Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW, 2351, Australia.
School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.
Genet Sel Evol. 2024 Sep 3;56(1):61. doi: 10.1186/s12711-024-00928-0.
The objective of this study was to introduce a genome-wide association study (GWAS) in conjunction with segregation analysis on monogenic categorical traits. Genotype probabilities calculated from phenotypes, mode of inheritance and pedigree information, are expressed as the expected allele count (EAC) (range 0 to 2), and are inherited additively, by definition, unlike the original phenotypes, which are non-additive and could be of incomplete penetrance. The EAC are regressed on the single nucleotide polymorphism (SNP) genotypes, similar to an additive GWAS. In this study, horn phenotypes in Merino sheep are used to illustrate the advantages of using the segregation GWAS, a trait believed to be monogenic, affected by dominance, sex-dependent expression and likely affected by incomplete penetrance. We also used simulation to investigate whether incomplete penetrance can cause prediction errors in Merino sheep for horn status.
Estimated penetrance values differed between the sexes, where males showed almost complete penetrance, especially for horned and polled phenotypes, while females had low penetrance values for the horned status. This suggests that females homozygous for the 'horned allele' have a horned phenotype in only 22% of the cases while 78% will be knobbed or have scurs. The GWAS using EAC on 4001 animals and 510,174 SNP genotypes from the Illumina Ovine high-density (600k) chip gave a stronger association compared to using actual phenotypes. The correlation between the EAC and the allele count of the SNP with the highest -log10(p-value) was 0.73 in males and 0.67 in females. Simulations using penetrance values found by the segregation analyses resulted in higher correlations between the EAC and the causative mutation (0.95 for males and 0.89 for females, respectively), suggesting that the most predictive SNP is not in full LD with the causative mutation.
Our results show clear differences in penetrance values between males and female Merino sheep for horn status. Segregation analysis for a trait with mutually exclusive phenotypes, non-additive inheritance, and/or incomplete penetrance can lead to considerably more power in a GWAS because the linearized genotype probabilities are additive and can accommodate incomplete penetrance. This method can be extended to any monogenic controlled categorical trait of which the phenotypes are mutually exclusive.
本研究旨在介绍一种结合单基因分类性状分离分析的全基因组关联研究（GWAS）。从表型、遗传方式和系谱信息计算出的基因型概率表示为预期等位基因数（EAC）（范围为 0 到 2），并且根据定义是可加性遗传的，与原始表型不同，原始表型是非加性的，可能不完全外显。EAC 回归于单核苷酸多态性（SNP）基因型，类似于加性 GWAS。在这项研究中，我们使用美利奴羊的角型表型来说明使用分离 GWAS 的优势，该性状被认为是单基因的，受显性、性别依赖表达的影响，并且可能受不完全外显的影响。我们还使用模拟来研究不完全外显是否会导致美利奴羊角型状态的预测错误。
估计的外显率在性别之间存在差异，雄性表现出几乎完全的外显率，尤其是对于有角和无角表型，而雌性的有角表型外显率较低。这表明，雌性纯合“有角等位基因”的有角表型仅占 22%，而 78%将为结节状或有残肢。使用 EAC 对 4001 只动物和 510174 个来自 Illumina 绵羊高密度（600k）芯片的 SNP 基因型进行 GWAS 比使用实际表型的关联更强。在雄性中，EAC 与 SNP 最高-log10(p 值)的等位基因数之间的相关性为 0.73，在雌性中为 0.67。使用分离分析中找到的外显率进行模拟导致 EAC 与致病突变之间的相关性更高（雄性分别为 0.95 和 0.89，雌性），这表明最具预测性的 SNP 与致病突变不完全连锁。
我们的结果表明，美利奴羊的角型表型在雄性和雌性之间存在明显的外显率差异。对于具有互斥表型、非加性遗传和/或不完全外显的性状进行分离分析，可以在 GWAS 中获得更大的功效，因为线性化的基因型概率是可加的，并且可以容纳不完全外显。这种方法可以扩展到任何具有互斥表型的单基因控制的分类性状。
