Understanding the genetic architecture of dairy cattle health using whole genome re-sequencing data

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Susceptibility of dairy cattle to certain health problems, such as mastitis (a painful inflammation of udder tissue), Johne’s disease (chronic, sometimes fatal infection of the intestine), metabolic diseases (displaced abomasum, clinical ketosis, milk fever, etc.) and fertility disorders (retained placenta, metritis, cystic ovaries, etc.) is partially genetic in nature. In Canada, the prevalence of these health problems in lactating dairy cows is substantial: 20-40% have one or more of these disorders. With a Canadian dairy population of 1 million cows and a global dairy population of 270 million cows, genetic improvement of health traits could positively impact a huge number of animals. The advantages of using genetic improvement to improve health traits are manifold: cows benefit through increased life expectancy / quality of life; farmers benefit through increased milk production, reduced veterinary costs, and decreased culling rate; consumers benefit through high-quality milk products; and the amount of antibiotics in the environment is reduced. Despite efforts to improve health through conventional breeding programmes, progress has been limited by the shortage of reliable phenotypes and a limited understanding of the genetic architecture underlying complex traits. Fortunately, the use of inexpensive genomic data is creating new opportunities in this field. The main challenges now facing animal breeders are the scarcity of accurate phenotypes, the lack of efficient bioinformatics tools and methodologies, and the shortage of highly qualified personnel capable of handling these massive amounts of data. Furthermore, although genomics provides an array of benefits, one of the technology's current drawbacks is an increased level of inbreeding in populations undergoing intense selection. To address these issues, novel insights into the genetic architecture underlying quantitative traits in dairy cattle are required. My long-term goal is thus to use high-density genomic data to gain fundamental insights into the genetic architecture of health and fitness traits in dairy cattle, and to train personnel qualified to work in this field. This will be done by 1) efficiently and accurately identifying relevant genetic variants, 2) developing statistical methodology and software to calculate genomic relationships from whole genome re-sequence data, 3) conducting genome-wide association studies to identify single or multiple variants or genomic regions influencing phenotype variability, specifically for health traits, and 4) creating a tool for the management of increasing homozygosity in Canadian dairy cattle breeding programs. Through incorporation of the latest genomic technologies in the development of breeding strategies, I will make a substantial contribution to breeding healthy, fertile, and productive cows for both Canadian and global dairy systems.