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Review
. 2019 Apr;211(4):1131-1141.
doi: 10.1534/genetics.119.301859.

Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction

Naomi R Wray  1   2 , Kathryn E Kemper  3 , Benjamin J Hayes  4 , Michael E Goddard  5   6 , Peter M Visscher  3   2
Affiliations
Review

Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction

Naomi R Wray et al. Genetics. 2019 Apr.

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.

Keywords: EBV; GenPred; Genomic Prediction; PRS; estimated breeding values; polygenic risk score; segregation variance; within family variance.

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Figures

Figure 1
Figure 1
Variance explained in out of sample prediction. Using Equation 1, we assume hM2 = 0.3 associated with common variants of frequency >0.1. In humans, the effective number of markers whose effect sizes are estimated is M∼50,000. Discovery sample GWAS of >1 million people are needed to achieve out-of-sample that achieves R2 approaching the upper limit of hM2. The red line bench marks out of sample prediction for M∼10,000, representative of a species with a smaller effective population size, or if, in humans, we achieve statistical methodologies that allow identification of a smaller number of DNA variants associated with the same hM2.
Figure 2
Figure 2
Increase in milk yield in black and white Holstein cattle since 1957. The mean EBV has increased by 3916 kg or 66 kg per cow per year. The phenotypic and genetic SD of milk yield in 1957 were ∼1200 and ∼600 kg. Hence, the genetic contribution to milk yield has increased by ∼6.5 genetic SD since 1957. Source: Council on Dairy Cattle Breeding (https://queries.uscdcb.com/eval/summary/trend.cfm)
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