 What may be the greatest impediment to causal interpretations of SNP heritability is that it is biologically vacuous. SNP heritability is constructed using all genetic variants included in any given SNP chip, a significant portion of which are statistically insignificant. Moreover, although SNP heritability represents another figure of phenotypic variance ‘explained’ by genetic differences, SNP heritability has not led to the discovery of meaningful biological stories or etiologies that would causally link patterns of nucleotides to patterns of complex human behaviors. Judea Pearl and Dana MacKenzie refer to GWAS as a “prototypical ‘big data’ method that allows researchers to comb through the whole genome statistically, looking for genes that happen to show up more in people with a certain disease” (2018, 339–340). They go on to say “It is important to notice the word ‘association’ in the term GWAS. This method does not prove causality; it only identifies genes associated with a certain disease in the given sample” (340). They echo our point here, in saying that the methods used in GWAS hamper efforts at making causal inferences.