Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
J Anim Sci. 2021 Mar 1;99(3). doi: 10.1093/jas/skab041.
Inclusion of crossbred (CB) data into traditionally purebred (PB) genetic evaluations has been shown to increase the response in CB performance. Currently, it is unrealistic to collect data on all CB animals in swine production systems, thus, a subset of CB animals must be selected to contribute genomic/phenotypic information. The aim of this study was to evaluate selective genotyping strategies in a simulated 3-way swine crossbreeding scheme. The swine crossbreeding scheme was simulated and produced 3-way CB animals for 6 generations with 3 distinct PB breeds each with 25 and 175 mating males and females, respectively. F1 crosses (400 mating females) produced 4,000 terminal CB progeny which were subjected to selective genotyping. The genome consisted of 18 chromosomes with 1,800 QTL and 72k SNP markers. Selection was performed using estimated breeding values (EBV) for CB performance. It was assumed that both PB and CB performance was moderately heritable (h2=0.4). Several scenarios altering the genetic correlation between PB and CB performance (rpc=0.1, 0.3, 0.5, 0.7,or 0.9) were considered. CB animals were chosen based on phenotypes to select 200, 400, or 800 CB animals to genotype per generation. Selection strategies included: (1) Random: random selection, (2) Top: highest phenotype, (3) Bottom: lowest phenotype, (4) Extreme: half highest and half lowest phenotypes, and (5) Middle: average phenotype. Each selective genotyping strategy, except for Random, was considered by selecting animals in half-sib (HS) or full-sib (FS) families. The number of PB animals with genotypes and phenotypes each generation was fixed at 1,680. Each unique genotyping strategy and rpc scenario was replicated 10 times. Selection of CB animals based on the Extreme strategy resulted in the highest (P < 0.05) rates of genetic gain in CB performance (ΔG) when rpc<0.9. For highly correlated traits (rpc=0.9) selective genotyping did not impact (P > 0.05) ΔG. No differences (P > 0.05) were observed in ΔG between top, bottom, or middle when rpc>0.1. Higher correlations between true breeding values (TBV) and EBV were observed using Extreme when rpc<0.9. In general, family sampling method did not impact ΔG or the correlation between TBV and EBV. Overall, the Extreme genotyping strategy produced the greatest genetic gain and the highest correlations between TBV and EBV, suggesting that 2-tailed sampling of CB animals is the most informative when CB performance is the selection goal.
将杂交（CB）数据纳入传统的纯种（PB）遗传评估中已被证明可以提高 CB 性能的反应。目前，在猪生产系统中收集所有 CB 动物的数据是不现实的，因此，必须选择一组 CB 动物来提供基因组/表型信息。本研究的目的是在模拟的 3 向猪杂交方案中评估选择性基因分型策略。模拟了猪杂交方案，并产生了 6 代 3 向 CB 动物，每个方案都有 3 个不同的 PB 品种，每个品种有 25 个和 175 个交配雄性和雌性。F1 杂交（400 个交配雌性）产生了 4000 个终端 CB 后代，对其进行了选择性基因分型。基因组由 18 条染色体组成，包含 1800 个 QTL 和 72k SNP 标记。选择是基于 CB 性能的估计育种值（EBV）进行的。假设 PB 和 CB 性能的遗传力都适中（h2=0.4）。考虑了改变 PB 和 CB 性能之间遗传相关性（rpc=0.1、0.3、0.5、0.7 或 0.9）的几种情况。根据表型选择 CB 动物，每代选择 200、400 或 800 个 CB 动物进行基因分型。选择策略包括：（1）随机：随机选择，（2）最高：最高表型，（3）最低：最低表型，（4）极端：最高和最低表型的一半，和（5）中间：平均表型。除了随机选择外，每种选择性基因分型策略都通过选择半同胞（HS）或全同胞（FS）家族中的动物来考虑。每一代具有基因型和表型的 PB 动物的数量固定在 1680 个。每个独特的基因分型策略和 rpc 方案都复制了 10 次。当 rpc<0.9 时，基于极端策略选择 CB 动物导致 CB 性能的遗传增益（ΔG）最高（P<0.05）。对于高度相关的性状（rpc=0.9），选择性基因分型对（P>0.05）ΔG 没有影响。当 rpc>0.1 时，在 top、bottom 或 middle 之间没有观察到（P>0.05）ΔG 的差异。当 rpc<0.9 时，使用极端法观察到真实育种值（TBV）和 EBV 之间的相关性更高。一般来说，家族抽样方法对ΔG 或 TBV 和 EBV 之间的相关性没有影响。总体而言，极端基因分型策略产生了最大的遗传增益和 TBV 和 EBV 之间的最高相关性，表明当 CB 性能是选择目标时，CB 动物的 2 尾抽样是最具信息量的。
