MAOS

Data from genome-wide association studies are often analyzed jointly for the purposes of combining information from multiple studies of the same disease or comparing results across different disorders. In many instances, the same subjects appear in multiple studies. Failure to account for overlapping subjects can greatly inflate type I error when combining results from multiple studies of the same disease and can drastically reduce power when comparing results across different disorders. MAOS implements valid and efficient statistical methods for meta-analysis of genomewide association studies with overlapping subjects, as described in Lin and Sullivan (submitted for publication, 2009). The current release performs logistic regression analysis of individual level data under the additive mode of inheritance. (Meta-analysis of summary results is much simpler to implement.) We are working intensely to improve the capabilities of MAOS, so please check back frequently for updates.

SPREG

SPREG is a computer program for performing regression analysis of secondary phenotype data in case-control association studies. Secondary phenotypes are quantitative or qualitative traits other than the case-control status. Because the case-control sample is not a random sample of the general population, standard statistical analysis of secondary phenotype data can yield very misleading results. SPREG implements valid and efficient statistical methods, as described in Lin and Zeng (submitted for publication, 2008).

HAPSTAT

HAPSTAT is a user-friendly software interface for the statistical analysis of haplotype-disease association. HAPSTAT allows the user to estimate or test haplotype effects and haplotype-environment interactions by maximizing the (observed-data) likelihood that properly accounts for phase uncertainty and study design. Cross-sectional, longitudinal, case-control and cohort studies are considered. The underlying methodology and a subset of the numerical algorithms used in HAPSTAT are found in Lin and Zeng (JASA, 2006), Lin, Zeng and Millikan (Genetic Epidemiology, 2005) Zeng, Lin, Avery, North and Bray (Biostatistics, 2006) and Lin, Hu and Huang (The American Journal of Human Genetics, 2008). The current version allows haplotype analysis of multiple genes as well as single- and multi-SNP analysis with missing genotypes, as described in Lin, Hu and Huang (The American Journal of Human Genetics, 2008).

SNPMStat

SNPMStat is a program for the statistical analysis of SNP-disease association in case-control studies with potentially missing genotype data. For SNPs without missing data, the program performs the standard association analysis and provides the estimated odds ratios and standard error estimates, together with the Armitage trend tests and p-values. For typed SNPs with missing data or untyped SNPs, the program performs the maximum-likelihood analysis described in Lin, Hu and Huang (The American Journal of Human Genetics, 2008) and provides the estimated odds ratios and standard error estimates, together with the Wald statistics and p-values. The current release performs single-SNP analysis under additive, recessive or dominant mode of inheritance without environmental factors. The related software interface HAPSTAT allows very general analysis (including multiple-SNP analysis, all modes of inheritance, Hardy-Weinberg disequilibrium, all study designs and phenotypes, and gene-environment interactions), but requires the user to specify the set of SNPs used to infer the unknown genotypes of the SNP with missing data. We are working intensely to improve the capabilities of SNPMStat, so please check back frequently for updates.

GAS2

GAS2 provides a Fortran-77 program to evaluate statistical significance in two-stage genomewide association studies, based on the method proposed by Lin (The American Journal of Human Genetics, 2006).

SQTL

We implement the algorithm of Diao and Lin (The American Journal of Human Genetics, 2005) for the semiparametric QTL mapping method in general pedigrees in a console application for the Linux platform.

SQTDT/SPDT

The efficient and reliable algorithms of Diao and Lin (Genetic Epidemiology, 2006) for the semiparametric family-based tests of association are available for the Linux platform.

SVCC

The semiparametric variance-component models for linkage and association analysis of censored trait data, as described in Diao and Lin (Genetic Epidemiology, 2006), are implemented in a Linux console application.

NPMLE

We have done a considerable amount of work on the nonparametric likelihood estimation (NPMLE) of semiparametric transformation models with censored data. Software for much of our work can be found at http://www.bios.unc.edu/~dzeng/Transform.html.