ArticleHeredity2011
Detection of epistatic interactions in association mapping populations: an example from tetraploid potato.
Article in Heredity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations.G3 (Bethesda, Md.) · 2021Article
- Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato.Frontiers in plant science · 2021Article
- Adaptive selection of founder segments and epistatic control of plant height in the MAGIC winter wheat population WM-800.BMC genomics · 2018Article
- Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.Frontiers in plant science · 2018Article
- Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epistatic interactions among loci are expected to contribute substantially to variation of quantitative traits. The objectives of our research were to (i) compare a classical mixed-model approach with a combined mixed-model and analysis of variance approach for detecting epistatic interactions; (ii) examine using computer simulations the statistical power to detect additive-additive, additive-dominance and dominance-dominance epistatic interactions and (iii) detect epistatic interactions between candidate genes for resistance to leaf blight in a set of tetraploid potato clones. Our study was based on the genotypic and phenotypic data of 184 tetraploid potato cultivars as well as computer simulations. The number of significant (α* =1 × 10(-6)) epistatic interactions ranged for the three examined traits from 3 to 32. Our findings suggested that the combined mixed-model and analysis of variance approach leads in comparison with the classical mixed-model approach not to an increased rate of false-positives. The results of the computer simulations suggested that, if molecular markers are available that are in high LD (D'>0.9) with the trait-coding loci, the statistical power to detect epistatic interactions, which explain 5-10% of the phenotypic variance, was of a size that seems promising for their detection.
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