Genome based genetic evaluation and genome wide selection using supervised dimension reduction based on partial least squares

The method of partial least squares was applied to the prediction of genetic merit using whole genome scan data consisting of 10715 SNP. The method is particularly suited to data sets that have many more markers than observations and in which markers are collinear due to high linkage disequilibrium. A SNP ranking method was applied to select a subset of markers which have equal predictive power compared to using all SNP simultaneously

QC analyses of SNP array data: Experiences from a large population of dairy sires with 23.8 million data points

The use of a high throughput SNP genotyping platform with 15,380 bovine SNP assays, across 1546 dairy bulls resulted in a data set of approximately 23.8 M SNP data points. Stringent control measures based around low polymorphic content, sample failure, deviation from HWE, low call rate, non-Mendelian inheritance, tri-allelic SNP, and incompatible clustering of data, resulted in removal of 4321 SNPs. The majority (2973) were due to low polymorphic content (MAF99%) across repeat samples, and between platforms. SNP technology has now matured where comprehensive genome-wide analyses can be conducted in cattle with a high degree of robustness

Genome-wide mapping of loci explaining variance in scrotal circumference in Nellore Cattle

The reproductive performance of bulls has a high impact on the beef cattle industry. Scrotal circumference (SC) is the most recorded reproductive trait in beef herds, and is used as a major selection criterion to improve precocity and fertility. The characterization of genomic regions affecting SC can contribute to the identification of diagnostic markers for reproductive performance and uncover molecular mechanisms underlying complex aspects of bovine reproductive biology. In this paper, we report a genome-wide scan for chromosome segments explaining differences in SC, using data of 861 Nellore bulls (Bos indicus) genotyped for over 777,000 single nucleotide polymorphisms. Loci that excel from the genome background were identified on chromosomes 4, 6, 7, 10, 14, 18 and 21. The majority of these regions were previously found to be associated with reproductive and body size traits in cattle. The signal on chromosome 14 replicates the pleiotropic quantitative trait locus encompassing PLAG1 that affects male fertility in cattle and stature in several species. Based on intensive literature mining, SP4, MAGEL2, SH3RF2, PDE5A and SNAI2 are proposed as novel candidate genes for SC, as they affect growth and testicular size in other animal models. These findings contribute to linking reproductive phenotypes to gene functions, and may offer new insights on the molecular biology of male fertility

Genome-wide selection in dairy cattle: Use of genetic algorithms in the estimation of molecular breeding values

A procedure has been developed for the prediction of genetic merit and the simultaneous assessment of multiple genotypes for subsequent use in gene detection. The system utilises a large volume of genotype information but ignores pedigree. With a simple additive model of inheritance, high correlations between estimates of molecular breeding value and highly reliable progeny test estimated breeding values were observed (0.70–0.77)

Genome wide selection in dairy cattle based on high-density genome-wide SNP analysis: From discovery to application

A genome wide selection (GWS) platform was developed for prediction of genetic merit in dairy cattle. The critical components of the GWS platform included a genome wide SNP analysis assay representing 15,036 SNPs, 1546 progeny tested Holstein Friesian sires with EBV (ABV) for 42 lactation performance traits, and a series of complexity reduction methods with internal and external cross validation. Derived Molecular Breeding Values (MBV) using a fraction of the available SNP information, were shown to have high predictive value for genetic merit (r=0.65-0.87 with ABV) in bulls not used in the training data from which the SNP effects were derived. GWS can be used in the absence of SNP location and pedigree to make potentially highly accurate predictions of genetic merit at an early age from DNA analyses