and tissue samples, including 85 cell types studied under the ENCODE Project (10) and 264 sam- ples studied under the Roadmap Epigenomics Program (11). These encompass several classes nome. In total, we identified 3,899,693 distinct DHS positions along the genome (collectively spanning 42.2%), each of which was detected in one or more cell or tissue types (median = 5). Disease- and trait-associated variants are concentrated in regulatory DNA. We examined the distribution of 5654 noncoding genome-wide significant associations [5134 unique single- nucleotide polymorphisms (SNPs); fig. S1 and table S2] for 207 diseases and 447 quantitative traits (2) with the deep genome-scale maps of regulatory DNA marked by DHSs. This revealed a collective 40% enrichment of GWAS SNPs in DHSs (fig. S1C, P < 10βˆ’55 , binomial, compared to the distribution of HapMap SNPs). Fully 76.6% of all noncoding GWAS SNPs either lie within a DHS (57.1%, 2931 SNPs) or are in complete linkage disequilibrium (LD) with SNPs in a near- by DHS (19.5%, 999 SNPs) (Fig. 1A) (12). To con- firm this enrichment, we sampled variants from the 1000 Genomes Project (13) with the same ge- nomic feature localization (intronic versus inter- genic), distance from the nearest transcriptional start site, and allele frequency in individuals of European ancestry. We confirmed significant en- richment both for SNPs within DHSs (P < 10βˆ’59 , simulation) and also including variants in com- plete LD (r 2 = 1) with SNPs in DHSs (P < 10βˆ’37 , simulation) (fig. S2). In total, 47.5% of GWAS SNPs fall within gene bodies (fig. S1B); however, only 10.9% of intronic GWAS SNPs within DHSs are in strong LD (r2 β‰₯ 0.8) with a coding SNP, indicating that the vast majority of noncoding genic variants are not simply tagging coding sequence. Analo- gously, only 16.3% of GWAS variants within coding sequences are in strong LD with variants in DHSs. SNPs on widely used genotyping arrays (e.g., Affymetrix) were modestly enriched with- in DHSs (fig. S2), possibly due to selection of SNPs with robust experimental performance in genotyping assays. However, we found no evi- dence for sequence composition bias (table S3). To further examine the enrichment of GWAS SNPs in regulatory DNA, we systematically clas- sified all noncoding GWAS SNPs by the quality 1 Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. 2 Laboratory of Disease Genomics RESEARCH ARTICLE onSeptember12,2012www.sciencemag.orgDownloadedfrom
  • 52. There have been few, if any, similar bursts of discovery in the history of medical research. David Hunter and Peter Kraft, NEJM, 2007
  • 53. Common claims discussed in regards to GWAS: Despite issues, yielded many discoveries vs. cost to a doubling of the number of associated variants discov- ered. The proportion of genetic variation explained by significantly associated SNPs is usually low (typically less than 10%) for many complex traits, but for diseases such as CD and multiple sclerosis (MS [MIM 126200]), and for quantitative traits such as height and lipid traits, between Figure 1. GWAS Discoveries over Time Data obtained from the Published GWAS Catalog (see Web Resources). Only the top SNPs representing loci with association p values < 5 3 10Γ€8 are included, and so that multiple counting is avoided, SNPs identified for the same traits with LD r2 > 0.8 esti- mated from the entire HapMap samples are excluded. ~500,000 SNP chips x ~$500/chip = $250M Five years of GWAS Discovery (Visscher, 2012) $250M / ~2000 loci = $125K/locus Candidate genes: >$250M! 100 NIH R01s Fighter jet Hadron Collider: $9B
  • 54. P = G + EType 2 Diabetes Cancer Alzheimer’s Gene expression Phenotype Genome Variants Environment Infectious agents Nutrients Pollutants Drugs Complex traits are a function of genes and environment...
  • 55. Nothing comparable to elucidate E influence! We lack high-throughput methods and data to discover new E in P… E: ???
  • 56. A similar paradigm for discovery should exist for E! Why?
  • 57. Οƒ2 P = Οƒ2 G + Οƒ2 E
  • 58. Οƒ2 G Οƒ2 P H2 = Heritability (H2) is the range of phenotypic variability attributed to genetic variability in a population Indicator of the proportion of phenotypic differences attributed to G.
  • 59. Height is an example of a heritable trait: Francis Galton shows how its done (1887) β€œmid-height of 205 parents described 60% of variability of 928 offspring”