@article {59,
	title = {Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.},
	journal = {Genetics},
	volume = {211},
	year = {2019},
	month = {2019 04},
	pages = {1179-1189},
	abstract = {<p>High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.</p>},
	keywords = {Animals, Genome-Wide Association Study, Humans, Models, Genetic, Phenotype, Principal Component Analysis, Quantitative Trait Loci, Reproducibility of Results},
	issn = {1943-2631},
	doi = {10.1534/genetics.118.301768},
	author = {Dahl, Andy and Guillemot, Vincent and Mefford, Joel and Aschard, Hugues and Zaitlen, Noah}
}
@article {23,
	title = {Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.},
	journal = {Am J Epidemiol},
	volume = {186},
	year = {2017},
	month = {2017 Oct 01},
	pages = {771-777},
	abstract = {<p>A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.</p>},
	keywords = {Animals, Disease, Gene-Environment Interaction, Genome-Wide Association Study, Genomics, Humans, Models, Animal, Sequence Analysis, RNA},
	issn = {1476-6256},
	doi = {10.1093/aje/kwx229},
	author = {Ritchie, Marylyn D and Davis, Joe R and Aschard, Hugues and Battle, Alexis and Conti, David and Du, Mengmeng and Eskin, Eleazar and Fallin, M Daniele and Hsu, Li and Kraft, Peter and Moore, Jason H and Pierce, Brandon L and Bien, Stephanie A and Thomas, Duncan C and Wei, Peng and Montgomery, Stephen B}
}
