Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

Wallace, Chris

doi:10.1371/journal.pgen.1008720

Cited by 274 publications

(249 citation statements)

References 57 publications

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“…However, we find that neither LocalAA nor GlobalAA in eQTL mapping of seven different tissues yield systematically stronger colocalizations across 142 GWAS. Limitations of our colocalization analyses include our use of the assumption of one causal variant per trait and our lack of an attempt to colocalize secondary signals [43,44].…”

Section: Discussionmentioning

confidence: 99%

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

et al. 2020

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Background Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization. Results Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up. Conclusions We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

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Section: Discussionmentioning

confidence: 99%

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

et al. 2020

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“…In the case that such historical information is unavailable, we recommend to estimate required hyper-parameters from the observed data. Recently, [17] proposes to perform sensitivity analysis of the priors for identified colocalization signals. While we com-pletely agree that understanding prior sensitivity is critical for practitioners, it should be noted that sensitivity analysis alone does not provide a justification for selecting a specific set of priors.…”

Section: Specification Of Enrichment Parametermentioning

confidence: 99%

Probabilistic Colocalization of Genetic Variants from Complex and Molecular Traits: Promise and Limitations

Hukku

Pividori

Luca

et al. 2020

Preprint

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AbstractColocalization analysis has emerged as a powerful tool to uncover the overlapping of causal variants responsible for both molecular and complex disease phenotypes. The findings from colocalization analysis yield insights into the molecular pathways of complex diseases. In this paper, we conduct an in-depth investigation of the promise and limitations of the available colocalization analysis approaches. Focusing on variant-level colocalization approaches, we first establish the connections between various existing methods. We proceed to discuss the impacts of various controllable analytical factors and uncontrollable practical factors on outcomes of colocalization analysis through realistic simulations and real data examples. We identify a single analytical factor, the specification of prior enrichment levels, which can lead to severe inflation of false-positive colocalization findings. Meanwhile, the combination of many other analytical and practical factors all lead to diminished power. Consequently, we recommend the following strategies for the best practice of colocalization analysis: i) estimating prior enrichment level from the observed data; and ii) separating fine-mapping and colocalization analysis. Our analysis of 4,091 complex traits and the multi-tissue eQTL data from the GTEx (version 8) suggests that colocalizations of molecular QTLs and GWAS traits are widespread in many complex traits. However, only a small proportion can be confidently identified from currently available data due to a lack of power. Our findings should serve as an important benchmark for the current and future integrative genetic association analysis applications.

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“…For example, linking MAPIT-R with a framework that explicitly follows up on marginal epistasis signals with locus-focused methods such as fine-mapping [161][162][163] or co-localization [164][165][166][167][168] Here, subgroups in the UK Biobank included individuals based on their self-identified ancestries: "African", "British", "Caribbean", "Chinese", "Indian", and "Pakistani" (see legend to the right of panel (b)). Genome-wide significance was determined by using Bonferroni-corrected p-value thresholds based on the number of pathways tested in each database-phenotype-subgroup combination (see Supplementary Table 1).…”

Section: Discussionmentioning

confidence: 99%

Pathway Analysis within Multiple Human Ancestries Reveals Novel Signals for Epistasis in Complex Traits

Turchin

Darnell

Crawford

et al. 2020

Preprint

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Genome-wide association (GWA) studies have identified thousands of significant genetic associations in humans across a number of complex traits. However, the majority of these studies focus on linear additive relationships between genotypic and phenotypic variation. Epistasis, or non-additive genetic interactions, has been identified as a major driver of both complex trait architecture and evolution in multiple model organisms; yet, this same phenomenon is not considered to be a significant factor underlying human complex traits. There are two possible reasons for this assumption. First, most large GWA studies are conducted solely with European cohorts; therefore, our understanding of broad-sense heritability for many complex traits is limited to just one ancestry group. Second, current epistasis mapping methods commonly identify significant genetic interactions by exhaustively searching across all possible pairs of SNPs. In these frameworks, estimated epistatic effects size are often small and power can be low due to the multiple testing burden. Here, we present a case study that uses a novel region-based mapping approach to analyze sets of variants for the presence of epistatic effects across six diverse subgroups within the UK Biobank. We refer to this method as the “MArginal ePIstasis Test for Regions” or MAPIT-R. Even with limited sample sizes, we find a total of 245 pathways within the KEGG and REACTOME databases that are significantly enriched for epistatic effects in height and body mass index (BMI), with 67% of these pathways being detected within individuals of African ancestry. As a secondary analysis, we introduce a novel region-based “leave-one-out” approach to localize pathway-level epistatic signals to specific interacting genes in BMI. Overall, our results indicate that non-European ancestry populations may be better suited for the discovery of non-additive genetic variation in human complex traits — further underscoring the need for publicly available, biobank-sized datasets of diverse groups of individuals.

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Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

Cited by 274 publications

References 57 publications

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

Probabilistic Colocalization of Genetic Variants from Complex and Molecular Traits: Promise and Limitations

Pathway Analysis within Multiple Human Ancestries Reveals Novel Signals for Epistasis in Complex Traits

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