Sample menu:

The iHS

[C-Source: iHS] This program is designed to calculate the integrated Hapotype Score (iHS), described in Voight and Kudaravelli et al. (PLoS Biology, 2006). This tool modifies slightly upon that program found elsewhere, reporting scores of all SNPs with error codes. Compliation instructions can be found here. Older version can be found there.

[C-Source: iHS] includes the most recently updated version, which includes a computational speed-up as reported in Johnson and Voight (2018).

Beta: A test for Balancing Selection

[Github: BetaScan] This program is designed to calculate Beta, a summary statistic for the frequency spectrum (similar to Fay and Wu's H, or Taajima's D) sensitive to the signature of balancing selection, described in Siewert and Voight (MBE, 2017).

ibdibsR: Inference of rare, non-IBD mutations from sequencing data

[Github: ibdibsR]. This program implements a implements a Gibbs sampler for the Bayesian hierarchical model described in Johnson and Voight 2020. The purpose of this method is to classify variants as likely IBD or non-IBD using the pairwise recombination distances between allele pairs.

WHAMM Website

[WHAMM Website] A.k.a. Whole-genome Association Mapping Machina (WHAMM), this tool was designed for the calculation and association testing of stretches of homozygosity in genome-wide association data, as well as calculation of the intergrated haplotype score (iHS). Note that this tool is currently unpublished, and many of the website links are not fully updated.

The CPMA Statistic

[R-Package: CPMA] This R package is designed to calculate the Cross-Phenotype Meta-Analysis statistic, described in Cotsapas and Voight et al. (PLoS Genetics, 2011).

[R-Package: CPMA-signed] A slight update to the above, ensuring that the statistics is signed given the direction of the non-randomness (negative values if the distribution of p-values are non-random in favor of p-values > 0.5, say).

Tools for Mendelian Randomization

[GitHub: MR_Predictor] This simulation program is to generate data from various genetic and epidemiological models useful and relavent for causal inference studies (e.g., Mendelian Randomization) described by Voight, 2014 (Bioinformatics). A website documentation for the program is here.

[GitHub: MeRP] This tool is used to automate several of the more labor-intensive steps to generate valid genetic instruments used in causal inference studies (e.g., Mendelian Randomization), described in Yin and Voight, 2015 (Bioinformatics). A website documentation for the program is here.