Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution

Wu, Ying; Broadaway, K. Alaine; Raulerson, Chelsea K.; Scott, Laura J.; Pan, Calvin; Ko, Arthur; He, Aiqing; Tilford, Charles; Fuchsberger, Christian; Locke, Adam E.; Stringham, Heather M.; Jackson, Anne; Narisu, Narisu; Kuusisto, Johanna; Pajukanta, Päivi; Collins, Francis S.; Boehnke, Michael; Laakso, Markku; Lusis, Aldons J.; Civelek, Mete; Mohlke, Karen L.

doi:10.1093/hmg/ddz263

Cited by 47 publications

(37 citation statements)

References 47 publications

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“…Of note, only two-thirds of our putative causal associations had strong evidence of colocalization, suggesting that a substantial proportion of the initial findings were likely to be driven by genetic confounding through LD between pQTLs and other disease-causal SNPs. To avoid misleading results, we suggest that for regions with multiple molecular trait QTLs, it is important to consider methods such as PWCoCo, which can avoid the assumptions of traditional colocalization approaches of just a single association signal per region 46 . In the current study, application of PWCoCo identified evidence of colocalization for 23 additional protein-phenotype associations hidden to marginal colocalization 46 .…”

Section: Discussionmentioning

confidence: 99%

“…To avoid misleading results, we suggest that for regions with multiple molecular trait QTLs, it is important to consider methods such as PWCoCo, which can avoid the assumptions of traditional colocalization approaches of just a single association signal per region 46 . In the current study, application of PWCoCo identified evidence of colocalization for 23 additional protein-phenotype associations hidden to marginal colocalization 46 . We note that recent recommendations support the use of colocalization as a follow up analysis to reduce false positives 47 .…”

Section: Discussionmentioning

confidence: 99%

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Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

et al. 2020

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The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium (LD) is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

et al. 2020

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“…This analysis incorporated information about significantly associated variants for AD risk obtained from a recent large GWAS 25 and lead eQTL variants each defined as the eSNP showing the strongest association with gene expression. Following recommended guidelines, the variants were deemed to be colocalized by a high posterior probability that a single shared variant is responsible for both signals (PP4 > 0.8) 24, 26 . A lower threshold for statistical significance with a false discovery rate (FDR) < 0.05 for eQTL significant results was applied to maximize detection of colocalized pairs.…”

Section: Materials Subjects and Methodsmentioning

confidence: 99%

Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

Patel

Farrell

Chung

et al. 2020

Preprint

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Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

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“…We performed a colocalization analysis using the method similar to that implemented by Wu et al [43]. For the colocalization of eQTL and pQTL signals, we were unable to determine lead eQTL variants that had the strongest evidence of association with mRNA expression because eQTLs were obtained from multiple studies.…”

Section: Colocalization Analysismentioning

confidence: 99%

Genome-wide pQTL analysis of protein expression regulatory networks in the human liver

Shi

Wang

et al. 2020

BMC Biol

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Background: Previous expression quantitative trait loci (eQTL) studies have identified thousands of genetic variants to be associated with gene expression at the mRNA level in the human liver. However, protein expression often correlates poorly with mRNA levels. Thus, protein quantitative trait loci (pQTL) study is required to identify genetic variants that regulate protein expression in human livers. Results: We conducted a genome-wide pQTL study in 287 normal human liver samples and identified 900 local pQTL variants and 4026 distant pQTL variants. We further discovered 53 genome hotspots of pQTL variants. Transcriptional region mapping analysis showed that 1133 pQTL variants are in transcriptional regulatory regions. Genomic region enrichment analysis of the identified pQTL variants revealed 804 potential regulatory interactions among 595 predicted regulators (e.g., non-coding RNAs) and 394 proteins. Moreover, pQTL variants and trait-variant integration analysis implied several novel mechanisms underlying the relationships between protein expression and liver diseases, such as alcohol dependence. Notably, over 2000 of the identified pQTL variants have not been reported in previous eQTL studies, suggesting extensive involvement of genetic polymorphisms in posttranscriptional regulation of protein expression in human livers. Conclusions: We have partially established protein expression regulation networks in human livers and generated a wealth of pQTL data that could serve as a valuable resource for the scientific community.

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Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution

Cited by 47 publications

References 47 publications

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

Genome-wide pQTL analysis of protein expression regulatory networks in the human liver

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