Package index • pecotmr

Class definitions

S4 class definitions. The -class topic documents the slots and validity constraints; the matching constructor topic (next section) documents the user-facing factory function. Virtual classes have no constructor, and neither does H2Estimate – estimateH2() builds it.

AnnotationMatrix-class: Genomic Annotation Matrix
ColocBoostResult-class: ColocBoost Result
ColocResult-class: Colocalization Result
ColocResultBase-class: Colocalization Result Base
CtwasResult-class: cTWAS Result Collection
CtwasResultEntry-class: cTWAS Per-Run Payload
FineMappingResultBase-class: Fine-Mapping Result Base Class
FineMappingRow-class: Fine-Mapping Row
GwasFineMappingResult-class: GWAS Fine-Mapping Result Collection
GwasSumStats-class: GWAS Summary-Statistic Collection
H2Estimate-class: Heritability Estimate
`[`(<LdData>,<ANY>,<ANY>,<ANY>): LD Data Container
`[`(<LdEigen>,<ANY>,<ANY>,<ANY>): Eigendecomposition-Based LD Statistic
LdScore-class: LD Score-Based LD Statistic
LdStatistic-class: LD Statistic (Virtual Base Class)
MashPrior-class: Data-Driven (mash) Prior Bundle
MultiStudyQtlDataset-class: Multi-Study QTL Dataset
`[`(<QtlDataset>,<ANY>,<ANY>,<ANY>) longForm(<QtlDataset>): QTL Dataset (individual-level data for one study)
QtlFineMappingResult-class: QTL Fine-Mapping Result Collection
QtlSumStats-class: QTL Summary-Statistic Collection
RangedTupleList-class: Ranged Tuple List
SldscData-class: S-LDSC input data container
SumStatsBase-class: Summary Statistics Base Class
TwasWeights-class: TWAS Weights Collection
TwasWeightsRow-class: TWAS Weights Row

Class constructors

User-facing constructors – one per class, plus the two alternative QtlSumStats builders. Every class here also has a -class topic in the section above: prefer the constructor for day-to-day use; the -class topic is the authoritative slot reference.

AnnotationMatrix(): Create an AnnotationMatrix Object
ColocBoostResult(): Build a ColocBoostResult
ColocResult(): Build a ColocResult
CtwasResult(): Create a CtwasResult Collection
CtwasResultEntry(): Create a CtwasResultEntry
fineMappingRow(): Build One Fine-Mapping Row
GwasFineMappingResult(): Create a GwasFineMappingResult Collection
GwasSumStats(): Create a GwasSumStats Collection Object
LdData(): Create an LdData Object
LdEigen(): Create an LdEigen
LdScore(): Create an LdScore
MashPrior(): Create a MashPrior Object
MultiStudyQtlDataset(): Create a MultiStudyQtlDataset Object
QtlDataset(): Create a QtlDataset Object
QtlFineMappingResult(): Create a QtlFineMappingResult Collection
QtlSumStats(): Create a QtlSumStats Collection Object
qtlSumStatsFromBetaMatrix(): Build a QtlSumStats from Bhat / Shat (effect-size) Matrices
qtlSumStatsFromZMatrix(): Build a QtlSumStats from a Z-score matrix
SldscData() show(<SldscData>): Construct an SldscData object
TwasWeights(): Create a TwasWeights Collection Object
twasWeightsRow(): Build One TWAS-Weight Row

Manifest loaders

Build the individual-level and summary-statistic containers from a manifest (a data.frame or a delimited file) that points at the phenotype / genotype / summary-statistic sources.

loadQtlDatasetFromManifest(): Load a QtlDataset from a manifest
loadMultiStudyQtlDatasetFromManifest(): Load a MultiStudyQtlDataset from manifests
loadQtlSumStatsFromManifest(): Load a QtlSumStats collection from a manifest
loadGwasSumStatsFromManifest(): Load a GwasSumStats collection from a manifest

Class methods

Accessor and behaviour methods, grouped by the class each method is defined on. A generic with methods on several classes is listed under every one of them, so each block is the complete callable surface for that class (a subclass also inherits everything listed under its base). S4 pipeline dispatch methods (fineMappingPipeline, colocboostPipeline, twasWeightsPipeline) are listed under “Pipelines” further down rather than repeated per class.

