MultiGWAS

MultiGWAS integrates results from GWASpoly, SHEsis, GAPIT, and TASSEL to perform genome-wide association analyses for detecting SNP–trait associations in diploid and tetraploid organisms.

Key Features:

• Software integration: Integrates outputs from GWASpoly and SHEsis for polyploid data and GAPIT and TASSEL for diploid data to combine complementary association results.
• Parallel execution: Executes the four GWAS software packages concurrently to produce comparable results across methods.
• Genotype format support: Accepts multiple genotype formats, including VCF (Variant Call Format), for input flexibility.
• Population structure and relatedness control: Incorporates controls for population structure and relatedness to reduce confounding in association tests.
• Quality control: Performs quality control checks on genotype data prior to association analysis.
• Model testing (additive and dominant): Supports testing additive and dominant gene action models to evaluate different genetic architectures.
• Proprietary scoring function: Applies a proprietary scoring function to identify and report the best-fitting SNP association models.
• Cross-software comparison reporting: Generates reports comparing significant SNPs across the four integrated platforms to aid identification of consistent associations.

Scientific Applications:

• Polyploid crop genetics: Enables GWAS in tetraploid crops such as potato by using polyploid-aware software (GWASpoly, SHEsis) alongside diploid methods.
• Trait mapping in model and non-model organisms: Facilitates detection of SNP–trait associations in both model and non-model species using multiple GWAS algorithms.
• Population phenotypic variation studies: Supports studies of ecological and economic phenotypic variation within populations by accounting for structure and relatedness.

Methodology:

MultiGWAS executes GWAS analyses simultaneously with GWASpoly, SHEsis, GAPIT, and TASSEL, compares significant SNPs across these packages for cross-validation, applies a proprietary scoring function to select best-fitting SNP models, and has been validated using public tetraploid potato data and simulated datasets under additive and dominant models.