A sequence of methodological changes due to sequencingBurkett, Kelly; Greenwood, CeliaCurrent Opinion in Allergy & Clinical Immunology: October 2013 - Volume 13 - Issue 5 - p 470–477 doi: 10.1097/ACI.0b013e3283648f68 GENETICS AND EPIDEMIOLOGY: Edited by Catherine Laprise and Emmanuelle Bouzigon Abstract Author Information Abstract 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. Recent findings: 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. Summary: 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. Author Information aDepartment of Epidemiology, Biostatistics and Occupational Health bDepartment of Oncology cDepartment of Human Genetics, McGill University dLady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada Correspondence to Celia Greenwood, Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Côte Sainte Catherine, Montreal, Quebec, Canada. Tel: +1 514 340 8222x8397; e-mail: firstname.lastname@example.org Copyright © 2013 Wolters Kluwer Health, Inc. All rights reserved.