 One simple method of increasing the amount of phenotypic variance accounted for by GWAS (sometimes referred to as ‘h2GWAS’) is to sum the total effects of genome-wide significant SNP hits. The first efforts to do this were met with disappointing results as well. Weedon et al. (2008) conducted a GWAS of height which identified 20 SNPs with a combined effect of 3%. These meager results inspired an impetus in the GWAS community to conduct bigger and better studies that would be required to power the small effect sizes of individual SNPs. Bigger data meant more SNPs, which meant more variance explained and, consequently, increased h2GWAS. Even when summing the small effects of hundreds of genome-wide significant SNPs, variance explained by GWAS results are still quite small. For example, the traditional heritability of IQ is 50%, while the largest GWAS of IQ to date explains less than 5% total variance (Plomin and von Stumm 2018).