State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
College of Animal Science and Technology, China Agricultural University, Beijing, China.
Genet Sel Evol. 2021 Oct 27;53(1):82. doi: 10.1186/s12711-021-00672-9.
Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features.
We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model.
A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.
生长性状对家禽的选育和生产至关重要，已成为广泛研究的课题，许多数量性状基因座（QTL）已被检测到。然而，由于其复杂的遗传背景，很少有因果基因得到确认，其潜在的分子机制也不清楚，因此限制了我们对 QTL 的理解及其在家禽遗传改良中的潜在应用。因此，破译遗传结构是优化基因组预测策略和利用基因组信息进行商业育种的有前途的途径。本研究的目的是：（1）进行全基因组关联研究，以确定关键遗传因素，探索鸡生长性状的多基因性；（2）研究肉鸡基因组预测的效率；（3）评估利用基因组特征进行基因组预测。
我们鉴定了五个显著的 QTL，包括一个在染色体 4 上具有主效的 QTL 和四个在染色体 1、2、17 和 27 上具有微效的 QTL，分别占基因组加性遗传方差的 14.5%至 34.1%和 0.2%至 2.6%，占观察到的育种值预测准确性的 23.3%至 46.7%和 0.6%至 4.5%。进一步分析表明，具有微效的 QTL 共同具有相当大的影响，反映了遗传背景的多基因性。与传统的基于系谱的 BLUP 模型相比，基因组最佳线性无偏预测（BLUP）的准确性提高了 22.0%至 70.3%。与基因组 BLUP 模型相比，基因组特征 BLUP 模型进一步提高了 13.8%至 15.2%的观察预测准确性。
鉴定到一个主效 QTL 和四个微效 QTL 用于生长性状；其余的变异是由于 QTL 效应太小而无法检测到。与基于系谱的 BLUP 模型相比，基因组 BLUP 和基因组特征 BLUP 模型产生了更高的预测准确性。本研究揭示了黄羽鸡生长性状的多基因性，并表明通过使用基因组信息和相关特征可以极大地提高预测能力。
