Concepedia

Reliability of Genomic Predictions Across Multiple Populations

A.P.W. de Roos, Ben J. Hayes, Michael E. Goddard

Genetics · 2009 · 342 citations · 27 references

Open access

TL;DR

Genomic prediction uses dense SNP data to estimate genetic merit, with reliability defined as the squared correlation to true merit, and while combining phenotypes from multiple populations can boost reliability when data are scarce, it may also reduce it if marker effects differ markedly across populations. The study aims to evaluate how combining phenotypes from multiple populations affects the reliability of genomic predictions. The authors simulated two cattle populations diverging for 6, 30, or 300 generations, training genomic models on 1000 A individuals plus 0–1000 B individuals while varying marker density and heritability. Adding phenotypes from a second population increased reliability in the first population by up to 0.12 at high marker density and T = 6, but decreased it by up to 0.07 at low density and T = 300; without cross‑population data, reliability in the second population was up to 0.77 lower than in the first, yet incorporating such data raised it to comparable levels when marker density was high, indicating that combining all phenotypes yields the most accurate predictions, especially for highly diverged populations that require denser markers.

Abstract

Genomic prediction of future phenotypes or genetic merit using dense SNP genotypes can be used for prediction of disease risk, forensics, and genomic selection of livestock and domesticated plant species. The reliability of genomic predictions is their squared correlation with the true genetic merit and indicates the proportion of the genetic variance that is explained. As reliability relies heavily on the number of phenotypes, combining data sets from multiple populations may be attractive as a way to increase reliabilities, particularly when phenotypes are scarce. However, this strategy may also decrease reliabilities if the marker effects are very different between the populations. The effect of combining multiple populations on the reliability of genomic predictions was assessed for two simulated cattle populations, A and B, that had diverged for T = 6, 30, or 300 generations. The training set comprised phenotypes of 1000 individuals from population A and 0, 300, 600, or 1000 individuals from population B, while marker density and trait heritability were varied. Adding individuals from population B to the training set increased the reliability in population A by up to 0.12 when the marker density was high and T = 6, whereas it decreased the reliability in population A by up to 0.07 when the marker density was low and T = 300. Without individuals from population B in the training set, the reliability in population B was up to 0.77 lower than in population A, especially for large T. Adding individuals from population B to the training set increased the reliability in population B to close to the same level as in population A when the marker density was sufficiently high for the marker-QTL linkage disequilibrium to persist across populations. Our results suggest that the most accurate genomic predictions are achieved when phenotypes from all populations are combined in one training set, while for more diverged populations a higher marker density is required.