GENOME-WIDE DETECTION OF QTL AND CNVS IN DAIRY CATTLE POPULATION

The QTL involved in susceptibility/resistance of infectious diseases and in the productive traits variations, are characterized by genetic heterogeneity and multifactorial inheritance, involving gene polymorphisms from different alternative pathways. With the availavility of single nucleotide polymorphism (SNP) genotyping arrays, the genome-wide association studies (GWAS) have been frequently used to determine the genetic component of complex trait. The Copy Number Variations (CNVs) are another genomic marker that can be possibly used in GWAS and that can be identified from SNP chips themselves. The aims and related discussions for each of the studies presented in this thesis were grouped into three different chapters. • Chapter 1 described the QTL mapping analysis to identify the existence of genetic variability associated to the CLA, VA and D9D contents in milk of the Italian Brown Swiss dairy cattle breed. For this study a selective DNA pooling in a daughter design was adopted, using the Illumina Bovine SNP50 Bead Chip to genotype the pools. Milk samples from 60 animals with higher values (after correction for environmental factors) and 60 animals with lower values for each of these traits from each of five half-sib families were pooled separately. Allele frequencies were compared between pools of high and low value at the sire and marker level for each SNPs for which the sires were heterozygous. An R procedure was implemented to perform data analysis. A correction for multiple tests was applied using the proportion of false positives approach. BTA 19 showed the largest number of markers in association with CLA. Associations between SNPs and the VA and D9-desaturase traits were found on several chromosomes. A bioinformatics survey identified genes with an important role in pathways for milk fat and fatty acids metabolism within 1 Mb distance from SNP markers associated with fatty acids contents. This is the first available mapping for fatty acid content in the Brown Swiss population. • Chapter 2 described a genome-wide association study for somatic cell score (SCS) in the Valdostana Red Pied cattle, with a selective DNA pooling analysis, using the Illumina BovineHD BeadChip. The phenotypes of 275 sires for SCS were expressed as Deregressed Proofs (DP-EBVs) for SCS. The sires were ranked according to DP-EBVs for SCS and the 20% high and 20% low sires included in the pools. The multiple marker test was performed in R software. On BTAs 1, 2, 3, 4, 9, 13, 15, 17, 21 and 22 the largest number of markers in association to the trait was found identifying novel genomic regions related to mastitis (1-Mb SNP windows) and confirming others already mapped. The largest number of significant SNPs exceeding the threshold for genome-wide significant signal was found on BTA 15, located at 50.43-51.63 Mb. The genomic regions identified in this study contribute to a better understanding of the genetic control of the mastitis immune response in cattle and may allow the inclusion of more detailed QTL information in selection programs. • Chapter 3 described a genome wide CNVs discovery in 651 bulls of the Italian Brown Swiss breed using the Illumina Bovine SNP50 BeadChip data. Hidden Markov Model (HMM) of PennCNV and SVS7 software (Golden Helix) were used for the identification of the CNVs and Copy Number Variation Regions (CNVRs). A total of 5,099 and 1,289 CNVs were identified using PennCNV and SVS7 software, respectively. These were grouped at the population level into 1,101 (220 losses, 774 gains, 107 complex) and 277 (185 losses, 56 gains and 36 complex) CNVRs, covering 682 Mb (27.14%) and 33.7 Mb (1.35%) of the autosome, respectively. Ten of the selected CNVRs were experimentally validated with qPCR and the proportions of confirmed positive samples for each region varied from 50% to 100%. The GO and pathway analyses identified genes (false discovery rate corrected) in the CNVRs related to biological processes, cellular component, molecular function and metabolic pathways. Although there is variability in the CNVRs detection across methods, platforms, this study allowed the identification CNVRs in Italian Brown Swiss, overlapping those already detected in other breeds and finding additional ones.