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NIMH Issues FOA to Study Deep Sequencing, Haplotype Profiling in Mental Disorders

NEW YORK (GenomeWeb News) — The US National Institutes of Health has issues a funding opportunity announcement that seeks to identify specific genomic and functional variants that influence susceptibility to mental disorders.
 
The FOA, called “Deep Sequencing and Haplotype Profiling of Mental Disorders,” are expected to focus on a topic “related to large-scale genomic or genetic studies in sufficiently powered studies using clinically well-characterized populations from the [Center for Collaborative Genetics Studies] and employing innovative analytical study designs.”
 
According to the National Institute of Mental Health, possible areas of investigation may include the following areas of genetic analysis of the mental disorder under study:
 
High-density whole genome association analyses using existing technology platforms for SNP typing; identification and analysis of tag SNPs for genome wide analysis; genome wide haplotype analysis and the development of novel statistical methods; use of high-density SNP chips or other platforms and development of new statistical methods for the analysis of large number of SNPs; use of “innovative analytical approaches” such as admixture designs; Identification and analysis of clinical sub-phenotypes in large-scale genetic studies; or use of “experimental technologies” for large scale SNP typing that would “dramatically increase” the possible SNP density or “dramatically decrease” genotyping costs.
 
Other areas of genetic analysis include sequencing studies of candidate regions across the genome; sequencing studies of candidate genes across the genome and the “development of new bioinformatics and other analytical tools;” analysis of genomic structure … across the human genome; integration of computational and experimental components, such as comparative sequence analysis of candidate genes with model organisms in existing databases; or “experimental technologies” for large scale sequencing that would dramatically decrease sequencing costs.
 
Additional information can be found here.