Strategies for Genotyping

Abstract

The identification of genomic loci linked to or associated with human disease has been greatly facilitated by the evolution of genotyping strategies and techniques. The success of these strategies continues to be based upon clear clinical assessment, accurate sample handling, and careful data management, but also increasingly upon experimental design. Technological advances in the field of genotyping have permitted increasingly complex and large population studies to be performed. An understanding of publicly available genetic variation databases, including an awareness of the limitations of these data, and an appreciation of the strategic approaches that should be used to exploit this information will provide tremendous insight for researchers are aiming to utilize this accessible technology. As genome-wide association studies (GWAS) and Next Generation (NextGen) sequencing become the mainstays of genetic analyses, it is important that their technical strengths and limitations, as well as their impact on study design, be understood before use in a linkage or genetic association study.

Characterizing genetic interactions in human disease association studies using statistical epistasis networks

“…Identifying associations between genetic and phenotypic variation is crucial to understanding the genetic basis of disease susceptibility and disease etiology [ 1 ], and to devising diagnostic tests and useful treatments [ 2 , 3 ]. With the rapid expansion of open-access single nucleotide polymorphism (SNP) databases [ 4 ], the progress in genotyping technologies [ 5 ], and the availability of immense computational resources [ 6 ], mapping the genes that underlie common diseases and quantitative traits is now feasible.…”

A Two‐Platform Design for Next Generation Genome‐Wide Association Studies

“…Based on a history of such successes, together with evaluations of imputation accuracy (Biernacka et al 2009, Pei 2008, Servin 2007, Zhao 2008, imputation offers a more economical alternative to directly genotyping all individuals (Servin 2007, Scheet and Stephens 2006, Li and Stephens 2003, Mott et al 2000, Howie et al 2009, Hao et al 2009, Browning 2006, Anderson 2008, Daly et al 2001, Purcell et al 2007). With this design, if the ancestry of the study population is not adequately represented in the database, the imputation accuracy for uncommon SNPs can be less than ideal and confound study results (Coventry et al 2010, Spencer et al 2011, Crawford and Dilks 2011, Howie et al 2009. With this design, if the ancestry of the study population is not adequately represented in the database, the imputation accuracy for uncommon SNPs can be less than ideal and confound study results (Coventry et al 2010, Spencer et al 2011, Crawford and Dilks 2011, Howie et al 2009.…”

“…One option is to genotype all individuals on a less-dense platform (e.g., OmniExpress), and then use an imputation procedure, trained on one of the publicly available databases, to estimate the missing genotypes (1000Genomes 2010, Consortium 2010, Metzker 2010. With this design, if the ancestry of the study population is not adequately represented in the database, the imputation accuracy for uncommon SNPs can be less than ideal and confound study results (Coventry et al 2010, Spencer et al 2011, Crawford and Dilks 2011, Howie et al 2009.…”

“…We then impute the missing genotypes for those participants not genotyped on the denser platform and perform the GWAS on the augmented dataset. Instead of depending only on a public dataset, the imputation reference set now includes a genotyped subset of the study population (Holm et al 2011, Zeggini 2011, Crawford and Dilks 2011, Howie et al 2009). Our goal is to show that this two platform method, which safeguards against confounding from inadequately chosen reference sets, can be efficient, while identifying its limitations.…”