h2_snp {iGasso}

An exact method for SNP-heritability estimation using GWAS summary statistics

Description

h2_snp calculates heritability explained by a set of SNPs

Usage

h2_snp(beta, SE, N, R, alpha)

Arguments

beta

a vector of beta coefficients for a set of SNPs. These coefficients are from a GWAS.

SE

a vector of the standard errors of the beta coefficients.

N

a vector of sample sizes used by the GWAS at these SNPs.

R

LD matrix for these SNPs.

alpha

1-\alpha is the confidence level of the confidence interval.

Value

A list containing the following components:

* MLE of the heritability.

* umvu (uniformly minimum variance unbiased) estimator of the heritability.

* interval estimate for the heritability.

Author(s)

Kai Wang <[e-mail]>

References

Wang, K. (2023) An exact method for SNP-heritability estimation using GWAS summary statistics without heritability modeling. submitted

Examples

beta = c(0.225269, 0.221270, 0.162635, 0.261669, 0.150887, 
         0.214515, 0.170296, 0.204454, 0.254811, 0.213803)
SE = c(0.033244, 0.032551, 0.032171, 0.031042, 0.032815, 
       0.031908, 0.031717, 0.032023, 0.031907, 0.032291)
N = 10000
R = diag(1, 10)
alpha = 0.05
h2_snp(beta, SE, N, R, alpha)