gQTLstats: computationally efficient analysis and

In gQTLstats: gQTLstats: computationally efficient analysis for eQTL and allied studies

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title: "gQTLstats: computationally efficient analysis and interpretation of large eQTL, mQTL, etc. archives" author: "Vincent J. Carey, stvjc at channing.harvard.edu" date: "Jun 2015" output: BiocStyle::html_document: highlight: pygments number_sections: yes theme: united toc: yes BiocStyle::pdf_document: toc: yes number_sections: yes

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BiocStyle::markdown()

Introduction

The software in this package aims to support refinements and functional interpretation of members of a collection of association statistics on a family of feature $\times$ genome hypotheses. provide a basis for refinement or functional interpretation.

We take for granted the use of the gQTL* infrastructure for testing and management of test results. We use for examples elements of the r Biocexptpkg("geuvPack") and r Biocexptpkg("geuvStore2") packages.

Basic infrastructure for statistics on a distributed store of eQTL results

We work with a ciseStore instance based on a small subset of transcriptome-wide cis-eQTL tests for GEUVADIS FPKM data. The overall testing procedure was conducted for all SNP:probe pairs for which SNP minor allele frequency (MAF) is at least 1\% and for which the minimum distance between SNP and either boundary of the gene coding region for the probe is at most 1 million bp.

suppressPackageStartupMessages({
library(SummarizedExperiment)
library(Homo.sapiens)
library(org.Hs.eg.db)
library(geuvStore2)
library(gQTLBase)
library(gQTLstats)
})
```r
library(geuvStore2)
library(gQTLBase)
library(gQTLstats)
library(parallel)
nco = detectCores()
library(doParallel)
registerDoSEQ()
if (.Platform$OS.type != "windows") {
  registerDoParallel(cores=max(c(1, floor(nco/2))))
}
prst = makeGeuvStore2() 

Estimation of quantiles of the distribution of observed associations

Quantile estimation is very memory-efficient, based on a temporary ff representation of the vector of all association test results.

qassoc = storeToQuantiles(prst, field="chisq",
    probs=c(seq(0,.999,.001), 1-(c(1e-4,1e-5,1e-6))))
tail(qassoc)

Estimation of histogram of the distribution of association scores under permutation

Because we compute fixed breaks, contributions to the overall histogram can be assembled in parallel, with small footprint. This is a tremendous reduction of data.

hh = storeToHist( prst, breaks= c(0,qassoc,1e9) )
tail(hh$counts)

Computing FDR from a gqtlStore

FDR computation is post-hoc relative to filtering that need not be specified prior to testing. For illustration, we survey the results in r Biocexptpkg("geuvStore2") to obtain FDRs for each SNP:probe pair in two forms. First, we obtain FDR without any filtering. Second, we compute an a FDR for those SNP:probe pairs separated by at most 500kb, and for which the MAF for the SNP is at least 5 per cent.

rawFDR = storeToFDR(prst, 
   xprobs=c(seq(.05,.95,.05),.975,.990,.995,.9975,.999, 
   .9995, .9999, .99999) )
```r
dmfilt = function(x)  # define the filtering function
     x[ which(x$MAF >= 0.05 & x$mindist <= 500000) ] 
```r
filtFDR = storeToFDR(prst, 
   xprobs=c(seq(.05,.95,.05),.975,.990,.995,.9975,.999, 
   .9995, .9999, .99999), filter = dmfilt )

rawFDR
filtFDR

The filtering leads to a lower FDR for a given strength of association. This is an inspiration for sensitivity analysis. Even with 5 million observations there is an effect of histogram bin selection in summarizing the permutation distribution of association. This can be seen fairly clearly in the wiggliness of the trace over the unfiltered association score:FDR plot.

