ArticleGenetics, selection, evolution : GSE2018
Impact of rare and low-frequency sequence variants on reliability of genomic prediction in dairy cattle.
Article in Genetics, selection, evolution : GSE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Transmission Ratio Distortion Detection Using Microsatellite and Single Nucleotide Polymorphism Markers in Hanwoo of Gyeongnam Province.Animal genetics · 2026Article
- The effect of marker types and density on genomic prediction and GWAS of key performance traits in tetraploid potato.Frontiers in plant science · 2024Article
- Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.Frontiers in plant science · 2023Article
- Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation.Genetics, selection, evolution : GSE · 2022Article
- On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL.Genetics, selection, evolution : GSE · 2021Article
- A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle.Genetics, selection, evolution : GSE · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundAvailability of whole-genome sequence data for a large number of cattle and efficient imputation methodologies open a new opportunity to include rare and low-frequency variants (RLFV) in genomic prediction in dairy cattle. The objective of this study was to examine the impact of including RLFV that are within genes and selected from whole-genome sequence variants, on the reliability of genomic prediction for fertility, health and longevity in dairy cattle.
resultsAll genic RLFV with a minor allele frequency lower than 0.05 were extracted from imputed sequence data and subsets were created using different strategies. These subsets were subsequently combined with Illumina 50 k single nucleotide polymorphism (SNP) data and used for genomic prediction. Reliability of prediction obtained by using 50 k SNP data alone was used as reference value and absolute changes in reliabilities are referred to as changes in percentage points. Adding a component that included either all the genic or a subset of selected RLFV into the model in addition to the 50 k component changed the reliability of predictions by - 2.2 to 1.1%, i.e. hardly no change in reliability of prediction was found, regardless of how the RLFV were selected. In addition to these empirical analyses, a simulation study was performed to evaluate the potential impact of adding RLFV in the model on the reliability of prediction. Three sets of causal RLFV (containing 21,468, 1348 and 235 RLFV) that were randomly selected from different numbers of genes were generated and accounted for 10% additional genetic variance of the estimated variance explained by the 50 k SNPs. When genic RLFV based on mapping results were included in the prediction model, reliabilities improved by up to 4.0% and when the causal RLFV were included they improved by up to 6.8%.
conclusionsUsing selected RLFV from whole-genome sequence data had only a small impact on the empirical reliability of genomic prediction in dairy cattle. Our simulations revealed that for sequence data to bring a benefit, the key is to identify causal RLFV.
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