QTCC: From Quantitative Trait Correlation to Causation in dairy cattle

奶牛育种的育种目标包括性能性状（如产奶量）、生育力和健康性状以及寿命。饲料效率性状将在不久的将来成为育种目标的一部分。现代基因组工具应用于大数据集，并增加了关于基因组序列变异的外部信息，可以对数量性状的遗传结构进行全面的解剖，除此之外，还可以对多个性状的共享结构进行解剖。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。所考虑的性状属于复合性状，包括产奶性能性状、育性性状、健康性状和效率性状。特别是（但不是唯一的），产奶性能和饲料效率将与生育和健康性状进行对比。以下假设将被检验。(i)从数量性状到功能和进化相关的基因组区域的全球遗传相关性分析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铅变异体的鉴定允许分离共同代谢途径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。（三）产奶性能和饲料效率是影响奶牛生育和健康的重要暴露性状。这些假设将使用现代基因组方法进行测试，部分增加外部信息，应用于包含150K头奶牛的独特和破坏性观察数据集，其中包含snp芯片信息，全基因组序列变异，以及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遗传结构直至单个变异水平具有重要意义。

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.