We assessed the cumulative effects of the 97 GWS loci on mean BMI and on their ability to predict obesity (BMI ≥ 30 kg m−2) using the c statistic from logistic regression models in the Health and Retirement Study17 , a longitudinal study of 26,000 European Americans 50 years or older. The variance explained (VarExp) by each SNP was calculated using the effect allele frequency (f) and beta (β) from the meta-analyses using the formula VarExp = β2(1 − f)2f.
For polygene analyses, the approximate conditional analysis from GCTA19 (link),20 (link), was used to select SNPs using a range of P value thresholds (that is, 5 × 10−8, 5 × 10−7, …, 5 × 10−3) based on summary data from the European sex-combined meta-analysis excluding TwinGene and QIMR studies. We performed a within-family prediction analysis using full-sib pairs selected from independent families (1,622 pairs from the QIMR cohort and 2,758 pairs from the TwinGene cohort) and then SNPs at each threshold were used to calculate the percentage of phenotypic variance explained and predict risk (Extended Data Figs 2 and 3). We then confirmed the results from population-based prediction and estimation analyses in an independent sample of unrelated individuals from the TwinGene (n = 5,668) and QIMR (n = 3,953) studies (Extended Data Fig. 3 and Fig. 1c). The SNP-derived predictor was calculated using the profile scoring approach implemented in PLINK and estimation analyses were performed using the all-SNP estimation approach implemented in GCTA.