1. VAAST
Deciphering Genetic Disease with Next-Generation
Sequencing
Barry Moore, M.S.
Research Scientist
Department of Human Genetics
Department of Biomedical Informatics

2. Outline
 The VAAST Analysis Pipeline
 Ogden Syndrome: Application of VAAST to a Genetic Disease
of Unknown Cause
 The Future of VAAST Development

3. $10,000,000
Venter Genome
$1,000,000
Watson
$5,000
You?

4. Next Generation Sequencing
Disease
Healthy
geneA geneB geneX geneY geneZ

5. Variant
Variant Annotation
Annotation Tool
Variant
Variant Selection
Selection Tool
Variant
Variant Annotation
Analysis
Analysis Search
Tool

6. GVF
VAAST Pipeline 3.5 Million
Variants
Reference VAT Reference
Genome (Variant Annotation Tool) Genes
Fasta GFF3
Annotated Annotated Annotated
GVF
Variants Variants Variants
VST
(Variant Selection Tool)
CDR
Merged
Variant Sets

7. GVF
VAAST Pipeline Variant Effect
3.5 Million
•sequence_variant
Variants
•gene_variant
Reference VAT Reference
•five_prime_UTR_variant
Genome Type
Variant Genes
•three_prime_UTR_variant
(Variant Annotation Tool)
•sequence_alteration
Fasta •exon_variant GFF3
•deletion •splice_region_variant
•insertion •splice_donor_variant
•duplication
Annotated Annotated •splice_acceptor_variant
Annotated
•inversion GVF •intron_variant
Variants
•substitution
Variants Variants
•coding_sequence_variant
•SNV •stop_retained
•MNP •stop_lost
•complex substitution •stop_gained
•translocation VST •synonymous_codon
•non_synonymous_codon
(Variant Selection Tool)
•amino_acid_substitution
•frameshift_variant
•inframe_variant
CDR
Merged
Variant Sets

8. GVF
VAAST Pipeline Variant Effect
3.5 Million
•sequence_variant
Variants
•gene_variant
Reference VAT Reference
•five_prime_UTR_variant
Genome Type
Variant Genes
•three_prime_UTR_variant
(Variant Annotation Tool)
•sequence_alteration
Fasta •exon_variant GFF3
•deletion •splice_region_variant
•insertion •splice_donor_variant
•duplication
Annotated Annotated •splice_acceptor_variant
Annotated
•inversion GVF •intron_variant
Variants
•substitution
Variants Variants
•coding_sequence_variant
•SNV •stop_retained
•MNP •stop_lost
•complex substitution •stop_gained
•translocation VST •synonymous_codon
•non_synonymous_codon
(Variant Selection Tool)
•amino_acid_substitution
•frameshift_variant
•inframe_variant
CDR
Merged
Variant Sets

9. CDR CDR
Background Target
Genomes Genomes
VAAST
Prioritized
Candidate
Genes
VAAST
Report

10. Key Features of VAAST
• Probabilistic
• Feature Based
• Both Allele and AAS Frequencies
• Considers Inheritance Model
• Fast
• Standardized Ontology Based Format
• Modular and Flexible in Design

11. VAAST Uses Variant Frequencies in a
Probabilistic Fashion
Likelihood Ratio Test
Maximum Likelihood
of the Null Model
(No Difference)
Maximum Likelihood
of the Alternate Model
(There is Difference)

12. VAAST Uses Variant Frequencies in a
Probabilistic Fashion

13. VAAST Uses Variant Frequencies in a
Probabilistic Fashion
• VAAST gives us the likelihood of the composite genotype
at GENE X in the target given the background.
• Do allele frequencies differ between Background and
Target genomes within a given gene or feature?
• Composite likelihood calculation assumes independence
across sites. To control for LD, statistical significance is
estimated by permutation test.
• Multiple test correction for number of features (~20,000)
is two orders of magnitude better than for the number of
variants (~3,500,000).

