| Statistical challenges for genome-wide association studies of suicidality using family data. | |
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MedLine Citation:
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PMID: 20447807 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The etiology of suicide is complex in nature with both environmental and genetic causes that are extremely diverse. This extensive heterogeneity weakens the relationship between genotype and phenotype and as a result, we face many challenges when studying the genetic etiology of suicide. We are now in the midst of a genetics revolution, where genotyping costs are decreasing and genotyping speed is increasing at a fast rate, allowing genetic association studies to genotype thousands to millions of SNPs that cover the entire human genome. As such, genome-wide association studies (GWAS) are now the norm. In this article we address several statistical challenges that occur when studying the genetic etiology of suicidality in the age of the genetics revolution. These challenges include: (1) the large number of statistical tests; (2) complex phenotypes that are difficult to quantify; and (3) modest genetic effect sizes. We address these statistical issues in the context of family-based study designs. Specifically, we discuss several statistical extensions of family-based association tests (FBATs) that work to alleviate these challenges. As our intention is to describe how statistical methodology may work to identify disease variants for suicidality, we avoid the mathematical details of the methodologies presented. |
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Authors:
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J Lasky-Su; C Lange |
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Publication Detail:
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Type: Journal Article; Review Date: 2010-05-05 |
Journal Detail:
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Title: European psychiatry : the journal of the Association of European Psychiatrists Volume: 25 ISSN: 1778-3585 ISO Abbreviation: Eur. Psychiatry Publication Date: 2010 Jun |
Date Detail:
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Created Date: 2010-06-14 Completed Date: 2010-09-20 Revised Date: 2011-07-28 |
Medline Journal Info:
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Nlm Unique ID: 9111820 Medline TA: Eur Psychiatry Country: France |
Other Details:
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Languages: eng Pagination: 307-9 Citation Subset: IM |
Copyright Information:
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(c) 2010 Elsevier Masson SAS. All rights reserved. |
Affiliation:
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Channing Laboratory, Brigham and Women's Hospital, Boston, 181, Longwood Ave., Boston, MA 02115, USA. rejas@channing.harvard.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Family Genetic Predisposition to Disease* Genome-Wide Association Study Genotype Humans Social Environment Statistics as Topic* Suicide / statistics & numerical data* |
| Grant Support | |
ID/Acronym/Agency:
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K99 HL096840-01/HL/NHLBI NIH HHS |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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