If there are unknown related samples in the data (cryptic relatedness), will possibly be inflated due to shared environment between close relatives and/or the effects of causal variants in LD with the SNPs but captured by family relatedness, and will be deflated due to the increase of . In fact, the interpretation of changes if there is a substantial proportion of close relatives in the data [30], [31]. This, however, affects GWAS result in a similar way, where the SE of the estimate of a SNP effect from a single SNP analysis (e.g. linear regression for a quantitative trait and logistic regression for a case-control study) will be deflated, causing an inflation of the test-statistics GWAS (often called “genomic inflation” [32]). For the estimation of using all common SNPs, to avoid possible confounding from shared environments and uncaptured causal variants, we suggested in Yang et al. (2010) a stringent threshold, i.e. 0.025, to remove cryptic relatedness from the data so that the estimate of can be compared directly to the results from GWAS in response to the “missing heritability” problem
