QTCC: From Quantitative Trait Correlation to Causation in dairy cattle

奶牛育种中的育种目标包括性能性状（例如产奶量）、生育力和健康性状以及寿命。饲料效率性状将是不久的将来育种目标的一部分。现代基因组工具应用于大型数据集，并增加了有关基因组序列变异的外部信息，可以全面剖析数量性状的遗传结构，除此之外，还可以剖析多个性状的共享结构。该项目的目的是分析奶牛数量性状的相互关系，超越全球相关性，走向局部相关性和因果关系。所考虑的性状属于综合的产奶性能性状、生育性状、健康性状和效率性状。特别是（但不排他），牛奶性能和饲料效率将与生育力和健康性状进行对比。将检验以下假设。(i)对数量性状到功能和进化相关基因组区域之间的全局遗传相关性的解剖确定了性状共享和性状特异性遗传结构。(ii)多效性和性状特异性先导变异的鉴定允许分离常见和性状特异性代谢途径，这反过来又有助于鉴定性状之间的因果关系。(iii)产奶性能和饲料效率是显著的暴露性状，其因果关系损害奶牛的生育力和健康。这些假设将使用现代基因组方法进行测试，部分增加了外部信息，应用于独特的和破坏性的观察数据集，涉及15万头具有SNP芯片信息的奶牛，估算的全基因组序列变异和大量数量性状的记录。这些结果对于理解和剖析性状复合体的相互关系和共享的遗传结构到单个变异水平是重要的。

The breeding goal in dairy cattle breeding includes performance traits (e.g. milk yield) and fertility and health traits, and longevity. Feed efficiency traits will be part of the breeding goal in the near future. Modern genomic tools applied to large data sets and augmented with external information about genome sequence variants allows for the comprehensive dissection of the genetic architecture of quantitative traits and, beyond that, of the shared architecture of multiple traits. The aim of the project is to analyse the interrelationships of the quantitative traits in dairy cattle beyond global correlations towards local correlation and causations. The traits considered belong to the complexes milk performance traits, fertility traits, health traits, and efficiency traits. In particular (but not exclusive), milk performance and feed efficiency will be contrasted against fertility and health traits. The following hypotheses will be tested. (i) The dissection of global genetic correlations between quantitative traits down to functional and evolutionary relevant genomic regions identifies trait-shared and trait-specific genetic architectures. (ii) The identification of pleiotropic and trait-specific lead variants allows for the separation of common and trait-specific metabolic pathways, and this in turn facilitates the identification of causative relationships between traits. (iii) Milk performance and feed efficiency are significant exposure traits which causally compromise the cow’s fertility and health. These hypotheses will be tested using modern genomic methods, partly augmented with external information, applied to a unique and disruptive observational data sets involving 150K cows with SNP-chip information, imputed whole genome sequence variants, and records for a large panel of quantitative traits. The results are important to understand and dissect the interrelationship and shared genetic architecture of the trait complexes down to single variants levels.