Software

CoaCC - simulates a case-control study using a coalescent framework. It assumes a haploid sample of cases and a second haploid sample of controls. Of these two samples the genealogy is generated, dependent on the user-specified population history. From this genealogy a distribution of marker-haplotypes is generated by allowing for marker-mutation and recombinations between marker and gene as well as between markers. The current version is 1.0.1. If you use CoaCC please e-mail szoellne@umich.edu or fill out the registration form. The software and files can be donwloaded here. Simulated Datasets - In the paper (Zöllner and Pritchard 04) we presented the analysis of 50 simulated datasets. Each dataset consists of 30 diploid cases and 30 diploid controls and about 50 markers. If you use this please e-mail szoellne@umich.edu or fill out the registration form. The files can be downloaded here. TreeLD - The package TreeLD is a free software tool for mapping complex trait loci. TreeLD performs a multipoint LD-analysis by inferring the ancestry of a genomic region and analyzing this ancestry for signals of disease mutations. The generated likelihoods can be used to test for the presence of a disease locus and to fine-map its location, providing a point estimate and a credible region. Furthermore, the package provides a novel way of visualizing the association signal in a sample. TreeLD is designed for high-density SNP haplotypes and can be applied to case-control data, TDT trio data and quantitative trait data. The package has a user friendly point and click interface, allowing an in depth exploration of the data. The methods in this program are detailed in (Zöllner and Pritchard 04). The current version is 1.0.1. If you use TreeLD please e-mail szoellne@umich.edu or fill out the registration form. The software and files can be downloaded here. CopyMap - CopyMap is based on a hidden Markov Model (HMM), predicting the location of CNVs and their allele frequencies using data from a set of CGH experiments. Each hybridization experiment is treated as one realization of the same Markov process, where the hidden states represent the presence or absence of a CNV and the transition probabilities are dependent on its population frequency. We apply a variation of Baum's algorithm to estimate these transition probabilities and the likelihood of each individual to carry a given CNV variant, combining information from all hybridization experiments. The algorithm is implemented in a program controlled by a command line interface. The current version is 0.806. If you use CopyMap please e-mail szoellne@umich.edu or fill out the registration form. The software and files can be downloaded here. CNVEM - CNVEM is a Bayesian Expectation-Maximization algorithm that infers carrier status of CNVs in large samples from SNP genotyping data, such as are available in genome-wide association studies. Using Bayesian computations the program calculates the posterior probability for carrier status of known CNV in each individual of a sample by jointly analyzing genotype information and hybridization intensity. Signal intensity is modeled as a mixture of normal distributions, allowing for locus-specific and allele-specific distributions. Using an expectation maximization algorithm, these distributions are estimated and then used to infer the carrier status of each individual the boundaries of the CNV. Presently, the program is implemented only for deletions, a version analyzing duplications is in development. The algorithm is implemented in a program controlled by a command line interface. The current version is 0.710. If you use CNVEM please e-mail szoellne@umich.edu or fill out the registration form. The C source code and a short manual can be downloaded here. To unpack the file use tar -xvf CNVEM.tar.gz, then follow the instructions in the manual.