GWAS and fine-mapping of livability and six disease traits in Holstein cattle
GWAS and fine-mapping of livability and six disease traits in Holstein cattle
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DOI：
10.1186/s12864-020-6461-z
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发表时间：
2020-01-13
期刊：
影响因子：
4.4
通讯作者：
Ma, Li
中科院分区：
文献类型：
--
作者：
Freebern, Ellen;Santos, Daniel J. A.;Ma, Li
健康性状对乳制品行业具有重要的经济意义，因为它们对牛奶产量和相关的处理成本有影响。全基因组关联研究（GWAS）提供了一种识别相关基因组变异的手段，从而揭示了复杂性状和疾病的遗传结构。本研究的目的是研究奶牛7个健康性状的遗传基础，并利用GWAS、精细定位和多组织转录组数据分析确定与奶牛健康相关的潜在候选基因。结果利用去回归育种值和超过300万个输入的DNA序列变异，研究了奶牛的宜居性和6个直接疾病特征：乳腺炎、酮症、低钙血症、皱胃移位、子宫炎和保留胎盘。经过数据编辑和可靠性筛选，纳入分析的公牛数量从11880只（低钙）到24699只（宜居性）不等。使用混合模型关联检验进行GWAS，并进行贝叶斯精细映射程序来计算候选区域中每个变异和基因的后验因果关系概率。GWAS检测到奶牛的存活率、酮症和低钙血症三个性状，包括与存活率相关的牛主要组织相容性复合体（MHC）区域，共8个全基因组显著关联。我们对相关区域的精细定位报告了20个候选基因，它们对牛健康的因果关系具有最高的后验概率。结合牛多个组织的转录组数据，我们进一步利用这些候选基因来确定疾病相关组织中的特定表达模式和相关的生物学解释，如肝脏中群体特异性成分（GC）的表达和与乳腺炎的关联，以及CD8细胞中含有88C的线圈结构域（CCDC88C）的表达和与奶牛存活率的关联。总的来说，我们的分析报告了牛健康的6个显著关联和20个候选基因。通过整合多组织转录组数据，我们的研究结果为未来的功能研究和更好地理解牛遗传与疾病易感性之间的生物学关系提供了有用的信息。
Background Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. Results We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability. Conclusions Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.