show(<GenotypeHandle>) show(<AnnotationMatrix>) show(<ColocResult>) show(<ColocBoostResult>) show(<CtwasResult>) show(<GwasFineMappingResult>) show(<H2Estimate>) show(<LdData>) show(<LdEigen>) show(<LdScore>) show(<MashPrior>) show(<QtlDataset>) show(<MultiStudyQtlDataset>) show(<QtlFineMappingResult>) show(<QtlSumStats>) show(<GwasSumStats>) show(<FineMappingRow>) show(<TwasWeights>) show(<TwasWeightsRow>): Show methods for pecotmr S4 classes

RangedTupleList

flattenTupleRanges(): Flatten a tuple collection to a plain GRanges
nestTupleRanges(): Re-nest a flattened GRanges into a tuple collection
nrow(<RangedTupleList>) ncol(<RangedTupleList>) colnames(<RangedTupleList>) `$`(<RangedTupleList>) `$<-`(<RangedTupleList>) `[`(<RangedTupleList>,<ANY>,<ANY>,<ANY>) `[[<-`(<RangedTupleList>,<ANY>,<ANY>) endoapply(<RangedTupleList>) bindROWS(<RangedTupleList>): RangedTupleList methods
subsetRegion(): Restrict a Collection to a Region

FineMappingResultBase

computeCsCorrelation(): Compute Between-Credible-Set Correlation On Demand
fsusieAffectedRegions(): fSuSiE affected genomic regions
fsusieCredibleBand(): fSuSiE credible band (fitted effect + uncertainty band)
getCredibleSetSummary(): Per-credible-set summary of a fine-mapping result
getCs(): Get Credible Sets
getLbf(): Per-variant per-effect log Bayes factors (wide)
getLdSketch(): Get LD Sketch
getMarginalEffects(): Get Marginal Effects
getMethodNames(): Get Method Names
getRegion(): Get Per-Row Genomic Regions
getRetainedMass(): Per-Effect Retained Posterior Mass
getStudy(): Get Study Identifier
getSusieFit(): Get SuSiE Fit
getTopLoci(): Get Top Loci (posterior view)
getTraitPosition(): Get Per-Row Trait Positions
getVariantIds(): Get Variant IDs
intersectVariants(): Reconcile Two Collections to Their Shared Variants
resolveWeights(): Resolve Per-Variant Weights From a Weight Source
writeSumstatsVcf(): Write summary statistics or fine-mapping results to VCF/BCF

QtlFineMappingResult

getContexts(): Get Context Names
getCvResult(): Get Cross-Validation Result
getFineMappingResult(): Get a Single Fine-Mapping Entry
getPip(): Get PIP Values
getTraits(): Get Unique Trait Names

GwasFineMappingResult

getContexts(): Get Context Names
getFineMappingResult(): Get a Single Fine-Mapping Entry
getPip(): Get PIP Values
getTraits(): Get Unique Trait Names

FineMappingRow

getCvResult(): Get Cross-Validation Result
getSusieFit(): Get SuSiE Fit
getVariantIds(): Get Variant IDs

ColocResultBase / ColocResult / ColocBoostResult

as.data.frame(<ColocBoostResult>): Coerce a ColocBoostResult to a data frame
as.data.frame(<ColocResult>): Coerce a ColocResult to a data frame
getColocPairs() getColocVariants() getColocCredibleSets() getColocGenes(): Colocalization Views
getColocBoostOutcomes(): ColocBoost Outcome View
getComputingTime(): Per-Analysis Timings
getLdSketch(): Get LD Sketch
getRegionVcp(): Region-Wide Variant Colocalization Probabilities

SumStatsBase

getBeta(): Get Marginal Effect Sizes
getGenome(): Get the Genome Build
getLdSketch(): Get LD Sketch
getMaf(): Get Minor Allele Frequencies
getN(): Get Sample Sizes
getP(): Get Association P-values
getQcDiagnostics(): Get SLALOM / DENTIST Diagnostics
getQcInfo(): Get QC Audit Record
getSe(): Get Effect-Size Standard Errors
getStudy(): Get Study Identifier
getVariantIds(): Get Variant IDs
getZ(): Get Z-scores
nSnps(): Get Number of SNPs
subsetChr(): Subset by Chromosome

