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Tree-based QTL mapping with expected local genetic relatedness matrices
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 tes...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104234/ https://www.ncbi.nlm.nih.gov/pubmed/37066144 http://dx.doi.org/10.1101/2023.04.07.536093 |
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author | Link, Vivian Schraiber, Joshua G. Fan, Caoqi Dinh, Bryan Mancuso, Nicholas Chiang, Charleston W.K. Edge, Michael D. |
author_facet | Link, Vivian Schraiber, Joshua G. Fan, Caoqi Dinh, Bryan Mancuso, Nicholas Chiang, Charleston W.K. Edge, Michael D. |
author_sort | Link, Vivian |
collection | PubMed |
description | 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 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. |
format | Online Article Text |
id | pubmed-10104234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-101042342023-04-15 Tree-based QTL mapping with expected local genetic relatedness matrices Link, Vivian Schraiber, Joshua G. Fan, Caoqi Dinh, Bryan Mancuso, Nicholas Chiang, Charleston W.K. Edge, Michael D. bioRxiv Article 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 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. Cold Spring Harbor Laboratory 2023-04-08 /pmc/articles/PMC10104234/ /pubmed/37066144 http://dx.doi.org/10.1101/2023.04.07.536093 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Link, Vivian Schraiber, Joshua G. Fan, Caoqi Dinh, Bryan Mancuso, Nicholas Chiang, Charleston W.K. Edge, Michael D. Tree-based QTL mapping with expected local genetic relatedness matrices |
title | Tree-based QTL mapping with expected local genetic relatedness matrices |
title_full | Tree-based QTL mapping with expected local genetic relatedness matrices |
title_fullStr | Tree-based QTL mapping with expected local genetic relatedness matrices |
title_full_unstemmed | Tree-based QTL mapping with expected local genetic relatedness matrices |
title_short | Tree-based QTL mapping with expected local genetic relatedness matrices |
title_sort | tree-based qtl mapping with expected local genetic relatedness matrices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104234/ https://www.ncbi.nlm.nih.gov/pubmed/37066144 http://dx.doi.org/10.1101/2023.04.07.536093 |
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