2010-Tossi_et_al - Genomic selection in admixed and...

A. Toosi, R. L. Fernando and J. C. M. Dekkers
Genomic selection in admixed and crossbred populations
doi: 10.2527/jas.2009-1975 originally published online Sep 11, 2009;
2010.88:32-46. J Anim Sci
http://jas.fass.org/cgi/content/full/88/1/32
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ABSTRACT: In livestock, genomic selection (GS) has
primarily been investigated by simulation of purebred
populations. Traits of interest are, however, often mea-
sured 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 popu-
lation 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 cre-
ate 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 correla-
tion 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 pop-
ulations 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 with-
out the need for pedigree or breed information. Results
also showed that haplotype segments with strong link-
age disequilibrium are shorter in crossbred and admixed
populations than in purebreds, providing opportunities
for QTL fine mapping.