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
show(<GenotypeMixture>): Mixture of Genotype Panels
`[`(<LdEigen>,<ANY>,<ANY>,<ANY>): Eigendecomposition-Based LD Statistic
show(<LdMatrix>): LD Correlation Matrix
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
GenotypeMixture(): Build a GenotypeMixture
GwasFineMappingResult(): Create a GwasFineMappingResult Collection
GwasSumStats(): Create a GwasSumStats Collection Object
LdEigen(): Create an LdEigen
LdMatrix(): Build an LdMatrix
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

Engine options and method parameters

Two disjoint families of argument record. An *Options() bundle carries arguments bound for an external engine and is validated against that engine’s own formals, so an unknown name is refused at construction. A *Param() record is pecotmr-internal and typed, built on the MethodParam virtual base, and reports only the fields that have actually been set.

show(<MethodOptions>): Arguments For One Fitting Engine
`$`(<MethodParam>) `[[`(<MethodParam>,<ANY>,<ANY>) names(<MethodParam>) length(<MethodParam>) as.list(<MethodParam>) show(<MethodParam>): Typed Settings For One Pipeline Stage

Engine options

AspuOptions(): Options for the aSPU Combination Test
ColocOptions(): Arguments For coloc's Bayes-Factor Scorer
ColocboostOptions(): colocboost Engine Settings
CorShrinkOptions(): Settings For The CorShrink Null-Correlation Estimator
CovCanonicalOptions(): Arguments For mashr's Canonical Covariance Components
CovEdOptions(): Arguments For Extreme Deconvolution Refinement
CovFlashOptions(): Arguments For mashr's FLASH Covariance Components
CovMcdOptions(): Options for the Robust Outlier Covariance Estimator
CovPcaOptions(): Arguments For mashr's PCA Covariance Components
CovUdrOptions(): Arguments For Unconstrained Deconvolution Refinement
CtwasOptions(): Arguments For The ctwas Fitting Steps
CtwasBoundaryGenesOptions(): Arguments For ctwas's Boundary-Gene Step
CtwasEstParamOptions(): Arguments For ctwas's Group-Prior EM
CtwasExpandOptions(): Arguments For ctwas's Region-Expansion Step
CtwasFinemapOptions(): Arguments For ctwas's Fine-Mapping Step
CtwasGeneZOptions(): Arguments For ctwas's Gene Z-Score Step
CtwasMergeOptions(): Arguments For ctwas's Region-Merging Postprocess
CtwasRegionDataOptions(): Arguments For ctwas's Region-Assembly Step
CtwasScreenOptions(): Arguments For ctwas's Region-Screening Step
SusieOptions() SusieRssOptions() SusieInfOptions() SusieInfRssOptions() SusieAshOptions() SusieAshRssOptions() SerOptions() MvsusieOptions() MvsusieRssOptions() FsusieOptions(): Arguments For A Fine-Mapping Engine
GbjOptions(): Options for the GBJ Family of Combination Tests
GbjOmniOptions(): Options for the GBJ Omnibus Combination Test
KrigingOptions(): Options for the susieR Kriging RSS Diagnostic
LdPruningOptions(): Options for the SNPRelate LD-Pruning Backend
MashCorEmOptions(): Settings For The EM Null-Correlation Estimator
MashCorSimpleOptions(): Settings For The Simple Null-Correlation Estimator
MashDataOptions(): Settings For mashr Data Assembly
MashOptions(): Settings For The mash Mixture-Weight Fit
MashPosteriorOptions(): Settings For mashr Posterior Computation
MrmashCanonicalCovsOptions(): Arguments For mr.mashr's Canonical-Covariance Builder
MrmashExpandCovsOptions(): Arguments For mr.mashr's Covariance Expansion
MvsusieMixturePriorOptions(): Arguments For mvsusieR's Mixture-Prior Builder
PlinkQcOptions(): Additional Arguments For plinkQC::relatednessFilter
PoolrOptions(): Options for the poolr Combination Tests
QvalueOptions(): Options for Storey q-value Estimation
RmaOptions(): Options for the Random-Effects Meta-Analysis Engine
SusieRssControlOptions(): Arguments For susieR's RSS Control Block
GlmnetOptions() QggOptions() BglrOptions() DprOptions() NcvregOptions() L0learnOptions() MrashOptions() MrashRssOptions() PrsCsOptions() LassosumOptions() PenalizedRssOptions() SdprOptions() MrmashOptions() MrmashRssOptions(): Arguments For A TWAS Weight-Fitting Engine
UdFitOptions(): Arguments For udr's Fitting Step
UdInitOptions(): Arguments For udr's Initialisation Step