GwasSumStats

combineGwasSumStats(): Combine GwasSumStats collections
getSumstatDf(): Get Standardized Sumstat Data Frame for One Tuple
getSumStats(<GwasSumStats>): Get a GWAS Study's Summary-Statistic GRanges
getVarY(): Get Phenotype Variance
writeSumstatsVcf(): Write summary statistics or fine-mapping results to VCF/BCF

QtlSumStats

combineQtlSumStats(): Combine QtlSumStats collections
getContexts(): Get Context Names
getSignificantQtls(): Extract Significant cis-QTL Variants
getSumstatDf(): Get Standardized Sumstat Data Frame for One Tuple
getSumStats(): Get a Single Summary-Statistic Entry or Embedded Collection
getTraitPosition(): Get Per-Row Trait Positions
getTraits(): Get Unique Trait Names
getVarY(): Get Phenotype Variance
qtlAssociationPostprocess(): Hierarchical Multiple-Testing Correction for cis-QTL Association

TwasWeights

getContexts(): Get Context Names
getCvResult(): Get Cross-Validation Result
getDataType(): Get Data Type
getFits(): Get Model Fits
getLdSketch(): Get LD Sketch
getMethodNames(): Get Method Names
getRegion(): Get Per-Row Genomic Regions
getStandardized(): Get Standardized Flag
getStudy(): Get Study Identifier
getTraitPosition(): Get Per-Row Trait Positions
getTraits(): Get Unique Trait Names
getTwasWeights(): Get a Single TWAS Weights Entry
getVariantIds(): Get Variant IDs
getWeights(): Get TWAS Weights
resolveWeights(): Resolve Per-Variant Weights From a Weight Source

TwasWeightsRow

getCvResult(): Get Cross-Validation Result
getDataType(): Get Data Type
getFits(): Get Model Fits
getStandardized(): Get Standardized Flag
getVariantIds(): Get Variant IDs
getWeights(): Get TWAS Weights

AnnotationMatrix

getBaseline(): Get Baseline Annotations
getCandidates(): Get Candidate Annotations
getGenome(): Get the Genome Build

CtwasResult / CtwasResultEntry

getContexts(): Get Context Names
getCtwasParam(): Get cTWAS Group Prior Parameters
getFinemap(): Get cTWAS Fine-mapping Posteriors
getMethodNames(): Get Method Names
getStudy(): Get Study Identifier
getSusieAlpha(): Get cTWAS Per-effect Susie Alpha Table

H2Estimate

getEnrichment(): Get Enrichment Estimates
getH2(): Get Global SNP Heritability
getH2Se(): Get Heritability Standard Error
getIntercept(): Get LD-Score Regression Intercept
getInterceptSe(): Get Intercept Standard Error
getLocal(): Get Local Estimates
getMethodNames(): Get Method Names
getNSnps(): Get Variant Count
getScoreStats(): Get Score Statistics
getTauBlocks(): Get Per-Block tau Matrix
getTraitName(): Get Trait Name

LdData

getBlockMetadata(): Get Block Metadata
getCorrelation(): Get LD Correlation Matrix
getGenotypes(): Get Genotype Matrix
getMixtureWeights(): Get Mixture Weights
getNRef(): Get LD Reference Panel Size
getRefPanel(): Get Reference Panel (data.frame)
getSnpIdx(): Get SNP Indices
getVariantIds(): Get Variant IDs
getVariantInfo(): Get Variant GRanges
hasGenotypes(): Check Genotype Availability

LdEigen

getEigenList(): Get Per-Block Eigendecompositions

LdScore

getLdMatrixList(): Get Per-Block LD Matrix List
getLdScores(): Get LD Scores
getLdScoreWeights(): Get LD-Score Regression Weights

LdStatistic

getGenome(): Get the Genome Build
getInSample(): Get In-Sample Flag
getLdBlocks(): Get LD Block Container
getNRef(): Get LD Reference Panel Size

MashPrior

getCvFits(): Get the Per-Fold Priors from a MashPrior
getFullFit(): Get the Full-Data Prior from a MashPrior

MultiStudyQtlDataset

getQtlDatasets(): Get the Embedded QtlDataset List
getStudy(): Get Study Identifier
getSumStats(): Get a Single Summary-Statistic Entry or Embedded Collection

