ArticleGenetics2013
Mapping quantitative trait loci by controlling polygenic background effects.
Article in Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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Who cites it
72 citing papers in PubMed.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Genome-wide association studies and QTL mapping for traits deviating from normal distribution.National science review · 2026Article
- Powerful one-dimensional scan to detect heterotic quantitative trait loci.Nature communications · 2025Article
- Fast3VmrMLM: A fast algorithm that integrates genome-wide scanning with machine learning to accelerate gene mining and breeding by design for polygenic traits in large-scale GWAS datasets.Plant communications · 2025Article
- FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.Briefings in bioinformatics · 2024Article
- Genome-Wide Association Study for Maize Hybrid Performance in a Typical Breeder Population.International journal of molecular sciences · 2024Article
- PyAGH: a python package to fast construct kinship matrices based on different levels of omic data.BMC bioinformatics · 2023Article
- Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize.International journal of molecular sciences · 2022Article
- Article
- A new approach fits multivariate genomic prediction models efficiently.Genetics, selection, evolution : GSE · 2022Article
- Identification of a Major QTL and Validation of Related Genes for Tiller Angle in Rice Based on QTL Analysis.International journal of molecular sciences · 2022Article
- Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize.International journal of molecular sciences · 2022Article
- Phenotype and Genotype Interaction Underlying Distributive Characteristic for Awn Development in Rice.Plants (Basel, Switzerland) · 2022Article
- Estimating genetic variance contributed by a quantitative trait locus: A random model approach.PLoS computational biology · 2022Article
- Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Epistasis Activation Contributes Substantially to Heterosis in Temperate by Tropical Maize Hybrids.Frontiers in plant science · 2022Article
- Two decades of association mapping: Insights on disease resistance in major crops.Frontiers in plant science · 2022Review
- Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests.Genetics · 2021Article
12 more citing papers are in PubMed but not listed here.
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1 author.
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Abstract
A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive × additive, dominance × dominance, additive × dominance, and dominance × additive variances. The six different kinship matrices along with the six estimated polygenic variances were used to control the genetic background of a QTL mapping model. Simulation studies showed that incorporating epistatic polygenic covariance structure can improve QTL mapping resolution. The method was applied to yield component traits of rice. We analyzed four traits (yield, tiller number, grain number, and grain weight) using 278 immortal F2 crosses (crosses between recombinant inbred lines) and 1619 markers. We found that the relative importance of each type of genetic variance varies across different traits. The total genetic variance of yield is contributed by additive × additive (18%), dominance × dominance (14%), additive × dominance (48%), and dominance × additive (15%) variances. Tiller number is contributed by additive (17%), additive × additive (22%), and dominance × additive (43%) variances. Grain number is mainly contributed by additive (42%), additive × additive (19%), and additive × dominance (31%) variances. Grain weight is almost exclusively contributed by the additive (73%) variance plus a small contribution from the additive × additive (10%) variance. Using the estimated genetic variance components to capture the polygenic covariance structure, we detected 39 effects for yield, 39 effects for tiller number, 24 for grain number, and 15 for grain weight. The new method can be directly applied to polygenic-effect-adjusted genome-wide association studies (GWAS) in human and other species.
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