Method parameters

BoundaryMergeParam(): Boundary-Region Merge Settings
ColocLbfFilterParam(): Credible-Set Filtering For Colocalisation
ColocPriorParam(): Prior Probabilities For Colocalisation
csCoverage() `csCoverage<-`() secondaryCoverage() `secondaryCoverage<-`() signalCutoff() `signalCutoff<-`() minAbsCorr() `minAbsCorr<-`() medianAbsCorr() `medianAbsCorr<-`() includeAllCs() `includeAllCs<-`() perCsColumns() `perCsColumns<-`() maxNumSingleEffects() maxNumSingleEffectsGreedy() CredibleSetParam(): Credible-Set Construction And Reporting
CrossValidationParam(): Cross-Validation Settings
CtwasPriorParam(): cTWAS Prior-Estimation Settings
DentistParam(): Arguments For The DENTIST LD-Mismatch Check
EnsembleParam(): SR-TWAS Ensemble Settings
FineMappingMethodsParam(): Which Fine-Mapping Methods To Run, And How
GenotypeFilterParam(): Genotype Filtering Options
fineMappingMethods() `fineMappingMethods<-`() credibleSetParam() `credibleSetParam<-`() susieRssParam() `susieRssParam<-`() panelFilterParam() `panelFilterParam<-`() initializeWithSusieInf() `initializeWithSusieInf<-`() fitRetention() `fitRetention<-`() GwasFineMappingParam(): Inline GWAS Fine-Mapping Settings
MashComponentParam(): Per-Component Arguments For mashPriorCovariances
MashPriorParam(): mash Prior-Covariance Settings
MethodsSelectionParam-class: Which Methods A Pipeline Runs, And How
MrmashPriorParam(): Prior-Covariance Arguments For mr.mash
MrParam(): Mendelian-Randomization Settings
PanelFilterParam(): LD Reference Panel Filtering Options
PvalMethodsParam(): Per-Method Options for combinePValues
QtlEnrichmentParam(): Arguments For The QTL Enrichment Estimator
RaissParam(): Arguments For RAISS Imputation During Summary-Statistic QC
RelatednessColumnsParam(): Column Names In A Relatedness Table
RelatednessPruningParam(): Graph Pre-Pruning Controls For filterRelatedness
ResidualizationParam(): Covariate Residualization Settings
SignalScreenParam(): Signal Pre-Screen Settings
SlalomParam(): Arguments For The SLALOM LD-Mismatch Check
SumstatsCleaningParam(): Summary-Statistics Cleaning Settings
SumstatsFilterParam(): Summary-Statistic Row Filtering Options
SusieRssParam(): SuSiE-RSS Solver Settings
TwasWeightsMethodsParam(): Which TWAS Weight Methods To Run, And How
VariantPruningParam(): Per-Gene Variant-Pruning Settings
WeightSelectionParam(): TWAS Weight-Selection Settings

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(<LdEigen>) show(<LdScore>) show(<MashPrior>) show(<QtlSumStats>) show(<QtlDataset>) show(<MultiStudyQtlDataset>) show(<QtlFineMappingResult>) 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)
fsusieTraitEffects(): fSuSiE effect at each joint trait
credibleSetSummary(): Per-credible-set summary of a fine-mapping result
credibleSets(): Get Credible Sets
lbf(): Per-variant per-effect log Bayes factors (wide)
ldReference(): Get LD Reference
marginalEffects(): Get Marginal Effects
methodNames(): Get Method Names
genomicRegion(): Get Per-Row Analysis Windows
retainedMass(): Per-Effect Retained Posterior Mass
studyName(): Get Study Identifier
susieFit(): Get SuSiE Fit
topLoci(): Get Top Loci (posterior view)
traitPosition(): Get Per-Row Trait Positions
variantIds(): Get Variant IDs
intersectVariants(): Reconcile Two Collections to Their Shared Variants
resolveWeights(): Resolve Per-Variant Weights From a Weight Source
exportToVcf(): Export a result to VCF/BCF
exportToBed(): Export a result to a published BED table
readFineMappingResult(): Read A Published Fine-Mapping Result
blockId(): Get The LD Block Identifier

