Biology subjects

Crum, T. E.

Publications and source records attributed to Crum, T. E..

3 recordsLinked to original sources

bioRxiv2022

Analysis of polygenic selection in purebred and crossbred pig genomes using Generation Proxy Selection Mapping

BackgroundArtificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time, termed polygenic selection, at causal loci and the surrounding genomic regions. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied Generation Proxy Selection Mapping (GPSM), a genome-wide association analysis of SNP genotype (38,294 to 46,458 SNPs) of birth date, in four pig populations (15,457, 15,772, 16,595 and 8,447 pigs per population) to identify loci responding to artificial selection over a span of five to ten years. Gene-drop simulation analyses were conducted to validate GPSM results. Selection signatures within and across each population of pigs were compared in the context of commercial pork production. ResultsForty-nine to 854 loci were identified by GPSM as under selection (Q-values less than 0.10) across 15 subsets of pigs based on population combinations. The number of significant associations increased as populations of pigs were pooled. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across populations. Negligible error rates (less than or equal to 0.02%) of false-positive associations were identified when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations. ConclusionsThis work confirms the efficacy and accuracy of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. Identified polygenic selection highlights loci important to swine production.

genomics↗

bioRxiv2019

A Multi-Breed Reference Panel and Additional Rare Variation Maximizes Imputation Accuracy in Cattle

BackgroundThe use of array-based SNP genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data in the last decade. While low-density assays work exceptionally well in the context of genomic prediction, they are less useful in mapping and causal variant discovery. This project focuses on maximizing imputation accuracies to the marker set of two high-density research assays, the Illumina Bovine HD, and the GGP-F250 which contains a large proportion of rare and potentially functional variants (~850,000 total SNPs). This 850K SNP set is well-suited for both imputation to sequence-level genotypes and direct downstream analysis.\n\nResultsWe find that a large multi-breed composite imputation reference comprised of 36,131 samples with either HD and/or F250 genotypes significantly increases imputation accuracy compared to a standard within-breed reference panel, particularly at low minor allele frequencies. Imputation accuracies were maximized when an individuals ancestry was adequately represented in the composite reference, particularly with complete 850K genotypes. The addition of rare content from the F250 to our composite reference panel significantly increased the imputation accuracy of rare variants found exclusively on the HD. Additionally, we identify 50,000 variants as an ideal starting density for 850K imputation.\n\nConclusionUsing high-density genotypes on all available individuals in a multi-breed reference panel maximizes imputation accuracy for all cattle populations. Admixed breeds or those sparsely represented in the composite reference are still imputed at high accuracy which will increase further as the reference panel grows. We expect that the addition of rare variation from the F250 will increase the accuracy of imputation at the sequence level.

genomics↗

bioRxiv2018

CRUMBLER: A tool for the Prediction of Ancestry in Cattle

BackgroundIn many beef and some dairy production systems, crossbreeding is used to take advantage of breed complementarity and heterosis. Admixed animals are frequently identified by their coat color and body conformation phenotypes, however, without pedigree information it is not possible to identify the expected breed composition of an admixed animal and in the presence of selection, the actual composition may differ from expectation.\n\nResultsWe tested an approach to estimate the global ancestry of individuals using ADMIXTURE and SNPweights. ADMIXTURE estimates ancestry using a model-based approach applied to large single nucleotide polymorphism (SNP) genotype datasets. Individuals are assumed to be unrelated and a supervised analysis can be performed using reference animals sampled to represent ancestral populations. SNPweights infers ancestry using weights estimated by principal component analysis for genome-wide SNP panels that have been genotyped in the reference panel animals. We constructed analysis pipelines to determine the ancestry of individuals with potentially complex ancestries using both methods and a specified reference population SNP dataset. The reference population was constructed using breed association pedigree information and an iterative analysis to identify sets of purebred individuals representative of each breed.\n\nConclusionThe finally adopted CRUMBLER pipeline extracts a subset of genotypes that are common to all current commercially available genotyping platforms and processes these into the file formats required for the analysis software and predicts admixture proportions using the reference population dataset.

genomics↗