Abstract

We previously developed statistical methodology designed to partition the genetic variance of a quantitative trait to the effects of loci located in specific chromosomal regions (Goldgar 1990). This method models the covariance of sibship trait values as a function of the pattern of identity and recombination of a set of marker loci defining a given chromosomal region. This multipoint IBD method (MIM) has been shown in preliminary studies to be considerably more powerful than existing methods based upon identity by descent at individual loci in sib-pairs. The studies described in this proposal are designed to further characterize and extend this method in a number of ways. Specifically, we will use Monte-Carlo simulation techniques to examine the robustness of the method to departures from the underlying assumptions and to compare its power and robustness to both sib-pair and traditional model-dependent methods of linkage analysis. Other studies are designed to extend the MIM method to the analysis of discrete traits such as complex disease phenotypes with a genetic susceptibility. Similar simulation studies of robustness and power will be performed for the analysis of discrete traits as for quantitative traits. As part of the GENOME initiative, a set of highly polymorphic index markers is being developed for every human chromosome. A second aspect of this proposal is to determine through simulation studies the most efficient strategy for utilizing these index markers in conducting genomic searches for all loci which may influence a given complex phenotype. The MIM method will be implemented in a computer program to facilitate the analysis of several phenotypes in three data sets collected as part of separately funded concurrent studies. Phenotypes to be analyzed include: a number of quantitative traits related to cognitive abilities in a set of families ascertained for reading disability; and both quantitative and discrete phenotypes hypothesized to be precursors to common cancer.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
1R01HG000571-01
Application #
3333753
Study Section
Genome Study Section (GNM)
Project Start
1992-03-15
Project End
1995-02-28
Budget Start
1992-03-15
Budget End
1993-02-28
Support Year
1
Fiscal Year
1992
Total Cost
Indirect Cost

  Institution

Name
University of Utah
Department
Type
Schools of Medicine
DUNS #
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112

  Publications

Shugart, Y Y; Goldgar, D E (1999) Multipoint genomic scanning for quantitative loci: effects of map density, sibship size and computational approach. Eur J Hum Genet 7:103-9
Whittaker, J C; Lewis, C M (1998) The effect of family structure on linkage tests using allelic association. Am J Hum Genet 63:889-97
Goldgar, D E; Easton, D F (1997) Optimal strategies for mapping complex diseases in the presence of multiple loci. Am J Hum Genet 60:1222-32
Lewis, C M; Kort, E N (1997) Multilocus quantitative trait analysis using the multipoint identity-by-descent method. Genet Epidemiol 14:839-44
Lewis, C M; Goldgar, D E (1995) Screening for linkage using a multipoint identity-by-descent method. Genet Epidemiol 12:777-82
Goldgar, D E; Neuhausen, S L; Steele, L et al. (1995) A 45-year follow-up of kindred 107 and the search for BRCA2. J Natl Cancer Inst Monogr :15-9
Thomas, A; Skolnick, M H; Lewis, C M (1994) Genomic mismatch scanning in pedigrees. IMA J Math Appl Med Biol 11:1-16
Wooster, R; Neuhausen, S L; Mangion, J et al. (1994) Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12-13. Science 265:2088-90
Thomas, A; Skolnick, M H (1994) A probabilistic model for detecting coding regions in DNA sequences. IMA J Math Appl Med Biol 11:149-60
Gholami, K; Thomas, A (1994) A linear time algorithm for calculation of multiple pairwise kinship coefficients and the genetic index of familiality. Comput Biomed Res 27:342-50

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