MFKM: KM for Quantitative Traits in Multivariate Family GWAS Data...
In KMgene: Gene-Based Association Analysis for Complex Traits

Description Usage Arguments Value Examples

This function (MFKM) is used to perform KM analysis (Yan et al., 2015) for quantitative traits in GWAS multivariate family data.

MFKM(
  obj,
  genotypes,
  weights = NULL,
  acc = 1e-04,
  append.write = NULL,
  eq.gen.effect = F
)

`obj`	results saved from MFKM_Null_Model.
`genotypes`	1st column: gene name; 2nd column: snp name; 3rd-end columns: A matrix of genotypes for each subject (class: data.frame). The order of 3rd-end columns should match unique(yid). Coded as 0, 1, 2 and no missing. This genotype file can be a big file containing all genes or it can be files containing one single gene.
`weights`	1st column: gene name; 2nd column: snp name; 3rd column: A vector with the length equal to the number of variants in the test (class: data.frame). Default is Null indicating equal weight for all markers.
`acc`	Accuracy of numerical integration used in Davies' method. Default 1e-4.
`append.write`	The name of pvalue output file. Write out p-values in real time. Don't need to wait until all genes are processed to get the final output.
`eq.gen.effect`	Whether assume equal genetic effects on different traits (default = False).

output: multivariate family KM (MF-KM) p-value

########################################################################################
### Examples for Multivariate (two) Continuous Traits in Familial GWAS Data using KM ###
########################################################################################
### Subject IDs are numeric ###
data("MFKM_numID")
#If Ninitial=1, the initial value "cor" is always equal to
#correlation(trait1|covariates, trait2|covariates).
obj1 <- MFKM_Null_Model(phenotype=mfkm_n_y$y, trait=mfkm_n_y$trait, yid=mfkm_n_y$id,
gid=mfkm_n_geneid$gid, fa=mfkm_n_geneid$fa, mo=mfkm_n_geneid$mo, covariates=NULL,
Ninitial=1)
#One should try multiple initial values in order to find max log-likelihood. The default
#is 10 times.
#This could be time consuming, depends on the sample size. The good thing is that null
#model only needs to be fitted once for the whole genome, so it's worth trying many
#initial values. The initial value with max logl can be saved in LogLikelihood.txt
#(LL.output="LogLikelihood.txt") for reuse.
pvalue1 <- MFKM(obj=obj1, genotypes=mfkm_n_gene, weights=NULL)
#If one wants to replicate the results, finds the initial value "cor" with the max
#loglikelihood in LogLikelihood.txt that is output by LL.output. The 1st column is
#"cor"; 2nd column is "log-likelihood".
obj1 <- MFKM_Null_Model(phenotype=mfkm_n_y$y, trait=mfkm_n_y$trait, yid=mfkm_n_y$id,
gid=mfkm_n_geneid$gid, fa=mfkm_n_geneid$fa, mo=mfkm_n_geneid$mo, covariates=NULL,
cor=0.687439771651474)
#This "cor" is calcualted based on 3 initial values, "Ninitial=3".
pvalue1 <- MFKM(obj=obj1, genotypes=mfkm_n_gene, weights=NULL)
#Introduce missing into covariates and outcome.
#When samples have missing values in outcome or covariates, those samples are used only for
#kinship calculation.
mfkm_n_covariates[1,] = NA
mfkm_n_y$y[4] = NA
obj1 <- MFKM_Null_Model(phenotype=mfkm_n_y$y, trait=mfkm_n_y$trait, yid=mfkm_n_y$id,
gid=mfkm_n_geneid$gid, fa=mfkm_n_geneid$fa, mo=mfkm_n_geneid$mo, covariates=mfkm_n_covariates,
Ninitial=1)
pvalue1 <- MFKM(obj=obj1, genotypes=mfkm_n_gene, weights=NULL)
#Read in a list of genes files instead of a big file containing all genes
obj <- MFKM_Null_Model(phenotype=mfkm_n_y$y, trait=mfkm_n_y$trait, yid=mfkm_n_y$id,
gid=mfkm_n_geneid$gid, fa=mfkm_n_geneid$fa, mo=mfkm_n_geneid$mo, covariates=NULL,
Ninitial=1)
gene <- split(mfkm_n_gene, mfkm_n_gene[,1])
for (k in 1:2) {
  gene[[k]]$gene <- as.character(gene[[k]]$gene)
  pvalue1 <- MFKM(obj=obj, genotypes=gene[[k]], weights=NULL)
}
### Subject IDs are character ###
data("MFKM_charID")
obj1 <- MFKM_Null_Model(phenotype=mfkm_c_y$y, trait=mfkm_c_y$trait,
yid=as.character(mfkm_c_y$id),
gid=as.character(mfkm_c_geneid$gid), fa=as.character(mfkm_c_geneid$fa),
mo=as.character(mfkm_c_geneid$mo), covariates=NULL, Ninitial=1)
pvalue1 <- MFKM(obj=obj1, genotypes=mfkm_c_gene, weights=NULL)