Replication attempts using studies unselected for BMI have limited power to achieve genome-wide significance, even with thousands of subjects (Sebastiani et al. 2009). Since there is strong a priori evidence for genome-wide significant and suggestive variants from the large metaanalyses, alternative approaches to replication can be applied. Instead of testing individual loci sequentially, a genetic risk sum score (GRSS) summarizing the total number of risk alleles can be constructed and tested. The aggregate risk should be significant if a sufficient proportion of the variants have real effects. GRSS have been used to test the total impact of associated variants on complex traits and disease. For example, Aulchenko et al. (2009) used 54 variants in a GRSS which accounted for ∼4% of the phenotypic variance in height. Risk scores incorporating 18–20 genome-wide significant variants have been shown to be associated and predictive of type II diabetes, though algorithms including family history and additional risk factors perform better (Meigs et al. 2008; Talmud et al. 2010). GRSS have also been applied to BMI and obesity in populations of European and Chinese descent