QtlDataset

getAf(): Get Effect-Allele Frequencies
getContexts(): Get Context Names
getGenotypeCovariates(): Get Genotype Covariates
getGenotypes(): Get Genotype Matrix
getMaf(): Get Minor Allele Frequencies
getPhenotypeCovariates(): Get Per-Context Phenotype Covariates
getPhenotypes(): Get Phenotype List
getResidualizedGenotypes(): Get Residualized Genotypes
getResidualizedPhenotypes(): Get Residualized Phenotypes
getScaleResiduals(): Get scaleResiduals Flag
getStudy(): Get Study Identifier
getTraitPosition(): Get Per-Row Trait Positions
getMafCutoff() getMacCutoff() getXvarCutoff() getImissCutoff() getKeepVariants() getKeepIndel(): QtlDataset Filter Settings

SldscData

getAnnotCols(): Get the annotation column names from an SldscData
getAnnotData(): Get the annotation table from an SldscData
getFrqData(): Get the allele-frequency table from an SldscData
getTraitNames(): Get the trait names from an SldscData
getTraitRun(): Get one trait's run from an SldscData
getTraitRuns(): Get the per-trait runs list from an SldscData

Pipelines

End-to-end pipeline entry points. Each takes one or more S4 input classes and returns a SumStats, FineMappingResult, TwasWeights collection, or a tabular summary.

causalInferencePipeline(): Causal Inference Pipeline (TWAS-Z + Mendelian Randomization)
colocboostPipeline(): ColocBoost multi-trait colocalization pipeline (S4)
colocPipeline(): Colocalization Pipeline (coloc.bf_bf over QTL + GWAS LBF matrices)
ctwasPipeline(): Causal TWAS Pipeline (cTWAS, multi LD block)
fineMappingPipeline(): Fine-Mapping Pipeline
mashPipeline(): Run mashr Across Multi-Context QTL or GWAS Summary Statistics
qtlEnrichmentPipeline(): QTL Enrichment Pipeline (Genome-Wide)
sldscPostprocessingPipeline(): sLDSC Postprocessing Pipeline
twasWeightsPipeline(): TWAS Weights Pipeline

cTWAS

The four chainable steps that ctwasPipeline is built from: assembleCtwasInputs → estCtwasParam → screenCtwasRegions → finemapCtwasRegions. Use the steps directly when you want to override the estimated priors (e.g. fall back to the prefit EM values after an accurate-EM divergence), inspect the assembled region data, or run only part of the pipeline.

assembleCtwasInputs(): Assemble cTWAS inputs from S4 GwasSumStats / TwasWeights
estCtwasParam(): Estimate cTWAS group prior + prior variance
screenCtwasRegions(): Screen cTWAS regions
finemapCtwasRegions(): Fine-map cTWAS regions
mergeCtwasBoundaryRegions(): Merge boundary cTWAS regions and re-fine-map
asCtwasResult(): Structure a granular cTWAS finemap result as a CtwasResult

Quality control

Summary-statistics QC orchestrator plus the underlying allele harmonization, LD-mismatch, kriging, SLALOM, DENTIST, RAISS, and relatedness checks.

summaryStatsQc(): Run QC on a SumStats Collection
effectiveN(): Effective sample size for a case/control study
ldMismatchQc(): Detect LD-Summary Statistic Mismatches
krigingOutlierQc(): Kriging-style LD-consistency outlier QC
slalom(): Slalom Function for Summary Statistics QC for Fine-Mapping Analysis
dentist(): Detect Outliers Using Dentist Algorithm
dentistSingleWindow(): Perform DENTIST on a single window
autoDecision(): Process Credible Set Information and Determine Updating Strategy
raiss(): Impute Summary Statistics Using LD (RAISS)
mergeVariantInfo(): Merge variant info from two sources with allele-flip-aware matching
filterRelatedness(): Filter related individuals from a study

LD infrastructure

Computing, loading, and manipulating LD matrices, plus design-matrix conditioning utilities.