QtlFineMappingResult

contexts(): Get Context Names
cvResult(): Get Cross-Validation Result
fineMappingResult(): Get a Single Fine-Mapping Entry
pip(): Get PIP Values
traitNames(): Get Unique Trait Names

GwasFineMappingResult

contexts(): Get Context Names
fineMappingResult(): Get a Single Fine-Mapping Entry
pip(): Get PIP Values
traitNames(): Get Unique Trait Names

FineMappingRow

cvResult(): Get Cross-Validation Result
susieFit(): Get SuSiE Fit
variantIds(): 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
colocPairs() colocVariants() colocCredibleSets() colocGenes(): Colocalization Views
colocBoostOutcomes(): ColocBoost Outcome View
computingTime(): Per-Analysis Timings
ldReference(): Get LD Reference
regionVcp(): Region-Wide Variant Colocalization Probabilities

SumStatsBase

af(): Get Effect-Allele Frequencies
coef(): Get Marginal Effect Sizes
ldReference(): Get LD Reference
maf(): Get Minor Allele Frequencies
nSamples(): Get Sample Sizes
`af<-`() `maf<-`() `nSamples<-`(): Replace a Per-Variant Column
pval(): Get Association P-values
qcDiagnostics(): Get SLALOM / DENTIST Diagnostics
qcInfo(): Get QC Audit Record
se(): Get Effect-Size Standard Errors
studyName(): Get Study Identifier
variantIds(): Get Variant IDs
z(): Get Z-scores
nSnps(): Get Number of SNPs
subsetChr(): Subset by Chromosome

GwasSumStats

combineGwasSumStats(): Combine GwasSumStats collections
as.data.frame(<QtlSumStats>) as.data.frame(<GwasSumStats>): Coerce Summary Statistics to a Data Frame
sumStats(<GwasSumStats>): Get a GWAS Study's Summary-Statistic GRanges
varY(): Get Phenotype Variance
exportToVcf(): Export a result to VCF/BCF

QtlSumStats

combineQtlSumStats(): Combine QtlSumStats collections
contexts(): Get Context Names
significantQtls(): Extract Significant cis-QTL Variants
as.data.frame(<QtlSumStats>) as.data.frame(<GwasSumStats>): Coerce Summary Statistics to a Data Frame
sumStats(): Get a Single Summary-Statistic Entry or Embedded Collection
traitPosition(): Get Per-Row Trait Positions
traitNames(): Get Unique Trait Names
varY(): Get Phenotype Variance
qtlAssociationPostprocess(): Hierarchical Multiple-Testing Correction for cis-QTL Association

TwasWeights

contexts(): Get Context Names
cvResult(): Get Cross-Validation Result
weightsDataType(): Get Data Type
methodFits(): Get Model Fits
ldReference(): Get LD Reference
methodNames(): Get Method Names
genomicRegion(): Get Per-Row Analysis Windows
weightStandardized(): Get Standardized Flag
studyName(): Get Study Identifier
traitPosition(): Get Per-Row Trait Positions
traitNames(): Get Unique Trait Names
twasWeights(): Get a Single TWAS Weights Entry
variantIds(): Get Variant IDs
weights(<TwasWeights>) weights(<TwasWeightsRow>): Get TWAS Weights
resolveWeights(): Resolve Per-Variant Weights From a Weight Source
exportToVcf(): Export a result to VCF/BCF
exportToBed(): Export a result to a published BED table
readTwasWeights(): Read Published TWAS Weights

TwasWeightsRow

cvResult(): Get Cross-Validation Result
weightsDataType(): Get Data Type
methodFits(): Get Model Fits
weightStandardized(): Get Standardized Flag
variantIds(): Get Variant IDs
weights(<TwasWeights>) weights(<TwasWeightsRow>): Get TWAS Weights

AnnotationMatrix

baselineAnnotations(): Get Baseline Annotations
testedAnnotations(): Get Tested Annotations

