Last updated: 2020-06-23
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Knit directory: rss/
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Details of MCMC algorithms for rss_bvsr.m and rss_bslmm.m are available in Supplementary Appendix B of Zhu and Stephens (2017) Details of MCMC algorithms for rss_ash.m are available in this unpublished note1. Note that only rss_bvsr.m and rss_bslmm.m were used to generate results in Zhu and Stephens (2017).
rss_bvsr.mFit the RSS-BVSR model that consists of RSS likelihood and BVSR prior (Guan and Stephens, 2011):
\[ \begin{aligned} \widehat{\boldsymbol\beta} &\sim {\cal N}({\bf SRS}^{-1}{\boldsymbol\beta},{\bf SRS}),\\ \beta_j &\sim \pi\cdot{\cal N}(0,\sigma_B^2) + (1-\pi)\cdot\delta_0, \end{aligned} \]
using a Metropolis-Hastings algorithm.
rss_bslmm.mFit the RSS-BSLMM model that consists of RSS likelihood and BSLMM prior (Zhou et al, 2013):
\[ \begin{aligned} \widehat{\boldsymbol\beta} &\sim {\cal N}({\bf SRS}^{-1}{\boldsymbol\beta},{\bf SRS}),\\ \beta_j &\sim \pi\cdot{\cal N}(0,\sigma_B^2+\sigma_P^2) + (1-\pi)\cdot{\cal N}(0,\sigma_P^2), \end{aligned} \]
using a component-wise MCMC algorithm.
rss_ash.mFit the RSS-ASH model that consists of RSS likelihood and ASH prior (Stephens, 2017):
\[ \begin{aligned} \widehat{\boldsymbol\beta} &\sim {\cal N}({\bf SRS}^{-1}{\boldsymbol\beta},{\bf SRS}),\\ \beta_j &\sim \pi_0 \cdot \delta_0 + {\textstyle\sum}_{k=1}^K \pi_k \cdot {\cal N}(0,\sigma_k^2), \end{aligned} \]
using a component-wise MCMC algorithm.
Details of VB algorithms, SQUAREM accelerator and parallel implementation are available in Supplementary Notes of Zhu and Stephens (2018). Note that only rss_varbvsr_squarem.m and rss_varbvsr_bigmem_squarem.m were used to generate results in Zhu and Stephens (2018). The other functions were developed merely for testing and benchmarking.
rss_varbvsr.mFit the following extended RSS-BVSR model
\[ \begin{aligned} \widehat{\boldsymbol\beta} &\sim {\cal N}({\bf SRS}^{-1}{\boldsymbol\beta},{\bf SRS}),\\ \beta_j &\sim \pi_j\cdot{\cal N}(0,\sigma_j^2) + (1-\pi_j)\cdot\delta_0, \end{aligned} \]
using a mean-field VB algorithm. The VB algorithm largely follows Carbonetto and Stephens (2012). This is an extended RSS-BVSR model because each SNP \(j\) can have its own hyper-parameters \(\{\pi_j,\sigma_j^2\}\), whereas the standard RSS-BVSR model assumes that all SNPs share the same hyper-parameters \(\{\pi,\sigma_B^2\}\).
rss_varbvsr_squarem.mThis is a variant of rss_varbvsr.m with the SQUAREM accelerator (Varadhan and Roland, 2008) added.
rss_varbvsr_parallel.mThis is a parallel implementation of rss_varbvsr.m based on MATLAB Parallel Computing Toolbox.
rss_varbvsr_pasquarem.mThis is a parallel implementation of rss_varbvsr_squarem.m based on MATLAB Parallel Computing Toolbox.
rss_varbvsr_bigmem.mThis is a memory-efficient implementation of rss_varbvsr_parallel.m.
rss_varbvsr_bigmem_squarem.mThis is a memory-efficient implementation of rss_varbvsr_pasquarem.m.
import_1000g_vcf.shOutput 1000 Genomes phased haplotypes of a given list of SNPs in IMPUTE reference-panel format.
compute_pve.mUse GWAS summary data to estimate PVE (or SNP heritability), a quantity defined by Equation 2.10 in Guan and Stephens (2011). This function corresponds to Equation 3.7 in Zhu and Stephens (2017).
band_storage.mConvert a symmetric, banded matrix to a compact matrix in such a way that only the main diagonal and the nonzero super-diagonals are stored. This function is used to reduce the file size of a large LD matrix.
find_bandwidth.mFind the bandwidth of a symmetric, banded matrix.
get_corr.mCompute linkage disequilibrium (LD) matrix using the shrinkage estimator proposed in Wen and Stephens (2010). This function is also implemented in an R package ldshrink.
data_maker.mSimulate phenotype data from the genome-wide multiple-SNP model described in Zhou et al (2013), and then compute the single-SNP summary statistics for each SNP. This function was used in some simulation studies of Zhu and Stephens (2017).
enrich_datamaker.mSimulate phenotype data from the genetic association enrichment model described in Carbonetto and Stephens (2013), and then compute the single-SNP summary statistics for each SNP. This function was used in some simulation studies of Zhu and Stephens (2018).
null_single.m & null_template.mFit genome-wide multiple-SNP “baseline model” to single-SNP summary data, using rss/src_vb functions. These scripts were used in data analyses of Zhu and Stephens (2018).
gsea_wrapper.m & gsea_template.mFit genome-wide multiple-SNP “enrichment model” to single-SNP summary data, using rss/src_vb functions. These scripts were used in data analyses of Zhu and Stephens (2018).
null_wrapper_fixsb.m & gsea_wrapper_fixsb.mFit genome-wide multiple-SNP “baseline model” and “enrichment model” to single-SNP summary data, using a fixed prior variance of causal genetic effects (\(\sigma_B^2\)) in rss/src_vb functions. These scripts were used in simulation studies of Zhu and Stephens (2018).
ash_lrt_31traits.RCompute a simple likelihood ratio as a sanity check for the more complicated enrichment analysis method developed in Zhu and Stephens (2018). This likelihood ratio calculation is based on an R package ashr.
Footnotes: