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Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study

Population structure or genetic relatedness should be considered in genome association studies to avoid spurious association. The most used methods for genome-wide association studies (GWAS) account for population structure but are limited to genotyped individuals with phenotypes. Single-step GWAS (...

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Autores principales: Mancin, Enrico, Lourenco, Daniela, Bermann, Matias, Mantovani, Roberto, Misztal, Ignacy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117227/
https://www.ncbi.nlm.nih.gov/pubmed/33995481
http://dx.doi.org/10.3389/fgene.2021.642065
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author Mancin, Enrico
Lourenco, Daniela
Bermann, Matias
Mantovani, Roberto
Misztal, Ignacy
author_facet Mancin, Enrico
Lourenco, Daniela
Bermann, Matias
Mantovani, Roberto
Misztal, Ignacy
author_sort Mancin, Enrico
collection PubMed
description Population structure or genetic relatedness should be considered in genome association studies to avoid spurious association. The most used methods for genome-wide association studies (GWAS) account for population structure but are limited to genotyped individuals with phenotypes. Single-step GWAS (ssGWAS) can use phenotypes from non-genotyped relatives; however, its ability to account for population structure has not been explored. Here we investigate the equivalence among ssGWAS, efficient mixed-model association expedited (EMMAX), and genomic best linear unbiased prediction GWAS (GBLUP-GWAS), and how they differ from the single-SNP analysis without correction for population structure (SSA-NoCor). We used simulated, structured populations that mimicked fish, beef cattle, and dairy cattle populations with 1040, 5525, and 1,400 genotyped individuals, respectively. Larger populations were also simulated that had up to 10-fold more genotyped animals. The genomes were composed by 29 chromosomes, each harboring one QTN, and the number of simulated SNPs was 35,000 for the fish and 65,000 for the beef and dairy cattle populations. Males and females were genotyped in the fish and beef cattle populations, whereas only males had genotypes in the dairy population. Phenotypes for a trait with heritability varying from 0.25 to 0.35 were available in both sexes for the fish population, but only for females in the beef and dairy cattle populations. In the latter, phenotypes of daughters were projected into genotyped sires (i.e., deregressed proofs) before applying EMMAX and SSA-NoCor. Although SSA-NoCor had the largest number of true positive SNPs among the four methods, the number of false negatives was two–fivefold that of true positives. GBLUP-GWAS and EMMAX had a similar number of true positives, which was slightly smaller than in ssGWAS, although the difference was not significant. Additionally, no significant differences were observed when deregressed proofs were used as pseudo-phenotypes in EMMAX compared to daughter phenotypes in ssGWAS for the dairy cattle population. Single-step GWAS accounts for population structure and is a straightforward method for association analysis when only a fraction of the population is genotyped and/or when phenotypes are available on non-genotyped relatives.
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spelling pubmed-81172272021-05-14 Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study Mancin, Enrico Lourenco, Daniela Bermann, Matias Mantovani, Roberto Misztal, Ignacy Front Genet Genetics Population structure or genetic relatedness should be considered in genome association studies to avoid spurious association. The most used methods for genome-wide association studies (GWAS) account for population structure but are limited to genotyped individuals with phenotypes. Single-step GWAS (ssGWAS) can use phenotypes from non-genotyped relatives; however, its ability to account for population structure has not been explored. Here we investigate the equivalence among ssGWAS, efficient mixed-model association expedited (EMMAX), and genomic best linear unbiased prediction GWAS (GBLUP-GWAS), and how they differ from the single-SNP analysis without correction for population structure (SSA-NoCor). We used simulated, structured populations that mimicked fish, beef cattle, and dairy cattle populations with 1040, 5525, and 1,400 genotyped individuals, respectively. Larger populations were also simulated that had up to 10-fold more genotyped animals. The genomes were composed by 29 chromosomes, each harboring one QTN, and the number of simulated SNPs was 35,000 for the fish and 65,000 for the beef and dairy cattle populations. Males and females were genotyped in the fish and beef cattle populations, whereas only males had genotypes in the dairy population. Phenotypes for a trait with heritability varying from 0.25 to 0.35 were available in both sexes for the fish population, but only for females in the beef and dairy cattle populations. In the latter, phenotypes of daughters were projected into genotyped sires (i.e., deregressed proofs) before applying EMMAX and SSA-NoCor. Although SSA-NoCor had the largest number of true positive SNPs among the four methods, the number of false negatives was two–fivefold that of true positives. GBLUP-GWAS and EMMAX had a similar number of true positives, which was slightly smaller than in ssGWAS, although the difference was not significant. Additionally, no significant differences were observed when deregressed proofs were used as pseudo-phenotypes in EMMAX compared to daughter phenotypes in ssGWAS for the dairy cattle population. Single-step GWAS accounts for population structure and is a straightforward method for association analysis when only a fraction of the population is genotyped and/or when phenotypes are available on non-genotyped relatives. Frontiers Media S.A. 2021-04-29 /pmc/articles/PMC8117227/ /pubmed/33995481 http://dx.doi.org/10.3389/fgene.2021.642065 Text en Copyright © 2021 Mancin, Lourenco, Bermann, Mantovani and Misztal. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Mancin, Enrico
Lourenco, Daniela
Bermann, Matias
Mantovani, Roberto
Misztal, Ignacy
Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study
title Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study
title_full Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study
title_fullStr Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study
title_full_unstemmed Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study
title_short Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study
title_sort accounting for population structure and phenotypes from relatives in association mapping for farm animals: a simulation study
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117227/
https://www.ncbi.nlm.nih.gov/pubmed/33995481
http://dx.doi.org/10.3389/fgene.2021.642065
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