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.
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.
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 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.
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.
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).
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.