Linkage disequilibrium among commonly genotyped SNP variants detected from bull sequence,

Variance component estimates for growth traits in beef cattle using selected variants from imputed low-pass sequence data

A beef cattle population (n = 2,343) was used to assess the impact of variants identified from the imputed low-pass sequence (LPS) on the estimation of variance components and genetic parameters of birth weight (BWT) and post-weaning gain (PWG). Variants were selected based on functional impact and were partitioned into four groups (low, modifier, moderate, high) based on predicted functional impact and re-partitioned based on the consequence of mutation, such as missense and untranslated region variants, into six groups (G1-G6). Each subset was used to construct a genomic relationship matrix (GRM) for univariate animal models. Multiple analyses were conducted to compare the proportion of additive genetic variation explained by the different subsets individually and collectively, and these estimates were benchmarked against all LPS variants in a single GRM and array (e.g., GeneSeek Genomic Profiler 100K) genotypes. When all variants were included in a single GRM, heritability estimates for BWT and PWG were 0.43 ± 0.05 and 0.38 ± 0.05, respectively. Heritability estimates for BWT ranged from 0.10 to 0.42 dependent on which variant subsets were included. Similarly, estimates for PWG ranged from 0.05 to 0.38. Results showed that variants in the subsets modifier and G1 (untranslated region) yielded the highest heritability estimates and were similar to the inclusion of all variants, while estimates from GRM containing only variants in the categories High, G4 (non-coding transcript exon), and G6 (start and stop loss/gain) were the lowest. All variants combined provided similar heritability estimates to chip genotypes and provided minimal to no additional information when combined with chip data. This suggests that the chip single nucleotide polymorphisms and the variants from LPS predicted to be less consequential are in relatively high linkage disequilibrium with the underlying causal variants as a whole and sufficiently spread throughout the genome to capture larger proportions of additive genetic variation.

Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers

Decreasing costs are making low coverage sequencing with imputation to a comprehensive reference panel an attractive alternative to obtain functional variant genotypes that can increase the accuracy of genomic prediction. To assess the potential of low-pass sequencing, genomic sequence of 77 steers sequenced to >10X coverage was downsampled to 1X and imputed to a reference of 946 cattle representing multiple Bos taurus and Bos indicus-influenced breeds. Genotypes for nearly 60 million variants detected in the reference were imputed from the downsampled sequence. The imputed genotypes strongly agreed with the SNP array genotypes (r¯=0.99) and the genotypes called from the transcript sequence (r¯=0.97). Effects of BovineSNP50 and GGP-F250 variants on birth weight, postweaning gain, and marbling were solved without the steers' phenotypes and genotypes, then applied to their genotypes, to predict the molecular breeding values (MBV). The steers' MBV were similar when using imputed and array genotypes. Replacing array variants with functional sequence variants might allow more robust MBV. Imputation from low coverage sequence offers a viable, low-cost approach to obtain functional variant genotypes that could improve genomic prediction.

Assessment of genetic diversity and population structure in cultured Australian Pacific oysters

The recent development of Pacific oyster (Crassostrea gigas) SNP genotyping arrays has allowed detailed characterisation of genetic diversity and population structure within and between oyster populations. It also raises the potential of harnessing genomic selection for genetic improvement in oyster breeding programmes. The aim of this study was to characterise a breeding population of Australian oysters through genotyping and analysis of 18 027 SNPs, followed by comparison with genotypes of oyster sampled from Europe and Asia. This revealed that the Australian populations had similar population diversity (H_E ) to oysters from New Zealand, the British Isles, France and Japan. Population divergence was assessed using PCA of genetic distance and revealed that Australian oysters were distinct from all other populations tested. Australian Pacific oysters originate from planned introductions sourced from three Japanese populations. Approximately 95% of these introductions were from geographically, and potentially genetically, distinct populations from the Nagasaki oysters assessed in this study. Finally, in preparation for the application of genomic selection in oyster breeding programmes, the strength of LD was evaluated and subsets of loci were tested for their ability to accurately infer relationships. Weak LD was observed on average; however, SNP subsets were shown to accurately reconstitute a genomic relationship matrix constructed using all loci. This suggests that low-density SNP panels may have utility in the Australian population tested, and the findings represent an important first step towards the design and implementation of genomic approaches for applied breeding in Pacific oysters.

QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection

BACKGROUND

National genetic evaluations for disease resistance do not exist, precluding the genetic improvement of cattle for these traits. We imputed BovineHD genotypes to whole genome sequence for 2703 Holsteins that were cases or controls for Bovine Respiratory Disease and sampled from either California or New Mexico to construct and compare genomic prediction models. The sequence variation reference dataset comprised variants called for 1578 animals from Run 5 of the 1000 Bull Genomes Project, including 450 Holsteins and 29 animals sequenced from this study population. Genotypes for 9,282,726 variants with minor allele frequencies ≥5% were imputed and used to obtain genomic predictions in GEMMA using a Bayesian Sparse Linear Mixed Model.

RESULTS

Variation explained by markers increased from 13.6% using BovineHD data to 14.4% using imputed whole genome sequence data and the resolution of genomic regions detected as harbouring QTL substantially increased. Explained variation in the analysis of the combined California and New Mexico data was less than when data for each state were separately analysed and the estimated genetic correlation between risk of Bovine Respiratory Disease in California and New Mexico Holsteins was - 0.36. Consequently, genomic predictions trained using the data from one state did not accurately predict disease risk in the other state. To determine if a prediction model could be developed with utility in both states, we selected variants within genomic regions harbouring: 1) genes involved in the normal immune response to infection by pathogens responsible for Bovine Respiratory Disease detected by RNA-Seq analysis, and/or 2) QTL identified in the association analysis of the imputed sequence variants. The model based on QTL selected variants is biased but when trained in one state generated BRD risk predictions with positive accuracies in the other state.

CONCLUSIONS

We demonstrate the utility of sequence-based and biology-driven model development for genomic selection. Disease phenotypes cannot be routinely recorded in most livestock species and the observed phenotypes may vary in their genomic architecture due to variation in the pathogen composition across environments. Elucidation of trait biology and genetic architecture may guide the development of prediction models with utility across breeds and environments.