Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions

We introduce new statistical methods for analyzing genomic data sets that measure many effects in many conditions (for example, gene expression changes under many treatments). These new methods improve on existing methods by allowing for arbitrary correlations in effect sizes among conditions. This...

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Bibliographic Details
Published in:	Nature genetics Vol. 51; no. 1; pp. 187 - 195
Main Authors:	Urbut, Sarah M., Wang, Gao, Carbonetto, Peter, Stephens, Matthew
Format:	Journal Article
Language:	English
Published:	New York Nature Publishing Group US 01.01.2019 Nature Publishing Group
Subjects:	38 45 45/23 45/43 45/91 631/208/212 639/705/531 Agriculture Animal Genetics and Genomics Assessments Biomedical and Life Sciences Biomedicine Brain Cancer Research Data processing Estimates Gene expression Gene Expression - genetics Gene Expression Profiling - statistics & numerical data Gene Expression Regulation - genetics Gene Function Gene mapping Genetic effects Genetics Genomics - statistics & numerical data Heterogeneity Human Genetics Human tissues Humans Polymorphism, Single Nucleotide - genetics Power Principal components analysis Quantitative trait loci Quantitative Trait Loci - genetics Statistical methods Statistics technical-report Tissue analysis Tissues
ISSN:	1061-4036, 1546-1718, 1546-1718
Online Access:	Get full text
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