Possibility to estimate the genetic correlation between populations unbiasedly

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Possibility to estimate the genetic correlation between populations unbiasedly

Gepubliceerd op
1 februari 2019

The genetic correlation between populations is a critical parameter that determines whether it is useful to combine information from multiple populations for genetic analyses. Therefore, it is important to have unbiased estimates of this genetic correlation. In two papers, the genomic relationship matrix and essential marker properties to unbiasedly estimate this correlation are described.

Genetic correlation between populations

Populations generally differ in their genetic makeup and in the environment in which they are kept. This can result in differences in the effects of the causal genetic loci influencing a trait across populations. The correlation between the effects of causal loci across populations is known as the genetic correlation between populations.

Definition genomic relationship matrix

For estimating the genetic correlation between populations, it is essential to know the relationships between animals within and between the populations. In a first paper, a multi-population genomic relationship matrix is defined that unbiasedly estimates the genetic correlation between populations. Using simulations, it was shown that this method indeed unbiasedly estimated the genetic correlation when genotypes of causal loci were used to estimate the relationships. Other definitions of the relationship matrix either resulted in biased estimates of the genetic correlation or the genetic variances.

Essential marker properties

In practice, the causal loci influencing a trait are unknown, and cannot be used to estimate the relationships. Genetic markers are used instead. In a second paper, the required properties of the genetic markers to unbiasedly estimate the genetic correlation were investigated. It was shown that it was essential that the markers and causal loci had a similar difference in allele frequencies between populations. In contrast to the expectation, it was shown that the differences in linkage phase between markers and causal loci across populations only had a very minor effect on the genetic correlation estimate.

Combining or not

The power of genomic prediction or to identify causal genetic loci may be improved by combining information from multiple populations. The genetic correlation between these populations is a critical parameter that determines whether combining information from multiple populations is useful. The results of these two papers provide tools to breeding companies to help in deciding whether populations from different breeds or countries should be combined in a genetic evaluation or not.