@article {16,
	title = {Testing for the indirect effect under the null for genome-wide mediation analyses.},
	journal = {Genet Epidemiol},
	volume = {41},
	year = {2017},
	month = {2017 Dec},
	pages = {824-833},
	abstract = {<p>Mediation analysis helps researchers assess whether part or all of an exposure{\textquoteright}s effect on an outcome is due to an intermediate variable. The indirect effect can help in designing interventions on the mediator as opposed to the exposure and better understanding the outcome{\textquoteright}s mechanisms. Mediation analysis has seen increased use in genome-wide epidemiological studies to test for an exposure of interest being mediated through a genomic measure such as gene expression or DNA methylation (DNAm). Testing for the indirect effect is challenged by the fact that the null hypothesis is composite. We examined the performance of commonly used mediation testing methods for the indirect effect in genome-wide mediation studies. When there is no association between the exposure and the mediator and no association between the mediator and the outcome, we show that these common tests are overly conservative. This is a case that will arise frequently in genome-wide mediation studies. Caution is hence needed when applying the commonly used mediation tests in genome-wide mediation studies. We evaluated the performance of these methods using simulation studies, and performed an epigenome-wide mediation association study in the Normative Aging Study, analyzing DNAm as a mediator of the effect of pack-years on FEV .</p>},
	keywords = {Basic Helix-Loop-Helix Transcription Factors, DNA Methylation, Epigenomics, Genome-Wide Association Study, Humans, Lung Neoplasms, Models, Genetic, Repressor Proteins},
	issn = {1098-2272},
	doi = {10.1002/gepi.22084},
	author = {Barfield, Richard and Shen, Jincheng and Just, Allan C and Vokonas, Pantel S and Schwartz, Joel and Baccarelli, Andrea A and VanderWeele, Tyler J and Lin, Xihong}
}
@article {47,
	title = {Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies.},
	journal = {Biometrics},
	volume = {73},
	year = {2017},
	month = {2017 Dec},
	pages = {1210-1220},
	abstract = {<p>We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information-like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.</p>},
	keywords = {Case-Control Studies, Computer Simulation, Genetic Association Studies, Humans, Likelihood Functions, Lung Neoplasms, Phenotype, Polymorphism, Single Nucleotide, Smoking},
	issn = {1541-0420},
	doi = {10.1111/biom.12680},
	author = {Sofer, Tamar and Schifano, Elizabeth D and Christiani, David C and Lin, Xihong}
}
