PGS Case vs Control Analysis

Compare polygenic score distributions between cases and controls with statistical analysis.

Configuration

PGS ID

Cohort 1 (case)

Cohort 2 (control)

Plot Title

Normalize Bins?

Normalizes bin heights for better comparison.

Odds Ratio

Calculate Odds Ratio?

Compare risk between the two selected cohorts.

Subcohort Comparison

Select a PGS ID, score type, and two cohorts to generate visualizations.

Overview

Subcohort Analysis allows you to compare polygenic score distributions between two specific cohorts. This tool generates three types of visualizations:

Distribution Curve: Shows the overall score distribution for the combined cohorts
Cohort Histograms: Displays frequency distributions for each cohort separately
Odds Ratio Plot: (Optional) Compares risk between cohorts using statistical measures

Visualization Components

Distribution Curve

Smooth curve showing combined score distribution

Cohort Histograms

Side-by-side frequency counts for each cohort

Odds Ratio Plot

Statistical comparison of risk between cohorts

How to Use

Select PGS ID: Choose the polygenic score you want to analyze
Choose Score Type: Select the appropriate normalization (SUM, Z_MostSimilarPop, Z_norm1, or Z_norm2)
Pick Two Cohorts: Select the two groups you want to compare
Configure Options:
- Add a custom plot title if desired
- Enable "Normalize Bins" for better visual comparison of differently-sized cohorts
Optional - Add Odds Ratio: Enable odds ratio analysis to quantify risk differences
Generate Plot: Click the generate button to create your visualization

Configuration Options

Normalize Bins

When enabled, adjusts histogram bin heights to account for different cohort sizes. This makes it easier to compare distribution shapes when your two cohorts have very different sample sizes.

Recommendation: Enable this when comparing cohorts of very different sizes.

Primary Application: This analysis is particularly powerful for case vs control comparisons, where you want to compare polygenic score distributions between individuals with and without a specific condition or trait.

What is an Odds Ratio?

An odds ratio (OR) compares the likelihood of an outcome between two groups. In our context, it compares the likelihood of having a high polygenic score between two cohorts.

OR = 1: Equal risk between cohorts
OR > 1: Cohort 1 has higher risk than Cohort 2
OR < 1: Cohort 1 has lower risk than Cohort 2

Single Threshold Method

What it does: Compares the proportion of individuals above vs below a specific score value.

How it works:

	Score ≥ Threshold	Score < Threshold
Cohort 1	A	B
Cohort 2	C	D

Odds Ratio = (A × D) / (B × C)

Best for: Testing a specific clinically relevant threshold (e.g., a risk cutoff used in practice).

Example: If you know that scores above 1.5 indicate high risk, use this method with threshold = 1.5.

Multiple Thresholds (Cumulative)

What it does: Calculates odds ratios at multiple score thresholds, showing how risk changes across the score distribution.

How it works:

Divides all scores into equal-sized groups (e.g., 10 groups)
For each threshold, compares individuals above vs below that point
Creates a series of odds ratios showing cumulative risk

Best for: Understanding how risk differences change across the entire score range, identifying optimal thresholds.

Example: Shows whether risk differences are consistent across low, medium, and high score ranges.

Risk Categories vs Lowest Risk

What it does: Divides scores into risk categories and compares each category against the lowest risk group.

How it works:

Divides all scores into equal-sized risk categories (e.g., deciles)
Uses the lowest category (1st decile) as the reference baseline
Compares each higher category against this baseline

Best for: Clinical interpretation, creating risk stratification systems, comparing high-risk to low-risk groups.

Example: "Individuals in the top 10% of scores have 3x higher odds compared to those in the bottom 10%."

Which Method Should You Choose?

Single Threshold: When you have a specific, clinically meaningful cutoff value
Multiple Thresholds: For exploratory analysis to find optimal thresholds
Risk Categories: For clinical applications and risk stratification

Statistical Notes

All methods include 95% confidence intervals
Small cell counts are adjusted using continuity correction (ε = 0.5)
Confidence intervals that cross 1.0 indicate non-significant differences

PGS Case vs Control Analysis

Configuration

Subcohort Comparison

Create New Database

Add Temporary Score Data

Session Information