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Analysis of exome sequences with and without incorporating prior biological knowledge
Author(s) -
Namkung Junghyun,
Raska Paola,
Kang Jia,
Liu Yunlong,
Lu Qing,
Zhu Xiaofeng
Publication year - 2011
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20649
Subject(s) - exome , computational biology , exome sequencing , bayesian network , bayesian probability , genetic association , biology , population , genetic variants , computer science , genetics , gene , mutation , machine learning , artificial intelligence , genotype , medicine , single nucleotide polymorphism , environmental health
Next‐generation sequencing technology provides new opportunities and challenges in the search for genetic variants that underlie complex traits. It will also presumably uncover many new rare variants, but exactly how these variants should be incorporated into the data analysis remains a question. Several papers in our group from Genetic Analysis Workshop 17 evaluated different methods of rare variant analysis, including single‐variant, gene‐based, and pathway‐based analyses and analyses that incorporated biological information. Although the performance of some of these methods strongly depends on the underlying disease model, integration of known biological information is helpful in detecting causal genes. Two work groups demonstrated that use of a Bayesian network and a collapsing receiver operating characteristic curve approach improves risk prediction when a disease is caused by many rare variants. Another work group suggested that modeling local rather than global ancestry may be beneficial when controlling the effect of population structure in rare variant association analysis. Genet. Epidemiol . 35:S48–S55, 2011. © 2011 Wiley Periodicals, Inc.