ArticleEcology and evolution2021
MultiGWAS: An integrative tool for Genome Wide Association Studies in tetraploid organisms.
Article in Ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Disentangling direct and pleiotropic SNP effects in alfalfa (Medicago sativa L.) using causal graph learning.Scientific reports · 2026Article
- Unveiling the Genomic Architecture of Phenotypic Plasticity Using Multiple GWAS Approaches Under Contrasting Conditions of Water Availability: A Model for Barley.International journal of molecular sciences · 2026Article
- Genomic and Transcriptomic Insights into Stage-Specific Drought Resilience in Diverse Myanmar Rice Germplasm.Rice (New York, N.Y.) · 2025Article
- Unlocking the molecular basis of wheat straw composition and morphological traits through multi-locus GWAS.BMC plant biology · 2022Article
- Two decades of association mapping: Insights on disease resistance in major crops.Frontiers in plant science · 2022Review
- MultiGWAS: An integrative tool for Genome Wide Association Studies in tetraploid organisms.Ecology and evolution · 2021Article
- Continuous Mapping Identifies Loci Associated With Weevil Resistance [Frontiers in plant science · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genome-wide association studies (GWASs) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of the model and nonmodel organisms. For this research question, the GWAS replication testing different parameters and models to validate the results' reproducibility is common. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software packages, two designed for polyploid data (GWASpoly and SHEsis) and two designed for diploid data (GAPIT and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in a graphical interface; it manages different genotype formats, including VCF. Moreover, it allows control for population structure, relatedness, and several quality control checks on genotype data. Besides, MultiGWAS can test for additive and dominant gene action models, and, through a proprietary scoring function, select the best model to report its associations. Finally, it generates several reports that facilitate identifying false associations from both the significant and the best-ranked association Single Nucleotide Polymorphisms (SNPs) among the four software packages. We tested MultiGWAS with public tetraploid potato data for tuber shape and several simulated data under both additive and dominant models. These tests demonstrated that MultiGWAS is better at detecting reliable associations than using each of the four software packages individually. Moreover, the parallel analysis of polyploid and diploid software that only offers MultiGWAS demonstrates its utility in understanding the best genetic model behind the SNP association in tetraploid organisms. Therefore, MultiGWAS probed to be an excellent alternative for wrapping GWAS replication in diploid and tetraploid organisms in a single analysis environment.
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