Estimating the causal tissues for complex traits and diseases

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

doi:10.1038/ng.3981

Cited by 175 publications

(147 citation statements)

References 39 publications

(38 reference statements)

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“…4). Consistent with previous reports (11,38), we identied several instances in which the most signicant tissue is supported by current biological knowledge. For example, blood cell count traits were enriched in whole blood, neuroticism and uid intelligence in brain/pituitary, hypothyrodism in thyroid, coronary artery disease in artery, and cholesterol-related traits in liver.…”

Section: Tissue Enrichment Of Gwas Signalssupporting

confidence: 89%

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Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Barbeira

Bonazzola

Gamazon

et al. 2019

Preprint

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The resources generated by the GTEx consortium oer unprecedented opportunities to advance our understanding of the biology of human traits and diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genetic loci discovered by genome-wide association studies (GWAS). Across a broad set of complex traits and diseases, we nd widespread dosedependent eects of RNA expression and splicing, with higher impact on molecular phenotypes translating into higher impact downstream. Using colocalization and association approaches that take into account the observed allelic heterogeneity, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotypephenotype link. Harmonized GWAS and QTL datasetsThe nal GTEx data release (v8) includes 54 primary human tissues, 49 of which included at least 65 samples and were used for cis-QTL mapping ( Fig. 1) (9). This phase increases the number of available tissues relative to previous GTEx publications (v6p; 44 tissues) (8) and doubles the sample size from 7,051 RNA-Seq samples from 449 individuals to 15,253 samples from 838 individuals, now all with whole genome sequencing data as opposed to genotype imputation in v6p. Furthermore, the v8 core data resources now include splicing QTLs (9), allowing parallel analysis of both expression and splicing variation underlying complex traits. Using these resources, we investigated the contribution of expression and splicing QTLs in cis (eQTL and sQTL, respectively) to complex trait variance and etiology.We retained 87 GWAS datasets representing 74 distinct complex traits for further analyses (table S1 and g. S1) after stringent quality control (g. S2; (21)) and data harmonization(g. S3, g. S4). 6We found a signicantly higher correlation in mediating eect between primary and secondary eQTLs for a given gene compared to a null distribution obtained by sampling GWAS eect sizes from a bivariate normal distribution to account for the small observed LD between primary and secondary eQTLs ( Fig. 2D-E) while keeping the observed eQTL eect sizes (p < 1 × 10 −30 ).Interestingly, the correlation between primary and secondary eQTLs for non-colocalized genes (rcp < 0.01), which were used as controls (9, 21), was signicantly higher than this more accurate null, indicating that even eQTLs with very low colocalization probability include many genes that are likely causal. Given this concordance between multiple independent eQTLs, it is clear that with widespread allelic heterogeneity detected with currently available sample sizes, methods that assume single causal variants are highly limited. The approaches described here enable insights into how multiple regulatory effects converge to mediate the same trait association. 7 * alphabetic order

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Section: Tissue Enrichment Of Gwas Signalssupporting

confidence: 89%

“…GWAS imputation quality Original versus imputed zscores for palindromic variants in chromosome 1 for 3 traits. 38 Supplementary Fig. S4.…”

Section: Genome-wide Association Studies (Gwas) Harmonizationmentioning

confidence: 99%

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Barbeira

Bonazzola

Gamazon

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…It is also possible that CoCoNet is not yet optimized to extract and take advantage of the tissue specific gene co-expression information accurately and in a maximized fashion. Certainly, similar negative results are frequently observed in existing literature on trait-relevance inference [72][73][74]. For example, lung is inferred as the top tissue for autoimmune disease UC while…”

Section: Plos Geneticssupporting

confidence: 78%

Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies

2020

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Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect measurements for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases.

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“…WAA percentage, gender, age, platform and batch were used as covariates for downstream analysis. Probabilistic estimation of expression residuals (PEER) method v1.3 was used to identify PEER factors and linear regression was run on inverse normal transformed expression data using five PEER factors, based on GTEx's determination of number of factors as a function of sample size (Consortium et al, 2017;Stegle, Parts, Piipari, Winn, & Durbin, 2012).…”

Section: Rna-seq Data Analysismentioning

confidence: 99%

“…Due to the key role of the liver in biosynthesis, drug metabolism and complex human diseases, genetic and epigenetic differences in the liver may uncover the underlying causal genes responsible for health disparities in AAs (Ongen et al, 2017;F. S. Wang, Fan, Zhang, Gao, & Wang, 2014).…”

Section: Introductionmentioning

confidence: 99%

Uncovering the role of admixture in disease and drug response: Association of hepatocyte gene expression and DNA methylation with African Ancestry in African Americans

Park¹,

De²,

et al. 2018

Preprint

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BackgroundAfrican Americans (AAs) are an admixed population with portions of their genome derived from West Africans and Europeans. In AAs, the proportion of West African ancestry (WAA) can vary widely and may explain the genetic drivers of disease, specifically those that disproportionately affect this understudied population. To examine the relationship between the proportion of WAA and gene expression, we used high dimensional data obtained from AA primary hepatocytes, a tissue important in disease and drug response.MethodsRNA sequencing (Illumina HiSeq Platform) was conducted on 60 AA-derived primary hepatocytes, with methylation profiling (Illumina MethylationEPIC BeadChip) of 44 overlapping samples. WAA for each sample was calculated using fastSTRUCTURE and correlated to both gene expression and DNA methylation. The GTEx consortium (n = 15) was used for replication and a second cohort (n = 206) was using used for validation using differential gene expression between AAs and European-Americans.ResultsWe identified 131 genes associated with WAA (FDR< 0.1), of which 28 gene expression traits were replicated (FDR<0.1) and enriched in angiogenesis and inflammatory pathways (FDR<0.1). These 28 replicated gene expression traits represented 257 GWAS catalog phenotypes. Among the PharmGKB pharmacogenes, VDR, PTGIS, ALDH1A1, CYP2C19 and P2RY1 were associated with WAA (p < 0.05) with replication of CYP2C19 and VDR in GTEx. Association of DNA methylation with WAA identified 1037 differentially methylated regions (FDR<0.05), with hypomethylated genes enriched in drug response pathways. Overlapping of differentially methylated regions with the 131 significantly correlated gene expression traits identified 5 genes with concordant directions of effect: COL26A1, HIC1, MKNK2, RNF135, SNAI1 and TRIM39.ConclusionsWe conclude that WAA contributes to variability in hepatic gene expression and DNA methylation with identified genes indicative of diseases disproportionately affecting AAs. Specifically, WAA-associated genes were linked to previously identified loci in cardiovascular disease (PTGIS, PLAT), renal disease (APOL1) and drug response (CYP2C19).

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Estimating the causal tissues for complex traits and diseases

Cited by 175 publications

References 39 publications

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies

Uncovering the role of admixture in disease and drug response: Association of hepatocyte gene expression and DNA methylation with African Ancestry in African Americans

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