gmap: Finemapping using Bayesian Linear Regression Models

In psoerensen/qgg: Statistical Tools for Quantitative Genetic Analyses

Finemapping using Bayesian Linear Regression Models

Description

In the Bayesian multiple regression model, the posterior density of the model parameters depends on the likelihood of the data given the parameters and a prior probability for the model parameters. The choice of the prior for marker effects can influence the type and extent of shrinkage induced in the model.

Usage

gmap(
  y = NULL,
  X = NULL,
  W = NULL,
  stat = NULL,
  trait = NULL,
  sets = NULL,
  fit = NULL,
  Glist = NULL,
  chr = NULL,
  rsids = NULL,
  ids = NULL,
  b = NULL,
  bm = NULL,
  seb = NULL,
  mask = NULL,
  LD = NULL,
  n = NULL,
  vg = NULL,
  vb = NULL,
  ve = NULL,
  ssg_prior = NULL,
  ssb_prior = NULL,
  sse_prior = NULL,
  lambda = NULL,
  scaleY = TRUE,
  shrinkLD = FALSE,
  shrinkCor = FALSE,
  formatLD = "dense",
  pruneLD = TRUE,
  r2 = 0.05,
  checkLD = TRUE,
  h2 = NULL,
  pi = 0.001,
  updateB = TRUE,
  updateG = TRUE,
  updateE = TRUE,
  updatePi = TRUE,
  adjustE = TRUE,
  models = NULL,
  checkConvergence = FALSE,
  critVe = 3,
  critVg = 5,
  critVb = 5,
  critPi = 3,
  ntrial = 1,
  nug = 4,
  nub = 4,
  nue = 4,
  verbose = FALSE,
  msize = 100,
  threshold = NULL,
  ve_prior = NULL,
  vg_prior = NULL,
  tol = 0.001,
  nit = 100,
  nburn = 50,
  nit_local = NULL,
  nit_global = NULL,
  method = "bayesC",
  algorithm = "mcmc"
)

Arguments

y	A vector or matrix of phenotypes.
X	A matrix of covariates.
W	A matrix of centered and scaled genotypes.
stat	Dataframe with marker summary statistics.
trait	Integer used for selection traits in covs object.
sets	A list of character vectors where each vector represents a set of items. If the names of the sets are not provided, they are named as "Set1", "Set2", etc.
fit	List of results from gbayes.
Glist	List of information about genotype matrix stored on disk.
chr	Chromosome for which to fit BLR models.
rsids	Character vector of rsids.
ids	vector of individuals used in the study
b	Vector or matrix of marginal marker effects.
bm	Vector or matrix of adjusted marker effects for the BLR model.
seb	Vector or matrix of standard error of marginal effects.
mask	Vector or matrix specifying if marker should be ignored.
LD	List with sparse LD matrices.
n	Scalar or vector of number of observations for each trait.
vg	Scalar or matrix of genetic (co)variances.
vb	Scalar or matrix of marker (co)variances.
ve	Scalar or matrix of residual (co)variances.
ssg_prior	Scalar or matrix of prior genetic (co)variances.
ssb_prior	Scalar or matrix of prior marker (co)variances.
sse_prior	Scalar or matrix of prior residual (co)variances.
lambda	Vector or matrix of lambda values
scaleY	Logical indicating if y should be scaled.
shrinkLD	Logical indicating if LD should be shrunk.
shrinkCor	Logical indicating if cor should be shrunk.
formatLD	Character specifying LD format (default is "dense").
pruneLD	Logical indicating if LD pruning should be applied.
r2	Scalar providing value for r2 threshold used in pruning
checkLD	Logical indicating if LD matches summary statistics.
h2	Trait heritability.
pi	Proportion of markers in each marker variance class.
updateB	Logical indicating if marker (co)variances should be updated.
updateG	Logical indicating if genetic (co)variances should be updated.
updateE	Logical indicating if residual (co)variances should be updated.
updatePi	Logical indicating if pi should be updated.
adjustE	Logical indicating if residual variance should be adjusted.
models	List structure with models evaluated in bayesC.
checkConvergence	Logical indicating if convergences should be checked.
critVe	Scalar providing value for z-score threshold used in checking convergence for Ve
critVg	Scalar providing value for z-score threshold used in checking convergence for Vg
critVb	Scalar providing value for z-score threshold used in checking convergence for Vg
critPi	Scalar providing value for z-score threshold used in checking convergence for Pi
ntrial	Integer providing number of trials used if convergence is not obtaines
nug	Scalar or vector of prior degrees of freedom for genetic (co)variances.
nub	Scalar or vector of prior degrees of freedom for marker (co)variances.
nue	Scalar or vector of prior degrees of freedom for residual (co)variances.
verbose	Logical; if TRUE, it prints more details during iteration.
msize	Integer providing number of markers used in computation of sparseld
threshold	Scalar providing value for threshold used in adjustment of B
ve_prior	Scalar or matrix of prior residual (co)variances.
vg_prior	Scalar or matrix of prior genetic (co)variances.
tol	Convergence criteria used in gbayes.
nit	Number of iterations.
nburn	Number of burnin iterations.
nit_local	Number of local iterations.
nit_global	Number of global iterations.
method	Method used (e.g. "bayesN","bayesA","bayesL","bayesC","bayesR").
algorithm	Specifies the algorithm.

Details

This function implements Bayesian linear regression models to provide unified mapping of genetic variants, estimate genetic parameters (e.g. heritability), and predict disease risk. It is designed to handle various genetic architectures and scale efficiently with large datasets.

Value

Returns a list structure including the following components:

bm	Vector or matrix of posterior means for marker effects.
dm	Vector or matrix of posterior means for marker inclusion probabilities.
vb	Scalar or vector of posterior means for marker variances.
vg	Scalar or vector of posterior means for genomic variances.
ve	Scalar or vector of posterior means for residual variances.
rb	Matrix of posterior means for marker correlations.
rg	Matrix of posterior means for genomic correlations.
re	Matrix of posterior means for residual correlations.
pi	Vector of posterior probabilities for models.
h2	Vector of posterior means for model probability.
param	List of current parameters used for restarting the analysis.
stat	Matrix of marker information and effects used for genomic risk scoring.

Author(s)

Peter Sørensen

Examples

# Plink bed/bim/fam files
bedfiles <- system.file("extdata", paste0("sample_chr",1:2,".bed"), package = "qgg")
bimfiles <- system.file("extdata", paste0("sample_chr",1:2,".bim"), package = "qgg")
famfiles <- system.file("extdata", paste0("sample_chr",1:2,".fam"), package = "qgg")

# Prepare Glist
Glist <- gprep(study="Example", bedfiles=bedfiles, bimfiles=bimfiles, famfiles=famfiles)

# Simulate phenotype
sim <- gsim(Glist=Glist, chr=1, nt=1)

# Compute single marker summary statistics
stat <- glma(y=sim$y, Glist=Glist, scale=FALSE)
str(stat)

# Define fine-mapping regions 
sets <- Glist$rsids
Glist$chr[[1]] <- gsub("21","1",Glist$chr[[1]]) 
Glist$chr[[2]] <- gsub("22","2",Glist$chr[[2]]) 

# Fine map
fit <- gmap(Glist=Glist, stat=stat, sets=sets, verbose=FALSE, 
            method="bayesC", nit=1500, nburn=500, pi=0.001)
            
fit$post  # Posterior inference for every fine-mapped region
fit$conv  # Convergence statistics for every fine-mapped region

# Posterior inference for marker effect
head(fit$stat)             

psoerensen/qgg documentation built on March 9, 2024, 10:02 p.m.