Our aim was to define a multi-population genomic relationship matrix to estimate genetic correlations between populations. We propose a genomic relationship matrix (G_New) where relationships represent the correlations between genotypes of individuals. Using simulations, we validated that G_New unbiasedly estimated current genetic variances and correlations when causal loci were used to calculate the relationships. When markers and causal loci both have different allele frequencies across populations, genetic variances and correlations were accurately estimated using markers, even when linkage disequilibrium patterns were different between the populations. When markers had similar allele frequencies and causal loci different allele frequencies across populations, genetic variances were slightly overestimated ( 4%) and genetic correlations underestimated ( 31%). We showed that an unbiased estimation of the genetic correlation between populations depends on the scaling of the relationships and that the difference in allele frequencies of causal loci across populations should be represented by the markers. Moreover, we showed that all loci should be included to calculate relationships, including the ones segregating in only one population. Keywords: genomic relationships, multi-population, genetic correlations, genetic variance
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Methods and Tools - Models and Computing Strategies 2, , 30, 2018
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