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A sequence of methodological changes due to sequencing

Burkett, Kelly; Greenwood, Celia

Current Opinion in Allergy and 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

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.

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:

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