Tree-based QTL mapping with expected local genetic relatedness matrices

Abstract

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the \textit{CREBRF} gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics

“…These LD graphical models enable orders-of-magnitude reductions in computation time and memory usage for LD matrix computations and facilitate better polygenic prediction compared to a similar method using the LD correlation matrix. As another example, [ 76 ] found that an expected genetic relatedness matrix calculated from an ARG in a given genomic region more accurately captures relationships than the empirical genetic relatedness matrix calculated in the same region. The higher accuracy of these approaches may seem counterintuitive; after all, empirical ARGs are estimated from genomic data, so how could statistical inferences conducted on an ARG be more accurate than those made directly from the genotype matrix?…”

The Promise of Inferring the Past Using the Ancestral Recombination Graph

“…For instance, researchers have studied the evolution of complex traits by combining the inferred ARG with genome-wide association studies data to analyze how directional selection has potentially shaped the evolution of phenotypic traits ( Speidel et al 2019 ; Stern et al 2020 ) or to analyze the evolution of polygenic scores ( Edge and Coop 2019 ). The inferred ARG and the GRM derived from it can also improve the robustness and power of association analysis to identify novel trait-associated loci, particularly in under-resourced populations or under complicated models of genetic architecture such as allelic heterogeneity ( Link et al 2023 ; Zhang et al 2023 ).…”

“…Across these applications, 2 broad categories of analysis currently leveraging the inferred ARG emerge. One is based on computing the expectation of a statistic from the inferred ARG ( Ralph et al 2020 ) as in the case of the eGRM ( Fan et al 2022 ; Link et al 2023 ; Zhang et al 2023 ). Another category of analyses uses a model-based approach to estimate an evolutionary parameter of interest as in CLUES ( Stern et al 2019 ), PALM ( Stern et al 2020 ), or SIA ( Hejase et al 2021 ).…”