CCpop (version 1.0)

pairwise.assoc.test.unconstrained.chisq: Pairwise association tests

Description

Tests for joint association of a pair of SNP loci and a binary phenotype in case-only, case-control, case-population, and case-control-population designs, under various assumptions.

Usage

pairwise.assoc.test.unconstrained.chisq(t0, t1) pairwise.assoc.test.unconstrained.gsq(t0, t1) pairwise.assoc.test.case.only(t1) pairwise.assoc.test.ind.3d(t0, t1) pairwise.assoc.test.pure.unconstrained(t0, t1) pairwise.assoc.test.kpy(t0, t1, prevalence, pen.initial = NULL, pxx.initial = NULL) pairwise.assoc.test.hwe.le.kpy(t0, t1, prevalence,  pen.initial = NULL, f1.initial = NULL, f2.initial = NULL) pairwise.assoc.test.kpx.kpy(t0, t1, prevalence, pxx, pen.initial = NULL) pairwise.assoc.test.pop.kpy(t0, t1, tp, prevalence,  pen.initial = NULL, pxx.initial = NULL) pairwise.assoc.test.pop.hwe.le.kpy(t0, t1, tp1, tp2, prevalence,  pen.initial = NULL, f1.initial = NULL, f2.initial = NULL) pairwise.assoc.test.pure.pop.kpy(t0, t1, tp, prevalence,  pen.initial = NULL, pxx.initial = NULL) pairwise.assoc.test.pure.pop.hwe.le.kpy(t0, t1, tp1, tp2, prevalence,  pen.initial = NULL, f1.initial = NULL, f2.initial = NULL) conditional.assoc.test.pure.pop.hwe.le.kpy(t0, t1, tp1, tp2, prevalence,  pen.initial = NULL, f1.initial = NULL, f2.initial = NULL)

Arguments

t0

A 3x3 matrix of genotype counts in the controls cohort

t1

A 3x3 matrix of genotype counts in the cases cohort

tp

A 3x3 matrix of genotype counts in the population cohort

tp1

A 3-component vector of genotype counts for the first (t0/t1 rows) locus in the population cohort

tp2

A 3-component vector of genotype counts for the second (t0/t1 columns) locus in the population cohort

prevalence

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

pxx

A 3x3 matrix of known genotypic distribution in the population Pr(x1,x2)

pen.initial

A 3x3 matrix of initial guess for the phenotypic penetrance Pr(y=1|x1,x2)

pxx.initial

A 3x3 matrix of initial guess for the genotypic distribution in the population Pr(x1,x2)

f1.initial

Initial guess for the MAF at the first (rows) locus

f2.initial

Initial guess for the MAF at the second (columns) locus

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

pairwise.assoc.test.unconstrained.chisq -- Pearson's chi-squared test for the 2x9 contingency table of a standard case-control design. pairwise.assoc.test.unconstrained.gsq -- Likelihood ratio ("G") test for the 2x9 contingency table of a standard case-control design. pairwise.assoc.test.case.only -- The likelihood ratio ("G") test for the 3x3 contingency table of the two SNPs in cases only. pairwise.assoc.test.ind.3d -- The likelihood ratio ("G") test for the 2x3x3 contingency table of the two SNPs and phenotype value (i.e., a case-control design). pairwise.assoc.test.pure.unconstrained -- The logistic regression based GLRT for full model vs. main effects model (within a standard case-control design). pairwise.assoc.test.kpy -- The GLRT based on constrained maximum likelihood estimation assuming that phenotype prevalence in the population is known (within a standard case-control design). pairwise.assoc.test.hwe.le.kpy -- The GLRT based on constrained maximum likelihood estimation assuming Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE) in the general population, and that phenotype prevalence in the population is known (within a standard case-control design). pairwise.assoc.test.kpx.kpy -- The GLRT based on constrained maximum likelihood estimation assuming that marginal genotypic and phenotypic distributions in the population are both known (within a standard case-control design). pairwise.assoc.test.pop.kpy -- The GLRT based on constrained maximum likelihood estimation assuming that phenotype prevalence in the population is known (within a case-control-population design). pairwise.assoc.test.pop.hwe.le.kpy -- The GLRT based on constrained maximum likelihood estimation assuming Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE) in the general population, and that phenotype prevalence in the population is known (within a case-control-population design). pairwise.assoc.test.pure.pop.kpy -- A GLRT based on constrained maximum likelihood estimation assuming that phenotype prevalence is known (within a case-control-population design). The test contrasts a general pairwise association versus a logit-additive "main-effects" model. pairwise.assoc.test.pure.pop.hwe.le.kpy -- Like pairwise.assoc.test.pure.pop.kpy, but done under the assumption of HWE and LE. conditional.assoc.test.pure.pop.hwe.le.kpy -- A GLRT based on constrained maximum likelihood estimation assuming that phenotype prevalence is known and assuming HWE and LE (within a case-control-population design). This tests for a pairwise association above and beyond a marginal association of x1 alone, or of x2 alone (and the *maximum* p-value of these two options is returned, i.e., the test is conservative)

References

Kaufman, S., & Rosset, S. (2014). Exploiting Population Samples To Enhance Genome-Wide Association Studies of Disease. Genetics, 114 (early electronic).

See Also

marginal.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)