hibayes (version 3.0.3)

sbrm: SBayes model

Description

Bayes linear regression model using summary level data

Usage

sbrm(
  sumstat,
  ldm,
  method = c("BayesB", "BayesA", "BayesL", "BayesRR", "BayesBpi", "BayesC", "BayesCpi",
    "BayesR", "CG"),
  map = NULL,
  Pi = NULL,
  lambda = NULL,
  fold = NULL,
  niter = NULL,
  nburn = NULL,
  thin = 5,
  windsize = NULL,
  windnum = NULL,
  vg = NULL,
  dfvg = NULL,
  s2vg = NULL,
  ve = NULL,
  dfve = NULL,
  s2ve = NULL,
  printfreq = 100,
  seed = 666666,
  threads = 4,
  verbose = TRUE
)

Value

the function returns a 'blrMod' object containing

$pi

estimated proportion of zero effect and non-zero effect SNPs

$Vg

estimated genetic variance

$Ve

estimated residual variance

$h2

estimated heritability (h2 = Vg / (Vg + Ve))

$alpha

estimated effect size of all markers

$pip

the frequency for markers to be included in the model during MCMC iteration, also known as posterior inclusive probability (PIP)

$gwas

WPPA is defined to be the window posterior probability of association, it is estimated by counting the number of MCMC samples in which $$\alpha$$ is nonzero for at least one SNP in the window

$MCMCsamples

the collected samples of posterior estimation for all the above parameters across MCMC iterations

Arguments

sumstat

matrix of summary data, details refer to https://cnsgenomics.com/software/gcta/#COJO.

ldm

dense or sparse matrix, ld for reference panel (m * m, m is the number of SNPs). NOTE that the order of SNPs should be consistent with summary data.

method

bayes methods including: "BayesB", "BayesA", "BayesL", "BayesRR", "BayesBpi", "BayesC", "BayesCpi", "BayesR", "CG".

• "BayesRR": Bayes Ridge Regression, all SNPs have non-zero effects and share the same variance, equals to RRBLUP or GBLUP.

• "BayesA": all SNPs have non-zero effects, and take different variance which follows an inverse chi-square distribution.

• "BayesB": only a small proportion of SNPs (1-Pi) have non-zero effects, and take different variance which follows an inverse chi-square distribution.

• "BayesBpi": the same with "BayesB", but 'Pi' is not fixed.

• "BayesC": only a small proportion of SNPs (1-Pi) have non-zero effects, and share the same variance.

• "BayesCpi": the same with "BayesC", but 'Pi' is not fixed.

• "BayesL": BayesLASSO, all SNPs have non-zero effects, and take different variance which follows an exponential distribution.

• "BayesR": only a small proportion of SNPs have non-zero effects, and the SNPs are allocated into different groups, each group has the same variance.

• "CG": conjugate gradient algorithm with assigned lambda.

map

(optional, only for GWAS) the map information of genotype, at least 3 columns are: SNPs, chromosome, physical position.

Pi

vector, the proportion of zero effect and non-zero effect SNPs, the first value must be the proportion of non-effect markers.

lambda

value or vector, the ridge regression value for each SNPs.

fold

percentage of variance explained for groups of SNPs, the default is c(0, 0.0001, 0.001, 0.01).

niter

the number of MCMC iteration.

nburn

the number of iterations to be discarded.

thin

the number of thinning after burn-in. Note that smaller thinning frequency may have higher accuracy of estimated parameters, but would result in more memory for collecting process, on contrary, bigger frequency may have negative effect on accuracy of estimations.

windsize

window size in bp for GWAS, the default is 1e6.

windnum

fixed number of SNPs in a window for GWAS, if it is specified, 'windsize' will be invalid, the default is NULL.

vg

prior value of genetic variance.

dfvg

the number of degrees of freedom for the distribution of genetic variance.

s2vg

scale parameter for the distribution of genetic variance.

ve

prior value of residual variance.

dfve

the number of degrees of freedom for the distribution of residual variance.

s2ve

scale parameter for the distribution of residual variance.

printfreq

frequency of collecting the estimated parameters and printing on console. Note that smaller frequency may have higher accuracy of estimated parameters, but would result in more time and memory for collecting process, on contrary, bigger frequency may have an negative effect on accuracy of estimations.

seed

seed for random sample.

threads

number of threads used for OpenMP.

verbose

whether to print the iteration information on console.

Details

• if any one of the options 'windsize' and 'windnum' is specified, the GWAS results will be returned, and the 'map' information must be provided, in which the physical positions should be all in digital values.

• the 'windsize' or 'windnum' option only works for the methods of which the assumption has a proportion of zero effect markers, e.g., BayesB, BayesBpi, BayesC, BayesCpi, BSLMM, and BayesR.

References

Lloyd-Jones, Luke R., et al. "Improved polygenic prediction by Bayesian multiple regression on summary statistics." Nature communications 10.1 (2019): 1-11.

Examples

bfile_path = system.file("extdata", "demo", package = "hibayes")
bin = read_plink(bfile_path, threads=1)
fam = bin$fam
geno = bin$geno
map = bin$map

sumstat_path = system.file("extdata", "demo.ma", package = "hibayes")
sumstat = read.table(sumstat_path, header=TRUE)
head(sumstat)

# \donttest{
# computate ld variance covariance matrix
## construct genome wide full variance-covariance matrix
ldm1 <- ldmat(geno, threads=4)	
## construct genome wide sparse variance-covariance matrix
# ldm2 <- ldmat(geno, chisq=5, threads=4)	
## construct chromosome wide full variance-covariance matrix
# ldm3 <- ldmat(geno, map, ldchr=FALSE, threads=4)	
## construct chromosome wide sparse variance-covariance matrix
# ldm4 <- ldmat(geno, map, ldchr=FALSE, chisq=5, threads=4)

# if the order of SNPs in genotype is not consistent with the order in sumstat file, 
# prior adjusting is necessary.
indx = match(map[, 1], sumstat[, 1])
sumstat = sumstat[indx, ]

# fit model
fit = sbrm(sumstat=sumstat, ldm=ldm1, method="BayesCpi", Pi = c(0.95, 0.05), 
	niter=20000, nburn=12000, seed=666666, map=map, windsize=1e6, threads=1)

# overview of the returned results
summary(fit)

# get the SD of estimated SNP effects for markers
summary(fit)$alpha
# }