Purpose of review
During the past 2 years, next-generation sequencing studies have revolutionized the field of genetic association studies. We review the concomitant evolution of statistical methods.
As much of the genetic variability identified with sequencing is extremely rare, many new methods have been developed for rare variant association studies. Sequencing data available as a result of large public projects are also being integrated with genome-wide association study (GWAS) chip data to improve genotype imputation. A further trend in recent methodological development has been the use of the linear mixed effect model (LMM). LMMs are used for rare variant association to handle effect heterogeneity. They are also used more generally in GWAS to account for population structure.
Many rare variant association tests have been developed to analyze the genetic variation discovered with large-scale DNA sequencing; however, no single approach outperforms others under all disease models and power tends to be low. Sequencing data are also contributing to improved imputation of uncommon genetic variants, although imputation of rare variants remains a challenge. The appropriate correction for population structure in rare variant analyses remains unclear; specialized adjustment techniques may be necessary.