LDSC (Pulit et al, BMI)
Linkage Disequilibrium Score Regression1 was applied to the Pulit et al.2 meta-analysis of BMI.
The aim was to estimate heritability and look for signs of stratification and confounding.
The results are below:
| Parameter | Value |
|---|---|
| h2_liab | 0.1962 |
| h2_liab_se | 0.006186 |
| Lambda_gc | 2.685 |
| Mean_chi2 | 3.908 |
| Intercept | 1.268 |
| Intercept_se | 0.02052 |
| Ratio | 0.09215 |
| Ratio_se | 0.007059 |
To illustrate the fit of the LDSC model the BMI GWAS, I generated the following LDSC diagnostic plot:
LDSC diagnostic plot. Genetic variants are binned by LD Score. For each bin, we plot the mean LD score of variants in the bin (x axis) against Wald chi squared statistic (y axis 1) and scaled Wald chi squared statistic (y axis 2). Each plotted bin includes error bars indicating the standard error of bin chi squared mean. We also plot the LDSC regression line (solid) and the line corresponding to chi squared =1 (dashed). If the LDSC model fits the data perfectly, we should expect that the intercept of the regression line is at chi squared=1
LDSC produces a heritability estimate of approximately \(0.2\). While the LDSC intercept is significantly greater than 1, the attenuation ratio is small, indicating relatively low risk of confounding.
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Brendan K Bulik-Sullivan, Po-Ru Loh, Hilary K Finucane, Stephan Ripke, Jian Yang, Nick Patterson, Mark J Daly, Alkes L Price, and Benjamin M Neale. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3):291–295, 2015. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC4495769/. ↩
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Sara L Pulit, Charli Stoneman, Andrew P Morris, Andrew R Wood, Craig A Glastonbury, Jessica Tyrrell, Loïc Yengo, Teresa Ferreira, Eirini Marouli, Yingjie Ji, and others. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of european ancestry. Human molecular genetics, 28(1):166–174, 2019. URL: https://academic.oup.com/hmg/article/28/1/166/5098227. ↩