STATISTICAL METHODS FOR MAPPING MULTIVARIATE PHENOTYPES

Summary

Principal Investigator: Saurabh Ghosh
Abstract: DESCRIPTION (provided by applicant) The overarching goal of this research proposal is to devise efficient and robust statistical methods for genetic dissection of complex human traits, which are determined by a complex interplay of gene-gene and gene-environment interactions. Our basic tenet is that for the dissection of the determinants of such traits, specifically to map the underlying genes, the study of the precursor variables that modulate an end-point trait is statistically more powerful than studying the end-point trait itself, which is usually dichotomized (affected/unaffected) by defining a threshold on the frequency distribution of a quantitative trait. The major aims of this research are: (i) to develop non-parametric methods including kernel smoothing and quantile based regression techniques for linkage and association mapping multivariate phenotypes (possibly comprising a mixture of quantitative and binary variables) using data on different types of relative-sets and also unrelated individuals. (ii) to compare the proposed distribution-free methods with existing distribution based methods through extensive computer simulations, (ii) to statistically assess the advantages of using SNP markers in haplotype blocks for QTL mapping, (iv) to develop user-friendly computer programs incorporating the methodologies, (v) to modify the proposed methods to incorporate inbreeding practiced in some populations and (vi) to apply the new methods to data on different types of complex traits/disorders in disparate ethnic populations. The major statistical thrust of this research will be on development of distribution-free gene-mapping methodologies for mixed (quantitative and binary) multivariate phenotypes in the presence of epistatic and gene-environment interactions. Our past studies on univariate phenotypes (Ghosh and Majumder, American Journal of Human Genetics, 2000, 66:1046-1061) have shown that this approach is efficient and robust, especially when distributional assumptions (such as, normality) and model assumptions (such as, dominance at the QTL) are not valid.
Funding Period: 2003-09-30 - 2010-03-31
more information: NIH RePORT

Top Publications

  1. pmc Effect of population stratification on false positive rates of population-based association analyses of quantitative traits
    Tanushree Haldar
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Ann Hum Genet 76:237-45. 2012
  2. ncbi Dissecting the correlation structure of a bivariate phenotype: common genes or shared environment?
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, 203 BT Road, Kolkata 700 108, India
    J Genet 84:143-6. 2005
  3. pmc Linkage mapping of a complex trait in the New York population of the GAW14 simulated dataset: a multivariate phenotype approach
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institiute, 203 B, T, Road, Kolkata 700 108, India
    BMC Genet 6:S19. 2005
  4. ncbi Association analysis of population-based quantitative trait data: an assessment of ANOVA
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Hum Hered 64:82-8. 2007
  5. pmc A novel non-parametric regression reveals linkage on chromosome 4 for the number of externalizing symptoms in sib-pairs
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Am J Med Genet B Neuropsychiatr Genet 147:1301-5. 2008
  6. pmc Genome-wide association analyses of quantitative traits: the GAW16 experience
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Genet Epidemiol 33:S13-8. 2009

Scientific Experts

Detail Information

Publications6

  1. pmc Effect of population stratification on false positive rates of population-based association analyses of quantitative traits
    Tanushree Haldar
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Ann Hum Genet 76:237-45. 2012
    ..We find that the rate of false positives increases very quickly with simultaneous increase in differences in the standardized phenotypic means and marker allele frequencies in the subpopulations...
  2. ncbi Dissecting the correlation structure of a bivariate phenotype: common genes or shared environment?
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, 203 BT Road, Kolkata 700 108, India
    J Genet 84:143-6. 2005
    ..An application of the method is illustrated using data on two alcohol-related phenotypes from a project on the collaborative study on the genetics of alcoholism...
  3. pmc Linkage mapping of a complex trait in the New York population of the GAW14 simulated dataset: a multivariate phenotype approach
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institiute, 203 B, T, Road, Kolkata 700 108, India
    BMC Genet 6:S19. 2005
    ..On the other hand, the linkage analysis based on Kofendrerd Personality Disorder status as a phenotype produced significant findings only near two of the loci and in a smaller proportion of replicates...
  4. ncbi Association analysis of population-based quantitative trait data: an assessment of ANOVA
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Hum Hered 64:82-8. 2007
    ....
  5. pmc A novel non-parametric regression reveals linkage on chromosome 4 for the number of externalizing symptoms in sib-pairs
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Am J Med Genet B Neuropsychiatr Genet 147:1301-5. 2008
    ..We also obtain evidence for epistatic interaction between a region on Chromosome 1 and one on Chromosome 15. Although alcoholism as a covariate does not have any effect on the linkage scan, it has an effect on the epistatic interaction...
  6. pmc Genome-wide association analyses of quantitative traits: the GAW16 experience
    Saurabh Ghosh
    Human Genetics Unit, Indian Statistical Institute, Kolkata, India
    Genet Epidemiol 33:S13-8. 2009
    ..In this report, we discuss the different strategies explored by the different investigators with the common goal of improving the power to detect association...