This application will develop statistical methodology and software for genetic association studies with special emphasis on complex disorders in mental health. The success of the Human Genome Project and related efforts, as well as the new genotyping technologies, has revolutionized our ability to understand the genetic underpinnings of complex disorders. The widespread availability of Single Nucleotide Polymorphisms (SNPs) means that genomic regions can be saturated with thousands of SNPs with sufficient density so that, with sufficient sample sizes and appropriate methods for handling the multiple comparisons problem, we can locate Disease Susceptibility Loci using ordinary association studies. Conventional case/control and case/cohort studies are often used in this setting because of their relative ease of collection and good power. Our work has focused on family based genetic association tests because they can protect against potentially spurious results that can arise when there is population substructure. We have previously developed a general approach to the analysis of family data which maintains robustness in a variety of non-standard designs, including missing parents and measured or time-to-onset phenotypes. A potential criticism of our approach is that we do not use 'non- informative'families, or families which do not contain within family information about association. We have turned this potential criticism to an advantage by developing a unique 'screening'algorithm which enables us to handle the multiple comparisons problem quite effectively. In this application we plan to develop additional methodology for family based association tests, and accompanying software in the following areas: tests for gene-gene and gene-environment interaction, tests for whole genome scans involving dichotomous outcomes, methods for identification of complex networks of cis- and trans- acting genes using gene expression data in pedigrees, and tests for association with genes on the x- chromosome. The methods development and implementation will utilize real data from our collaborators in Bipolar Disorder, Nicotine Addiction, Attention Deficit Hyperactivity Disorder, Alzheimer's disease, Asthma, Chronic Obstructive Pulmonary Disease, among others.
| 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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