Project description:The genomic distribution of trait-associated SNPs (TASs) discovered in genome-wide association studies (GWAS) can provide insight into the genetic architecture of complex traits and the design of future studies. Here we report on a maize GWAS that identified TASs underlying five quantitative traits measured across a large panel of samples and examine the characteristics of these TASs. A set of SNPs obtained via RNA sequencing (RNA-seq), most of which are located within annotated genes (~87%) were complemented with additional SNPs from the maize HapMap Project that contains approximately equal proportions of intragenic and intergenic SNPs. TASs were identified via a genome scan while controlling for polygenic background effects. The diverse functions of TAS-containing candidate genes indicate that complex genetic networks shape these traits. The vast majority of the TAS-containing candidate genes have dynamic expression levels among developmental stages. Overall, TASs explain 44~54% of the total phenotypic variation for these traits, with equal contributions from intra- and inter-genic TASs. Association of ligueless2 with upper leaf angle was implicated by two intragenic TASs; rough sheath1 was associated with leaf width by an upstream intergenic TAS; and Zea agamous5 was associated with days to silking by both intra- and inter-genic TASs. A large proportion (82%) of these TASs comes from noncoding regions, similar to findings from human diseases and traits. However, TASs were enriched in both intergenic (53%) and promoter 5kb (24%) regions, but under-represented in a set of nonsynonymous SNPs.
Project description:We investigated whether intersecting functional genomic data (ATAC-seq + promoter focused Capture C) with increasingly powered publically available GWAS for Body Mass Index and Waist to Hip Ratio could identify additional true postiive subsignficant signals (5x10-8< P value < 5x10-4) without increasing the GWAS sample size
Project description:We investigated whether intersecting functional genomic data (ATAC-seq + promoter focused Capture C) with increasingly powered publically available GWAS for Body Mass Index and Waist to Hip Ratio could identify additional true postiive subsignficant signals (5x10-8< P value < 5x10-4) without increasing the GWAS sample size
Project description:In this work we present an analytical strategy to systematically identify early regulators by combining gene regulatory networks (GRN) with GWAS. We hypothesized that early regulators in T-cell associated diseases could be found by defining upstream transcription factors (TFs) in T-cell differentiation. Time series expression and DNA methylation profiling of T-cell differentiation identified several upstream TFs, of which TFs involved in Th1/2 differentiation were most enriched for disease associated SNPs identified by GWAS.
Project description:In this work we present an analytical strategy to systematically identify early regulators by combining gene regulatory networks (GRN) with GWAS. We hypothesized that early regulators in T-cell associated diseases could be found by defining upstream transcription factors (TFs) in T-cell differentiation. Time series expression and DNA methylation profiling of T-cell differentiation identified several upstream TFs, of which TFs involved in Th1/2 differentiation were most enriched for disease associated SNPs identified by GWAS.
Project description:In this work we present an analytical strategy to systematically identify early regulators by combining gene regulatory networks (GRN) with GWAS. We hypothesized that early regulators in T-cell associated diseases could be found by defining upstream transcription factors (TFs) in T-cell differentiation. Time series expression and DNA methylation profiling of T-cell differentiation identified several upstream TFs, of which TFs involved in Th1/2 differentiation were most enriched for disease associated SNPs identified by GWAS.