Understanding mechanisms underlying human gene expression variation with RNA sequencing

Goals: ,[object Object],[object Object],[object Object]

Lymphoblastoid cell lines: a model system for understanding the genetics of gene regulation ,[object Object],Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Genomic Data : mRNA expression, DNA methylation, histone marks, etc.

Lymphoblastoid cell lines: a model system for understanding the genetics of gene regulation Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project This talk: 69 cell lines derived from white blood cells from Nigerian individuals

AAAAAAAAAA DNA RNA RNA-Seq 1: Isolate poly-A RNA, convert to cDNA

AAAAAAAAAA DNA RNA RNA-Seq 2: Generate short sequencing reads from cDNA library (using Illumina GA2)

AAAAAAAAAA DNA RNA RNA-Seq 3: Map short reads back to genome; count of reads/gene measures expression

Lymphoblastoid cell lines: a model system for understanding the genetics of gene regulation ,[object Object],AAAAAAAAAA Sequence RNA from each line (total 1.2 billion sequencing reads), average expression levels across lines

RNA-Seq identifies new polyadenylation sites

Revisiting gene annotations in these cells ,[object Object],[object Object],[object Object],[object Object],[object Object]

Lymphoblastoid cell lines: a model system for understanding the genetics of gene regulation ,[object Object],AAAAAAAAAA Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Genotypes : > 4M Single Nucleotide Polymorphisms (SNPs) from the HapMap Project Expression : from RNA-Seq

(Natural) Genetic variation potentially affects many levels of gene regulation DNA 1. Transcription Initiation Chromatin accessibility TF Binding 2. mRNA processing Splicing Polyadenylation Capping, export 3. mRNA degradation microRNA regulation NMD 4. Translation, etc tRNA abundances Protein localization Protein degradation

AAAAAAAAAA DNA RNA RNA-Seq 3: Map short reads back to genome; count of reads/gene measures expression C

AAAAAAAAAA DNA RNA AAAAAAAAAA T

[object Object],[object Object]

Polymorphisms near the transcription start site of a gene are the most likely to affect its transcription ,[object Object],[object Object],Black: bins within the genic region Blue: bins outside the genic region L See also Veyrieras et al. (2008), Stranger et al. (2007), Cheung et al. (2005)

where are SNPs that affect splicing? ,[object Object],[object Object]

Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]

http://dx.doi.org/10.1038/nature08872 http://eqtl.uchicago.edu

Understanding mechanisms underlying human gene expression variation with RNA sequencing

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Destaque

Destaque (20)

Semelhante a Understanding mechanisms underlying human gene expression variation with RNA sequencing

Semelhante a Understanding mechanisms underlying human gene expression variation with RNA sequencing (20)

Último

Último (20)

Understanding mechanisms underlying human gene expression variation with RNA sequencing