ArticlePLoS genetics2021
Average semivariance yields accurate estimates of the fraction of marker-associated genetic variance and heritability in complex trait analyses.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Genetic architecture of angular leaf spot resistance in cultivated strawberry shaped by epistasis and genotype-by-environment interactions.The plant genome · 2026Article
- Whole-genome sequencing bulked segregant analysis uncovered FW7, a Fusarium wilt resistance gene masked by epistasis in octoploid strawberry.The plant genome · 2025Article
- Regulation of heterosis-associated gene expression complementation in maize hybrids.Genome biology · 2025Article
- The PERPETUAL FLOWERING locus: Necessary but insufficient for genomic prediction of runnerless and other asexual reproduction phenotypes in strawberry.The plant genome · 2025Article
- Leveraging genetic resources and genomic prediction to enhance flavonol content in cranberry fruit.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
- Loss-of-function mutations in the fruit softening geneHorticulture research · 2025Article
- Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance toHorticulture research · 2024Article
- Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures.G3 (Bethesda, Md.) · 2023Article
- Harnessing underutilized gene bank diversity and genomic prediction of cross usefulness to enhance resistance to Phytophthora cactorum in strawberry.The plant genome · 2023Article
- Novel Fusarium wilt resistance genes uncovered in natural and cultivated strawberry populations are found on three non-homoeologous chromosomes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses.G3 (Bethesda, Md.) · 2022Article
- Identification of QTNs, QTN-by-environment interactions and genes for yield-related traits in rice using 3VmrMLM.Frontiers in plant science · 2022Article
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4 authors.
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Abstract
The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more 'large-effect' loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability ([Formula: see text]) for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and [Formula: see text]. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and [Formula: see text] were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and [Formula: see text], in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and [Formula: see text]. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance.
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