stylistic:style_transfer, structural:reformatting
Methods

Variations on the case-control approach. A common alternative to case-control GWA studies is the analysis of quantitative phenotypic data, e.g. height or biomarker concentrations or even gene expression. Likewise, alternative statistics designed for dominance or recessive penetrance patterns can be used. Calculations are typically done using bioinformatics software such as SNPTEST and PLINK, which also include support for many of these alternative statistics. GWAS focuses on the effect of individual SNPs. However, it is also possible that complex interactions among two or more SNPs (epistasis) might contribute to complex diseases. Due to the potentially exponential number of interactions, detecting statistically significant interactions in GWAS data is both computationally and statistically challenging. This task has been tackled in existing publications that use algorithms inspired from data mining. Moreover, the researchers try to integrate GWA data with other biological data such as protein-protein interaction network to extract more informative results. Despite the previously perceived challenge posed by the vast number of SNP combinations, a recent study has successfully unveiled complete epistatic maps at a gene-level resolution in plants/Arabidopsis thaliana