BayesCpi: Estimte SNP Effects by MCMC - Bayes C and Cpi - beta

In gdmp: Genomic Data Management

Description

BayesCpi is an implementation of Bayes Cpi to extend Bayes A and B for estimating direct SNP effects in high dimensional data problems (p >> N). BayesCpi treats the prior probability, pi = P(SNP has zero effect), as unknown. The C Function cBaysCpi is utilized for for speed ..

Usage

BayesCpi(ga, numiter = 5000, Pi = .9, y)

Arguments

ga	A matrix of genotypes with a number of rows identical to the number of genotyped individuals and a number of columns identical to the number of SNPs. Values in the matrix are 0, 1, 2, & 5 for homozygous, heterozygous, other homozygous, & unknown genotypes, respectively.
numiter	Number of iterations
Pi	Proportion of SNP loci with 0 effect for Bayes C
y	Trait phenotypes or conventional breeding values

Details

This function runs Bayes C and Cpi to estimate direct SNP effects and the proportion of loci with nonzero effects based on a matrix of genotypes, ga and a vector of adjusted phenotypes, y, (Habier et al., 2011; BMC Bioinformatics 12:186). As in other bayesian alphabet, Bayes Cpi is essential in high dimensional data problems with highly overparameterized models (p >> N). It extends Bayes A and B to estimate the proportion of loci with nonzero effect.

Other data management functions in gdmp can be used to construct the integer matrix of genotypes, ga, to use as input to BayesCpi.

Value

A list object with a vector of SNP estimates meanb and a vector of genomic values for individuals, aHat are returned. It is also possible to extract the estimated number of SNP loci in nLoci.

References

Habier et al. (2011). Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 12, 186.

See Also

BayesAB

gdmp documentation built on May 1, 2019, 8:07 p.m.