susie_rss: Sum of Single Effects (SuSiE) Regression using summary...

In simingz/ctwas: Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits

Sum of Single Effects (SuSiE) Regression using summary statistics

Description

susie_rss performs sum of single-effect linear regression with z scores; all posterior calculations are for z-scores. This function fits the regression model z = \sum_l R*b_l + e, where e is N(0,R) and \sum_l b_l is a p-vector of effects to be estimated. The required summary data are the p by p correlation matrix, R, and the p-vector z.

Usage

susie_rss(
  z,
  R,
  maf = NULL,
  maf_thresh = 0,
  z_ld_weight = 0,
  L = 10,
  prior_variance = 50,
  residual_variance = NULL,
  prior_weights = NULL,
  null_weight = NULL,
  estimate_residual_variance = FALSE,
  estimate_prior_variance = TRUE,
  estimate_prior_method = c("optim", "EM", "simple"),
  check_null_threshold = 0,
  prior_tol = 1e-09,
  max_iter = 100,
  s_init = NULL,
  intercept_value = 0,
  coverage = 0.95,
  min_abs_corr = 0.5,
  tol = 0.001,
  verbose = FALSE,
  track_fit = FALSE,
  check_R = FALSE,
  r_tol = 1e-08,
  refine = FALSE
)

Arguments

z	A p-vector of z scores.
R	A p by p symmetric, positive semidefinite correlation matrix.
maf	Minor allele frequency; to be used along with maf_thresh to filter input summary statistics.
maf_thresh	Variants having a minor allele frequency smaller than this threshold are not used.
z_ld_weight	This feature is not recommended. The weights assigned to the z scores in the LD matrix. If z_ld_weight > 0, the LD matrix used in the model is cov2cor((1-w)*R + w*tcrossprod(z)), where w = z_ld_weight.
L	Number of components (nonzero coefficients) in the susie regression model. If L is larger than the number of covariates, p, L is set to p.
prior_variance	The prior variance. It is either a scalar or a vector of length L.
residual_variance	Variance of the residual. If it is not specified, we set it to 1.
prior_weights	A vector of length p, in which each entry gives the prior probability that SNP j has non-zero effect.
null_weight	Prior probability of no effect (a number between 0 and 1, and cannot be exactly 1).
estimate_residual_variance	The residual variance is fixed to the value supplied by residual_variance. We don't estimate residual variance in susie_rss.
estimate_prior_variance	If estimate_prior_variance = TRUE, the prior variance is estimated (this is a separate parameter for each of the L effects). If provided, prior_variance is then used as an initial value for the optimization. When estimate_prior_variance = FALSE, the prior variance for each of the L effects is determined by the value supplied to prior_variance.
estimate_prior_method	The method used for estimating prior variance. When estimate_prior_method = "simple" is used, the likelihood at the specified prior variance is compared to the likelihood at a variance of zero, and the setting with the larger likelihood is retained.
check_null_threshold	When the prior variance is estimated, compare the estimate with the null, and set the prior variance to zero unless the log-likelihood using the estimate is larger by this threshold amount. For example, if you set check_null_threshold = 0.1, this will "nudge" the estimate towards zero when the difference in log-likelihoods is small. A note of caution that setting this to a value greater than zero may lead the IBSS fitting procedure to occasionally decrease the ELBO.
prior_tol	When the prior variance is estimated, compare the estimated value to prior_tol at the end of the computation, and exclude a single effect from PIP computation if the estimated prior variance is smaller than this tolerance value.
max_iter	Maximum number of IBSS iterations to perform.
s_init	A previous susie fit with which to initialize.
intercept_value	The intercept. (The intercept cannot be estimated from centered summary data.) This setting will be used by coef to assign an intercept value, mainly for consistency with susie. Set to NULL if you want coef not to include an intercept term (and so only a p-vector is returned).
coverage	A number between 0 and 1 specifying the “coverage” of the estimated confidence sets.
min_abs_corr	Minimum absolute correlation allowed in a credible set. The default, 0.5, corresponds to a squared correlation of 0.25, which is a commonly used threshold for genotype data in genetic studies.
tol	A small, non-negative number specifying the convergence tolerance for the IBSS fitting procedure. The fitting procedure will halt when the difference in the variational lower bound, or “ELBO” (the objective function to be maximized), is less than tol.
verbose	If verbose = TRUE, the algorithm's progress, and a summary of the optimization settings, are printed to the console.
track_fit	If track_fit = TRUE, trace is also returned containing detailed information about the estimates at each iteration of the IBSS fitting procedure.
check_R	If check_R = TRUE, check that R is positive semidefinite.
r_tol	Tolerance level for eigenvalue check of positive semidefinite matrix of R.
refine	If refine = TRUE, we use a procedure to help SuSiE get out of local optimum.

Value

A "susie" object with some or all of the following elements:

alpha	An L by p matrix of posterior inclusion probabilites.
mu	An L by p matrix of posterior means, conditional on inclusion.
mu2	An L by p matrix of posterior second moments, conditional on inclusion.
lbf	log-Bayes Factor for each single effect.
lbf_variable	log-Bayes Factor for each variable and single effect.
intercept	Fixed Intercept.
sigma2	Fixed Residual variance.
V	Prior variance of the non-zero elements of b, equal to scaled_prior_variance * var(Y).
elbo	The value of the variational lower bound, or “ELBO” (objective function to be maximized), achieved at each iteration of the IBSS fitting procedure.
fitted	Vector of length n containing the fitted values of the outcome.
sets	Credible sets estimated from model fit; see susie_get_cs for details.
pip	A vector of length p giving the (marginal) posterior inclusion probabilities for all p covariates.
niter	Number of IBSS iterations that were performed.
converged	TRUE or FALSE indicating whether the IBSS converged to a solution within the chosen tolerance level.
Rr	An p-vector of t(X) times fitted values, X %*% colSums(alpha*mu).

Examples

set.seed(1)
n = 1000
p = 1000
beta = rep(0,p)
beta[1:4] = 1
X = matrix(rnorm(n*p),nrow = n,ncol = p)
X = scale(X,center = TRUE,scale = TRUE)
y = drop(X %*% beta + rnorm(n))

input_ss <- compute_ss(X,y,standardize = TRUE)
ss   <- univariate_regression(X,y)
R    <- with(input_ss,cov2cor(XtX))
zhat <- with(ss,betahat/sebetahat)
res  <- susie_rss(zhat,R,L = 10)

simingz/ctwas documentation built on Sept. 17, 2024, 10:55 p.m.