A well-rounded toolbox: Multiple approaches of animal breeding and genetics to improve livestock production, conservation and food security.

To meet the demands of the 21st century, the livestock sector needs to efficiently and sustainably increase production paying close attention to consumers demands for animal welfare and social responsibility. Breeding and genetics provides a great approach to achieve these goals given that genetic gains are cumulative. Additionally, the fast pace at which technology advances provides geneticists with several molecular tools to tackle a series of challenges both productive and environmental. The manuscripts presented in this work represent varied applications of genomics to tackle these issues.The first manuscript presented an approach to improve food and nutritional security in developing countries through the discovery of genes related to the content of beta-carotene in cow and buffalo milk using a candidate gene approach. Blood for DNA and milk samples for Beta carotene (BC) were obtained from 2,291 Indian cows of 5 different breeds (Gir, Holstein cross, Jersey Cross, Tharparkar, and Sahiwal) and 2,242 Indian buffaloes (Jafarabadi, Murrah, Pandharpuri, and Surti breeds). Multiple significant SNP were found using Bayesian and frequentist methodologies with allele substitution effects ranging from 6.21 (3.13) to 9.10 (5.43) µg of BC per 100 mL of milk. Total gene effects exceeded the mean BC value for all breeds with both analysis methods. Moreover, the recommendation of selection for significant specific alleles of some gene markers provides a route to effectively increase the BC content in milk in the Indian cattle and buffalo populations. The second manuscript focused on exploring the usefulness of blood-based traits as indicators of health and performance in beef cattle at weaning and identify the genetic basis underlying the different blood parameters obtained from complete blood counts (CBCs) CBCs were recorded from approximately 570 Angus based, crossbred beef calves at weaning born between 2015 and 2016 and raised on toxic or novel tall fescue. The calves were genotyped using 50k SNPs and the genotypes were imputed to a density of 270k SNPs. Genetic parameters were estimated for 15 blood and 4 production traits. Finally, genome-wide association studies (GWAS) were performed for all traits. Heritability estimates ranged from 0.11 to 0.60, and generally weak phenotypic correlations and strong genetic correlations were observed among blood- based traits only. The genome-wide association study identified ninety-one 1-Mb windows that accounted for 0.5% or more of the estimated genetic variance for at least 1 trait with 21 windows overlapping across 2 or more traits (explaining more than 0.5% of estimated genetic variance for two or more traits) and 5 candidate genes were identified in the most interesting overlapping regions related to blood-based traits. Finally, there is evidence of an important overlap of genetic control among similar blood-based traits which will allow for their use in improvement programs in beef cattle. The third manuscript aimed to develop an effective set of SNPs to estimate breed composition of pigs, focusing on those with a Mangalitsa background. The manuscript also explored different methods to estimate breed composition. Genotypes from 648 pigs and 11 breeds were used to develop marker panels. Two sets of panels were created. The first set was composed of the 10, 50, 100, 500 and 1000 markers with the highest Fst scores across the pig genome. The second set was composed by randomly selected markers and had the same number of markers as the Fst-derived panels. Linear regression and random forest methods were then used on the marker panels to estimate breed composition, of 107 pigs including 47 individuals known to have Mangalitsa background. The Fst approach appeared to be better at identifying Mangalitsa individuals while random markers were more accurate at estimating breed composition for non-Mangalitsa individuals. When the results were compared across methods for estimating breed composition, linear regression produced more accurate estimates of breed composition than random forest. Importantly, accuracy of estimation depends on the right set of animals being used as reference for the estimation. The last manuscript presented was the first to examine the genomics of brood stock Muskellunge (Esox masquinongy) from Iowa and showed marked genetic differences with a Canadian population. The genome of the Northern pike (Esox Lucius) was used as a reference genome to align whole genome sequence from 12 brood individuals from Iowa and publicly available RAD-seq of 625 individuals from Saint-Lawrence river in Canada. Analyses were performed using 16,867 high-quality SNPs common between both populations. The Ti/Tv values were 1.09 and 1.29 for samples from Iowa and Canada, respectively. PCA and Admixture analyses showed large genetic differences between Canadian and Iowan populations. Window-based pooled heterozygosity found 6 highly heterozygous windows containing 244 genes in the Iowa population and Fst comparing the Iowa and Canadian populations found 14 windows with Fst values larger than 0.9 containing 641 genes. Finally, these results prove the validity of using genomes of closely related species to perform genomic analyses when no reference genome assembly is available. Overall, the manuscripts included in this thesis show the wide variety of applications and methods of genomics to tackle the most important challenges that the biological fields, especially the livestock sector will face in the years to come.

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