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FunGen-xQTL Computational Protocol

Getting started

Illustration of xQTL protocol

Command Generator

RNA-seq calling and QC
Univariate xQTL Discovery

Reference data

Reference Data

Molecular Phenotypes

RNA-seq expression
scRNA-seq expression calling
Alternative polyadenylation
- APA Calling
Methylation
- Quantification of methylation data
Alternative splicing from RNA-seq data
- Quantifying alternative splicing from RNA-seq data
- Normalization and phenotype table generation for splicingQTL analysis

Data Pre-processing

Genotype data preprocessing
Phenotype data preprocessing
Covariate Data Preprocessing
- Covariate Data Formatting
- Hidden Factor Analysis

QTL Association Testing

QTL Association Analysis
- QTL Association Testing
- Quantile regression for QTL association testing
QTL data postprocessing

Multivariate Mixture Model

Mixture Multivariate Distribution Estimate
- A multivariate EBNM approach for mixture multivariate distribution estimate
- MASH analysis pipeline with data-driven prior matrices

Multiomics Regression Models

Fine-Mapping

GWAS Integration

xQTL-GWAS pairwise enrichment and colocalization
TWAS, cTWAS and MR
Multi-trait colocalization using ColocBoost

Enrichment and Validation

Chromosome-Specific Enrichment Analysis of Annotations Using Block Jackknife
Pathway Analysis
GREGOR enrichment analysis
Stratified LD Score Regression

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scRNA-seq expression calling

scRNA-seq expression calling#

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Bulk RNA-seq counts normalization

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Alternative polyadenylation

By The NIH/NIA Alzheimer's Disease Sequencing Project Functional Genomics xQTL Consortium

© Copyright 2021+, FunGen xQTL Analysis Working Group.