Software Downloads

APL-OSA

In the presence of genetic heterogeneity, APL-OSA can identify a genetically homogenous subset of families based on a trait-related covariate. APL-OSA then tests the relationship between the association statistics (i.e., the APL statistics) calculated based on the subset and the family-specific covariate. APL-OSA is based on the OSA method for linkage (Hauser et al. 2004) and the family-based association test, APL (Martin et al. 2003; Chung et al. 2006). Thus, APL-OSA has similar properties with OSA and APL. Bi-alleleic markers such as SNPs are accepted by APL-OSA. APL-OSA is a single-marker test and considers one covariate each time. APL-OSA is written in C++.

COMBINED LIKELIHOOD RATIO TEST FOR CANDIDATE GENE STUDIES

The Combined Likelihood Ratio Test (Combined_LRT) is an extension of the original likelihood ratio test (LRT) proposed by Weinberg et al. (1999) to test child genotype risk, maternal genotype effects and parent-of-origin (POO) effects. This program uses single locus genotype data from case-parent triads to fit a log-linear model with genotype and imprinting effects at a candidate gene. The model is fit to bi-allelic data with possible missing genotype data for the mother or father, and includes sibling genotypes. Likelihood ratio tests for parameter effects are obtained from program output.

Reference:   Rampersaud E, Morris RW, Weinberg CR, Speer MC, Martin ER. Power calculations for likelihood ratio tests for offspring genotype risks, maternal effects, and parent-of-origin (POO) effects in the presence of missing parental genotypes when unaffected siblings are available. Genet Epidemiol. 2006 Nov 9

ORDERED SUBSET ANALYSIS PROGRAM

The OSA program was developed by researchers at the University of Michigan and the Center for Human Genetics (now the Duke Molecular Physiology Institute) at Duke University Medical Center. This work was funded by NIH grant RO1 MH59528.

OSA allows the researcher to evaluate evidence for linkage even when heterogeneity is present in a data set. This is not an unusual occurrence when studying diseases of complex origin. Families are ranked by covariate values in order to test evidence for linkage among homogeneous subsets of families. Because families are ranked, a priori covariate cutpoints are not necessary. Covariates may include linkage evidence at other genes, environmental exposures, or biological trait values such as cholesterol, age at onset, etc.

ORDERED SUBSET ANALYSIS CASE-CONTROL DATA PROGRAM

Genetic heterogeneity can reduce the power for complex disease gene mapping since only a fraction of the cases in the collected dataset may carry a specific disease susceptibility allele. The Ordered Subset Analysis for Case-Control data (OSACC) program was designed to evaluate evidence for association in the presence of genetic heterogeneity. For a more detailed description of the method and results of an extensive simulation study based on different models of genetic heterogeneity, please see Qin et al. (2010), Ordered Subset Analysis for Case-Control Studies Genetic Epidemiology 34(5), 407-417

SIBLINK

SIBLINK allows the user to perform multipoint linkage analysis based on estimated IBD sharing between affected sibpairs. IBD sharing is inferred from IBS status, given marker genotypes, frequencies, and locations. Resulting LOD scores are maximized across a grid of possible disease locations and IBD sharing vectors.

SIMLA

SIMLA is a SIMuLAtion program that generates data sets of families for use in Linkage and Association studies. It allows the user flexibility in specifying marker and disease placement, locus heterogeneity, disequilibrium between markers and between markers and disease loci. Output is in the form of a LINKAGE (Lathrop et al., Proc Natl Acad Sci USA 81, 1984) pedigree file and is easily utilized, either directly or with minimal reformatting, as input for various genetic analysis packages.

SIMLA_3.2 is a major upgrade to versions 2.3 and 3.0 that provides the ability to simulate two disease loci and two environmental covariates. Gene-gene and gene-environment interactions may also be simulated which jointly determine the disease risk of all pedigree members. Further enhancements include the simulation of X-linked markers and QTL effects with up to 12000 marker loci.

A description of the underlying simulation algorithm can be found in:
Schmidt MA, Hauser ER, Martin ER, Schmidt S. Extension of the SIMLA package for generating pedigrees with complex inheritance patterns: Environmental covariates, gene-gene a nd gene-environment interaction. Stat Appl Genet Mol Biol 4(1): article 15, 2005.

