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Integration of Diverse Statistical Evidence of GeneTrait Association in Systems Biology Studies
Author(s) -
Cheng Cheng
Publication year - 2012
Publication title -
chemistry and biodiversity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.427
H-Index - 70
eISSN - 1612-1880
pISSN - 1612-1872
DOI - 10.1002/cbdv.201100384
Subject(s) - trait , computational biology , biology , systems biology , genomics , inference , genetic association , genome wide association study , statistical inference , gene , genetics , data science , bioinformatics , evolutionary biology , genome , computer science , single nucleotide polymorphism , genotype , artificial intelligence , mathematics , statistics , programming language
The rapid advancement of high‐throughput genomic assay technologies has generated large amounts of diverse genomic data in disparate human populations and diseases. These data provide a unique opportunity for biomedical investigators to systematically study multifaceted aspects of genes' involvement in the biological processes underlying important traits from the systems biology perspective. An important component in such a study is the inference that integrates diverse lines of statistical evidence for genetrait association from the observed trait values and the massive numbers of measured genomic features. A novel integrated statistical analysis procedure is developed in this paper and is illustrated by an application in studying childhood leukemia.