less common variants. The former approach has been successfully done by the GIANT consortium, which reported that 10% and 1.5% of variation for height and BMI, respectively, can be accounted for by common SNPs using sample sizes of more than 100,00015,26. The latter will be facilitated by the 1000 Genomes Project33 and independently by efforts to sequence exomes and whole genomes. Experimental designs to discover causal variants that are in LD with common SNPs and those that interrogate less common or rare variants are complementary and recent publications that suggest that all or most variation for disease is to be found in less common or rare (coding) variants34,35 are not consistent with empirical data, at least for a range of complex traits, including height, BMI, lipids and schizophrenia15,26,29,36. For those causal variants that are rare in the population (say, with a frequency of less than 1%), an important but unanswered question is whether their effect sizes are large enough to be detected through conventional association analysis. The power of detection for a rare variant is proportional to the product of its frequency (which is small) and the square of its effect size. Hence rare variants will be detected only if
