Biology subjects

Herring, W. O.

Publications and source records attributed to Herring, W. O..

5 recordsLinked to original sources

bioRxiv2022

Optimisation of the core subset for the APY approximation of genomic relationships

BackgroundBy entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have have been proposed to address this challenge, like the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of genomic relationship matrix. The partitioning into subsets is often done at random. While APY is a good approximation of the full model, the random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. MethodsWe derived a novel algorithm for optimising the core subset based on the conditional genomic relationship matrix or the conditional SNP genotype matrix. We compared accuracy of genomic predictions with different core subsets on simulated and real pig data. The core subsets were constructed (1) at random, (2) based on the diagonal of genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we have visualised population structure of genotyped animals with the linear Principal Component Analysis and the non-linear Uniform Manifold Approximation and Projection. ResultsAll core subset constructions performed equally well when the number of core animals captured most of variation in genomic relationships, both in simulated and real data. When the number of core animals was not optimal, there was substantial variability in results with the random construction and no variability with the conditional construction. Visualisation of population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. ConclusionsOur results confirm that the size of the core subset in APY is critical. The results further show that the core subset can be optimised with the conditional algorithm that achieves a good and repeatable spread of core animals across the domain of genotyped animals.

genetics↗

bioRxiv2022

Genomic prediction with whole-genome sequence data in intensely selected pig lines

BackgroundEarly simulations indicated that whole-genome sequence data (WGS) could improve genomic prediction accuracy and its persistence across generations and breeds. However, empirical results have been ambiguous so far. Large data sets that capture most of the genome diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays, to identify scenarios in which WGS provides the largest advantage, and to identify potential pitfalls for its effective implementation. MethodsWe sequenced 6,931 individuals from seven commercial pig lines with different numerical size. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a marker array or variants preselected from WGS based on association tests. ResultsThe prediction accuracy with each set of preselected WGS variants was not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and used to preselect variants with statistically significant associations to the trait for augmenting the established marker array. With this method and training sets of around 80k individuals, average improvements of genomic prediction accuracy of 0.025 were observed in within-line scenarios. ConclusionsOur results showed that WGS has a small potential to improve genomic prediction accuracy compared to marker arrays in intensely selected pig lines in some settings. Thus, although we expect that more robust improvements could be attained with a combination of larger training sets and optimised pipelines, the use of WGS in the current implementations of genomic prediction should be carefully evaluated on a case-by-case basis against the cost of generating WGS at a large scale.

genomics↗

bioRxiv2022

Rare and population-specific functional variation across pig lines

BackgroundIt is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for phenotypic differences among breeds, genetic lines, and varieties of livestock and crop species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in livestock commercial populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction. MethodsWe re-sequenced the whole genome of 7,848 individuals from nine commercial pig breeding lines (average sequencing coverage: 4.1x) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of distribution along the genome, minor allele frequency, Wrights fixation index (FST), individual load, and association to production traits. ResultsOf the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower FST, and putatively functional and regulatory roles (including loss-of-function and deleterious missense variants). Only a small subset of low-prevalence variants was found at intermediate allele frequencies and had large estimated effects on production traits. Individuals on average carried less private deleterious missense alleles than expected compared to other predicted consequence types. A small subset of low-prevalence variants with intermediate allele frequencies and higher FST were detected as significantly associated to the production traits and explained small fractions of phenotypic variance (up to 3.2%). These associations were tagged by other more widespread variants, including intergenic variants. ConclusionsMost low-prevalence variants are kept at very low allele frequency and only a small subset contributed detectable fractions of phenotypic variance. Not accounting for low-prevalence variants is therefore unlikely to hinder across-breed analyses, in particular for genomic prediction of breeding values using reference populations of a different genetic background.

genomics↗

bioRxiv2020

Genetics of recombination rate variation in the pig

BackgroundIn this paper, we estimated recombination rate variation within the genome and between individuals in the pig using multiocus iterative peeling for 150,000 pigs across nine genotyped pedigrees. We used this to estimate the heritability of recombination and perform a genome-wide association study of recombination in the pig. ResultsOur results confirmed known features of the pig recombination landscape, including differences in chromosome length, and marked sex differences. The recombination landscape was repeatable between lines, but at the same time, the lines also showed differences in average genome-wide recombination rate. The heritability of genome-wide recombination was low but non-zero (on average 0.07 for females and 0.05 for males). We found three genomic regions associated with recombination rate, one of them harbouring the RNF212 gene, previously associated with recombination rate in several other species. ConclusionOur results from the pig agree with the picture of recombination rate variation in vertebrates, with low but nonzero heritability, and a major locus that is homologous to one detected in several other species. This work also highlights the utility of using large-scale livestock data to understand biological processes.

genetics↗

bioRxiv2019

Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations

BackgroundWe demonstrate high accuracy of whole-genome sequence imputation in large livestock populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage.\n\nMethodsWe used data from four pig populations of different sizes (18,349 to 107,815 individuals) that were broadly genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most at 1x or 2x and a small fraction at 30x; average coverage per individual: 4x). We imputed whole-genome sequence with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of a total of 284 individuals that had been sequenced at high coverage, using a leave-one-out design. We complemented these results with simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees.\n\nResultsImputation accuracy was high for the majority of individuals in all four populations (median individual-wise correlation was 0.97). Individuals in the earliest generations of each population had lower accuracy than the rest, likely due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status of the individual, the availability of marker array data for immediate ancestors, and the degree of connectedness of an individual to the rest of the population, but sequencing coverage had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. These results were validated with the empirical observations.\n\nConclusionsThe coupling of an appropriate sequencing strategy and imputation method, such as described and validated here, is a powerful strategy for generating whole-genome sequence data in large pedigreed populations with high accuracy. This is a critical step for the successful implementation of whole-genome sequence data for genomic predictions and fine-mapping of causal variants.

genomics↗