rawtab = getTab(rawFDR)
filttab = getTab(filtFDR)
 plot(rawtab[-(1:10),"assoc"], 
      -log10(rawtab[-(1:10),"fdr"]+1e-6), log="x", axes=FALSE,
  xlab="Observed association", ylab="-log10 plugin FDR")
 axis(1, at=c(seq(0,10,1),100,200))
 axis(2)
 points(filttab[-(1:10),1], -log10(filttab[-(1:10),2]+1e-6), pch=2)
 legend(1, 5, pch=c(1,2), legend=c("all loci", "MAF >= 0.05 & dist <= 500k"))

We'll address this below by fitting smooth functions for the score:FDR relationship.

Estimates of FDR at the probe level

The storeToFDRByProbe FDR function examines the maximal association score by gene, for observed and permuted measures.
Good performance of this procedure is obtained by using group_by and summarize utilities of r CRANpkg("dplyr"). Iteration employs r CRANpkg("foreach").

fdbp = storeToFDRByProbe( prst, xprobs=c(seq(.025,.975,.025),.99))
tail(getTab(fdbp),5)

fdAtM05bp = storeToFDRByProbe( prst, filter=function(x) x[which(x$MAF > .05)],
   xprobs=c(seq(.025,.975,.025),.99))
tail(getTab(fdAtM05bp),5)

Modeling the association-FDR relationship to support efficient variant selection and annotation

Choosing a smooth model for the association:FDR relationship

We'll focus here on all-pairs analysis, with and without filtering.

Especially in this small example there will be some wiggling or even non-monotonicity in the trace of empirical FDR against association. We want to be able to compute the approximate FDR quickly and with minimal assumptions and pathology. To accomplish this, we will bind an interpolating model to the FDR estimates that we have. Interpolation will be accomplished with scatterplot smoothing in the r CRANpkg("mgcv") framework.

The code that is used to fit the interpolating model is

 fdrmod = gam(-log10(fdr+fudge)~s(assoc,bs="tp"), data=...,
    subset=assoc<(1.1*maxch))

where fudge defaults to 1e-6 and maxch defaults to 30

library(mgcv)
rawFDR = setFDRfunc(rawFDR)
filtFDR = setFDRfunc(filtFDR)
par(mfrow=c(2,2))
txsPlot(rawFDR)
txsPlot(filtFDR)
directPlot(rawFDR)
directPlot(filtFDR)

More work is needed on assessing tolerability of relative error in FDR interpolation.

Enumerating significant cis-eQTL in a store

Recall that dmfilt is a function that obtains the SNP-probe pairs for which SNP has MAF at least five percent and SNP-probe distance at most 500kbp.

We use the FDRsupp instances with ciseStore to list the SNP-probe pairs with FDR lying beneath a given upper bound.

Unfiltered pairs:

rawEnum = enumerateByFDR(prst, rawFDR, threshold=.05) 
rawEnum[order(rawEnum$chisq,decreasing=TRUE)[1:3]]
length(rawEnum)

A small quantity of metadata is bound into the resulting GRanges instance.

names(metadata(rawEnum))

Pairs meeting MAF and distance conditions are obtained with a filter setting to the enumerating function.

filtEnum = enumerateByFDR(prst, filtFDR, threshold=.05,
   filter=dmfilt) 
filtEnum[order(filtEnum$chisq,decreasing=TRUE)[1:3]]
length(filtEnum)

Sensitivity analysis for eQTL enumeration

The yield of an enumeration procedure depends on filtering based on SNP-gene distance and SNP MAF. This can be illustrated as follows, with minimal computational effort owing to the retention of genome-scale permutations and the use of the plug-in FDR algorithm.

data(sensByProbe) # see example(senstab) for construction approach
tab = senstab( sensByProbe )
plot(tab)

If we wish to maximize the yield of eQTL enumeration at FDR at most 0.05, we can apply a filter to the store.

flens = storeApply( prst, function(x) {
    length(x[ which(x$MAF >= .08 & x$mindist <= 25000), ] )
})
```r
sum(unlist(flens))

