ArticleHeredity2022
Temporal and genomic analysis of additive genetic variance in breeding programmes.
Article in Heredity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Genomic models for accurate estimation of breeding values in Sitka spruce (Picea sitchensis Bong. Carr).Heredity · 2026Article
- Genetic prediction with ARG-powered linear algebra.Genetics · 2026Article
- Interpreting SNP heritability in admixed populations.Genetics · 2025Article
- Practical Considerations When Using Mendelian Sampling Variances for Selection Decisions in Genomic Selection Programs.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025Article
- Estimation of heritabilities and genetic correlations by time slices using predictivity in large genomic models.Genetics · 2025Article
- Interpreting SNP heritability in admixed populations.bioRxiv : the preprint server for biology · 2025Article
- SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection.The plant genome · 2025Article
- Simulation of functional additive and non-additive genetic effects using statistical estimates from quantitative genetic models.Heredity · 2024Article
- Temporal dynamics of genetic parameters and SNP effects for performance and disorder traits in poultry undergoing genomic selection.Journal of animal science · 2024Article
- Influence of the mating design on the additive genetic variance in plant breeding populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- A genome-wide association study for loin depth and muscle pH in pigs from intensely selected purebred lines.Genetics, selection, evolution : GSE · 2023Article
- A method for partitioning trends in genetic mean and variance to understand breeding practices.Genetics, selection, evolution : GSE · 2023Article
- Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study.Frontiers in genetics · 2023Article
- A joint learning approach for genomic prediction in polyploid grasses.Scientific reports · 2022Article
- Bovine host genome acts on rumen microbiome function linked to methane emissions.Communications biology · 2022Article
- Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes.American journal of human genetics · 2022Article
- A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens.Genetics, selection, evolution : GSE · 2021Article
- Impact of Genomic Prediction Model, Selection Intensity, and Breeding Strategy on the Long-Term Genetic Gain and Genetic Erosion in Soybean Breeding.Frontiers in genetics · 2021Article
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5 authors.
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Abstract
Genetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium.
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