GeneScan3D.UKB.GLMM: Conduct GeneScan3D analysis on UKBB data using fitted null...

In Iuliana-Ionita-Laza/BIGKnock: BIobank-scale Gene-based association test via Knockoffs

Conduct GeneScan3D analysis on UKBB data using fitted null GLMM results.

Description

This function perform the gene-based test for each gene using the fitted null GLMM and estimated variance ratio obtained from SAIGE/SAIGE-Gene package. For binary traits, we conduct SPA gene-based tests to deal with imbalance case-control issues.

Usage

GeneScan3D.UKB.GLMM(
  G = G_gene_buffer,
  G.EnhancerAll = G_EnhancerAll,
  R = length(p_EnhancerAll),
  p_Enhancer = p_EnhancerAll,
  window.size = c(1000, 5000, 10000),
  pos = pos_gene_buffer,
  MAC.threshold = 10,
  MAF.threshold = 0.01,
  Gsub.id = Gsub.id,
  result.null.model.GLMM = result.null.model.GLMM,
  outcome = "C",
  sparseSigma = sparseSigma,
  ratio = ratio
)

Arguments

G	The genotype matrix in the gene buffer region, which is a n*p matrix where n is the number of individuals and p is the number of genetic variants in the gene buffer region.
G.EnhancerAll	The genotype matrix for R enhancers, by combining the genotype matrix of each enhancer by columns.
R	Number of enhancers.
p_Enhancer	Number of variants in R enhancers, which is a 1*R vector.
window.size	The 1-D window sizes in base pairs to scan the gene buffer region. The recommended window sizes are c(1000,5000,10000).
pos	The positions of genetic variants in the gene buffer region, an p dimensional vector. Each position corresponds to a column in the genotype matrix G and a row in the functional annotation matrix Z.
MAC.threshold	Threshold for minor allele count. Variants below MAC.threshold are ultra-rare variants. The recommended level is 10.
MAF.threshold	Threshold for minor allele frequency. Variants below MAF.threshold are rare variants. The recommended level is 0.01.
Gsub.id	The subject id corresponding to the genotype matrix, an n dimensional vector. The default is NULL, where the matched phenotype and genotype matrices are assumed.
result.null.model.GLMM	The fitted null GLMM results obtained from SAIGE/SAIGE-Gene package.
outcome	'C' for quantitative trait, 'D' for binary trait.
sparseSigma	n by n sparse Sigma matrix obtained from SAIGE/SAIGE-Gene package.
ratio	Variance ratio to calibrate test statistics,obtained from SAIGE/SAIGE-Gene package.

Value

GeneScan3D.Cauchy.pvalue	Cauchy combination p-values of all, common and rare variants for GeneScan3D analysis.
M	Number of 1D scanning windows.
minp	Minimum p-values of all, common and rare variants for 3D windows.
RE_minp	The regulartory elements in the 3D windows corresponding to the minimum p-values, for all, common and rare variants. 0 represents promoter and a number from 1 to R represents promoter and r-th enhancer.

Examples

data("GeneScan3D.UKB.GLMM.example")
result.null.model.GLMM=GeneScan3D.UKB.GLMM.example$result.null.model.GLMM
ratio=GeneScan3D.UKB.GLMM.example$ratio
sparseSigma=GeneScan3D.UKB.GLMM.example$sparseSigma
G_gene_buffer=GeneScan3D.UKB.GLMM.example$G_gene_buffer
pos_gene_buffer=GeneScan3D.UKB.GLMM.example$pos_gene_buffer
G_gene_buffer_knockoff1=GeneScan3D.UKB.GLMM.example$G_gene_buffer_knockoff1
G_enhancer=GeneScan3D.UKB.GLMM.example$G_enhancer
G_enhancer_knockoff1=GeneScan3D.UKB.GLMM.example$G_enhancer_knockoff1
Gsub.id=result.null.model.GLMM$sampleID

G_EnhancerAll=G_enhancer
p_EnhancerAll=dim(G_enhancer)[2]
G_EnhancerAll_knockoff1=G_enhancer_knockoff1
p_EnhancerAll_knockoff1=dim(G_enhancer_knockoff1)[2]
R=1

result.GeneScan3D_orginal=GeneScan3D.UKB.GLMM(G=G_gene_buffer,
                                             G.EnhancerAll=G_EnhancerAll,
                                             R=R,
                                             p_Enhancer=p_EnhancerAll,
                                             window.size=c(1000,5000,10000),
                                             pos=pos_gene_buffer,
                                             MAC.threshold=10,
                                             MAF.threshold=0.01,
                                             Gsub.id=Gsub.id,
                                             result.null.model.GLMM,
                                             outcome='C',
                                            sparseSigma=sparseSigma,
                                            ratio=ratio)
result.GeneScan3D_orginal$GeneScan3D.Cauchy.pvalue[1]

Iuliana-Ionita-Laza/BIGKnock documentation built on July 31, 2023, 3:50 p.m.