CCpop-package {CCpop} | R Documentation |
Tests for marginal and pairwise SNP associations with binary phenotypes in case-control, case-population, and case-control-population studies.
Package: | CCpop |
Type: | Package |
Version: | 1.0 |
Date: | 2014-03-18 |
License: | GPL-2 |
Inputs to test functions are vectors (for marginal tests) and matrices (for pairwise tests) of genotypic counts and are given separately for cases, controls, and population cohorts (where relevant). The constrained maximum likelihood estimation based tests (Kaufman and Rosset, 2014) also require a value for the known phenotypic prevalence in the population (a crude estimate is usually sufficient). When applicable, tests assuming Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE) will be considerably more powerful.
See marginal.assoc.tests for single SNP tests, and pairwise.assoc.tests for pairwise joint, pure-interaction, and conditional tests.
Shachar Kaufman <shachark@post.tau.ac.il>
Kaufman, S., & Rosset, S. (2014). Exploiting Population Samples To Enhance Genome-Wide Association Studies of Disease. Genetics, genetics-114.
## An example marginal/pairwise association # Controls t0 = matrix(c(375, 240, 46, 640, 405, 62, 300, 169, 19), nrow = 3, byrow = TRUE) # Cases t1 = matrix(c(317, 162, 15, 459, 209, 22, 120, 76, 13), nrow = 3, byrow = TRUE) # Independent population sample, marginalized for SNP1 and SNP2 tp1 = c(2410, 4253, 1945) tp2 = c(4972, 3140, 496) ## The prevalence of the studied disease in the population prevalence = 0.001 marginal.assoc.test.pop.hwe.kpy(t0 = rowSums(t0), t1 = rowSums(t1), tp = tp1, prevalence) marginal.assoc.test.pop.hwe.kpy(t0 = colSums(t0), t1 = colSums(t1), tp = tp2, prevalence) pairwise.assoc.test.pop.hwe.le.kpy(t0, t1, tp1, tp2, prevalence) conditional.assoc.test.pure.pop.hwe.le.kpy(t0, t1, tp1, tp2, prevalence)