Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1204907 | ENA |
REPOSITORIES: ENA
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Project description:Alpine goat phenotypes for quality components have been routinely recorded for many years and deposited in the Council on Dairy Cattle Breeding (CDCB) repository. The data collected were used to conduct an exploratory genome-wide association study (GWAS) from 72 female Alpine goats originating from locations throughout the U.S. Genotypes were identified with the Illumina Goat 50K single nucleotide polymorphisms (SNP) Beadchip. The analysis used a polygenic model where the dropping criteria was the Call Rate ≥ 0.95. The initial dataset was composed of ~ 60,000 rows of SNPs, 21 columns of phenotypic traits and composed of 53,384 scaffolds containing other informative data points used for genomic predictive power. Phenotypic association with the 50KBeadchip revealed 26,074 reads of candidate genes. These candidate genes segregated as separate novel SNPs and were identified as statistically significant regions for genome and chromosome level trait associations. Candidate genes associated differently for each of the following phenotypic traits: test day milk yield (13,469 candidate genes), test day protein yield (25,690 candidate genes), test day fat yield (25,690 candidate genes), percentage protein (25,690 candidate genes), percentage fat (25,690 candidate genes), and percentage lactose content (25,690 candidate genes). The outcome of this study supports elucidation of novel genes that are important for livestock species in association to key phenotypic traits. Validation towards the development of marker-based selection that provide precision breeding methods will thereby increase breeding value. Specific aims: 1) Improve on contributions to the phenotype repository, the Council on Dairy Cattle Breeding (CDCB) for milk quality traits that are economically important for goat production while developing a corresponding DNA repository for each of the animals with significant genotype-phenotype associations. 2) Develop genomic prediction tools and provide data for a better database for tools to predict phenotypic traits by initially using the high density Goat50KSNP BeadChip for the selection of more specific SNPs associated with select signatures (genes) for phenotypic traits in American Alpine goats. 3) To establish whether a low number of goat subjects (< 300 goats) will provide statistically significant (p < 0.05) predictive capabilities for desired breeding traits in American Alpine dairy goats.
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