ArticleBMC genomics2016
Advantages of continuous genotype values over genotype classes for GWAS in higher polyploids: a comparative study in hexaploid chrysanthemum.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Use of continuous genotypes for genomic prediction in sugarcane.The plant genome · 2024Article
- To be or not to be tetraploid-the impact of marker ploidy on genomic prediction and GWAS of potato.Frontiers in plant science · 2024Article
- Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids.The plant genome · 2023Article
- Implementation of different relationship estimate methodologies in breeding value prediction in kiwiberry (Molecular breeding : new strategies in plant improvement · 2023Article
- Genome-Wide Identification of Loci Associated With Phenology-Related Traits and Their Adaptive Variations in a Highbush Blueberry Collection.Frontiers in plant science · 2021Article
- Article
- Genetic Mapping in Autohexaploid Sweet Potato with Low-Coverage NGS-Based Genotyping Data.G3 (Bethesda, Md.) · 2020Article
- Genome-wide study on the polysomic genetic factors conferring plasticity of flower sexuality in hexaploid persimmon.DNA research : an international journal for rapid publication of reports on genes and genomes · 2020Article
- Genomic Prediction of Autotetraploids; Influence of Relationship Matrices, Allele Dosage, and Continuous Genotyping Calls in Phenotype Prediction.G3 (Bethesda, Md.) · 2019Article
- polyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids.G3 (Bethesda, Md.) · 2019Article
- Genotyping Polyploids from Messy Sequencing Data.Genetics · 2018Article
- Development and Applications of a High Throughput Genotyping Tool for Polyploid Crops: Single Nucleotide Polymorphism (SNP) Array.Frontiers in plant science · 2018Review
- Conclusive evidence for hexasomic inheritance in chrysanthemum based on analysis of a 183 k SNP array.BMC genomics · 2017Article
- Variant calling in polyploids for population and quantitative genetics.Applications in plant sciencesArticle
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAssociation studies are an essential part of modern plant breeding, but are limited for polyploid crops. The increased number of possible genotype classes complicates the differentiation between them. Available methods are limited with respect to the ploidy level or data producing technologies. While genotype classification is an established noise reduction step in diploids, it gains complexity with increasing ploidy levels. Eventually, the errors produced by misclassifications exceed the benefits of genotype classes. Alternatively, continuous genotype values can be used for association analysis in higher polyploids. We associated continuous genotypes to three different traits and compared the results to the output of the genotype caller SuperMASSA. Linear, Bayesian and partial least squares regression were applied, to determine if the use of continuous genotypes is limited to a specific method. A disease, a flowering and a growth trait with h (2) of 0.51, 0.78 and 0.91 were associated with a hexaploid chrysanthemum genotypes. The data set consisted of 55,825 probes and 228 samples.
resultsWe were able to detect associating probes using continuous genotypes for multiple traits, using different regression methods. The identified probe sets were overlapping, but not identical between the methods. Baysian regression was the most restrictive method, resulting in ten probes for one trait and none for the others. Linear and partial least squares regression led to numerous associating probes. Association based on genotype classes resulted in similar values, but missed several significant probes. A simulation study was used to successfully validate the number of associating markers.
conclusionsAssociation of various phenotypic traits with continuous genotypes is successful with both uni- and multivariate regression methods. Genotype calling does not improve the association and shows no advantages in this study. Instead, use of continuous genotypes simplifies the analysis, saves computational time and results more potential markers.
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