Package 'RepeatABEL'

Title: GWAS for Multiple Observations on Related Individuals
Description: Performs genome-wide association studies (GWAS) on individuals that are both related and have repeated measurements. For each Single Nucleotide Polymorphism (SNP), it computes score statistic based p-values for a linear mixed model including random polygenic effects and a random effect for repeated measurements. The computed p-values can be visualized in a Manhattan plot. For more details see Ronnegard et al. (2016) <doi:10.1111/2041-210X.12535> and for more examples see <https://github.com/larsronn/RepeatABEL_Tutorials>.
Authors: Lars Ronnegard [aut, cre]
Maintainer: Lars Ronnegard <[email protected]>
License: GPL
Version: 2.0
Built: 2026-06-05 07:33:52 UTC
Source: https://github.com/cran/RepeatABEL

Help Index

Extracts the chromosome numbers

Description

Gets the chromosome numbers.

Usage

chromosome(genabel.data)

Arguments

genabel.data

A GenABEL-like data of class gwaa.data2.

Value

Returns an array of chromosome numbers

Author(s)

Lars Ronnegard

Computes a Genetic Relationship Matrix from a GenABEL-like object

Description

One method for GRM computations implemented.

Usage

compute.GRM(gen.data)

Arguments

gen.data

The GenABEL-like object.

Value

Returns a genomic relationship matrix.

Author(s)

Lars Ronnegard

Constructs the (co)variance matrix for y

Description

Constructs the (co)variance matrix for y.

Usage

constructV(Z, RandC, ratio)

Arguments

Z

The incidence matrix for the random effects column binded with the Cholesky of the GRM
RandC

The number of columns in the two matrices combined in Z.
ratio

The ratios between random effect variances and the residual variance.

Value

Returns a (co)variance matrix of y.

Author(s)

Lars Ronnegard

Creates a gwaa.data2 object

Description

Creates a gwaa.data2 object from input.

Usage

Create_gwaa_data2(genotypes, chromosome = NULL, map = NULL, phenotypes = NULL)

Arguments

genotypes

A matrix with genotype values coded as (0,1,2) or (-1,0,1)
chromosome

An array of characters for the chromosomes. Length equal to the number of SNPs.
map

An array with the order of the SNPs.
phenotypes

A data frame including columns with phenotypes and a column with ids, called "id"

Value

Returns a gwaa.data2-object.

Author(s)

Lars Ronnegard

Creates a scan.gwaa2 object

Description

Creates a scan.gwaa2 object from the rGLS output.

Usage

Create_gwaa_scan2(data, P1df, SNP.eff)

Arguments

data

A gwaa.data2 object
P1df

P-values computed from external analysis
SNP.eff

Estimated additive SNP effects

Value

Returns a scan.gwaa2 object.

Author(s)

Lars Ronnegard

Function to estimate lambda

Description

Estimates lambda from P-values. Most code copied from the archived GenABEL package

Usage

estlambda(data, plot = FALSE, method = "regression", filter = TRUE)

Arguments

data

An array of P-values
plot

Logical. TRUE to produce a plot
method

Either "regression" or "median".
filter

Logical. If TRUE the extreme P-values are not included in the estimate of lambda.

Value

Returns a list with estimate and standard error.

Author(s)

Lars Ronnegard

An S4 class to represent GWAS input data

Description

An S4 class to represent GWAS input data

Slots

phdata

Phenotype information including id

gtdata

object of class snp.data with genotype information

Extracts the id names

Description

Gets the idnames.

Usage

idnames(genabel.data)

Arguments

genabel.data

A GenABEL-like data of class gwaa.data2.

Value

Returns an array with the names of the individuals (as character).

Author(s)

Lars Ronnegard

A function to subset an gwaa.data2 object

Description

Extracts a subset of the data.

Usage

keep_gwaa_data(genabel.data, indx.keep = NULL)

Arguments

genabel.data

A GenABEL-like data of class gwaa.data2.
indx.keep

Indeces to extract.

Author(s)

Lars Ronnegard

Extracts the map information

Description

Gets the map.

Usage

map(genabel.data)

Arguments

genabel.data

A GenABEL-like data of class gwaa.data2.

Author(s)

Lars Ronnegard

Extracts the number of ids

Description

Gets nids.

Usage

nids(genabel.data)

Arguments

genabel.data

A GenABEL-like data of class gwaa.data2.

Value

Returns the number of individuals.

Author(s)

Lars Ronnegard

Function to plot P-values as a Manhattan plot

Description

Creates a Manhattan plot

Usage

## S3 method for class 'scan.gwaa2'
plot(
  x,
  y,
  ...,
  ystart = 0,
  col = c("blue", "green"),
  sort = TRUE,
  ylim,
  main = NULL
)

Arguments

x

A scan.gwaa2 object created by the rGLS function
y

A parameter not used in the current version
...