loadLdMatrix(): Load and Process Linkage Disequilibrium (LD) Matrix
loadLdSketch(): Load LD sketch genotypes for a region
loadLdBlock(): Load one LD block from an ldLoader spec
computeLd(): Compute an LD Correlation Matrix
checkLd(): Check and optionally repair LD matrix quality
enforceDesignFullRank(): Iteratively enforce full column rank on a design matrix
ldClumpByScore(): LD clumping by a per-variant score using bigsnpr
ldLoader(): Create an LD loader for on-demand block-wise LD retrieval
ldPruneByCorrelation(): Prune columns by pairwise correlation (LD-style prune)
filterVariantsByLdReference(): Filter variants by LD Reference

Genotype I/O

Reading genotype panels and per-region genotype data. A panel is a RangedSummarizedExperiment whose dosage assay reads from the file lazily, so the Bioconductor accessors apply to it directly.

readGenotypes(): Read a Genotype Panel
loadGenotypeRegion(): Load genotype data for a specific region
readAfreq(): Read a PLINK2 allele frequency file (.afreq or .afreq.zst)
getRefVariantInfo(): Get variant information from any LD reference source
invertMinmaxScaling(): Invert min-max [0,2] scaling to recover the original U matrix

Variant ID and region helpers

Parsing, formatting, and overlap checks for variant IDs (chr:pos:A2:A1) and genomic regions (chr:start-end).

parseVariantId(): Parse variant IDs into a data frame
variantIdToDf(): Parse variant IDs into a data frame
normalizeVariantId(): Re-format variant IDs to a chosen output convention
classifyVariantType(): Classify variant type from allele strings
parseRegion(): Parse a region string into its components
regionToDf(): Utility function to convert LD region_ids to `region of interest` dataframe
regionsOverlap(): Test whether two genomic regions overlap
findOverlappingRegions(): Find which target regions overlap a query region
asGranges(): Convert region specifications to a GRanges object

Fine-mapping

SuSiE-family fit wrappers (individual-level and summary-stat variants) plus the shared post-processing that produces the unified top_loci table. The VCF writer for fine-mapping results is documented under in the Class methods section above.

susieWeights(): Compute SuSiE TWAS weights
susieRssWeights(): Compute SuSiE-RSS TWAS weights
susieInfWeights(): Compute SuSiE-inf TWAS weights
susieInfRssWeights(): Compute SuSiE-inf-RSS TWAS weights
susieAshWeights(): Compute SuSiE-ASH TWAS weights
susieAshRssWeights(): Compute SuSiE-ASH-RSS TWAS weights
mvsusieWeights(): Compute mvSuSiE TWAS weights
mvsusieRssWeights(): Compute mvSuSiE-RSS TWAS weights from summary statistics
fsusieWeights(): Compute fSuSiE feature-level TWAS weights
fitMvsusie(): Fit mvSuSiE on individual-level (X, Y) data
fitMvsusieRss(): Fit mvSuSiE-RSS on summary-statistic (Z, R, N) data
fitFsusie(): Fit fSuSiE on individual-level (X, Y, pos) data
fitSusieInfThenSusieRss(): Two-stage SuSiE-RSS Fine-mapping
fsusieGetCs(): Create Sets Similar to SuSiE Output from fSuSiE Object
fsusieWrapper(): Wrapper for fsusie Function with Automatic Post-Processing
getSusieResult(): Extract the trimmed SuSiE fit from a finemapping pipeline result
computeCsTables(): Compute Credible-Set Tables From a Fine-Mapping Fit
buildTopLoci(): Build the unified top-loci table for one fit and one method
extractCsInfo(): Process Credible Sets (CS) from Finemapping Results
extractTopPipInfo(): Extract Information for Top Variant from Finemapping Results
formatFinemappingOutput(): Format Fine-mapping Post-processing for Protocol Output
lbfToAlpha(): Convert a log-Bayes-factor matrix to Single Effect PIPs
postprocessFinemappingFits(): Post-process Fine-mapping Fits
combineFineMappingResults(): Combine FineMappingResult collections
mergeSusieCs(): Merge SuSiE credible sets across conditions
overlapTopLoci(): Overlap QTL and GWAS top loci by allele-aware variant matching

TWAS

Per-method weight-training functions invoked by twasWeightsPipeline(), the cross-validation / ensembling machinery that combines them, and multi-method p-value combination. Grouped by method family below. (SuSiE-family weight functions live under “Fine-mapping” above since they double as fine-mappers.)

