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MultiGWAS: An integrative tool for Genome Wide Association Studies in tetraploid organisms. | LitMetric

MultiGWAS: An integrative tool for Genome Wide Association Studies in tetraploid organisms.

Ecol Evol

Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA) CI Tibaitatá Bogota Colombia.

Published: June 2021

AI Article Synopsis

  • Genome-wide association studies (GWASs) help identify the genetic factors behind phenotypic variations in various organisms, but current methods struggle with replication and tetraploid data.
  • The MultiGWAS tool was developed to conduct GWAS for both diploid and tetraploid organisms, integrating four software packages to enhance analysis capabilities.
  • Testing with public tetraploid potato data shows MultiGWAS outperforms individual software in detecting reliable genetic associations, making it a valuable option for researching polyploid organisms.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216910PMC
http://dx.doi.org/10.1002/ece3.7572DOI Listing

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