Dairy cattle breeds are populations of limited effective size, subject to recurrent outbreaks of recessive defects that are commonly studied using positional cloning. However, this strategy, based on the observation of animals with characteristic features, may overlook a number of conditions, such as immune or metabolic genetic disorders, which may be confused with pathologies of environmental etiology. We present a data mining framework specifically designed to detect recessive defects in livestock that have been previously missed due to a lack of specific signs, incomplete penetrance, or incomplete linkage disequilibrium. This approach leverages the massive data generated by genomic selection. Its basic principle is to compare the observed and expected numbers of homozygotes for sliding haplotypes in animals with different life histories. Within three cattle breeds, we report 33 new loci responsible for increased risk of juvenile mortality and present a series of validations based on large-scale genotyping, clinical examination, and functional studies for candidate variants affecting the NOA1, RFC5, and ITGB7 genes. In particular, we describe disorders associated with NOA1 and RFC5 mutations for the first time in vertebrates. The discovery of these many new defects will help to characterize the genetic basis of inbreeding depression, while their management will improve animal welfare and reduce losses to the industry.

中文翻译：

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大规模检测奶牛隐性遗传缺陷，挖掘数百万生命史


奶牛品种是有效规模有限的群体，经常出现隐性缺陷，通常使用定位克隆进行研究。然而，这种基于对具有特征的动物的观察的策略可能会忽略许多条件，例如免疫或代谢遗传疾病，这些疾病可能与环境病因学的病理学相混淆。我们提出了一个数据挖掘框架，专门设计用于检测牲畜的隐性缺陷，这些缺陷以前由于缺乏特定迹象、不完全外显率或不完全连锁不平衡而被遗漏。这种方法利用了基因组选择产生的大量数据。其基本原理是比较具有不同生活史的动物中滑动单倍型的观察到的和预期的纯合子数量。在三个牛品种中，我们报告了 33 个导致幼年死亡率风险增加的新位点，并基于大规模基因分型、临床检查和功能研究对影响 NOA1、RFC5 和 ITGB7 基因的候选变异进行了一系列验证。特别是，我们首次描述了脊椎动物中与 NOA1 和 RFC5 突变相关的疾病。这些新缺陷的发现将有助于描述近交衰退的遗传基础，而它们的管理将改善动物福利并减少行业损失。