@article {830,
	title = {Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.},
	journal = {Cell},
	volume = {180},
	year = {2020},
	month = {2020 02 06},
	pages = {568-584.e23},
	abstract = {<p>We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n~= 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.</p>},
	keywords = {Autistic Disorder, Case-Control Studies, Cell Lineage, Cerebral Cortex, Cohort Studies, Exome, Female, Gene Expression Regulation, Developmental, Gene Frequency, Genetic Predisposition to Disease, Humans, Male, Mutation, Missense, Neurobiology, Neurons, Phenotype, Sex Factors, Single-Cell Analysis, Whole Exome Sequencing},
	issn = {1097-4172},
	doi = {10.1016/j.cell.2019.12.036},
	author = {Satterstrom, F Kyle and Kosmicki, Jack A and Wang, Jiebiao and Breen, Michael S and De Rubeis, Silvia and An, Joon-Yong and Peng, Minshi and Collins, Ryan and Grove, Jakob and Klei, Lambertus and Stevens, Christine and Reichert, Jennifer and Mulhern, Maureen S and Artomov, Mykyta and Gerges, Sherif and Sheppard, Brooke and Xu, Xinyi and Bhaduri, Aparna and Norman, Utku and Brand, Harrison and Schwartz, Grace and Nguyen, Rachel and Guerrero, Elizabeth E and Dias, Caroline and Betancur, Catalina and Cook, Edwin H and Gallagher, Louise and Gill, Michael and Sutcliffe, James S and Thurm, Audrey and Zwick, Michael E and B{\o}rglum, Anders D and State, Matthew W and Cicek, A Ercument and Talkowski, Michael E and Cutler, David J and Devlin, Bernie and Sanders, Stephan J and Roeder, Kathryn and Daly, Mark J and Buxbaum, Joseph D}
}
@article {330,
	title = {Next generation analytic tools for large scale genetic epidemiology studies of complex diseases.},
	journal = {Genet Epidemiol},
	volume = {36},
	year = {2012},
	month = {2012 Jan},
	pages = {22-35},
	abstract = {<p>Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.</p>},
	keywords = {Data Mining, Gene-Environment Interaction, Genetic Variation, Genome-Wide Association Study, Humans, Molecular Epidemiology, National Institutes of Health (U.S.), Neoplasms, Phenotype, United States},
	issn = {1098-2272},
	doi = {10.1002/gepi.20652},
	author = {Mechanic, Leah E and Chen, Huann-Sheng and Amos, Christopher I and Chatterjee, Nilanjan and Cox, Nancy J and Divi, Rao L and Fan, Ruzong and Harris, Emily L and Jacobs, Kevin and Kraft, Peter and Leal, Suzanne M and McAllister, Kimberly and Moore, Jason H and Paltoo, Dina N and Province, Michael A and Ramos, Erin M and Ritchie, Marylyn D and Roeder, Kathryn and Schaid, Daniel J and Stephens, Matthew and Thomas, Duncan C and Weinberg, Clarice R and Witte, John S and Zhang, Shunpu and Z{\"o}llner, Sebastian and Feuer, Eric J and Gillanders, Elizabeth M}
}