Possible additional parameters (not used in the current version)
ystart

Lowest value on the y-axis
col

Default is c("blue","green")
sort

Logical. If TRUE the SNPs are sorted before plotting.
ylim

Limits of the y-axis
main

Plot title

Value

No return value, called for side effects

Author(s)

Lars Ronnegard

Fits a linear mixed model (without fixed SNP effects) and computes the fitted variance-covariance matrix for later use in the rGLS function.

Description

Uses a GenABEL-like object and phenotype data as input. The model is fitted using the hglm function in the hglm package.

Usage

preFitModel(
  fixed = y ~ 1,
  random = ~1 | id,
  id.name = "id",
  genabel.data,
  phenotype.data,
  corStruc = NULL,
  GRM = NULL,
  Neighbor.Matrix = NULL,
  verbose = TRUE
)

Arguments

fixed

A formula including the response and fixed effects
random

A formula for the random effects
id.name

The column name of the IDs in phen.data
genabel.data

An GenABEL-like object including marker information. This object has one observation per individual.
phenotype.data

A data frame including the repeated observations and IDs.
corStruc

A list specifying the correlation structure for each random effect. The options are: "Ind" for iid random effects, "GRM" for a correlation structure given by a genetic relationship matrix, or "CAR" for a spatial correlation structure given by a Conditional Autoregressive model specified by a neighborhood matrix.
GRM

A genetic relationship matrix. If not specified whilst the "GRM" option is given for corStruc then the GRM is computed internally within the function.
Neighbor.Matrix

A neighborhood matrix having non-zero value for an element (i,j) where the observations i and j come from neighboring locations. The diagonal elements should be zero.
verbose

If TRUE the progress of the computations is printed.

Value

Returns a list including the fitted hglm object fitted.hglm, the variance-covariance matrix V and the ratios between estimated variance components for the random effects divided by the residual variance, ratio.

Author(s)

Lars Ronnegard

Examples

####### FIRST EXAMPLE USING GRM #############
 set.seed(1234)
 Gen.Data <- simulate_gendata(n=100, p=200)
 Phen.Data <- simulate_PhenData(y ~ 1, genabel.data=Gen.Data,
                               n.obs=rep(4, nids(Gen.Data)), SNP.eff=2, SNP.nr=100, VC=c(1,1,1))
 GWAS1 <- rGLS(y ~ 1, genabel.data = Gen.Data, phenotype.data = Phen.Data)
 plot(GWAS1, main="")
 summary(GWAS1)
 #Summary for variance component estimation without SNP effects
 summary(GWAS1@call$hglm)
 #The same results can be computed using the preFitModel as follows
 fixed = y ~ 1
 Mod1 <- preFitModel(fixed, random=~1|id, genabel.data = Gen.Data,
                     phenotype.data = Phen.Data, corStruc=list( id=list("GRM","Ind") ))
 GWAS1b <- rGLS(fixed, genabel.data = Gen.Data,
                phenotype.data = Phen.Data, V = Mod1$V)
 plot(GWAS1b, main="Results using the preFitModel function")

GWAS for Studies having Repeated Measurements on Related Individuals

Description

It is used to perform genome-wide association studies on individuals that are both related and have repeated measurements. The function computes score statistic based p-values for a linear mixed model including random polygenic effects and a random effect for repeated measurements. A p-value is computed for each marker and the null hypothesis tested is a zero additive marker effect.

Usage

rGLS(
  formula.FixedEffects = y ~ 1,
  genabel.data,
  phenotype.data,
  id.name = "id",
  GRM = NULL,
  V = NULL,
  memory = 1e+08,
  verbose = TRUE
)

Arguments

formula.FixedEffects

Formula including the response variable and cofactors as fixed effects.
genabel.data

A GenABEL-like object including marker information. This object has one observation per individuals.
phenotype.data

A data frame including the repeated observations and IDs.
id.name

The column name of the IDs in phen.data
GRM

An optional genetic relationship matrix (GRM) can be included as input. Otherwise the GRM is computed within the function.
V

An optional (co)variance matrix can be included as input. Otherwise it is computed using the hglm function.
memory

Used to optimize computations. The maximum number of elements in a matrix that can be stored efficiently.
verbose

If TRUE the progress of the computations is printed.

Details

A generalized squares (GLS) is fitted for each marker given a (co)variance matrix V. The computations are made fast by transforming the GLS to an ordinary least-squares (OLS) problem using an eigen-decomposition of V. The OLS are computed using QR-factorization. If V is not specified then a model including random polygenic effects and permanent environmental effects is fitted (using the hglm package) to compute V. A GenABEL-like object (scan.gwaa2 class) is returned (including also the hglm results). Let e.g. GWAS1 be an object returned by the rGLS function. Then a Manhattan plot can be produced by calling plot(GWAS1) and the top SNPs using summary(GWAS1).
The results from the fitted linear mixed model without any SNP effect included are produced by calling summary(GWAS1@call$hglm).

Value

Returns a gwaa.scan2-object.

