Package 'RMR.ICP'

Robust Mendelian Randomization to account for Idiosyncratic and Correlated Pleiotropy

Description

Implements a robust Mendelian Randomization method (RMR-ICP) that accounts for both idiosyncratic and correlated pleiotropy using a Gibbs sampler.

Author(s)

Qing Cheng [email protected]

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Example datasets for RMR-ICP

Description

Simulated GWAS summary statistics and LD reference data for demonstrating the RMR-ICP method.

• gamma1_Indep, gamma1_LD: SNP-exposure effect estimates (independent and LD-structured scenarios, respectively).

• Gamma2_Indep, Gamma2_LD: SNP-outcome effect estimates (independent and LD-structured scenarios, respectively).

• se1_Indep, se1_LD: Standard errors for the exposure effect estimates.

• se2_Indep, se2_LD: Standard errors for the outcome effect estimates.

• R: LD correlation matrix (2000 x 2000) for the LD scenario.

• block_inf1: Block boundary matrix (200 x 2) giving the start and end indices of each LD block (0-indexed).

Usage

data(gamma1_Indep)
data(gamma1_LD)
data(Gamma2_Indep)
data(Gamma2_LD)
data(se1_Indep)
data(se1_LD)
data(se2_Indep)
data(se2_LD)
data(R)
data(block_inf1)

Format

Numeric matrices. The _Indep datasets have 100 rows (SNPs); the _LD datasets have 2000 rows. R is a 2000 x 2000 matrix. block_inf1 is a 200 x 2 integer matrix of block boundaries.

Examples

data(gamma1_Indep)
  data(Gamma2_Indep)
  data(se1_Indep)
  data(se2_Indep)
  res <- RMRICPindep(gamma1_Indep, Gamma2_Indep, se1_Indep, se2_Indep)

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Simulation helpers for RMR-ICP

Description

genRawGeno simulates a genotype matrix with a block correlation structure.

genSumStat takes a genotype matrix and generates GWAS summary statistics under a specified causal and pleiotropic model.

traceplot plots the Gibbs sampler trace of the causal effect estimate.

Usage

genRawGeno(maf, L, M, rho, n)

genSumStat(x12, n1, n2, M, L, b1, Alrate, sigma2g, dfA, delta, h2a, h2g)

traceplot(bhatpoint)

Arguments

maf	Numeric vector of minor allele frequencies (length L * M).
L	Number of LD blocks.
M	Number of SNPs per block.
rho	Within-block LD correlation parameter (AR(1) structure).
n	Total sample size.
x12	Genotype matrix ((n1+n2) rows, L*M columns).
n1	Sample size for exposure GWAS.
n2	Sample size for outcome GWAS.
b1	True causal effect of exposure on outcome.
Alrate	Proportion of blocks with correlated pleiotropic effects.
sigma2g	Variance of genetic effects.
dfA	Degrees of freedom for the t-distributed correlated pleiotropy.
delta	Scaling factor for correlated pleiotropy.
h2a	Heritability explained by pleiotropic effects.
h2g	Heritability of the exposure explained by genetics.
bhatpoint	Numeric vector of posterior samples of the causal effect.

Value

genRawGeno returns a numeric genotype matrix with n rows and L*M columns.

genSumStat returns a list with components gammah, se1, se2, Gammah, and CHPindex.

traceplot returns a ggplot object.

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Set of functions in example RcppArmadillo package

Description

These four functions are created when RcppArmadillo.package.skeleton() is invoked to create a skeleton packages.

Usage

rcpparma_hello_world()
rcpparma_outerproduct(x)
rcpparma_innerproduct(x)
rcpparma_bothproducts(x)

Arguments

x	a numeric vector

Details

These are example functions which should be largely self-explanatory. Their main benefit is to demonstrate how to write a function using the Armadillo C++ classes, and to have to such a function accessible from R.

Value

rcpparma_hello_world() does not return a value, but displays a message to the console.

rcpparma_outerproduct() returns a numeric matrix computed as the outer (vector) product of x.

rcpparma_innerproduct() returns a double computer as the inner (vector) product of x.

rcpparma_bothproducts() returns a list with both the outer and inner products.

Author(s)

Dirk Eddelbuettel

References

See the documentation for Armadillo, and RcppArmadillo, for more details.

Examples

x <- sqrt(1:4)
  rcpparma_innerproduct(x)
  rcpparma_outerproduct(x)

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Read and prepare GWAS summary statistics for RMR-ICP

Description

ReadSummaryStat reads GWAS summary statistics for the exposure and outcome, performs quality control (MHC region removal, allele filtering), aligns to a reference panel, and organises data into LD blocks ready for RMRICP.

EstRho estimates the correlation between exposure and outcome GWAS using null SNPs via a truncated Gibbs sampler.

Usage

ReadSummaryStat(fileexp, fileout, filepan, snpinfo, pva_cutoff, lambad)

EstRho(fileexp, fileout, filepan, snpinfo, ld_r2_thresh, lambad, pth)

Arguments

fileexp	Path to the exposure GWAS summary statistics file.
fileout	Path to the outcome GWAS summary statistics file.
filepan	Named list (by chromosome) of paths to LD panel RDS files.
snpinfo	Path to reference SNP information RDS file.
pva_cutoff	p-value threshold for selecting instruments from the exposure GWAS.
lambad	Shrinkage factor applied to off-diagonal LD correlations.
ld_r2_thresh	LD r-squared threshold for clumping (used in EstRho).
pth	Numeric vector of z-score thresholds used to define null SNPs for rho estimation.

Value

ReadSummaryStat returns a list of LD-block-structured summary statistics suitable for passing to RMRICP.

EstRho returns a list with components rhohat, pvalue, pres, and Rhores.

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Robust Mendelian Randomization accounting for Idiosyncratic and Correlated Pleiotropy

Description

Perform robust Mendelian Randomization using a Gibbs sampler that accounts for both idiosyncratic and correlated horizontal pleiotropy. RMRICP handles the general case with LD structure; RMRICPindep assumes independent instruments; RMRICPSim works with a pre-specified LD matrix and block structure.

Usage

RMRICP(F4gammah, F4Gammah, F4se1, F4se2, F4Rblock, opts = NULL)

RMRICPindep(gammah, Gammah, se1, se2, opts = NULL)

RMRICPSim(gammah, Gammah, se1, se2, R, block_inf, opts = NULL)

Arguments

F4gammah	List of numeric vectors of SNP-exposure effect estimates per LD block.
F4Gammah	List of numeric vectors of SNP-outcome effect estimates per LD block.
F4se1	List of numeric vectors of standard errors for F4gammah.
F4se2	List of numeric vectors of standard errors for F4Gammah.
F4Rblock	List of LD correlation matrices per block.
gammah	Numeric vector of SNP-exposure effect estimates.
Gammah	Numeric vector of SNP-outcome effect estimates.
se1	Numeric vector of standard errors for gammah.
se2	Numeric vector of standard errors for Gammah.
R	LD correlation matrix.
block_inf	Matrix of block boundary indices.
opts	Optional list of MCMC options (e.g., iterations, burn-in, thinning).

Value

A list containing posterior samples and summary estimates of the causal effect.

Examples

data(gamma1_Indep)
  data(Gamma2_Indep)
  data(se1_Indep)
  data(se2_Indep)
  res <- RMRICPindep(gamma1_Indep, Gamma2_Indep, se1_Indep, se2_Indep)

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