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

Abstract

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.

Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits

“…More recent polygenic epistasis models make simplifying assumptions to improve power and interpretation. For example, autosome-sex interactions represent a particular form of epistasis that strongly affects many complex traits [53][54][55][56], and interactions between a locus (or region) with a polygenic background can identify epistasis hubs [57][58][59][60][61][62]. EFA instead assumes that polygenic epistasis is driven by interactions among a few latent polygenic pathways.…”

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

“…The marginal epistatic testing strategy offers an alternative to traditional epistatic mapping methods by seeking to identify variants that exhibit nonzero interaction effects with any other variant in the data ( Crawford et al 2017 ; Crawford and Zhou 2018 ; Turchin et al 2020 ). This framework has been shown to drastically reduce the number of statistical tests needed to uncover evidence of significant nonadditive variation in complex traits and, as a result, alleviates much of the empirical power concerns and heavy computational burden associated with explicit search-based methods.…”

“…For example, the “MArginal ePIstasis Test” (MAPIT) ( Crawford et al 2017 ) assesses each variant (in turn) and identifies candidate markers that are involved in epistasis without the need to identify the exact partners with which the variants interact—thus, alleviating much of the statistical power concerns and heavy computational burdens associated with explicit search-based methods. As a framework, the marginal epistatic strategy has been implemented in both linear mixed models and machine learning and has been used for case–control studies ( Crawford and Zhou 2018 ), pathway enrichment applications ( Turchin et al 2020 ), heritability estimation ( Darnell et al 2022 ), and even extended to explore different sources of nonadditive genetic variation (e.g. gene-by-environment interactions) ( Moore et al 2019 ; Kerin and Marchini 2020b ).…”

Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies

“…The marginal epistatic testing strategy offers an alternative to traditional epistatic mapping methods by seeking to identify variants that exhibit non-zero interaction effects with any other variant in the data 81–83 . This framework has been shown to drastically reduce the number of statistical tests needed to uncover evidence of significant non-additive variation in complex traits and, as a result, alleviates much of the empirical power concerns and heavy computational burden associated with explicit search-based methods.…”

“…For example, the “MArginal ePIstasis Test” (MAPIT) 81 assesses each variant (in turn) and identifies candidate markers that are involved in epistasis without the need to identify the exact partners with which the variants interact — thus, alleviating much of the statistical power concerns and heavy computational burdens associated with explicit search-based methods. As a framework, the marginal epistatic strategy has been implemented in both linear mixed models and machine learning and has been used for case-control studies 82 , pathway enrichment applications 83 , heritability estimation 12 , and even extended to explore different sources of non-additive genetic variation (e.g., gene-by-environment interactions) 84,85 . However, despite its wide adoption, this approach can still be underpowered for traits with low heritability or “polygenic” traits which are generated by many mutations of small effect 81 .…”