CCpop (version 1.0)

marginal.assoc.test.unconstrained.chisq: Marginal association tests

Description

Tests for association of single SNP locus and a binary phenotype in case-control, case-population, and case-control-population designs, under various assumptions.

Usage

marginal.assoc.test.unconstrained.chisq(t0, t1) marginal.assoc.test.unconstrained.gsq(t0, t1) marginal.assoc.test.hwe.in.controls(t0, t1) marginal.assoc.test.pop.hwe.kpy(t0, t1, tp, prevalence,  pen.initial = NULL, f.initial = NULL) marginal.assoc.test.kpx.kpy(t0, t1, prevalence, px, pen.initial = NULL)

Arguments

t0

A 3-component vector of genotype counts in the controls cohort

t1

A 3-component vector of genotype counts in the cases cohort

tp

A 3-component vector of genotype counts in the population cohort

prevalence

The population prevalence of the phenotype Pr(y=1) < 0.5

px

A 3-component vector of known genotypic distribution in the population Pr(x)

pen.initial

A 3-component vector of initial guess for phenotypic penetrance Pr(y=1|x)

f.initial

Initial guess for the SNP's minor allele frequency (MAF)

Value

p.value

P-value indicating significance of the association

statistic

Value of the relevant test statistic

pen

The estimated case-control peneterance Pr(y=1|x,ascertained)

Details

marginal.assoc.test.unconstrained.chisq -- Pearson's chi-squared test for the 2x3 contingency table of a standard case-control design. marginal.assoc.test.unconstrained.gsq -- Likelihood ratio ("G") test for the 2x3 contingency table of a standard case-control design. marginal.assoc.test.hwe.in.controls -- A test for association in a standard case-control design, assuming HWE among the controls (Chen and Chatterjee, 2007). marginal.assoc.test.pop.hwe.kpy -- GLRT based on constrained maximum likelihood estimation assuming HWE in the general population, and that disease prevalence is known. marginal.assoc.test.kpx.kpy -- GLRT based on constrained maximum likelihood estimation assuming the marginal genotypic and phenotypic distributions are known.

References

Chen J., & Chatterjee, N. (2007). Exploiting hardy-weinberg equilibrium for effcient screening of single SNP associations from case-control studies. Human heredity, 63(3-4), 196-204. Kaufman, S., & Rosset, S. (2014). Exploiting Population Samples To Enhance Genome-Wide Association Studies of Disease. Genetics, 114 (early electronic).

See Also

pairwise.assoc.tests,

Examples

  ## 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)