@article {55,
	title = {Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.},
	journal = {Curr Protoc Hum Genet},
	volume = {101},
	year = {2019},
	month = {2019 04},
	pages = {e83},
	abstract = {<p>With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. {\textcopyright} 2019 by John Wiley \& Sons, Inc.</p>},
	keywords = {Algorithms, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genotype, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Whole Exome Sequencing, Whole Genome Sequencing},
	issn = {1934-8258},
	doi = {10.1002/cphg.83},
	author = {Weissenkampen, J Dylan and Jiang, Yu and Eckert, Scott and Jiang, Bibo and Li, Bingshan and Liu, Dajiang J}
}
@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}
}
