Computational tools to analyze SNP data from patients with mental illness

Information

  • Research Project
  • 7670133
  • ApplicationId
    7670133
  • Core Project Number
    R43MH086192
  • Full Project Number
    1R43MH086192-01
  • Serial Number
    86192
  • FOA Number
    PA-07-424
  • Sub Project Id
  • Project Start Date
    6/17/2009 - 15 years ago
  • Project End Date
    5/31/2010 - 14 years ago
  • Program Officer Name
    GRABB, MARGARET C.
  • Budget Start Date
    6/17/2009 - 15 years ago
  • Budget End Date
    5/31/2010 - 14 years ago
  • Fiscal Year
    2009
  • Support Year
    1
  • Suffix
  • Award Notice Date
    6/17/2009 - 15 years ago
Organizations

Computational tools to analyze SNP data from patients with mental illness

DESCRIPTION (provided by applicant): The broad, long-term objective of the proposed research is to develop a software product that can be used to facilitate the analysis of genetic changes in order to elucidate chromosomal abnormalities that underlie diseases such as autism spectrum disorder, bipolar disorder, and schizophrenia. Recent technological advances allow samples of DNA from patients to be analyzed on single nucleotide polymorphism (SNP) arrays, generating up to millions of data points from each sample. These data must be analyzed to identify chromosomal abnormalities (e.g. DNA mutations, hemizygous or homozygous deletions, or translocations) that confer risk for these diseases. Two main approaches to data analysis include copy number estimates (based on the intensity of hybridization of samples to SNP arrays) and genotype analysis (revealing heterozygosity and homozygosity). Software such as Partek Genomics Suite (GS) exists to perform data analysis and visualization. A goal of this proposal is to add another dimension to the analysis of high density SNP data by incorporating information about the genetic relatedness of individuals into the data analysis repertoire of Partek GS. The specific aims are as follows. (1) Incorporate SNPtrio into a new Partek GS module. This program analyzes genotype and copy number data from trios consisting of father, mother, and child and produces graphical and tabular descriptions of uniparental inheritance (e.g. uniparental isodisomy in which two copies of a chromosome or chromosomal segment are inherited from one parent;such a mechanism is known to cause a variety of mental retardation and other syndromes). (2) Incorporate SNPduo into PartekGS;this program performs pairwise analyses of SNP data sets, allowing the description of relatedness between individuals (by identity-by-state measurements). This is useful for a variety of purposes including identifying outliers, replicate samples, non-paternity, and confirming the genetic relatedness of members of a pedigree. (3) Incorporate a set of analytic tools that measure meiotic recombination in pedigrees consisting of one, two, or three generations. Such tools may be useful to exclude loci in association studies or to characterize mechanisms by which deletions or duplications occur. The software tools described in aims (1) to (3) will be assembled into a new prototype version of Partek GS. In aim (4), this prototype will be used to analyze a set of 500,000 SNPs measured in 2,883 individuals from 700 families having two or more individuals affected with autism. This analysis will demonstrate the functionality of the Partek GS prototype, demonstrating the usefulness of incorporating new tools for genetic analysis to discover chromosomal abnormalities that may have roles in autism. PUBLIC HEALTH RELEVANCE: Newly available technologies allow the measurement of millions of variations in DNA sequence between samples from individuals with diseases (such as autism and schizophrenia) relative to unaffected individuals (controls). The proposed research is designed to create software analysis tools that will facilitate the discovery of chromosomal abnormalities in diseases. This may lead to treatments for these disorders, serving a large public health need.

IC Name
NATIONAL INSTITUTE OF MENTAL HEALTH
  • Activity
    R43
  • Administering IC
    MH
  • Application Type
    1
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    243011
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    242
  • Ed Inst. Type
  • Funding ICs
    NIMH:243011\
  • Funding Mechanism
    SBIR-STTR
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    PARTEK, INC.
  • Organization Department
  • Organization DUNS
    877038133
  • Organization City
    CHESTERFIELD
  • Organization State
    MO
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    630051270
  • Organization District
    UNITED STATES