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.

中文翻译：

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分离 GWAS 线性化具有不完全外显率的非加性基因座：绵羊角状态的示例


本研究的目的是引入全基因组关联研究（GWAS）以及单基因分类性状的分离分析。根据表型、遗传模式和谱系信息计算的基因型概率表示为预期等位基因计数 (EAC)（范围 0 到 2），并且根据定义是加性遗传，这与原始表型不同，原始表型是非加性的，可以是不完全外显的。 EAC 在单核苷酸多态性 (SNP) 基因型上进行回归，类似于加性 GWAS。在这项研究中，美利奴羊的角表型被用来说明使用隔离 GWAS 的优势，该性状被认为是单基因的，受到显性、性别依赖性表达的影响，并且可能受到不完全外显率的影响。我们还使用模拟来研究不完全外显率是否会导致美利奴羊角状态的预测错误。估计的外显率值在性别之间有所不同，其中雄性表现出几乎完全的外显率，特别是对于有角和无角表型，而雌性对于有角状态的外显率较低。这表明，“有角等位基因”纯合的雌性仅在 22% 的情况下具有有角表型，而 78% 的情况下会出现瘤状或有杂毛。与使用实际表型相比，使用 EAC 对 4001 只动物和来自 Illumina Ovine 高密度 (600k) 芯片的 510,174 个 SNP 基因型进行的 GWAS 给出了更强的关联性。 EAC 与最高 –log10(p 值) SNP 的等位基因计数之间的相关性在男性中为 0.73，在女性中为 0.67。使用分离分析发现的外显率值进行的模拟导致 EAC 与致病突变之间具有更高的相关性（男性为 0.95，男性为 0.95）。女性分别为 89），表明最具预测性的 SNP 与致病突变并不完全 LD。我们的结果显示，雄性和雌性美利奴羊的角状态的外显率值存在明显差异。对具有互斥表型、非加性遗传和/或不完全外显率的性状进行分离分析可以在 GWAS 中产生更大的功效，因为线性化基因型概率是加性的并且可以适应不完全外显率。该方法可以扩展到任何表型相互排斥的单基因控制的分类性状。