Evaluates the functional impact of noncoding variants. PINES integrates diverse epigenetic annotations to work. It can map noncoding alleles at genome wide association study (GWAS) loci across a range of diseases. This tool enables the discovery of new causal risk alleles for Parkinson’s disease and inflammatory bowel disease. It can be useful to prioritize and fine map functional noncoding variants.
A probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus.
Queries users’ results with different thresholds of physical distance and/or Linkage Disequilibrium (LD). gwasMP is an online method for quantifies the physical distance, LD and difference in Minor Allele Frequency (MAF) between the top associated single-nucleotide polymorphism (SNPs) identified from genome wide association studies (GWAS) and the underlying causal variants. The results are from simulations based on whole-genome sequencing data.
A Bayesian framework to model genome-wide association study (GWAS) summary statistics in terms of P-values using large-scale reference datasets. The main novelty of RiVIERA is the ability to perform efficient Bayesian inference of the intrinsic causal signals across multiple traits while simultaneously inferring and exploiting enrichment signals and their correlation between traits over hundreds of tissue-specific epigenomic annotations. We achieve this efficiently via stochastic sampling of loci and powerful Hamiltonian Monte Carlo sampling of model parameters. In simulation, RiVIERA promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power.
Produces enrichment parameter estimate that can help with interpretation of association results. SFBA is a scalable Bayesian hierarchical method for integrating functional information in genome-wide association studies (GWASs) to help prioritize functional associations and understand underlying genetic architecture. It models both association probability and effect-size distribution as a function of annotation categories for improving fine-mapping resolution. In comparisons with competing methods, SFBA has higher power, especially in loci with multiple associated variants and when the sample size is large.
A software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects.
A method that is able to operate across large LD regions of the genome while also correcting for population structure. A key feature of this approach is that it provides as output a minimally sized set of genes that captures the genes which harbor causal variants with probability rho.
An approach for statistical fine mapping using meta-analysis summary statistics. HAPRAP uses haplotypes to represent linkage disequilibrium (LD) structure among multiple variants in a region. Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits (GIANT) height data, HAPRAP performs well with a small training sample size (N<2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by SNPs with low minor allele frequencies.
A computationally optimised C++ implementation of a fully Bayesian variable selection approach that can analyse, in a genome-wide context, single and multiple responses in an integrated way. The program uses packages from the GNU Scientific Library (GSL) and offers the possibility to re-route computationally intensive linear algebra operations towards the Graphical Processing Unit (GPU) through the use of proprietary CULA-dense library. The multi-SNP model of GUESS typically seeks for the best combinations of SNPs to predict the (possibly multivariate) outcome of interest.
Permits users to map the association of multiple sclerosis (MS) and type 1 diabetes (T1D) to an established susceptibility region for immune mediated diseases. GUESSFM was developed to also assists users in drawing other disease-associated regions, which is suitable for the design of functional follow-up studies. This method requires specification of the a priori expected number of causal variants in a region.
Employs marginal test statistics in a Bayesian framework to make mapping of single nucleotide polymorphisms (SNPs). CAVIARBF can be applied to both quantitative traits and binary traits. It can compute the test statistic for each SNP adjusted for other covariates. This tool is useful for genome-wide association scans to solve the association questions.
A free software package for imputing allele frequencies from pooled or summary-level genetic data. This approach, which predicts each allele frequency using a linear combination of observed frequencies, is statistically straight-forward, and related to a long history of the use of linear methods for estimating missing values (e.g. Kriging).
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