cis-eQTL Analysis with SAIGE-QTL

cis-eQTL analysis tests genetic variants located near genes (typically within 1 Mb) for their effects on gene expression. This approach is particularly powerful for identifying local genetic regulatory elements.

Analysis Overview

SAIGE-QTL supports two complementary approaches for cis-eQTL mapping:

🔹 Common Variant Analysis

  • Method: Single-variant tests
  • Steps: 3-step process (Step 1 → Step 2 → Step 3)
  • Best for: Variants with MAF > 1-5%
  • Output: Variant-level association statistics

🔹 Rare Variant Analysis

  • Method: Set-based tests (gene burden/variance component tests)
  • Steps: 2-step process (Step 1 → Step 2)
  • Best for: Variants with MAF < 1-5%
  • Output: Gene-level association statistics

Note: Step 1 (null model fitting) is shared between both approaches

Workflow Diagram

SAIGE-QTL cis-eQTL analysis workflow showing three-step process for common variants and two-step process for rare variants

When to Use cis-eQTL Analysis

Choose cis-eQTL analysis when:

  • You want to identify local regulatory variants near specific genes
  • You have candidate genes of interest
  • You need higher statistical power (smaller multiple testing burden than genome-wide)
  • You’re studying tissue-specific or cell-type-specific regulation
  • You want to validate known eQTLs in your population/tissue

Consider genome-wide analysis when:

  • You want to discover trans-eQTLs (distant regulatory effects)
  • You’re performing unbiased discovery across the genome
  • You have computational resources for larger-scale analysis

Getting Started

Prerequisites

  1. Install SAIGE-QTL using your preferred method
  2. Understand the workflow and analysis pipeline
  3. Prepare your data: phenotype files, genotype files, and covariates

Quick Start Guide

  1. Step 1: Fit null models - One model per gene of interest
  2. Step 2: Run association tests - Test cis-variants for each gene
  3. Step 3: Gene-level analysis (optional) - For rare variant burden tests

Analysis Considerations

Variant Selection

  • cis-window: Typically 1 Mb upstream and downstream of gene boundaries
  • MAF filtering: Consider separate analyses for common (MAF ≥ 5%) and rare (MAF < 5%) variants
  • Quality control: Apply standard variant QC before analysis

Statistical Power

  • Multiple testing: Fewer tests compared to genome-wide analysis
  • Effect sizes: cis-eQTLs typically have larger effect sizes
  • Sample size: Power increases with larger sample sizes and more cells per individual

Cell-Type Specificity

  • Analyze cell-type-specific expression when possible
  • Consider pseudobulk approaches for rare cell types
  • Account for cell composition effects in mixed populations

Next Steps

Ready to start your cis-eQTL analysis? Follow these detailed guides:

  1. Step 1: Null Model Fitting - Detailed parameter explanations and examples
  2. Step 2: Single-Variant Tests - cis-eQTL association testing
  3. Step 2: Set-Based Tests - Rare variant burden analysis
  4. Step 3: Gene-Level Analysis - Combine results across variants

Alternative Analysis