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Michael E. Zwick

Researcher at Emory University

Publications -  102
Citations -  7929

Michael E. Zwick is an academic researcher from Emory University. The author has contributed to research in topics: Population & Exome sequencing. The author has an hindex of 30, co-authored 97 publications receiving 6094 citations. Previous affiliations of Michael E. Zwick include Johns Hopkins University & University of California, Davis.

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Synaptic, transcriptional and chromatin genes disrupted in autism

Silvia De Rubeis, +99 more
- 13 Nov 2014 - 
TL;DR: Using exome sequencing, it is shown that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate of < 0.05, plus a set of 107 genes strongly enriched for those likely to affect risk (FDR < 0.30).
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Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism

F. Kyle Satterstrom, +201 more
- 06 Feb 2020 - 
TL;DR: The largest exome sequencing study of autism spectrum disorder (ASD) to date, using an enhanced analytical framework to integrate de novo and case-control rare variation, identifies 102 risk genes at a false discovery rate of 0.1 or less, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.
Journal ArticleDOI

Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism

F. Kyle Satterstrom, +153 more
TL;DR: Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, 102 risk genes are identified at a false discovery rate of ≤ 0.1, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.
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Microarray-based genomic selection for high-throughput resequencing.

TL;DR: It is demonstrated that large human genomic regions, on the order of hundreds of kilobases, can be enriched and resequenced with resequencing arrays.
Journal ArticleDOI

High-Throughput Variation Detection and Genotyping Using Microarrays

TL;DR: An automated statistical method (ABACUS) is developed to analyze microarray hybridization data and applied this method to Affymetrix Variation Detection Arrays (VDAs) to provide a quality score to individual genotypes, allowing investigators to focus their attention on sites that give accurate information.