SIMLA with GUI

SIMLA SIMULATION SOFTWARE VERSION 3.3 WITH GRAPHICAL INTERFACE

The SIMLA3.3 with Graphical User Interface (GUI) is designed for users to set up the SIMLA parameters conveniently. The GUI assists in creating a SIMLA control file based on the parameters specified on the GUI panels. It includes the SIMLA 3.2 program version.

The program is written in JAVA Swing and requires JAVA JDK 6 or above. 

In addition, SIMLA3.3 with GUI includes some tools that are written in R which should be installed before using these tools. R can be obtained at http://www.r-project.org.

Users are encouraged to read the SIMLA User's Manual and paper before running the program. Notice that parameters on the GUI panels are designed as buttons. Users can press these buttons to obtain more information about the functions of the parameters. Documentation, which includes a tutorial describing most of the features of the GUI, is included in the package as well.

SIMLAPLOT

SIMLAPLOT is a tool designed to help visualize the joint effect of genes and continuous environmental covariates on complex human disease simulation models (e.g. Schmidt et al. 2007). SIMLAPLOT takes as input the set of genetic model parameters used to generate the data, including risk ratios for genotypes (G), a normally distributed environmental covariate (E) and their interaction (GXE), or the risk ratio for a continuous covariate that is itself influenced by a quantitative trait locus (QTL) and thus follows a mixture normal distribution. It is also possible to use SIMLAPLOT to plot observed data distributions, if estimates of these parameters can be provided.

EMK Program

The EMK program implements two family-based association methods, allele- and genotype- based association methods, that we developed based on the framework of the allele-based method developed by Monks and Kaplan (2000) for quantitative traits (Li et al. 2008). Our version of allele-based EMK (allele-EMK) and genotype-based EMK (geno-EMK) methods are applicable to any pedigree structure, for instance, nuclear families with or without parental genotypes, and extended pedigrees. Currently, EMK program is fit to bi-allelic markers (e.g. single nucleotide polymorphisms).

References:
Li YW, Martin ER, Li YJ (2008) EMK: A Novel Program for Family-Based Allelic and Genotypic Association Tests on Quantitative Traits. Annals of Human Genetics 72 (Pt 3): 388-396. Monks SA and Kaplan NL (2000) Removing the sampling restrictions from family-based tests of association for a quantitative-trait locus. Am J Hum Genet 66:576-592

GENETIC ASSOCIATION TESTS BASED ON RANKS PROGRAM

GATOR is a program that implements the family-based association method for quantitative traits with and without censoring described in Allen et al. (2006). This program is distinctive in that it can handle quantitative phenotypes with skewed distributions, censored data, and/or outliers. Currently the program focuses on bi-allelic markers (e.g. single nucleotide polymorphisms). It can handle parent-offspring (Triads), sibships with (Quads) and without (Sibs) parents, and extended multi-generation pedigree data (General Pedigree). GATOR is able to perform association tests using four different genetic models: general, dominant, recessive, and additive.

References

Allen AS., Martin E., Qin X., Li Y.J: Genetic Association Tests Based on Ranks (GATOR) for Quantitative Traits With and Without Censoring. Genetic Epidemiology 2006; 30: 248-258.

Curry J.L., Li Y.W., Martin E., Allen, A.S, Li Y.J. The GATOR Program for association analysis on quantitative traits with and without censoring [abstract]. In: American Society of Hum an Genetics 56th Annual Meeting; 2006 Oct 9-13; New Orleans: 2216/A.

X-LRT

X-LRT is a suite of family-based tests for detecting association of X-chromosome genes. This is a likelihood-based approach which can perform hypothesis testing as well as estimation of disease-related marker relative risks under a case-parent design. This test uses nuclear family with a single affected proband and allows additional siblings and missing parental genotypes. This program can test both single marker and haplotypes association. The detail of the theoretical works can be found at Zhang et al. (2008)

Reference: Zhang L, Martin ER, Chung RH, Li YJ, Morris RW. (2008) X-LRT: a likelihood approach to estimate genetic risks and test association with X-linked markers using a case-parents design. Genetic Epidemiology May;32(4):370-80.

XQTL

XQTL is a family-based allelic/haplotype association test for quantitative traits using X-linked SNP/two-locus markers in a nuclear family design. XQTL adopts the framework of the orthogonal model implemented in the QTDT program with modification of the sex-specific score for X-linked genotypes. XQTL also takes into account the dosage effect due to female X chromosome inactivation.

References:
Zhang L, Martin ER, Morris RW, Li YJ. (2009) Association test for X-linked QTL in family-base d designs. American Journal of Human Genetics