Genomic selection in admixed and crossbred populations 1

Abstract

In livestock, genomic selection (GS) has primarily been investigated by simulation of purebred populations. Traits of interest are, however, often measured in crossbred or mixed populations with uncertain breed composition. If such data are used as the training data for GS without accounting for breed composition, estimates of marker effects may be biased due to population stratification and admixture. To investigate this, a genome of 100 cM was simulated with varying marker densities (5 to 40 segregating markers per cM). After 1,000 generations of random mating in a population of effective size 500, 4 lines with effective size 100 were isolated and mated for another 50 generations to create 4 pure breeds. These breeds were used to generate combined, F(1), F(2), 3- and 4-way crosses, and admixed training data sets of 1,000 individuals with phenotypes for an additive trait controlled by 100 segregating QTL and heritability of 0.30. The validation data set was a sample of 1,000 genotyped individuals from one pure breed. Method Bayes-B was used to simultaneously estimate the effects of all markers for breeding value estimation. With 5 (40) markers per cM, the correlation of true with estimated breeding value of selection candidates (accuracy) was greatest, 0.79 (0.85), when data from the same pure breed were used for training. When the training data set consisted of crossbreds, the accuracy ranged from 0.66 (0.79) to 0.74 (0.83) for the 2 marker densities, respectively. The admixed training data set resulted in nearly the same accuracies as when training was in the breed to which selection candidates belonged. However, accuracy was greatly reduced when genes from the target pure breed were not included in the admixed or crossbred population. This implies that, with high-density markers, admixed and crossbred populations can be used to develop GS prediction equations for all pure breeds that contributed to the population, without a substantial loss of accuracy compared with training on purebred data, even if breed origin has not been explicitly taken into account. In addition, using GS based on high-density marker data, purebreds can be accurately selected for crossbred performance without the need for pedigree or breed information. Results also showed that haplotype segments with strong linkage disequilibrium are shorter in crossbred and admixed populations than in purebreds, providing opportunities for QTL fine mapping.

Genomic selection in forest tree breeding

“…A difficulty with GS across multiple populations is the calculation of the genomic breeding values because the marker effects may differ across populations. Some recent studies for animal breeding, however, have shown that this is possible and, in spite of a reduced accuracy, GS should still provide accurate selection of purebreds for crossbred performance without the need for pedigree or breed information (de Roos et al 2009;Ibanz-Escriche et al 2009;Toosi et al 2009).…”

“…Several reports have analyzed the prospects of GS through simulation studies of the several parameters and analytical procedures that impact the predicted accuracy of genomic selection for domestic animal breeding schemes (Calus et al 2008;Dekkers 2007;Long et al 2007;Muir 2007;Schaeffer 2006;Solberg et al 2008). Recent studies have tackled further issues on the theme such as the reliability of genomic predictions across multiple populations (de Roos et al 2009), the application of GS in pure-bred versus crossbred populations (Ibanz-Escriche et al 2009;Toosi et al 2009), the use of lower-density genotyping panels when pedigrees can be tracked ), different testing strategies for GS in breeding programs , and reviewed realized gains over conventional progeny testing in dairy cattle worldwide ). Empirical results of GS in animal models have also been reported (Lee et al 2008;Legarra et al 2008) confirming the positive theoretical expectations.…”

Genomic selection for durable stem rust resistance in wheat

“…The highest GS accuracies are achieved when the TP is large, consists of the parents or very recent ancestors of the population under selection (Habier et al 2007;Goddard 2009;Hayes et al 2009b;Toosi et al 2010;Zhong et al 2009), and consists of multiple generations of training (Muir 2007). The study by Zhong et al (2009) examined the effects of different parameters on GEBV accuracy and found that doubling the size of the TP always increased accuracy.…”

“…Several simulation studies in cattle assessed the potential of using a large TP of various divergent breeds to calculate GEBVs in one breed (de Roos et al 2009;Hayes et al 2009a;Meuwissen 2009;Toosi et al 2010). These studies found high accuracies at very high marker densities.…”

Seleção genômica para melhoramento vegetal com diferentes estruturas populacionais

“…O cálculo dos GBVs dificulta a utilização da seleção genômica entre várias populações, uma vez que, na maioria dos casos, os efeitos de marcadores são diferentes entre elas (De Roos et al, 2009). Esses resultados estão de acordo com os obtidos por Toosi et al (2010), que observaram que a acurácia é reduzida drasticamente quando os alelos específicos de uma amostra, ou de um subgrupo, não estão incluídos na população de referência. Nakaya & Isobe (2012) argumentam que marcadores em LD significativo com os QTLs de interesse, em geral, não são mantidos em diferentes cruzamentos, o que dificulta o uso da GWS.…”