STATISTICAL METHODS FOR GENETIC ASSOCIATION STUDIES

Project: Research project

Project Details

Description

Defining the role of genetics in complex diseases demonstrating familial
clustering, yet no simple Mendelian mode of transmission, has immense
public health implications because these types of diseases are much more
common than the rare Mendelian diseases. The traditional method for
identifying disease susceptibility genes is based on selecting. unique
pedigrees, often rare, which are most informative for genetic linkage
analyses. Still, the analyses are often compromised by locus and allelic
genetic heterogeneity, incomplete penetrance, sporadic cases of disease,
and unknown or poorly estimated allele frequencies. An alternative method
depends on the association of candidate genes with disease in the
population. This approach, based on the genetic mechanisms of the disease
process, allows estimation of not only relative risks for the candidate
gene(s), but also the population attributable risk and gene-environmental
interactions. The goal of this project is to make available statistical
tools for design and analysis of population candidate-gene association
studies. These methods will be useful for a wide variety of diseases. The
choice of appropriate controls for association studies has been difficult,
mainly because the frequencies of alleles for candidate gene loci may vary
across ethnic subpopulations, and biased results can occur when cases and
controls have different ethnic backgrounds. Our methods are based on
family members as internal controls (with emphasis on parents). Unlike
current work in this field, we have developed likelihood-based methods of
analysis. The specific aims of this proposal are: (I) to expand the
theoretical development of the candidate gene likelihood-based relative
risk models to (a) appropriately and efficiently account for "messy" data
(such as partially missing genotype data), inclusion of multiple cases per
family, and inclusion of parental phenotype data; (b) determine the most
efficient test for association; (c) extent the relative risk models so
that the effects of measured covariates on the genotype risks can be
assessed by regression modles - important models will include the effects
of individual alleles, allelic interactions (within and between loci),
genic interaction with person-specific covariates (i.e., gene-environment
interaction), and genomic imprinting; (d) determine theoretical relative
risk parameters as functions of genetic parameters to use for study
design; (2) to validate by simulations the statistical methods developed
in Aim l in terms of robustness to deviations from underlying assumptions
and the adequacy of large sample approximations; and (3) to develop and
distribute user-friendly computer code for planning and analysis of
studies based on the methods developed by this grant.
StatusFinished
Effective start/end date8/1/947/31/99

ASJC

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)