Package 'colocPropTest'

adjust LD for variable sample size

Description

adjust LD for variable sample size

Usage

adjust_LD(S, LD)

Arguments

S	coloc style dataset, with additional entries n0 and n1 which are *vectors* giving the number of cases and controls genotyped at each SNP
LD	matrix of LD, with dimnames given by snps in S$snp

Value

adjusted LD matrix

Examples

library(coloc)

data(coloc_test_data)
attach(coloc_test_data)
LD=D1$LD
dimnames(LD)=list(D1$snp,D1$snp)
D1$type="cc"
D1$s=.5
D1$n1=D1$N * sample(c(0.25,.5),length(D1$snp), replace=TRUE)
D1$n0=rep(0.5*D1$N,length(D1$snp))
aLD=colocPropTest::adjust_LD(D1,LD)
LD[1:6,1:6]
aLD[1:6,1:6]
detach(coloc_test_data)

---

Proportional colocalisation testing supplying only a pair of regression coefficients.

Description

Proportional colocalisation testing supplying only a pair of regression coefficients.

Usage

estprop(b1, b2, V1, V2)

Arguments

b1	regression coefficients for trait 1, expect length(b1)=2
b2	regression coefficients for trait 2, expect length(b2)=2
V1	2x2 variance-covariance matrix for trait 1
V2	2x2 variance-covariance matrix for trait 2

Value

a list, containing * result: the test statistic * plot.data: dataset for plotting the input data * plot.eta: dataset for plotting chisq as a function of theta or eta

Author(s)

Chris Wallace

---

Proportional colocalisation testing

Description

This should return the same as estprop for a pair of snps, but is slower. Left here for checking. Also accomodates more than two snps.

Usage

estprop_slow(b1, b2, V1, V2)

Arguments

b1	regression coefficients for trait 1
b2	regression coefficients for trait 2
V1	variance-covariance matrix for trait 1
V2	variance-covariance matrix for trait 2

Value

a list, containing the test statistic and two datasets for plotting the input data or eta

Author(s)

Chris Wallace

---

keep snp subset of coloc dataset

Description

keep snp subset of coloc dataset

Usage

keep_from_S(S, keep)

Arguments

S	coloc dataset
keep	snps to keep

Value

subset of coloc dataset

---

logp

Description

uses logs in calculation to avoid numerical issues with very small std errors / p values

Usage

lp(beta, se)

Arguments

beta	coefficient
se	std error of coefficient

Value

-log10 p

---

create variance-covariance matrix for pair of marginal beta + vbeta, given estimate of r between snps

Description

create variance-covariance matrix for pair of marginal beta + vbeta, given estimate of r between snps

Usage

marg_with_V(beta, vbeta, rho)

Arguments

beta	vector of two coefficients at two snps
vbeta	vector of two variance of coefficients at the same two snps
rho	LD (r) between the two snps

Value

list of coefficient & variance-covariance matrix

---

Helper function to adjust LD parameter r for differential sample size between snps

Description

Estimate the r between effect estimates at snps which were genotyped on different sets of cases and controls. The adjusted r will be nform(...) * r (where r is the population correlation between snps).

Usage

nform(n0a, n1a, n0b, n1b, n0ab = pmin(n0a, n0b), n1ab = pmin(n1a, n1b))

Arguments

n0a	number of controls with data at snp a
n1a	number of cases with data at snp a
n0b	number of controls with data at snp b
n1b	number of cases with data at snp b
n0ab	number of controls with data at both snps a and b
n1ab	number of cases with data at both snps a and b

Value

proportionality constant that depends on sample size.

---

draw two ellipses

Description

draw two ellipses

Usage

plot_ellipses(
  b1,
  vb1,
  b2,
  vb2,
  legend = c("inferred", "observed"),
  include_origin = FALSE,
  ...
)

Arguments

b1	ellipse 1 centre (2d)
vb1	ellipse 1 vcov matrix
b2	ellipse 2 centre (2d)
vb2	ellipse 2 vcov matrix
legend	character vector length 2 naming ellipse 1 and 2
include_origin	if TRUE, ensure plot includes (0,0)
...	arguments passed to plot()

Value

draw ellipses on current graphics device

Author(s)

Chris Wallace

Examples

plot_ellipses(b1=c(5,5), vb1=diag(2),
              b2=c(2,2), vb2=matrix( c(1,0.5,0.5,1), 2, 2 ),
              legend=c("circle", "ellipse"),
              include.origin=TRUE)

---

run proportional tests on extreme subset of snp pairs from two coloc style datasets. Of all functions in this package, this is the main one that should be used.

Description

run proportional tests on extreme subset of snp pairs from two coloc style datasets. Of all functions in this package, this is the main one that should be used.

Usage

run_proptests(
  S1,
  S2,
  LD,
  topsnps = "auto",
  r2.thr = 0.95,
  maxtests = 10000,
  nauto = 200,
  adjust_n = FALSE
)

Arguments

S1	coloc dataset 1
S2	coloc dataset 2
LD	LD matrix - rownames, colnames capture the snps and S1$snp[j] must be represented
topsnps	list of topsnps to be considered for testing or, if "auto", will be automatically selected
r2.thr	r2 threshold for initial tagging step - includes only one of any set of snps in mutually high LD with r2 > r2.thr
maxtests	maximum number of test pairs to consider. if more than maxtests pairs available, will select a random sample.
nauto	number of snps to use when automatically defining topsnps. only has an effect if topsnps=="auto"
adjust_n	TRUE if you want to adjust for variable sample size between snps. This is only set up for case control data at the moment (ask if you need quantitative) and requires that you supply separately the number of cases and controls at each snp in each dataset, as vector elements of the lists called n1 (cases) and n0 (controls)

Value

data.table containing the tests run

Author(s)

Chris Wallace

Examples

library(colocPropTest)
library(coloc)
data(coloc_test_data)
attach(coloc_test_data)
LD=D1$LD
dimnames(LD)=list(D1$snp,D1$snp)
results=run_proptests(D1,D2,LD=LD,topsnps=D1$snp,maxtests=100)
min(results$fdr)

---

Derive tag SNPs using heirarchical clustering

Description

Uses complete linkage and the hclust function to define clusters, then cuts the tree at 1-tag.threshold

Usage

tag(r2, r2_threshold = 0.95, quiet = FALSE, method = "complete")

Arguments

r2	matrix of rsquared values
r2_threshold	only 1 of a set of snps with r2 > r2_threshold will be kept
quiet	if FALSE (default), show progress messages
method	method used for heirarchical clustering. See hclust for options.

Value

character vector, names are snps, values are the tag for each SNP

Author(s)

Chris Wallace

---

run proportional test directly on marginal test stats from coloc datasets

Description

run proportional test directly on marginal test stats from coloc datasets

Usage

tester_marg(j, S1, S2, LD1, LD2 = LD1)

Arguments

j	indices of thw two snps
S1	coloc dataset 1
S2	coloc dataset 2
LD1	LD matrix for dataset 1 - rownames, colnames capture the snps and S1$snp[j] must be represented
LD2	LD matrix for dataset 2 - rownames, colnames capture the snps and S2$snp[j] must be represented. if not supplied, defaults to LD1

Value

result from estprop

---