snp.PAR gives PAR for a particular SNP.
snp.ES gives the effect size estimate based on the linear regression coefficient and standard error. For logistic regression, we can have similar idea for log(OR) and log(SE(OR)).
snp.HWE gives an exact Hardy-Weinberg Equilibrium (HWE) test, and -1 in the case of misspecification of genotype counts.
Eventually, this will be a set of functions specifically for single nucleotide polymorphisms (SNPs), which are biallelic markers. This is particularly relevant to the genomewide association studies (GWAS) using GeneChips and in line with the classic generalised single-locus model. snp.HWE is from Abecasis's website and yet to adapt for chromosome X.
Internally, snp.PAR calls for an internal function PARn, whcih calculates the the population attributable risk (PAR) given a set of frequencies and associate relative risks (RR). Other 2x2 table statistics familiar to epidemiologists can be added when necessary.
snp.ES(beta,SE,N)
snp.HWE(g)
snp.PAR(RR,MAF,unit=2)Minar allele frequency
Relative risk
Unit to exponentiate for homozygote
Regression coefficient
Standard error for beta
Sample size
Observed genotype vector