Aliloo Hassan, Mrode Raphael, Okeyo A M, Gibson John P
School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.
Animal Biosciences, International Livestock Research Institute, Nairobi, Kenya.
Front Genet. 2020 Jun 9;11:544. doi: 10.3389/fgene.2020.00544. eCollection 2020.
Understanding the genetic structure of adaptation and productivity in challenging environments is necessary for designing breeding programs that suit such conditions. Crossbred dairy cattle in East Africa resulting from over 60 years of crossing exotic dairy breeds with indigenous cattle plus inter se matings form a highly variable admixed population. This population has been subject to natural selection in response to environmental stresses, such as harsh climate, low-quality feeds, poor management, and strong disease challenge. Here, we combine two complementary sets of analyses, genome-wide association (GWA) and signatures of selection (SoS), to identify genomic regions that contribute to variation in milk yield and/or contribute to adaptation in admixed dairy cattle of Kenya. Our GWA separates SNP effects due to ancestral origin of alleles from effects due to within-population linkage disequilibrium. The results indicate that many genomic regions contributed to the high milk production potential of modern dairy breeds with no region having an exceptional effect. For SoS, we used two haplotype-based tests to compare haplotype length variation within admixed and between admixed and East African Shorthorn Zebu cattle populations. The integrated haplotype score (iHS) analysis identified 16 candidate regions for positive selection in the admixed cattle while the between population Rsb test detected 24 divergently selected regions in the admixed cattle compared to East African Shorthorn Zebu. We compare the results from GWA and SoS in an attempt to validate the most significant SoS results. Only four candidate regions for SoS intersect with GWA regions using a low stringency test. The identified SoS candidate regions harbored genes in several enriched annotation clusters and overlapped with previously found QTLs and associations for different traits in cattle. If validated, the GWA and SoS results indicate potential for SNP-based genomic selection for genetic improvement of smallholder crossbred cattle.
了解在具有挑战性的环境中适应和生产力的遗传结构，对于设计适合此类条件的育种计划至关重要。东非的杂交奶牛是经过60多年将外来奶牛品种与本地牛杂交以及相互交配而形成的，构成了一个高度可变的混合种群。该种群因应对环境压力（如恶劣气候、低质量饲料、管理不善和严重疾病挑战）而经历了自然选择。在此，我们结合了两组互补分析，即全基因组关联分析（GWA）和选择特征分析（SoS），以识别对肯尼亚混合奶牛的产奶量变异有贡献和/或有助于其适应的基因组区域。我们的GWA将等位基因祖先来源引起的SNP效应与种群内连锁不平衡引起的效应区分开来。结果表明，许多基因组区域对现代奶牛品种的高产奶潜力有贡献，但没有一个区域具有特别突出的影响。对于SoS，我们使用了两种基于单倍型的测试，以比较混合种群内以及混合种群与东非短角瘤牛种群之间的单倍型长度变异。综合单倍型评分（iHS）分析在混合牛中确定了16个正选择候选区域，而种群间Rsb测试在混合牛中检测到24个与东非短角瘤牛相比有差异选择的区域。我们比较了GWA和SoS的结果，试图验证最显著的SoS结果。使用低严格度测试时，SoS的候选区域中只有四个与GWA区域相交。所确定的SoS候选区域在几个富集注释簇中含有基因，并与先前发现的牛不同性状的QTL和关联重叠。如果得到验证，GWA和SoS结果表明基于SNP的基因组选择对小农户杂交牛进行遗传改良具有潜力。