This is a count of gene-snp pairs satisfying structural and genetic criteria.

Visualizing and annotating significant loci

Re-binding probe annotation from RangedSummarizedExperiment

In the case of geuFPKM there is some relevant metadata in the rowRanges element. We will bind that into the collection of significant findings.

library(geuvPack)
data(geuFPKM)
basic = mcols(rowRanges(geuFPKM))[, c("gene_id", "gene_status", "gene_type",
    "gene_name")]
rownames(basic) = basic$gene_id
extr = basic[ filtEnum$probeid, ]
mcols(filtEnum) = cbind(mcols(filtEnum), extr)
stopifnot(all.equal(filtEnum$probeid, filtEnum$gene_id))
filtEnum[1:3]

Static visualization of FDR patterns

We have a utility to create an annotated Manhattan plot for a search cis to a gene. The basic ingredients are

• a ciseStore instance for basic location and association information
• a gene identifier that works for that store
• an FDRsupp instance that includes the function that maps from association scores to FDR, and the filter employed during FDR estimation
• an annotation resource; here we use ChromHMM labeling based on NA12878, in the hmm878 GRanges instance in gQTLstats/data.

It is important to recognize that, given an FDRsupp instance we can compute the FDR for any association score, but validity of the FDR attribution requires that we refrain from computing it for any locus excluded by filtering. the manhWngr executes the FDRsupp-resident filter by default.

data(hmm878)
library(geuvStore2)
prst = makeGeuvStore2() 
myg = "ENSG00000183814.10" # LIN9
data(filtFDR)
library(ggplot2)
manhWngr( store = prst, probeid = myg, sym="LIN9",
  fdrsupp=filtFDR, namedGR=hmm878 )

For a dynamic visualization procedure, see the vjcitn/gQTLbrowse github archive.

Basic structural variant annotation

We can use r Biocpkg("VariantAnnotation") to establish basic structural characteristics for all filtered variants. This code is blocked owing to changes to seqlevelsStyle that need to be reconciled at a deeper level. Please notify stvjc at channing.harvard.edu if there is a need to run this code.

suppressPackageStartupMessages({
library(VariantAnnotation)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
})
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene
seqlevelsStyle(filtEnum) = "UCSC"
#seqinfo(filtEnum) = seqinfo(txdb)
seqlengths(filtEnum)[paste0("chr", c(1:22,"M"))] = 
 seqlengths(txdb)[paste0("chr", c(1:22,"M"))]
filtEnum = keepStandardChromosomes(filtEnum)
suppressWarnings({
allv = locateVariants(filtEnum, txdb, AllVariants()) # multiple recs per eQTL 
})
table(allv$LOCATION)
hits = findOverlaps( filtEnum, allv )
filtEex = filtEnum[ queryHits(hits) ]
mcols(filtEex) = cbind(mcols(filtEex), mcols(allv[subjectHits(hits)])[,1:7])
filtEex[1:3]

The resulting table is SNP:transcript specific, and will likely need further processing.

Statistical modeling of phenorelevance of variant contexts, based on a cis-eQTL store

The following tasks need to be addressed in the modeling of phenorelevance

• Definition of outcome for a variant. In this example we consider identification of a variant as an NHGRI GWAS catalog hit.
• Definition of variant context. In this example we use Broad Institute ChromHMM states for NA12878, along with other information assembled in the store on MAF and distance
• Definition of the statistical model. We consider logistic regression modeling of the probability that a variant is a GWAS hit, employing LD-pruned variants only.
• Identification of a tractable approach to fitting and evaluating the statistical model. We'll use a test-train framework.

Visiting variants and updating with the relevant context and outcomes

We will make a temporary reconstruction of geuvStore2 contents with the enhanced information.

Support for trans-eQTL identification

The workhorse function is AllAssoc. The interface is

args(AllAssoc)

This differs from cisAssoc through the addition of a variantRange argument.

The basic operation will be as follows. For a given RangedSummarizedExperiment instance summex, all features will be tested for association with all SNP in the variantRange restriction of the VCF identified in vcf.tf. The basic iteration strategy is

a) tile the genome to obtain chunks of SNPs

b) decompose the SE into chunks of transcriptome (or other 'ome)

c) for each chunk of SNPs, for each chunk of transcriptome, seek associations and retain the top K in a buffering structure

Management of this buffering structure needs work.

require(GenomeInfoDb)
require(geuvPack)
require(Rsamtools)
data(geuFPKM)  # get a ranged summarized expt
lgeu = geuFPKM[ which(seqnames(geuFPKM)=="chr20"), ] # limit to chr20
seqlevelsStyle(lgeu) = "NCBI"
tf20 = TabixFile(system.file("vcf/c20exch.vcf.gz", package="gQTLstats"))
if (require(VariantAnnotation)) scanVcfHeader(tf20)
set.seed(1234)
mysr = GRanges("20", IRanges(33.099e6, 33.52e6))
lita = AllAssoc(geuFPKM[1:10,], tf20, mysr)
names(mcols(lita))

The trans search for this segment of chr20 proceeds by obtaining additional association scores for additional genes.

litb = AllAssoc(geuFPKM[11:20,], tf20, mysr)
litc = AllAssoc(geuFPKM[21:30,], tf20, mysr)

Now we want to reduce this information by collecting the strongest associations over the 30 genes tested.

buf = gQTLstats:::collapseToBuf(lita, litb, frag="_obs")
buf
buf = gQTLstats:::collapseToBuf(buf, litc, frag="_obs")
buf

Let's do the same buffering process for the first permutation.

pbuf = gQTLstats:::collapseToBuf(lita, litb, frag="_permScore_1")
pbuf = gQTLstats:::collapseToBuf(pbuf, litc, frag="_permScore_1")
pbuf

We can compare the distributions of maximal association per SNP as observed or under permutation.

plot(density(buf$scorebuf[,1]))
lines(density(pbuf$scorebuf[,1]), lty=2)

gQTLstats documentation built on Nov. 8, 2020, 7:53 p.m.