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Background: 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. Results: 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 ITGB7genes. In particular, we describe disorders associated with NOA1and RFC5mutations for the first time in vertebrates. Conclusions: 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.

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Summary:In dairy cattle at the herd level, Coxiella burnetiican be diagnosed by PCR on bulk tank milk (BTM). To facilitate the collection and shipment of BTM samples to the laboratory, we validated the use of Whatman FTA Elute Micro Cards for the conservation of BTM in 2 studies. The first study evaluated the stability of samples over time and at 2 different storage temperatures. The second compared results for BTM stored on FTA cards and raw BTM stored as is.