Author(s)

Lars Ronnegard

Examples

set.seed(1234)
 Gen.Data <- simulate_gendata(n=100, p=200)
 Phen.Data <- simulate_PhenData(y ~ 1, genabel.data=Gen.Data,
                               n.obs=rep(4, nids(Gen.Data)), SNP.eff=2, SNP.nr=100, VC=c(1,1,1))
 GWAS1 <- rGLS(y ~ 1, genabel.data = Gen.Data, phenotype.data = Phen.Data)
 plot(GWAS1, main="")
 summary(GWAS1)
 #Summary for variance component estimation without SNP effects
 summary(GWAS1@call$hglm)

An S4 class to represent SNP data

Description

An S4 class to represent SNP data

Slots

results

The results from the rGLS function as a data.frame

lambda

Computed inflation factor as list

idnames

Idnames as character

map

SNP order as numeric

chromosome

The chromosome name for each SNP as numeric

call

The call made by rGLS as call

family

The assumed distribution of the outcome. Only "gaussian" allowed.

Function to simulate genotype data for the RepeatABEL package.

Description

The function simulates n individuals and p SNPs, with linkage disequilibrium (LD) given by the LD-parameter

Usage

simulate_gendata(n = 100, p = 1000, LD = 0.9, n.chrom = 1)

Arguments

n

Number of individuals.
p

Number of SNPs.
LD

An LD-parameter. LD=1 gives complete LD and LD=0 no LD.
n.chrom

The size of a simulated SNP.effect.

Value

Returns a gwaa.data2 object.

Author(s)

Lars Ronnegard

Examples

set.seed(1234)
 Gen.Data <- simulate_gendata(n=100, p=200)

Simulation function for the RepeatABEL package.

Description

The function takes a GenABEL-like object (class gwaa.data2) as input and generates simulated phenotypic values for related individuals having repeated obserevations.

Usage

simulate_PhenData(
  formula.FixedEffects = y ~ 1,
  genabel.data,
  n.obs,
  SNP.eff = NULL,
  SNP.nr = NULL,
  beta = NULL,
  VC = c(1, 1, 1),
  GRM = NULL,
  sim.gamma = FALSE
)

Arguments

formula.FixedEffects

A formula including the name of the simulated variable as response, and cofactors as fixed effects.
genabel.data

A GenABEL-like object of class gwaa.data2.
n.obs

A vector including the number of observations per individual. The length of n.obs must be equal to the number if individuals in genabel.data.
SNP.eff

The size of a simulated SNP.effect.
SNP.nr

The SNP genotype that the SNP effect is simulated on. SNP.nr=i is the i:th SNP.
beta

The simulated fixed effects. Must be equal to the number of cofactors simulated (including the intercept term).
VC

A vector of length 3 including the simulated variances of the polygenic effect, permanent environmental effect and residuals, respectively.
GRM

An optional input where the Genetic Relationship Matrix can be given. Otherwise it is computed using the GenABEL package.
sim.gamma

A logical parameter specifying whether the residuals shuld be simulated from a gamma distribution or not. If specified as TRUE then residuals are drawn from a gamma distribution with variance equal to the residual variance specified in VC[3]

Value

Returns a data frame including the simulated phenotypic values, cofactors and IDs.

Author(s)

Lars Ronnegard

Examples

#Simulate 4 observations per individual
 set.seed(1234)
 Gen.Data <- simulate_gendata(n=100, p=200)
 Phen.Data <- simulate_PhenData(y ~ 1, genabel.data=Gen.Data,
                               n.obs=rep(4, nids(Gen.Data)), SNP.eff=2, SNP.nr=100, VC=c(1,1,1))
 GWAS1 <- rGLS(y ~ 1, genabel.data = Gen.Data, phenotype.data = Phen.Data)
 plot(GWAS1, main="Simulated Data Results")

Imputes column means to missing genotypes

Description

Imputes column means to missing genotypes.

Usage

SmoothSNPmatrix(SNP)

Arguments

SNP

A matrix including SNP coding.

Author(s)

Lars Ronnegard

An S4 class to represent SNP data

Description

An S4 class to represent SNP data

Slots

nids

The number of ids as numeric

idnames

The idnames as character

nsps

The number of SNPs as numeric

snpnames

The SNP names as character

map

The order of the SNPS as numeric

chromosome

The chromosome names for each SNP as numeric

gtps

The matrix with SNP coding

Extracts the snpnames

Description

Gets the SNP names.

Usage

snpnames(genabel.data)

Arguments

genabel.data

A GenABEL-like data of class gwaa.data2.

Author(s)

Lars Ronnegard

Summary function for the rGLS output

Description

Creates a Manhattan plot using a slimmed version of the summary.scan.gwaa() function in the GenABEL package

Usage

## S3 method for class 'scan.gwaa2'
summary(object, ...)

Arguments

object

A scan.gwaa2 object created by the rGLS function
...

Possible additional parameters (not used in the current version)

Value

Returns a data frame with estimated SNP effects, standard errors, test-statistic values, p-values, and corrected p-values.

Author(s)

Lars Ronnegard