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Genetic linkage analysis in the age of whole-genome sequencing
Jurg Ott1,2, Jing Wang1 and Suzanne M.Leal3
Abstract | For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data.
Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16Lincui Road, Beijing 100101, China.
Laboratory of Statistical Genetics, Rockefeller University, 1230 York Avenue, New York, New York 10065, USA.
Center for Statistical Genetics, Department of Human and Molecular Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA. Correspondence to J.O.and S.M.L.e-mails: mailto:[email protected]
Web End [email protected] ; mailto:[email protected]
Web End [email protected] doi:10.1038/nrg3908 Published online 31 March 2015
Linkage analysis was the predominant statistical genetic mapping approach used in the latter half of the twentieth century. More recently, the focus shifted to association studies of complex traits that analyse common variants, which have a modest effect. For such variants, association analyses are more powerful than linkage analyses, and genome-wide association studies (GWASs) using single-nucleotide polymorphism (SNP) marker loci became the preferred association mapping tool. However, an emerging view is that rare variants, which are not well interrogated by GWASs, could be responsible for a substantial proportion of complex human disease1. Importantly, the increased availability of exome and whole-genome sequence data has brought linkage analysis once again to the forefront owing to the development of powerful methods to detect rare variants involved in disease aetiology using family-based data; such an approach has many advantages over simply using filter methods to identify causal variants. Several reviews25 and books68 have been written on genetic linkage analysis, but none, to our knowledge, covers linkage analysis coupled with whole-genome sequencing (WGS).
Several recent studies have generated genome-wide association data for families. For example, the T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing...