Improving livestock production through high-throughput identification of functional regulatory variation

在过去的几十年里，全球投入了大量资金，试图绘制控制产量、疾病耐受性和福利表型的牲畜基因组区域。绘制这些DNA区域的最终目的是使研究人员能够使用先进的基因组学和育种方法来更快地提高牲畜的产量和福利。尽管许多重要的基因座已经被绘制出来，但我们通常不知道这些区域中哪些精确的遗传变化与观察到的表型差异有关，这使得应用基因编辑等先进方法来改善这些性状变得更加困难。然而，对牛的研究估计，改变下游表型的变体通过导致转录（即基因表达水平）变化而改变下游表型的可能性是偶然预期的18倍以上（Nat Genet 50，362-367（2018））。如果我们可以绘制哪些变体直接影响表达水平，我们就可以确定哪些遗传变化最有可能驱动关键性状的变化。在本项目中，我们将采用高通量方法，直接测试数百万种基因变化对基因表达的影响。这将使我们能够产生一个目录的牛的功能变异直接联系到转录的变化，因此可能是基础的基因座与重要性状。然而，我们还将进一步研究，并测试人类基因变化在牛细胞中的影响，反之亦然。某些物种比其他物种更好地注释了更丰富的数据集，我们将使用这些数据来确定哪些特征与影响跨物种基因调控的遗传变异有关。利用这些数据和机器学习方法，我们将开发统计模型，使研究人员能够预测哪些遗传变化可能会对物种产生影响。这将使研究人员能够利用更好地描述物种的数据来改善注释不太好的物种，进一步加速牲畜改良工作，但也可能为基于动物模型的人类疾病研究提供信息。通过绘制调控变异图谱和开发预测变异的统计模型，该项目有望大大提高对牛和人类表型的理解that impact影响transcription转录across横过species物种.

In the past several decades there has been a substantial global investment to try and map the regions of livestock genomes that control production, disease tolerance and welfare phenotypes. The ultimate aim of mapping these DNA regions is so that researchers can then use advanced genomics and breeding approaches to more rapidly improve the production and welfare of livestock. Although many important loci have been mapped, most often we do not know which precise genetic changes in these regions are linked to the observed differences in phenotypes, making it more difficult to apply advanced approaches such as gene editing to improve these traits. However, studies in cattle have estimated that variants that alter downstream phenotypes are over 18 times more likely to do so by leading to changes in transcription, i.e. the expression level of genes, than is expected by chance (Nat Genet 50, 362-367 (2018)). If we can map which variants directly impact expression levels, we can determine which genetic changes are most likely driving the observed changes in key traits. This will consequently substantially improve the rate at which we can improve important livestock phenotypes.In this project we will apply a high-throughput approach that directly tests the impact on gene expression of millions of genetic changes at the same time. This will allow us to generate a catalogue of cattle functional variants directly linked to changes in transcription, and which may therefore underlie loci linked to important traits. However, we will also take this further, and test the impact of human genetic changes when in cattle cells as well as vice versa. Certain species are much better annotated with richer datasets than others, and we will use these data to determine which features are linked to genetic variants that impact gene regulation across species. Using these data and machine learning approaches we will develop statistical models that will allow researchers to predict which genetic changes will likely have an impact across species. This will allow researchers to exploit the data in better characterised species to improve less well annotated ones, further accelerating livestock improvement efforts but also potentially, for example, informing human disease studies that are based on animal models.Consequently, this project is expected to substantially improve the understanding of both cattle and human phenotypes by mapping regulatory variants and developing statistical models for predicting variants that impact transcription across species.