GCTA_singleValue: GCTA_singleValue

Description

Function to return the heritability of an external phenotype for a single dataset

Usage

GCTA_singleValue(
  fileLoc,
  externalVar,
  gctaPath,
  remlAlg = 0,
  maxRemlIt = 100,
  numCovars = NULL,
  catCovars = NULL
)

Value

Row of GREML output containing heritability point estimate of external data and standard error

Arguments

fileLoc: absolute file path to bed, bim, and fam files, including prefix
externalVar: vector of length n of external variable values with no ID column; must be in the same sample order as bed, bim, fam files
gctaPath: absolute path to GCTA software
remlAlg: algorithm to run REML iterations in GCTA; 0 = average information (AI), 1 = Fisher-scoring, 2 = EM; default is 0 (AI)
maxRemlIt: the maximum number of REML iterations; default is 100
numCovars: n x c_n matrix of numerical covariates to adjust heritability model for; must be in same person order as fam file; default is NULL
catCovars: n x c_c matrix of categorical covariates to adjust heritability model for; must be in same person order as fam file; default is NULL

Author

Katelyn Queen, kjqueen@usc.edu

Details

Genome-wide Complex Trait Analysis (GCTA) is a suite of C++ functions. In order to use the GCTA functions, the user must specify the absolute path to the GCTA software, which can be downloaded from the Yang Lab website here.

Here, we use GCTA's Genomics REstricted Maximum Likelihood (GREML) method to estimate the heritability of an external phenotype.

References

Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011 Jan 7;88(1):76-82. doi: 10.1016/j.ajhg.2010.11.011. Epub 2010 Dec 17. PMID: 21167468; PMCID: PMC3014363.

Examples

Run this code


externalVar <- c()

# run function; input data before running
if (FALSE) OSCA_singleValue(fileLoc = "pathHere", 
                  externalVar = externalVar,
                  gctaPath = "pathHere")

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