PIAGE: Power of Indirect Association Studies of Gene-Environment Interactions

The program PIAGE performs estimation of power and sample sizes required to detect genetic and environmental main, as well as gene-environment interaction (GxE) effects in indirect matched case-control studies (1:1 matching). When the hypothesis of GxE is tested, power/sample size will be estimated for the detection of GxE, as well as for the detection of genetic and environmental marginal effects. Furthermore, power estimation is implemented for the joint test of genetic marginal and GxE effects (Kraft P et al., 2007).
The program is written in R.

SDMinP: a program to control the family wise error rate using step-down minP adjusted

SDMinP is an easy-to-use program for fast calculation of empirical
and adjusted p-values for correlated and uncorrelated hypotheses
in multiple testing experiments. It is based on the Free
Step-Down Resampling Method for controlling the Family Wise Error
Rate, originally proposed by Westfall and Young (1993), and implements
a variation of the efficient algorithm of Ge et al. (2003), who
reduced the originally necessary re-sampling effort considerably
and made the method computationally feasible. The program is
independent of the underlying test statistic and works with
provided observed and permutation test statistics.

The program is written in Python 2.3.5 and runs in a Windows, Linux and Unix environment.

Tomcat: Haplotype sharing analysis using mantel statistics

The program tomcat1.0 implements the Mantel statistics as proposed by Beckmann et al. (2005) to test for association between genetic markers and phenotypes in case-control studies using haplotype information. The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take linkage disequilibrium information between multiple markers into account, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for space time clustering, which is developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease. The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case-only and case-control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. To measure the phenotypic similarity, alternative measures are implemented.

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