Software


HS-TDT
You can download HS-TDT for Windows here, and HS-TDT for Unix here.

  • The program HS-TDT implements the haplotype sharing TDT methods proposed by Zhang et al. (2003; Am J Hum Genet 73:566-579), Zhang et al.(2004;Am J Hum Genet 74: 591-593), and Sha et al. (2004) ( pdf file) to test for association using tightly linked markers for general nuclear family data. It allows missing genotypes and aplicable both to quantitative traits and qualitative trait. By merging the rare haplotypes, it can well control the false-positive results caused by genotyping errors (Sha et al. 2004).

  • It also can estimate the haplotype frequencies based on nuclear family data. The method implemented in this version of HS-TDT uses Partition-Ligation EM algorithm to estimate haplotype frequencies by incorporating nuclear family information. The program allows missing genotype, missing parents, and can deal very large number of markers.


ELA
You can download ELA for Windows here, and ELA for Linux here.

  • The program ELA implements a genetic association mapping method for complex diseases [Zhaogong Zhang, Shuanglin Zhang, Man-Yu Wong, Nicholas J. Wareham, Qiuying Sha (2007) An Ensemble Learning Approach Jointly Modeling Main and Interaction Effects in Genetic Association Studies. Genetic Epi. Submitted]. The method implemented in this version of ELA uses an Ensemble Learning Approach (ELA) to jointly model main and interaction effects in genetic association mapping. The program allows missing genotypes and can deal with very large number of markers. The detail methods used are introduced in the above mentioned paper.

IPEM
You can download IPEM for Windows here and IPEM for Linux here.

  • In genome-wide association sudies, some causal variants may be completely untyped in that only the tagging single nucleotide polymorphisms (SNPs) are genotyped. This algorithm imputes the genotypes at untyped marker loci in a new study. It utilizes an available comprehensice reference haplotype dataset and incorporates the inherent multi-locus linkage disequilibrium. This software can impute the genotypes on 500,000 SNPs in 500 individuals within 32 hours.

Improved Score Test
You can download R code for Improved Score Test here.

  • Improved Score test is an association test proposed in the paper: Sha Q, Zhang Z, Zhang S (2011) An Improved Score Test for Genetic Association Studies. To be appeared in Genetic Epidemiology. Improved Score Test is uniformly more powerful than many commonly used association tests such as the Cochran-Armitage trend test, the allelic Chi-square test, the genotypic Chi-square test, the haplotypic Chi-square test, and the multi- marker genotypic Chi-square test among others.

Adaptive Weighting Test
You can download R code for Adaptive Weighting Test here.

  • Adaptive Weighting Test is an association test to test association between a phenotype and a group of variants (can be rare and common) in a genomic region by using an adaptive weighting method. The adaptive weighting test, proposed in the paper: Sha Q, Wang S, Zhang S (2011) Adaptive Clustering and Adaptive Weighting Methods to Detect Disease Associated Rare Variants, aims to test rare variant association in the presence of neutral and/or protective variants.

Hardy Weinberg Equilibrium Test in Structured Populations (HWES)
You can download R code for HWES here.

  • The HWES is a test to test HWE in structured populations proposed in the paper: Sha Q, Zhang S (2011) A Test of Hardy Weinberg Equilibrium in Structured Populations. To be appeared in Genetic Epidemiology. HWES can assess departure from HWE and take into account of population stratification at the same time. The HWES can distinguish departure from HWE caused by population stratification and departure from HWE caused by other factors.

Test the effect of the Optimally Weighted combination (TOW) of variants
You can download R code for TOW here.

  • TOW is an association test to test association between a phenotype and an optimal weighted combination of variants (can be rare and common) in a genomic region. The TOW, proposed in the paper: Sha Q, Wang X, Wang X, Zhang S (2012) Detecting Association of Rare and Common Variants by Testing an Optimally Weighted Combination of Variants (Genetic Epidemiology), aims to test association of an optimal weighted combination of rare variants.


PC based Nonparametric Regression (PC-nonp) Approach to Control for Population Stratification in Rare Variant Association Studies
You can download R code for PC-nonp here.

  • R code of PC-nonp includes three functions: PCA, choose_OPT_SMP, and Resid_Nonp. PCA gives the first k principal components of genotypes at genomic markers. choose_OPT_SMP chooses the optimal value of smoothing parameter. Given the value of smoothing parameter, Resid_Nonp calculates residuals of trait values and genotypes at candidate region by applying nonparametric regression for PCs of genotypes at genomic markers.

R code for Simulation
Type I error rates of simulation set 1 for quantitative trait and for qualitative trait. Type I error rates of simulation set 2 for quantitative trait and for qualitative trait. Power comparisons of simulation set 1 for quantitative trait and for qualitative trait. Power comparisons of simulation set 2 for quantitative trait and for qualitative trait . Subfunctions and Data sets used in simulations.
Test an Optimally Weighted combination of rare variants in Admixed populations (TOWA)
You can download R code for TOWA here.

  • TOWA is an association test to test association between a phenotype and an optimally weighted combination of variants (can be rare and common) in an admixture population.

Joint Analysis of Multiple Traits Using Optimal Maximum Heritability Test (MHT-O)
You can download R code for MHT-O here.

  • MHT-O is an association test to test association between multiple traits and a genetic variant.

An Adaptive Fisher’s Combination Method for Joint Analysis of Multiple Phenotypes in Association Studie
You can download R code for AFC here.

  • AFC is an association test to test associations between multiple phenotypes and a genetic marker. AFC was proposed in the paper: Liang X, Wang Z, Sha Q, Zhang S. An adaptive Fisher’s combination method for joint analysis of multiple phenotypes in association studies.

A Clustering Linear Combination Approach to Jointly Analyze Multiple Phenotypes for GWAS
You can download R code for CLC here.

  • CLC is an association test to test associations between multiple phenotypes and a genetic marker. CLC was proposed in the paper: Sha Q, Wang Z, Zhang X, Zhang S. A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.

Joint analysis of multiple phenotypes in association studies based on cross-validation prediction error
You can download R code for MultP-PE here.
You can download R code for models in simulation here.

  • MultP-PE is a statistical method to test associations between a genetic variant and multiple phenotypes based on cross-validation prediction error. MultP-PE was proposed in the paper: Yang X, Sha Q, Zhang S. Joint analysis of multiple phenotypes in association studies based on cross-validation prediction error.