ssbrm: Single-step Bayes model

Description

Single-step Bayes linear regression model using individual level data and pedigree information $$y = X \beta + R r + M \alpha + U \epsilon + e$$ where $y$ is the vector of phenotypic values for both genotyped and non-genotyped individuals, $\beta$ is a vector of estimated coefficient for covariates, $M$ contains the genotype ($M_2$) for genotyped individuals and the imputed genotype ($M_1 = A_{12}A_{22}^{-1}M_2$) for non-genotyped individuals, $\epsilon$ is the vector of genotype imputation error, $e$ is a vector of residuals.

Usage

ssbrm(
  formula,
  data = NULL,
  M = NULL,
  M.id = NULL,
  pedigree = NULL,
  method = c("BayesCpi", "BayesA", "BayesL", "BayesR", "BayesB", "BayesC", "BayesBpi",
    "BayesRR"),
  map = NULL,
  Pi = NULL,
  fold = NULL,
  niter = NULL,
  nburn = NULL,
  thin = 5,
  windsize = NULL,
  windnum = NULL,
  maf = 0.01,
  dfvr = NULL,
  s2vr = 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 a 'blrMod' object containing

$J: coefficient for genotype imputation residuals
$Veps: estimated variance of genotype imputation residuals
$epsilon: genotype imputation residuals
$mu: the regression intercept
$pi: estimated proportion of zero effect and non-zero effect SNPs
$beta: estimated coefficients for all covariates
$r: estimated environmental random effects
$Vr: estimated variance for all environmental random effect
$Vg: estimated genetic variance
$Ve: estimated residual variance
$h2: estimated heritability (h2 = Vg / (Vr + Vg + Ve))
$g: data.frame, the first column is the list of individual id, the second column is the genomic estimated breeding value for all individuals, including genotyped and non-genotyped.
$alpha: estimated effect size of all markers
$e: residuals of the model
$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

formula

a two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a ‘~’ operator and the terms, separated by ‘+’ operators, on the right. Random-effects terms are distinguished by vertical bars (1|’) separating expressions for design matrices from grouping factors.

data

the data frame containing the variables named in 'formula', NOTE that the first column in 'data' should be the individual id.

M

numeric matrix of genotype with individuals in rows and markers in columns, NAs are not allowed.

M.id

vector of id for genotype.

pedigree

matrix of pedigree, 3 columns limited, the order of columns shoud be "id", "sir", "dam".

method

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

"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.

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.

fold

proportion 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 NULL.

windnum

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

maf

the effects of markers whose MAF is lower than the threshold will not be estimated.

dfvr

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

s2vr

scale parameter for the distribution of environmental variance.

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 printing iterative details on console.

seed

seed for random sample.

threads

number of threads used for OpenMP.

verbose

whether to print the iteration information on console.

References

Fernando, Rohan L., Jack CM Dekkers, and Dorian J. Garrick. "A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses." Genetics Selection Evolution 46.1 (2014): 1-13.
Henderson, C.R.: A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32(1), 69-83 (1976).

Examples

Run this code

# Load the example data attached in the package
pheno_file_path = system.file("extdata", "demo.phe", package = "hibayes")
pheno = read.table(pheno_file_path, header=TRUE)

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

pedigree_file_path = system.file("extdata", "demo.ped", package = "hibayes")
ped = read.table(pedigree_file_path, header=TRUE)

# For GS/GP
## no environmental effects:
fit = ssbrm(T1~1, data=pheno, M=geno, M.id=fam[,2], pedigree=ped,
	method="BayesCpi", niter=1000, nburn=600, thin=5, printfreq=100, threads=1)

## overview of the returned results
summary(fit)

# \donttest{

## add fixed effects or covariates:
fit = ssbrm(T1~sex+bwt, data=pheno, M=geno, M.id=fam[,2], pedigree=ped,
	method="BayesCpi")

## add environmental random effects:
fit = ssbrm(T1~(1|loc)+(1|dam), data=pheno, M=geno, M.id=fam[,2],
	pedigree=ped, method="BayesCpi")

# For GWAS
fit = ssbrm(T1~sex+bwt+(1|dam), data=pheno, M=geno, M.id=fam[,2],
	pedigree=ped, method="BayesCpi", map=map, windsize=1e6)
# }

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

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