Division of Statistical Programming

Division of Statistical Programming


The Division of Statistical Programming resides within the Center for Genetic Epidemiology and Statistical Genetics and works closely with the Divisions of Genetic Epidemiology and Division of Research Informatics. The Division of Statistical faculty members and programmers focus their research on statistical genetics analysis and methods development in human genetics studies.

The Division of Statistical Programming specializes in:

  • Methods development for disease gene mapping
  • Simulation studies
  • Population genetics methods as they relate to understanding genetic variation in human disease
  • Large-scale statistical analysis methods

The Division of Statistical Programming is led by Michael Schmidt Ph.D., Research Assistant Professor with expertise in commercial software development, and QA engineering methods. Dr. Schmidt is involved in the software development and in the implementation of automated testing procedures for new statistical methods. His areas of interest include genetic simulations software, analysis programs and parallel processing. He was instrumental in the development of our in-house software’s and methods, which are maintained by the division and made easily accessible to the genetic research community.

  • SIMLA Simulation Software
  • PDT Analysis Program v 5.1
  • Combined Association in the Presence of Linkage (CAPL)
  • Extended Multifactor Dimensionality Reduction (EMDR), MDR-PDT and MDR-phenomics

SIMLA Simulation Software

SIMLA is a SIMulation program that generates data sets of families for use in linkage and association studies. SIMLA_3.1 is a major upgrade to version 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.
The SIBLINK program performs linkage analysis on affected sib-pairs.

PDT Analysis Program v 5.1

The Pedigree Disequilibrium Test (PDT) analysis program allows the user to test for linkage and association in general pedigree data. In addition to allele- and genotype- specific analysis of individual markers, PDT version 5.1 adds the ability to perform genotype – specific analysis over marker sets (Solaris, Linux, Windows). The extension versions allow for genotype association analysis (geno-PDT) and multi-locus effect estimate 9multi-locus geno-PDT).

Combined Association in the Presence of Linkage (CAPL)

CAPL is a C++ program that provides a novel test for association in the presence of linkage using general pedigree data and accounts for population stratification. The CAPL takes advantage of modern multi
core hardware architecture. Recently a function for analysis of X-linked markers was added.

Extended Multifactor Dimensionality Reduction (EMDR, MDR-PDT and MDR-phenomics)

EMDR and MDR-PDT are extensions of MDR, which is a model-free non-parametric method for identifying and characterizing gene-gene effect influencing complex diseases. These methods are more powerful for detecting high order gene-gene or gene-environment interactions in both family-based and case-control studies than traditional parametric methods.

MDR-Phenomics was developed to test for high-order interaction with the consideration of genetic heterogeneity. It inherits the advantages from MDR that no assumptions of genetic models are required and that high dimensionality can be processed by reductions. It also improves the power by integration of phenotypic covariate analysis.