Next-generation sequencing research has transformed the area of genetic association studies during the last two years. Researchers examined the parallel evolution of statistical approaches. Because most of the genetic diversity discovered by sequencing was highly uncommon, numerous novel methodologies for studying rare variant association studies were created. To enhance genotype imputation, sequencing data from major public projects were combined with genome-wide association study (GWAS) chip data. The employment of the linear mixed effect model was another recent trend in methodological progress (LMM). LMMs were used to address effect heterogeneity in rare variant associations. They were also employed in GWAS to account for population structure more broadly.

Many rare variant association tests were devised to assess genetic variation identified by large-scale DNA sequencing; however, no one strategy outperforms others across all disease models, and power is typically poor. Sequencing data was also helping to improve the imputation of uncommon genetic variations, while imputation of rare variants was still difficult. The best way to account for population structure in the rare variant analysis was unknown; specific adjustment approaches may be required.