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James A. Knowles

Researcher at University of Southern California

Publications -  121
Citations -  26985

James A. Knowles is an academic researcher from University of Southern California. The author has contributed to research in topics: Genome-wide association study & Gene. The author has an hindex of 49, co-authored 111 publications receiving 22785 citations. Previous affiliations of James A. Knowles include Zilkha Neurogenetic Institute.

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Biological insights from 108 schizophrenia-associated genetic loci

Stephan Ripke, +354 more
- 24 Jul 2014 - 
TL;DR: Associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses.
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Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

Shaun Purcell, +81 more
- 06 Aug 2009 - 
TL;DR: The extent to which common genetic variation underlies the risk of schizophrenia is shown, using two analytic approaches, and the major histocompatibility complex is implicate, which is shown to involve thousands of common alleles of very small effect.
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Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

S. Hong Lee, +405 more
- 01 Sep 2013 - 
TL;DR: Empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
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Rare chromosomal deletions and duplications increase risk of schizophrenia

Jennifer Stone, +91 more
- 11 Sep 2008 - 
TL;DR: A genome-wide survey of rare CNVs in 3,391 patients with schizophrenia and 3,181 ancestrally matched controls provides strong support for a model of schizophrenia pathogenesis that includes the effects of multiple rare structural variants, both genome- wide and at specific loci.
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Transcriptional landscape of the prenatal human brain

TL;DR: An anatomically comprehensive atlas of the mid-gestational human brain is described, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions.