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Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices

We propose a new method, G-REMLadp, to estimate the phenotypic variance explained by parent-of-origin effects (POEs) across the genome. Our method uses restricted maximum likelihood analysis of genome-wide genetic relatedness matrices based on individuals’ phased genotypes. Genome-wide SNP data from...

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Autores principales: Laurin, Charles, Cuellar-Partida, Gabriel, Hemani, Gibran, Smith, George Davey, Yang, Jian, Evans, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752821/
https://www.ncbi.nlm.nih.gov/pubmed/29098496
http://dx.doi.org/10.1007/s10519-017-9880-0
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author Laurin, Charles
Cuellar-Partida, Gabriel
Hemani, Gibran
Smith, George Davey
Yang, Jian
Evans, David M.
author_facet Laurin, Charles
Cuellar-Partida, Gabriel
Hemani, Gibran
Smith, George Davey
Yang, Jian
Evans, David M.
author_sort Laurin, Charles
collection PubMed
description We propose a new method, G-REMLadp, to estimate the phenotypic variance explained by parent-of-origin effects (POEs) across the genome. Our method uses restricted maximum likelihood analysis of genome-wide genetic relatedness matrices based on individuals’ phased genotypes. Genome-wide SNP data from parent child duos or trios is required to obtain relatedness matrices indexing the parental origin of offspring alleles, as well as offspring phenotype data to partition the trait variation into variance components. To calibrate the power of G-REMLadp to detect non-null POEs when they are present, we provide an analytic approximation derived from Haseman-Elston regression. We also used simulated data to quantify the power and Type I Error rates of G-REMLadp, as well as the sensitivity of its variance component estimates to violations of underlying assumptions. We subsequently applied G-REMLadp to 36 phenotypes in a sample of individuals from the Avon Longitudinal Study of Parents and Children (ALSPAC). We found that the method does not seem to be inherently biased in estimating variance due to POEs, and that substantial correlation between parental genotypes is necessary to generate biased estimates. Our empirical results, power calculations and simulations indicate that sample sizes over 10000 unrelated parent-offspring duos will be necessary to detect POEs explaining < 10% of the variance with moderate power. We conclude that POEs tagged by our genetic relationship matrices are unlikely to explain large proportions of the phenotypic variance (i.e. > 15%) for the 36 traits that we have examined. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10519-017-9880-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-57528212018-01-22 Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices Laurin, Charles Cuellar-Partida, Gabriel Hemani, Gibran Smith, George Davey Yang, Jian Evans, David M. Behav Genet Original Reasearch We propose a new method, G-REMLadp, to estimate the phenotypic variance explained by parent-of-origin effects (POEs) across the genome. Our method uses restricted maximum likelihood analysis of genome-wide genetic relatedness matrices based on individuals’ phased genotypes. Genome-wide SNP data from parent child duos or trios is required to obtain relatedness matrices indexing the parental origin of offspring alleles, as well as offspring phenotype data to partition the trait variation into variance components. To calibrate the power of G-REMLadp to detect non-null POEs when they are present, we provide an analytic approximation derived from Haseman-Elston regression. We also used simulated data to quantify the power and Type I Error rates of G-REMLadp, as well as the sensitivity of its variance component estimates to violations of underlying assumptions. We subsequently applied G-REMLadp to 36 phenotypes in a sample of individuals from the Avon Longitudinal Study of Parents and Children (ALSPAC). We found that the method does not seem to be inherently biased in estimating variance due to POEs, and that substantial correlation between parental genotypes is necessary to generate biased estimates. Our empirical results, power calculations and simulations indicate that sample sizes over 10000 unrelated parent-offspring duos will be necessary to detect POEs explaining < 10% of the variance with moderate power. We conclude that POEs tagged by our genetic relationship matrices are unlikely to explain large proportions of the phenotypic variance (i.e. > 15%) for the 36 traits that we have examined. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10519-017-9880-0) contains supplementary material, which is available to authorized users. Springer US 2017-11-02 2018 /pmc/articles/PMC5752821/ /pubmed/29098496 http://dx.doi.org/10.1007/s10519-017-9880-0 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Reasearch
Laurin, Charles
Cuellar-Partida, Gabriel
Hemani, Gibran
Smith, George Davey
Yang, Jian
Evans, David M.
Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices
title Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices
title_full Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices
title_fullStr Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices
title_full_unstemmed Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices
title_short Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices
title_sort partitioning phenotypic variance due to parent-of-origin effects using genomic relatedness matrices
topic Original Reasearch
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752821/
https://www.ncbi.nlm.nih.gov/pubmed/29098496
http://dx.doi.org/10.1007/s10519-017-9880-0
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