ArticleG3 (Bethesda, Md.)2022
Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses.
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Article
- Impact of scale parameter for marker variance prior in some Bayesian whole-genome regression methods.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- The PERPETUAL FLOWERING locus: Necessary but insufficient for genomic prediction of runnerless and other asexual reproduction phenotypes in strawberry.The plant genome · 2025Article
- A major QTL region associated with powdery mildew resistance in leaves and fruits of the reconstructed garden strawberry.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Maximizing the accuracy of genetic variance estimation and using a novel generalized effective sample size to improve simulations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic prediction of synthetic hexaploid wheat upon tetraploid durum and diploid Aegilops parental pools.The plant genome · 2024Article
- Quantitative genomics-enabled selection for simultaneous improvement of lint yield and seed traits in cotton (Gossypium hirsutum L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Leveraging trait and QTL covariates to improve genomic prediction of resistance to Fusarium head blight in Central European winter wheat.Frontiers in plant science · 2024Article
- A medium-density genotyping platform for cultivated strawberry using DArTag technology.The plant genome · 2023Article
- Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures.G3 (Bethesda, Md.) · 2023Article
- Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population.G3 (Bethesda, Md.) · 2023Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries' genetic values and predict unobserved entries' genetic values. One of the main parameters of such models is genomic variance (σg2), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (hg2); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of σg2 and hg2. With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, KASV, that directly yields accurate estimates of σg2 and hg2 in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.