publication . Part of book or chapter of book . Conference object . Other literature type . 2011

Penalized regression for genome-wide association screening of sequence data.

Zhou, H.; Alexander, D. H.; Sehl, M. E.; Sinsheimer, J. S.; Eric Sobel; Lange, K.;
Open Access
  • Published: 01 Jan 2011
W hole exome and whole genome sequencing are likely to be potent tools in the study of common diseases and complex traits. Despite this promise, some very difficult issues in data management and statistical analysis must be squarely faced. The number of rare variants identified by sequencing is apt to be much larger than the number of common variants encountered in current association studies. The low frequencies of rare variants alone will make association testing difficult. This article extends the penalized regression framework for model selection in genome-wide association data to sequencing data with both common and rare variants. Previous research has show...
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Genetic association, Regression analysis, Exome, Lasso (statistics), Biology, Model selection, Data mining, computer.software_genre, computer, Regression, Statistical genetics, Genome-wide association study, Bioinformatics, Article
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