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STATISTICAL METHODS FOR THE GENETIC ANALYSIS OF DEVELOPMENTAL DISORDERS

Sucheston, Lara E.
http://rave.ohiolink.edu/etdc/view?acc_num=case1175883318

Abstract Details

2007, Doctor of Philosophy, Case Western Reserve University, Epidemiology and Biostatistics.
This dissertation focuses on approaches to the genetic analysis of longitudinal measures of developmental disorders (DD) with specific application to a longitudinal pedigree study of children ascertained on the basis of a Speech Sound Disorder (SSD). Analysis of this longitudinal cohort is complicated by non-normal trait distributions and a potentially non-linear cognitive developmental trajectory. Prior to developing a longitudinal model I measured the power of the SSD dataset to correctly detect linkage of a quantitative trait to a genetic marker. Assuming that the function describing the genetic effect across time is correctly specified the power of the SSD data set is .18 at a .01 level of signficance. Additional data collection is planned and by doubling the sample size (from 200 to 400 sibling pairs) and number of measurement points (from 2 to 4) the power increases to .83 for the same significance level. It is therefore reasonable to develop a longitudinal approach for use at a later date. As an alternative to the longitudinal analysis, multivariate dependence functions, called copulas, are used to develop a cross-sectional model to test for polygenic*age interaction. These functions separate a multivariate joint distribution into two parts: one describing the interdependency of the probabilities (correlation), the other describing the distribution of the margins (the phenotypes). Using these functions for analysis simultaneously addresses both the non-normality problem, as the margins can be modeled with a wide variety of parametric probability distributions and the developmental trajectory question, as we incorporate age into the analysis through the use of a correlation function, the parameter estimate of which can be tested for significance using a chi-square test statistic. Four of the 13 SSD test measures showed nominal p-values less than .05. While at the broadest level the 4 tests measure different cognitive skills, short term memory plays an important role in each of these tests. This provides preliminary evidence that the genetic contribution to phenotypic variance of tasks involving memory is not stationary in children ages 6 to 18.
Sudha Iyengar (Advisor)
178 p.
Statistics
speech sound disorder; longitudinal model; gene age interaction; longitudinal copula model

Recommended Citations

Citations

  • Sucheston, L. E. (2007). STATISTICAL METHODS FOR THE GENETIC ANALYSIS OF DEVELOPMENTAL DISORDERS [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1175883318

    APA Style (7th edition)

  • Sucheston, Lara. STATISTICAL METHODS FOR THE GENETIC ANALYSIS OF DEVELOPMENTAL DISORDERS. 2007. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1175883318.

    MLA Style (8th edition)

  • Sucheston, Lara. "STATISTICAL METHODS FOR THE GENETIC ANALYSIS OF DEVELOPMENTAL DISORDERS." Doctoral dissertation, Case Western Reserve University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1175883318

    Chicago Manual of Style (17th edition)

case1175883318
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© 2007, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.