14. Noise Decreases Dramatically with
Increasing Number of Genomes
1 genome target
1 genome background

15. 1 genome target
10 genome background

16. 1 genome target
250 genome background

17. 1 genome target
250 genome background
Trio Data

19. Alleles Responsible for Miller
Syndrome in Utah Kindred
CHR 16: DHODH CHR 5: DNAH5
Mom Dad Mom Dad
G:R R:Q
G:A R:
*
Son Daughter Son Daughter
G:R G:R R:Q R:Q
R: R:
G:A G:A
* *
•Ng et al, Nature Genetics 42, 30–35 (2010) doi:10.1038/ng.499
•Roach, et al, Science , 328 636, 2101

20. Schematic of VAAST Analysis of Utah
Miller Kindred Using a Single Quartet
DHODH
DNAH5

21. Average Rank for 100 Dominant and
Recessive Diseases
1300
Ave. rank genome-wide
SIZE OF CASE COHORT
1100
2 allele copies
900
4 allele copies
700
6 allele copies
500
300
156 132
100 21 9 8 3
-100
DOMINANT RECESSIVE
-300
-500 443 genomes in background

22. Impact of Missing Data
4000
3500
2 of 6 allele copies
Ave. rank genome-wide
3000
4 of 6 allele copies
2500
6 of 6 allele copies
2000
1500
1000
639
500 373
61
21
9 3
0
-500
DOMINANT RECESSIVE
443 genomes in background

23. Outline
 The VAAST Analysis Pipeline
 Ogden Syndrome: Application of VAAST to a Genetic
Disease of Unknown Cause
 The Future of VAAST Development

24. An Rare X-linked Mendelian Disorder
• A Utah family coming to the
University Hospital for 20+
years
• About half of the male offspring
die around 1 year of age
• Aged appearance
• Craniofacial anomalies
• Hypotonia
• Global developmental delays
• Cardiac arrhythmias

25. Four Affected Boys over Two
Generations
I
II
III

26. Exome Sequencing
• Agilent SureSelect In-Solution X Chromosome Capture
• Covaris S series Sonication (150-200 bp)
• 76 bp single-end reads on one lane each of the
IlluminaGAIIx
Variant Calling
• Sequence alignment with bwa
• Remove duplicate reads with PICARD
• Realign indel regions with GATK
• Variant calling with Samtools, GATK

27. Identifying Candidate Genes
VAAST Identifies NAA10 as Candidate Gene
• About 20 min. run time
• 3 candidate genes (NAA10 ranked 2) proband only
• 1 candidate gene (NAA10) with pedigree

28. Additional Analyses
• Microarray based CNV analysis
• No likely causal variants found
• Sanger sequencing confirmation
• Variant segregates perfectly with disease in 13
family members
• Haplotype sharing (STR genotyping)
• ~11 MB shared between two affected boys
• A second family discovered – same mutation
• IBD relatedness analysis – independent mutational
events

29. N(alpha)-acetyltransferase
• N-alpha-acetylation is one of the most common protein
modifications that occurs during protein synthesis.
• NatA (catalytic subunit NAA10 (hARD1)
• Eight exons, Crick strand, highly conserved
• A:G transition causes p.Ser37Pro

30. Functional Analyses
• Quantitative in vitro N-terminal acetylation assay (RP-
HPLC).
• Four peptide substrates previously shown to be
acetylated by NatA (NAA10)
• Assays indicate loss-of-function allele.

31. Functional Analyses

33. VAAST in Summary
• Probabilistic Disease Gene Finder
• Feature Based not Variant Based
• Both Allele and AAS Frequencies
• Considers Inheritance Model
• As few as two target genomes can be sufficient to
identify causative gene.
• Background Genomes are “Reusable”
• Not Limited to Human Analyses

34. VAAST: Future Directions
• Indel support
• Splice-site
• No-call support
• Pedigree support
• Phylogenetic conservation

36. Acknowledgements
VAAST Development Ogden
•Chad Huff Syndrome •Thomas Arnesen
•HaoHu •John Carey •Rune Evjenth
•Lynn Jorde •Steven Chin •Johan R. Lillehaug
•Barry Moore •Heidi Deborah Fain
•Martin Reese •Gholson Lyon •Leslie G. Biesecker
•Marc Singleton •John Optiz •Jennifer J.
•Jinchuan Xing •Theodore J. Pysher Johnston
•Mark Yandell •Alan Rope •Cathy A. Stevens
Yandell Lab •Reid Robison
•Sarah T. South •Brian Dalley
•Michael Campbell •Tao Jiang
•Daniel Ence •JeffereySwensen
•Chad Huff
•Guozhen Fan
•Evan Johnson
•Steven Flygare •HakonHakonarson
•Barry Moore
•HaoHu •Lynn B. Jorde
•Christa Schank
•Zev Kronenberg •Mark Yandell
•Kai Wang
•Barry Moore
•Jinchuan Xing
•Marc Singleton
•Robert Ross
•Mark Yandell

37. Acknowledgements