h2D2 v1.1

The h2D2 package implements a fine-mapping method based on the "heritability-induced Dirichlet Decomposition" (h2-D2) prior. It is a novel fine-mapping method that utilizes a continuous global-local shrinkage prior. This method can be applied to both quantitative and binary traits. An MCMC algorithm is employed to obtain samples from the posterior distribution. This method can also provide "credible sets" of candidate causal variants, which are generally provided by fine-mapping methods based on discrete-mixture priors.

Quick Start

1. Install h2D2 from GitHub

devtools::install_github("https://github.com/xiangli428/h2D2")
library(h2D2)

2. Create an h2D2 object with summary data

h2D2 = Createh2D2Object(z,
                        R,
                        N,
                        SNP_ID = NULL,
                        trait = "quantitative",
                        in_sample_LD = F,
                        a = 0.005,
                        b = 1e4,
                        coverage = 0.95,
                        purity = 0.5)

where a and b specify the hyperparameters of the prior. In v1.1, if in_sample_LD = T, we recommend setting b = NULL and b will be estimated by a pre-training process before MCMC. If in_sample_LD = F, the LD matrix may be rank-deficient. In this case, the z-score vector will be projected to the column space of LD matrix. See https://arxiv.org/abs/2401.15014 for more details.

3. MCMC sampling

h2D2 = h2D2_MCMC(h2D2, mcmc_n = 10000, burn_in = 5000, 
                 thin = 1, stepsize = 2, seed = 428)

4. Results

Credible levels of SNPs:

h2D2@CL

95% Credible sets:

h2D2@CS

5. Citation

Li, X., Sham, P. C., & Zhang, Y. D. (2024). A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis. The American Journal of Human Genetics. https://doi.org/10.1016/j.ajhg.2023.12.007