How to interpret QQ-Plot
In genetics, the QQ Plot (Quantile-Quantile plot) is the "quality control" partner to your Manhattan plot. It tells you if your statistical model is working or if your data is "noisy." Looking at the QQ plot in your image (the small plot in the top right corner), here is how to interpret it for your viewers: 1. The Red Diagonal Line (The Expected) The red line represents the Null Hypothesis. It shows what the data would look like if there were zero genetic associations—essentially, if everything was just random chance. If all your black dots stayed on this red line, your study found nothing. 2. The Black Dots (The Observed) These are your actual results. You want to see them follow the red line for most of the way and then "break away" at the very end. The "Tail" (Upper Right): Notice how the dots at the far right of your image curve sharply upward away from the red line. Interpretation: This is exactly what you want to see! This "tail" represents the true genetic signals (the skyscrapers on your Manhattan plot). It means these SNPs are much more significant than what would happen by random chance. 3. Diagnosing Problems (The Shape) The shape of this curve tells a story about your data quality: Early Inflation (Bulging upward early): If the black dots lift off the red line right from the start (near the bottom left), it usually means Population Stratification. In plain English: It means your results are biased because of the ethnic makeup of your groups or cryptic relatedness (e.g., you accidentally included a bunch of cousins), rather than actual biology. Deflation (Sagging below the line): If the dots stay below the red line, your model is too strict. You are "killing" your signals, and you might miss real discoveries (False Negatives). The "Perfect" Plot (Like yours): Most dots stay on the line, and only the most significant ones curve up at the end. This suggests a clean study with real hits. https://www.youtube.com/watch?v=HimTorACbsE
In genetics, the QQ Plot (Quantile-Quantile plot) is the "quality control" partner to your Manhattan plot. It tells you if your statistical model is working or if your data is "noisy." Looking at the QQ plot in your image (the small plot in the top right corner), here is how to interpret it for your viewers: 1. The Red Diagonal Line (The Expected) The red line represents the Null Hypothesis. It shows what the data would look like if there were zero genetic associations—essentially, if everything was just random chance. If all your black dots stayed on this red line, your study found nothing. 2. The Black Dots (The Observed) These are your actual results. You want to see them follow the red line for most of the way and then "break away" at the very end. The "Tail" (Upper Right): Notice how the dots at the far right of your image curve sharply upward away from the red line. Interpretation: This is exactly what you want to see! This "tail" represents the true genetic signals (the skyscrapers on your Manhattan plot). It means these SNPs are much more significant than what would happen by random chance. 3. Diagnosing Problems (The Shape) The shape of this curve tells a story about your data quality: Early Inflation (Bulging upward early): If the black dots lift off the red line right from the start (near the bottom left), it usually means Population Stratification. In plain English: It means your results are biased because of the ethnic makeup of your groups or cryptic relatedness (e.g., you accidentally included a bunch of cousins), rather than actual biology. Deflation (Sagging below the line): If the dots stay below the red line, your model is too strict. You are "killing" your signals, and you might miss real discoveries (False Negatives). The "Perfect" Plot (Like yours): Most dots stay on the line, and only the most significant ones curve up at the end. This suggests a clean study with real hits. https://www.youtube.com/watch?v=HimTorACbsE