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Published on: June 15, 2011
Whole Genome Sequencing of Pedigrees With High Density of Substance Use and Psychiatric Disorders: A Meeting Report
Shirley Y Hill1, Howard J Edenberg2, Aiden Corvin3
1Behavioral Genetics Research Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Abstract:
The National Institute of Drug Abuse convened a panel of scientists with expertise in substance use disorders (SUD) and genetic methodologies primarily to determine the feasibility of performing whole genome sequencing utilizing existing pedigree collections with a high density of SUD and psychiatric disorders. A major focus was on determining if there had been any successes in identifying genetic variants for complex traits in family-based designs. Such information could provide assurance that whole genome sequencing might provide significant pay-offs particularly in the pursuit of rare variants and copy number variants. An important goal was to discuss and evaluate optimal strategies for studying genetic variants in human samples. Specific topics were (a) to consider whether a smaller number of cases typically available in family studies versus the larger number available in biobanks can reveal unique information; (b) to identify potential gaps in information available in biobank data that might be supplemented with family data; (c) to consider the optimal SUD phenotypic definitions (e.g., quantity of use, problem-oriented) and data collection instruments (self-report or clinician administered) that are both practical and efficient to collect, and likely to provide important insights concerning prevention, intervention, and medication development. Conclusions reached by the panel included optimism about the successes that have occurred in the existing family studies ascertained to include densely affected pedigrees. Evaluation of methodologies led, overall, to a panel consensus that steps should be taken to utilize biobank collection in conjunction with family-based investigations for optimal variant discovery.
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