Multi-population genomic relationships for estimating current genetic variances within and genetic correlations between populations

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17 Citations (Scopus)

Abstract

Different methods are available to calculate multi-population genomic relationship matrices. Since those matrices differ in base population, it is anticipated that the method used to calculate genomic relationships affects the estimate of genetic variances, covariances, and correlations. The aim of this article is to define the multi-population genomic relationship matrix to estimate current genetic variances within and genetic correlations between populations. The genomic relationship matrix containing two populations consists of four blocks, one block for population 1, one block for population 2, and two blocks for relationships between the populations. It is known, based on literature, that by using current allele frequencies to calculate genomic relationships within a population, current genetic variances are estimated. In this article, we theoretically derived the properties of the genomic relationship matrix to estimate genetic correlations between populations and validated it using simulations. When the scaling factor of across-population genomic relationships is equal to the product of the square roots of the scaling factors for within-population genomic relationships, the genetic correlation is estimated unbiasedly even though estimated genetic variances do not necessarily refer to the current population. When this property is not met, the correlation based on estimated variances should be multiplied by a correction factor based on the scaling factors. In this study, we present a genomic relationship matrix which directly estimates current genetic variances as well as genetic correlations between populations.

Original languageEnglish
Pages (from-to)503-515
JournalGenetics
Volume207
Issue number2
DOIs
Publication statusPublished - 1 Oct 2017

Keywords

  • Genetic correlation between populations
  • Genetic variance
  • Genomic relationships
  • Genomic Selection
  • GenPred
  • Multi-trait model
  • Shared Data Resources

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