Use of genomic information in genetic evaluation of livestock

The imprecise location of Quantitative Trait Loci (QTL) in the genome of livestock species has represented a relevant constraint for commercial application of Marker Assisted Selection (MAS) in the past. Nowadays, thanks to chip DNA technology, thousands of markers can be used in genomic selection of livestock. The overall objective of the thesis was to investigate on the use of genetic markers in animal breeding. Specific objectives were: i) a meta-analysis study of QTL data through factor analysis (FA); ii) to study factors affecting direct genomic value (DGV) accuracy; iii) to reduce the dimensionality of marker data through Principal Component Analysis (PCA) FA extracted three factor (F). F1 was associated (0.98) to marker location (marker map index); the F2 was correlated with population size (0.74) and the year (0.84) (dimension of the study); F3 was correlated to the significance level (0.78), the number of families (0.63) and, the marker density (-0.43) (power of the experiment index). The score that each QTL got on each factor may be used to classify the original QTL. The DGV accuracy increased with the heritability, marker density and number of daughters per bull. Whereas, the number of QTLs and generation of random mating did not show any relevant effect. The PC approach yielded the same accuracy of predicted DGV obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time by 99%

Multivariate meta-analysis of QTL mapping studies

A large number of quantitative trait loci (QTLs) for milk production and quality traits in dairy cattle has been reported in literature. The large amount of information available could be exploited by meta-analyses to draw more general conclusions from results obtained in different experimental conditions (animals, statistical methodologies). QTL meta-analyses have been carried out to estimate the distribution of QTL effects in livestock and to find consensus on QTL position. In this study, multivariate dimension reduction techniques are used to analyse a database of dairy cattle QTL published results, in order to extract latent variables able to characterise the research. A total of 92 papers by 72 authors were found on 25 scientific Journals for the period January 1995-February 2008. More than thirty parameters were picked up from the articles. To overcome the problem of different map location, the flanking markers were mapped on release 4.1 of the Bos taurus genome sequence (www.ensembl. org). Their position was retrieved from public databases and, when absent, was calculated in silico by blasting (http://blast.wustl.edu/) the markers’ nucleotide sequence against the genomic sequence. Records were discarded if flanking markers or P-values were not available. After these edits, the final archive consisted of 1,162 records. Seven selected variables were analysed both with the Factor Analysis (FA), combined with the varimax rotation technique, and Principal Component Analysis (PCA). FA was able to explain 68% of the original variability with 3 latent factors: the first factor extracted was highly associated (factor loading of 0.98) to marker location along the chromosome and could be considered as a marker map index; the second factor showed factor loadings of 0.74 and 0.84 related to the variable number of animals involved and year of the experiment, respectively, and it can be regarded as an indicator of the dimension of the study; the third factor was correlated to the significance level of the statistical test (0.78), number of families (0.63), and, negatively, to the marker density (-0.43). It can be named as index of power of the experiment. Same patterns can be observed in the eigenvectors of PCA. Four PCs were able to explain about 80% of the original variance. The first two PCs basically underlined accurately the same structure found with the first two factors in FA, whereas PC3 and PC4 summarized the structure of F3. The score that each QTL gets on each Factor or PC could be useful to classify the original QTL records and make them more comparable once that the redundancy of information has been removed

detectRUNS: Detect runs of homozygosity and runs of heterozygosity in diploid genomes

detectRUNS is a R package for the detection of runs of homozygosity (ROH/ROHom) and of heterozygosity (ROHet, a.k.a. “heterozygosity-rich regions”) in diploid genomes. ROH/ROHom were first studied in humans (e.g. McQuillan et al. 2008) and rapidly found applications not only in human genetics abut also in animal genetics (e.g. Ferencakovic et al., 2011, in Bos taurus). More recently, the idea of looking also at “runs of heterozygosity” (ROHet or, more appropriately, “heterozygosity-rich regions”) has been proposed (Wiliams et al. 2016)

Pre-selection of most significant SNPS for the estimation of genomic breeding values

The availability of a large amount of SNP markers throughout the genome of different livestock species offers the opportunity to estimate genomic breeding values (GEBVs). However, the estimation of many effects in a data set of limited size represent a severe statistical problem. A pre-selection of SNPS based on single regression may provide a reasonable compromise between accuracy of results, number of independent variables to be considered and computing requirements