Estimating the causal tissues for complex traits and diseases

Ongen, Halit; Brown, Andrew A.; Delaneau, Olivier; Panousis, Nikolaos; Nica, Alexandra C.; Dermitzakis, Emmanouil T.

doi:10.1101/074682

Cited by 55 publications

(108 citation statements)

References 24 publications

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“…Our analysis also highlights the importance of intermediate trait QTL, including QTLs for metabolic traits and gene expression (mQTLs, geQTLs, eeQTLs, sQTLs and aseQTLs). This is not a surprising result as the significant contribution of different intermediate trait QTLs to complex trait variations have been reported in humans (7,26,(39)(40)(41) and cattle (13,(42)(43)(44). To our knowledge, no study has systematically compared the genetic importance of mQTLs with eQTLs.…”

Section: Discussionmentioning

confidence: 81%

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

Xiang

Berg

MacLeod

et al. 2019

Preprint

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Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (eQTLs) and concentration of metabolites (mQTLs), and under histone modification marks in several tissues were discovered from multi-omics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants and for each set we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r>0.94) between bulls and cows.Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone modification marks and selection signatures. From these results, we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In 7,551 Danish cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution and trait heritability to rank variants and the publicly available FAETH data provides a set of biological priors for cattle genomic selection worldwide.

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Section: Discussionmentioning

confidence: 81%

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

Xiang

Berg

MacLeod

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…These methods fall into two classes. The first class operates on individual genes and includes colocalization tests [6][7][8][9][10] , which identify genes with shared causal variants for their expression levels and disease, and transcriptome-wide association studies [11][12][13][14][15][16] , which identify genes with significant cis-genetic correlations between their expression and disease. The second class of methods operates on the entire genome and partitions disease heritability by SNP categories defined by eQTL status (i.e.…”

Section: Introductionmentioning

confidence: 99%

Quantifying genetic effects on disease mediated by assayed gene expression levels

Yao

O’Connor

Price

et al. 2019

Preprint

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Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs). However, it remains unclear whether this overlap is driven by mediation of genetic effects on disease by expression levels, or whether it primarily reflects pleiotropic relationships instead. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of assayed steady-state gene expression levels, using summary association statistics from GWAS and eQTL studies. We show that MESC produces robust estimates of expression-mediated heritability across a wide range of simulations. We applied MESC to GWAS summary statistics for 42 diseases and complex traits (average N = 323K) and cis-eQTL data across 48 tissues from the GTEx consortium. We determined that a statistically significant but low proportion of disease heritability (mean estimate 11% with S.E. 2%) is mediated by the cis-genetic component of assayed gene expression levels, with substantial variation across diseases (point estimates from 0% to 38%). We further partitioned expression-mediated heritability across various gene sets. We observed an inverse relationship between cis-heritability of expression and disease heritability mediated by expression, suggesting that genes with weaker eQTLs have larger causal effects on disease. Moreover, we observed broad patterns of expression-mediated heritability enrichment across functional gene sets that implicate specific gene sets in disease, including loss-of-function intolerant genes and FDA-approved drug targets. Our results demonstrate that eQTLs estimated from steady-state expression levels in bulk tissues are informative of regulatory disease mechanisms, but that such eQTLs are insufficient to explain the majority of disease heritability. Instead, additional assays are necessary to more fully capture the regulatory effects of GWAS variants.

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“…Co-localization of GWAS variants and eQTLs were performed using both COLOC 40 and RTC 41 . For the analysis using COLOC, all variants within 250 kilobase flanking regions around the index variants were tested for co-localization using default parameters from the software were used on summary statistics from T2D GWAS 5 and fasting glucose 35 .…”

Section: Co-localization Of Islet Eqtl With T2d Gwasmentioning

confidence: 99%

Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes

Viñuela

Varshney

Bunt

et al. 2019

Preprint

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Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

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Estimating the causal tissues for complex traits and diseases

Cited by 55 publications

References 24 publications

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

Quantifying genetic effects on disease mediated by assayed gene expression levels

Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes

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