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RiVIERA / Risk Variant Inference using Epigenomic Reference Annotations
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.
HAPRAP / HAPlotype Regional Association analysis Program
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.
GUESS / Graphical processing Unit Evolutionary Stochastic Search
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.
Statistical approaches to this fine mapping problem have traditionally taken a stepwise search approach, beginning with the most associated variant in a region, then iteratively attempting to find additional associated variants. We adapted a stochastic search approach that avoids this stepwise process and is explicitly designed for dealing with highly correlated predictors to the fine mapping problem. We showed in simulated data that it outperforms its stepwise counterpart and other variable selection strategies such as the lasso. Our approach can be used to aid fine mapping of other disease-associated regions, which is critical for design of functional follow-up studies required to understand the mechanisms through which genetic variants influence disease.
A fine-mapping method using marginal test statistics in the Bayesian framework. An advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts.
PINES / Phenotype-Informed Noncoding Element Scoring
Evaluates the functional impact of noncoding variants by integrating diverse epigenetic annotations. PINES directs the analysis towards genomic annotations most relevant to phenotypes of interest. It identifies functional noncoding variation more accurately than methods that do not use phenotype-specific knowledge. PINES is applied to fine map noncoding alleles at GWAS loci across a range of diseases, and predict new causal risk alleles for Parkinson’s disease and inflammatory bowel disease. PINES is also used to confirm several high-penetrance variants implicated in Mendelian traits, as well as variants residing within known enhancer regions. It consistently identifies functional variants in fine mapping analyses, dissecting pathogenic loci while avoiding the resource-intensive traditional fine mapping studies. Due to its flexibility and ease of use through a dedicated web portal, PINES provides a powerful in silico method to prioritize and fine map functional noncoding variants.
SFBA / Scalable Functional Bayesian Association
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.
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