Penalized regression

glmnetWeights(): Compute TWAS weights via penalized regression (glmnet)
lassoWeights(): Compute TWAS weights via lasso (glmnet, alpha = 1)
enetWeights(): Compute TWAS weights via elastic net (glmnet, alpha = 0.5)
lassosumRss(): Lassosum RSS: LASSO on summary statistics with LD reference
lassosumRssWeights(): Extract weights from lassosumRss with shrinkage grid search
mcpWeights(): Compute Weights Using MCP-Penalized Regression
mcpRssWeights(): Compute MCP-Penalized Weights from Summary Statistics
scadWeights(): Compute Weights Using SCAD-Penalized Regression
scadRssWeights(): Compute SCAD-Penalized Weights from Summary Statistics
l0learnWeights(): Compute Weights Using L0Learn
l0learnRssWeights(): Compute L0-Penalized Weights from Summary Statistics
penalizedRss(): Penalized Regression on RSS (Summary Statistics) Objective

Bayesian alphabet

bayesAlphabetWeights(): Extract Coefficients From Bayesian Linear Regression
bayesAWeights(): BayesA TWAS weights (t-distribution prior)
bayesBWeights(): Compute Weights Using BayesB
bayesCWeights(): BayesC TWAS weights (rounded-spike prior)
bayesLWeights(): BayesL TWAS weights (Laplace prior, LASSO-equivalent)
bayesNWeights(): BayesN TWAS weights (Gaussian prior, ridge-equivalent)
bayesRWeights(): BayesR TWAS weights (hierarchical mixture prior)
bLassoWeights(): Compute Weights Using the Bayesian LASSO (BGLR)
prsCs(): PRS-CS: a polygenic prediction method that infers posterior SNP effect sizes under continuous shrinkage (CS) priors
prsCsWeights(): Extract weights from prsCs function
sdpr(): SDPR (Summary-Statistics-Based Dirichelt Process Regression for Polygenic Risk Prediction)
sdprWeights(): Extract weights from sdpr function
dprWeights() dprVbWeights() dprGibbsWeights() dprAdaptiveGibbsWeights(): Compute Weights Using Dirichlet Process Regression (RcppDPR)

mr.mash / mr.ash

mrashWeights(): Compute Weights Using mr.ash Shrinkage
mrAshRssWeights(): Extract weights from mr.ash.rss (susieR)
mrmashWeights(): Compute mr.mash TWAS weights
mrmashRssWeights(): Compute mr.mash-RSS TWAS weights from summary statistics
mrmashWrapper(): Mr.Mash Wrapper
buildMrmashPriorMatrices(): Build mr.mash prior covariance matrices
computeCoefficientsGlasso(): Compute initial mr.mash coefficients via group-lasso
computeCovDiag(): Compute diagonal covariance matrix
computeCovFlash(): Compute covariance matrix using FLASH

Ensembling / cross-validation / prediction

learnTwasWeights(): Run multiple TWAS weight methods
ensembleWeights(): Ensemble TWAS Weights via Stacked Regression
combineTwasWeights(): Combine TwasWeights collections
twasWeightsCv(): Cross-Validation for weights selection in Transcriptome-Wide Association Studies (TWAS)
twasPredict(): Predict outcomes using TWAS weights
estimateSparsity(): Estimate Sparsity from mr.ash Mixture Proportions

Association testing and p-value combination

twasZ(): Calculate TWAS Z-Statistics for One or More Methods / Contexts
combinePValues(): Combine P-values via Any of a Menu of Methods
waldTestPval(): Wald-test two-sided p-value

mash

The mash (multivariate adaptive shrinkage) track: the chainable steps -> -> (or ) -> -> that is built from, the posterior-contrast + feature-score layer, and the supporting utilities for contrast construction and meta-analysis.

