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SKAT / SNP-set (Sequence) Kernel Association Test
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A SNP-set (e.g., a gene or a region) level test for association between a set of rare (or common) variants and dichotomous or quantitative phenotypes. SKAT aggregates individual score test statistics of SNPs in a SNP set and efficiently computes SNP-set level p-values, e.g. a gene or a region level p-value, while adjusting for covariates, such as principal components to account for population stratification. SKAT also allows for power/sample size calculations for designing for sequence association studies.
WHaIT / Weighted Haplotype and Imputation-based Tests
A weighted haplotype-based approach and an imputation-based approach, to test for the effect of rare variants with GWAS data. Both methods can incorporate external sequencing data when available. We evaluated our methods and compared them with methods proposed in the sequencing setting through extensive simulations. Our methods clearly show enhanced statistical power over existing methods for a wide range of population-attributable risk, percentage of disease-contributing rare variants, and proportion of rare alleles working in different directions.
A flexible Bayesian framework for modeling haplotype association with disease in population-based studies of candidate genes or small candidate regions. Under this model, haplotypes are clustered according to their similarity, in terms of marker-single-nucleotide polymorphism (SNP) allele matches, which is used as a proxy for recent shared ancestry. GENEBPM has been developed to (i) obtain maximum-likelihood estimates of the relative frequencies of haplotypes consistent with a sample of observed SNP genotypes; (ii) implement the Markov Chain-Monte Carlo (MCMC) algorithm to sample over the space of covariate-regression coefficients under the null model of no association; (iii) implement the reversible-jump MCMC algorithm to sample over the space of haplotype clusters and the corresponding probabilities that they carry the causal variant at the functional polymorphism(s).
PEATH / Probabilistic Evolutionary Algorithm with Toggling for Haplotyping
Allows users to handle and solve the single individual haplotyping (SIH) problem. PEATH can identify reliable haplotypes (low error rates and reliably longer haplotype length). It shows the best phased length and N50 values: the length of the haplotype is initialized by the number of total mutation sites and the phasing blocks are divided only in cases with no connection by the overlapped sequence reads. Moreover, this algorithm can be useful for long read sequencing technologies.
Combines an algorithm designed to cluster haplotypes of interest from a given set of haplotypes with two existing tools: Haploview, for analyses of linkage disequilibrium blocks and haplotypes, and PLINK, to generate all possible diplotypes from given genotypes of samples and calculate linear or logistic regression. In addition, procedures for generating all possible diplotypes from the haplotype clusters and transforming these diplotypes into PLINK formats were implemented. Diplotyper is a fully automated tool for performing association analysis based on diplotypes in a population. Diplotyper is useful for identifying more precise and distinct signals over single-locus tests.
T-Trees / Trees inside Trees
A tree-based ensemble method that takes into account the correlation structure among the genetic markers implied by linkage disequilibrium in GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification.
A fast predictor for the inference of blood groups from single nucleotide variant (SNV) databases. BOOGIE correctly predicted the blood group with 94% accuracy for the Personal Genome Project whole genome profiles where good quality SNV annotation was available. Additionally, BOOGIE produces a high quality haplotype phase, which is of interest in the context of ethnicity-specific polymorphisms or traits. The versatility and simplicity of the analysis make it easily interpretable and allow easy extension of the protocol towards other phenotypes.
A generalized linear model with regularization approach for detecting disease-haplotype association using unphased single nucleotide polymorphisms data that is applicable to both common disease/common variant (CD/CV) and common disease/rare variant (CD/RV) scenarios. Our simulation study demonstrates the gain in power for detecting associations with moderate sample sizes. For detecting association under CD/RV, regression type methods such as that implemented in hapassoc may fail to provide coefficient estimates for rare associated haplotypes, resulting in a loss of power compared to rGLM. Furthermore, our results indicate that rGLM can uncover the associated variants much more frequently than can hapassoc.
THESIAS / Testing Haplotype Effects In Association Studies
Provides haplotype analysis in unrelated individuals that can treat quantitative, binary, survival and polychotomous phenotype analyses. THESIAS is a multiple-imputation algorithm that never assigns haplotype to individuals. It is based on the Stochastic Expectation Maximisation (SEM) algorithm, a method that has the advantage over the standard EM algorithm of being more robust to problems of lack of convergence and convergence to local minima.
A package for haplotype block identification, visualization and haplotype tagging single nucleotide polymorphism (htSNP) selection. HaploBlockFinder can also compare the haplotype block structure with local linkage disequilibrium (LD) pattern. As HaploBlockFinder is based on the greedy algorithm, an inherent limitation is that it does not guarantee that the haplotype blocks are globally optimal. In addition, this tool can be either run as a web service or standalone executables on local machine.
Estimates haplotypes within each haplotype block. htSNPer allows molecular geneticists to perform haplotype block analysis and haplotype tagging SNPs (htSNPs) selection using different definitions, different performance criteria, as well as different algorithms. It is a program that has integrated four haplotype block definitions: chromosome coverage, average pairwise LD |D’, estimated pairwise LD confidence limits with minor modifications, and no historical recombination.
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