In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in
livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for
those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different
viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective
population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In
human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship ,0.05) to estimate heritability
captured by common SNPs. In livestock data, all animals within a breed are to some extent “related,” and so it is not possible to select
unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data
analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is
directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in
polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the
expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of
prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.
%0 Journal Article
%1 wray2019complex
%A Wray, Naomi R.
%A Kemper, Kathryn E.
%A Hayes, Benjamin J.
%A Goddard, Michael E.
%A Visscher, Peter M.
%D 2019
%I Genetics Society of America
%J Genetics
%K animal_breeding domestication polygenic_scores polygenic_traits review
%N 4
%P 1131--1141
%R 10.1534/genetics.119.301859
%T Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans
%U https://doi.org/10.1534%2Fgenetics.119.301859
%V 211
%X In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in
livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for
those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different
viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective
population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In
human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship ,0.05) to estimate heritability
captured by common SNPs. In livestock data, all animals within a breed are to some extent “related,” and so it is not possible to select
unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data
analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is
directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in
polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the
expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of
prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.
@article{wray2019complex,
abstract = {In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in
livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for
those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different
viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective
population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In
human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship ,0.05) to estimate heritability
captured by common SNPs. In livestock data, all animals within a breed are to some extent “related,” and so it is not possible to select
unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data
analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is
directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in
polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the
expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of
prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.},
added-at = {2020-05-27T00:05:27.000+0200},
author = {Wray, Naomi R. and Kemper, Kathryn E. and Hayes, Benjamin J. and Goddard, Michael E. and Visscher, Peter M.},
biburl = {https://www.bibsonomy.org/bibtex/20a23586db96d20d14efc75a396e667fd/peter.ralph},
doi = {10.1534/genetics.119.301859},
interhash = {f13278e44d2bfa7a1b5d6689cc0947aa},
intrahash = {0a23586db96d20d14efc75a396e667fd},
journal = {Genetics},
keywords = {animal_breeding domestication polygenic_scores polygenic_traits review},
month = apr,
number = 4,
pages = {1131--1141},
publisher = {Genetics Society of America},
timestamp = {2020-05-27T00:05:27.000+0200},
title = {Complex Trait Prediction from Genome Data: Contrasting {EBV} in Livestock to {PRS} in Humans},
url = {https://doi.org/10.1534%2Fgenetics.119.301859},
volume = 211,
year = 2019
}