mashInput(): Assemble MASH strong / random / null input from S4 objects
mashResidualCorrelation(): Estimate the mash Residual Correlation Matrix (Vhat)
mashCovarianceComponents(): Build mash Data-Driven Covariance Components
mashPriorCovariances(): Estimate mash Prior Covariances and Mixture Weights
mashModelFit(): Fit a mash Model for Posterior Computation
mashPosterior(): Compute mash Posterior Matrices for a Target Set
mashPosteriorContrast(): Posterior contrast table over an entire mash posterior
calculateFeatureScores(): Feature score from deviation contrasts (random-effects meta per condition)
nSignificantScore(): Feature score from the fraction of significant deviation contrasts
scoreFromCs(): Feature score from fine-mapped credible sets
makePairwiseContrastCol(): Create a pairwise contrast column
fitMashContrast(): Compute pairwise contrasts from mash posterior
metaAnalysisPerCondition(): Random-Effects Meta-Analysis of Mash Pairwise Contrasts, per Condition
filterInvalidSummaryStat(): Filter invalid summary statistics for mash input
filterMixtureComponents(): Filter conditions from mash prior mixture components
mashRandNullSample(): Sample random and null variant subsets for mash
mergeMashData(): Merge two mash data lists
sanitizeMashData(): Sanitize NaN/Inf values in mash data
sliceMashData(): Subset mash data matrices to specific SNPs and conditions
updateMashModelCov(): Subset a fitted mash model to a subset of conditions

Heritability and enrichment

Heritability estimation, LD-score computation, stratified LD-score regression utilities, and per-variant QTL/GWAS enrichment analysis (low-level kernel that powers ).

estimateH2(): Estimate SNP Heritability
computeLdScores(): Compute LD Scores
computeSldscAnnotSd(): Compute per-annotation standard deviation, MAF-restricted
computeSldscMRef(): Reference-panel SNP count (the M_ref used to standardise tau*)
readAnnotations(): Read Annotations
readSldscTrait(): Read S-LDSC outputs from polyfun for one trait/run
readSldscAnnot(): Read target annotation files (.annot.gz) into one table
readSldscFrq(): Read PLINK allele-frequency files (.frq) into one table
standardizeSldscTrait(): Standardize tau and compute EnrichStat for one polyfun run
h2EstimateToSldscTrait(): Convert H2Estimate to S-LDSC Trait Format
isBinarySldscAnnot(): Detect whether each annotation is binary or continuous
metaSldscRandom(): Random-effects meta-analysis of S-LDSC quantities across traits
sldscSubsetMeta(): Random-effects meta-analysis over a subset of sLDSC traits
qtlEnrichment(): Implementation of enrichment analysis described in https://doi.org/10.1371/journal.pgen.1006646

Bundled example datasets

Synthetic data shipped with the package for vignettes and tests. Their bundled genotype-panel paths resolve to the installed inst/extdata/ location automatically at extraction time.

qtlDatasetExample: Example QtlDataset (S4)
multiStudyQtlDatasetExample: Example MultiStudyQtlDataset (S4)
qtlSumStatsExample: Example QtlSumStats (S4)
qtlSumStatsMulticontextExample: Example multi-context QtlSumStats (S4) for mash demos
gwasSumStatsS4Example: Example GwasSumStats (S4)
eqtlRegionExample: Example eQTL Region Data (Individual-Level)
gwasSumStatsExample: Example GWAS Summary Statistics
multiTraitData: Simulated Multi-condition Data for TWAS analysis
colocboostResultExample: Example ColocBoost Result (One Colocalized Set)
qtlFineMappingExample: Example QTL Fine-Mapping Results (SuSiE)
qtlFineMappingLbfExample: Example QTL Fine-Mapping Results with LBF (SuSiE)
qtlFineMappingPairedExample: Example QTL Fine-Mapping Result Paired With the LD-Source Fixtures
gwasFineMappingExample: Example GWAS Fine-Mapping Results (SuSiE)
gwasFineMappingLbfExample: Example GWAS Fine-Mapping Result with LBF (SuSiE)
mvsusieFineMappingExample: Example Multivariate-SuSiE Fine-Mapping Result
fsusieFineMappingExample: Example Functional-SuSiE Fine-Mapping Result
twasWeightsExample: Example TWAS Weights (Multiple Methods)
ctwasWeightsExample: Example cTWAS-formatted TWAS Weights
ctwasInputsExample: Example Assembled cTWAS Inputs
ctwasEstExample: Example cTWAS Parameter-Estimation State
ctwasFinemapExample: Example cTWAS Fine-Mapping Result
mashInputExample: Example mash Input Matrices (Multi-Condition)
mashPosteriorExample: Example mash Posterior Bundle
ldEigenExample: Example LD Eigen-Decomposition Reference
ldScoreExample: Example LD-Score Reference
h2EstimateExample: Example Heritability Estimate