PDF(Pubmed)
Abstract：
Identifying key causal genes is critical for unraveling the genetic basis of complex economic traits, yet it remains a formidable challenge. The advent of large-scale sequencing data and computational algorithms, such as transcriptome-wide association studies (TWASs), offers a promising avenue for identifying potential causal genes. In this study, we harnessed the power of TWAS to identify genes potentially responsible for milk production traits, including daily milk yield (MY), fat percentage (FP), and protein percentage (PP), within a cohort of 100 buffaloes. Our approach began by generating the genotype and expression profiles for these 100 buffaloes through whole-genome resequencing and RNA sequencing, respectively. Through comprehensive genome-wide association studies (GWAS), we pinpointed a total of seven and four single nucleotide polymorphisms (SNPs) significantly associated with MY and FP traits, respectively. By using TWAS, we identified 55, 71, and 101 genes as significant signals for MY, FP, and PP traits, respectively. To delve deeper, we conducted protein-protein interaction (PPI) analysis, revealing the categorization of these genes into distinct PPI networks. Interestingly, several TWAS-identified genes within the PPI network played a vital role in milk performance. These findings open new avenues for identifying potentially causal genes underlying important traits, thereby offering invaluable insights for genomics and breeding in buffalo populations.
摘要：
识别关键因果基因对于解开复杂经济性状的遗传基础至关重要，然而，这仍然是一个巨大的挑战。大规模测序数据和计算算法的出现，例如全转录组关联研究(TWAS)，为识别潜在的因果基因提供了一个有希望的途径。在这项研究中,我们利用TWAS的力量来鉴定可能导致产奶性状的基因，包括每日产奶量(MY)，脂肪百分比（FP），和蛋白质百分比(PP)，在100只水牛的队列中。我们的方法首先通过全基因组重测序和RNA测序生成这100只水牛的基因型和表达谱，分别。通过全面的全基因组关联研究(GWAS)，我们确定了总共七个和四个单核苷酸多态性（SNP）与MY和FP性状显著相关，分别。通过使用TWAS,我们确定了55、71和101个基因是MY的重要信号，FP,和PP性状，分别。为了更深入地研究，我们进行了蛋白质-蛋白质相互作用（PPI）分析，揭示了将这些基因分类为不同的PPI网络。有趣的是,PPI网络中几个TWAS鉴定的基因在牛奶性能中起着至关重要的作用。这些发现为确定重要性状的潜在因果基因开辟了新的途径。从而为水牛种群的基因组学和育种提供了宝贵的见解。
