Bold steps must be taken to advance our understanding of the genetic and associated co- variates affecting the inheritance of complex diseases. To that end, this proposal will develop improved quantitative methods to detect genetic factors contributing to increased susceptibility to complex disorders and implement these methods in software for distribution to the research community. The methods will concentrate on the use of classification techniques applied to allele sharing data and other risk factors which affect the trait. Allele sharing methods for mapping genes will be extended to include the classification methods known as latent class models, cluster analysis, and artificial neural networks, as well as a novel use of logistic regression Co-variates such as gender, parental diagnosis, or other concomitant factors will be systematically studied through applications to both stimulated and existing data sets. An additional goal is to determine the optimal distribution of relative pairs (e.g. siblings, first cousins) for these methods. Of great importance to this proposal is the development of well-documented, user-friendly software and documentation which will be distributed to the scientific community via the Internet. Existing software developed by the PI will be extensively expanded for latent class models. Existing cluster analysis software will be modified and combined for ease of use. This proposal consists of theoretical exploration, computer simulation, data analysis, and software development. First, solutions of theoretical questions relating to classification techniques will be pursued; second, adaptation of computer programs to implement the analytic methods, and investigation into alternative research strategies will be accomplished. The new strategies will be applied to stimulated data, and finally, to existing data sets of pedigrees in which a complex trait has been diagnosed. Findings from this research may contribute to the ability to locate susceptibility loci in complex traits and to the clarification of those etiological mechanisms responsible for susceptibility.
| Neuman, Rosalind J; Sitdhiraksa, Nantawat; Reich, Wendy et al. (2005) Estimation of prevalence of DSM-IV and latent class-defined ADHD subtypes in a population-based sample of child and adolescent twins. Twin Res Hum Genet 8:392-401 |
| Todd, R D; Joyner, C A; Ji, T H-C et al. (2004) Family factors and sampling approach differentially influence attention deficit/hyperactivity disorder subtypes. Mol Psychiatry 9:260-3 |
| Saccone, Nancy L; Neuman, Rosalind J; Saccone, Scott F et al. (2003) Genetic analysis of maximum cigarette-use phenotypes. BMC Genet 4 Suppl 1:S105 |
| Rice, J P; Saccone, N L; Rasmussen, E (2001) Definition of the phenotype. Adv Genet 42:69-76 |
| Neuman, R J; Bierut, L J; Rasmussen, E et al. (2001) A clustering approach for localizing disease susceptibility loci. Genet Epidemiol 21 Suppl 1:S534-9 |
| Neuman, R J; Heath, A; Reich, W et al. (2001) Latent class analysis of ADHD and comorbid symptoms in a population sample of adolescent female twins. J Child Psychol Psychiatry 42:933-42 |
| Todd, R D; Rasmussen, E R; Neuman, R J et al. (2001) Familiality and heritability of subtypes of attention deficit hyperactivity disorder in a population sample of adolescent female twins. Am J Psychiatry 158:1891-8 |
| Saccone, N L; Rochberg, N; Neuman, R J et al. (2001) Covariates in linkage analysis using sibling and cousin pairs. Genet Epidemiol 21 Suppl 1:S540-5 |
| Rice, J P (2001) Diagnosis as a covariate in sib-pair linkage analysis. Am J Med Genet 105:55-6 |
| Neuman, R J; Saccone, N L; Holmans, P et al. (2000) Clustering methods applied to allele sharing data. Genet Epidemiol 19 Suppl 1:S57-63 |
