@article {21,
	title = {Covariate selection for association screening in multiphenotype genetic studies.},
	journal = {Nat Genet},
	volume = {49},
	year = {2017},
	month = {2017 Dec},
	pages = {1789-1795},
	abstract = {<p>Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.</p>},
	keywords = {Algorithms, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Multivariate Analysis, Phenotype, Reproducibility of Results, Sample Size},
	issn = {1546-1718},
	doi = {10.1038/ng.3975},
	author = {Aschard, Hugues and Guillemot, Vincent and Vilhjalmsson, Bjarni and Patel, Chirag J and Skurnik, David and Ye, Chun J and Wolpin, Brian and Kraft, Peter and Zaitlen, Noah}
}
@article {22,
	title = {Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.},
	journal = {Am J Epidemiol},
	volume = {186},
	year = {2017},
	month = {2017 Oct 01},
	pages = {753-761},
	abstract = {<p>Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G{\texttimes}Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G{\texttimes}E discovery. This paper highlights current and critical issues and themes in G{\texttimes}E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.</p>},
	keywords = {Disease, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Software},
	issn = {1476-6256},
	doi = {10.1093/aje/kwx227},
	author = {McAllister, Kimberly and Mechanic, Leah E and Amos, Christopher and Aschard, Hugues and Blair, Ian A and Chatterjee, Nilanjan and Conti, David and Gauderman, W James and Hsu, Li and Hutter, Carolyn M and Jankowska, Marta M and Kerr, Jacqueline and Kraft, Peter and Montgomery, Stephen B and Mukherjee, Bhramar and Papanicolaou, George J and Patel, Chirag J and Ritchie, Marylyn D and Ritz, Beate R and Thomas, Duncan C and Wei, Peng and Witte, John S}
}
