The long-term objective of this proposal is to enable the discovery of genes underlying complex traits using family based association tests that detect linkage disequilibrium while protecting against spurious evidence of association due to population admixture. To achieve this, specific aims of this project are to: (i) develop computer program that implements family based association tests with arbitrary nuclear family structure including those with missing parental information; that can incorporate environmental covariates and multiple genes into the model; that can test for gene-gene and gene-environment interactions; and that may be applied to dichotomous, quantitative, and time-of-onset phenotypes; (ii) develop a simple and flexible use interface to the program and easy installation procedures, and make the program widely available via ftp; (iii) test the statistical properties of the methodology in simulations and in real data, and use the test results to develop guidelines for the use of the program; (iv) develop an online help system and well-indexed user's manual; provide user support for the program; and develop comprehensive documentation of the statistical procedures; and (v) to extend the methodology to handle pedigrees of arbitrary structure including families with missing founder information. The resulting methodology will be able to test candidate genes, to test for linkage in the presence of association, and to test for association in homogeneous populations where linkage has been established (fine mapping). The investigators also indicate that the new computer software will fill gaps in family based association tests, specifically in cases with arbitrary pedigrees missing parental information, multiple genes, covariates, gene-gene and gene-environment interactions, and survival end-points.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH059532-02
Application #
2891153
Study Section
Special Emphasis Panel (ZRG2-GNM (02))
Program Officer
Moldin, Steven Owen
Project Start
1998-09-30
Project End
2001-05-31
Budget Start
1999-06-01
Budget End
2000-05-31
Support Year
2
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
082359691
City
Boston
State
MA
Country
United States
Zip Code
02115
Hoffmann, Thomas J; Vansteelandt, Stijn; Lange, Christoph et al. (2011) Combining disease models to test for gene-environment interaction in nuclear families. Biometrics 67:1260-70
VanderWeele, Tyler J; Laird, Nan M (2011) Tests for compositional epistasis under single interaction-parameter models. Ann Hum Genet 75:146-56
Hoffmann, Thomas J; Lange, Christoph; Vansteelandt, Stijn et al. (2010) Parsing the effects of individual SNPs in candidate genes with family data. Hum Hered 69:91-103
Ding, Xiao; Laird, Nan (2009) Family-Based Association Tests with longitudinal measurements: handling missing data. Hum Hered 68:98-105
Hoffmann, Thomas J; Laird, Nan M (2009) fgui: A Method for Automatically Creating Graphical User Interfaces for Command-Line R Packages. J Stat Softw 30:
Ionita-Laza, Iuliana; Lange, Christoph; M Laird, Nan (2009) Estimating the number of unseen variants in the human genome. Proc Natl Acad Sci U S A 106:5008-13
Hoffmann, Thomas J; Lange, Christoph; Vansteelandt, Stijn et al. (2009) Gene-environment interaction tests for dichotomous traits in trios and sibships. Genet Epidemiol 33:691-9
Vansteelandt, Stijn; Demeo, Dawn L; Lasky-Su, Jessica et al. (2008) Testing and estimating gene-environment interactions in family-based association studies. Biometrics 64:458-67
Degnan, James H; Lasky-Su, Jessica; Raby, Benjamin A et al. (2008) Genomics and genome-wide association studies: an integrative approach to expression QTL mapping. Genomics 92:129-33
Ionita-Laza, Iuliana; Laird, Nan M; Raby, Benjamin A et al. (2008) On the frequency of copy number variants. Bioinformatics 24:2350-5

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