CtwasResult / CtwasResultEntry

contexts(): Get Context Names
ctwasGroupPriors(): Get cTWAS Group Prior Parameters
ctwasPosteriors(): Get cTWAS Fine-mapping Posteriors
methodNames(): Get Method Names
studyName(): Get Study Identifier
susieAlpha(): Get cTWAS Per-effect Susie Alpha Table

H2Estimate

stratifiedHeritabilityEnrichment(): Get Enrichment Estimates
heritabilityEstimate(): Get Global SNP Heritability
heritabilityEstimateSe(): Get Heritability Standard Error
heritabilityIntercept(): Get LD-Score Regression Intercept
heritabilityInterceptSe(): Get Intercept Standard Error
localH2Blocks(): Get Local Estimates
methodNames(): Get Method Names
nSnps(): Get Number of SNPs
scoreStats(): Get Score Statistics
annotationJackknifeCoefs(): Get Per-Block tau Matrix
traitNames(): Get Unique Trait Names

LdMatrix / GenotypeMixture

ldMatrix(): Get LD Correlation Matrix
mixtureWeights(): Get Mixture Weights
ldPanelNSamples(): Get LD Reference Panel Size
variantIds(): Get Variant IDs
genotypes(): Get Genotype Matrix

LdEigen

eigenList(): Get Per-Block Eigendecompositions

LdScore

ldMatrixList(): Get Per-Block LD Matrix List

LdStatistic

inSample(): Get In-Sample Flag
ldBlocks(): Get LD Block Container
ldPanelNSamples(): Get LD Reference Panel Size

MashPrior

cvFits(): Get the Per-Fold Priors from a MashPrior
fullFit(): Get the Full-Data Prior from a MashPrior

MultiStudyQtlDataset

qtlDatasets(): Get the Embedded QtlDataset List
studyName(): Get Study Identifier
sumStats(): Get a Single Summary-Statistic Entry or Embedded Collection

QtlDataset

contexts(): Get Context Names
genotypeCovariates(): Get Genotype Covariates
genotypes(): Get Genotype Matrix
maf(): Get Minor Allele Frequencies
phenotypeCovariates(): Get Per-Context Phenotype Covariates
molecularTraits(): Get Molecular Traits
residualizedGenotypes(): Get Residualized Genotypes
residualizedPhenotypes(): Get Residualized Phenotypes
scaleResiduals(): Get scaleResiduals Flag
studyName(): Get Study Identifier
traitPosition(): Get Per-Row Trait Positions
mafCutoff() macCutoff() xvarCutoff() imissCutoff() keepVariants() keepIndel(): QtlDataset Filter Settings

SldscData

annotCols(): Get the annotation column names from an SldscData
annotData(): Get the annotation table from an SldscData
frqData(): Get the allele-frequency table from an SldscData
sldscResults(): Get sLDSC Results 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 paired 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 → estCtwasGroupPriors → 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
estCtwasGroupPriors(): 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 and checking LD matrices, plus design-matrix conditioning utilities.

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
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)
refVariantInfo(): 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
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
asGranges(): Convert region specifications to a GRanges object
genomicOrder(): Order rows by genomic position, with chromosomes sorted naturally

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.

serWeights(): Compute single-effect (SER) TWAS weights
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
buildMvsusieMixturePrior(): Build the mvSuSiE mixture prior
fitFsusie(): Fit fSuSiE on individual-level (X, Y, pos) data
fsusieGetCs(): Create Sets Similar to SuSiE Output from fSuSiE Object
susieResult(): 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 fine-mapping result 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

fitGlmnetCv(): Fit a cross-validated glmnet model
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)
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

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)
prsCsWeights(): Extract weights from prsCs function
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

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, the two builders that turn a loaded LD reference into the statistic each estimator needs ( for LDER/HDL, for g-LDSC), LD-score computation, stratified LD-score regression utilities, and per-variant QTL/GWAS enrichment analysis (low-level kernel that powers ).

estimateH2(): Estimate SNP Heritability
buildLdEigen(): Build an LdEigen from an LD reference
buildLdScore(): Build an LdScore from an LD reference
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
enlocClusterProbabilities(): enloc Colocalisation Probabilities For One Signal Cluster

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