We introduce a similarity representation framework for robust heritability estimation in Genome-Wide Association Studies (GWAS). This problem parallels the signal-to-noise ratio estimation problem in the presence of a large number of predictors in linear models. Traditional fixed and random-effects methods for heritability estimation often impose restrictive assumptions on regression coefficients or the design (genotype) matrix. These assumptions are usually violated by the heterogeneous effects of genetic variants (regression coefficients) that depend on the genotype distribution and the correlation among genotypes due to linkage disequilibrium. This leads to the non-robust estimation of heritability in practice. To overcome these limitations, we propose a SiMILarity rEpresentation method (SMILE) by modeling the dependence of the gram matrix of the outcome vector (outcome similarity) on the gram matrix of the genetic signal vector (genetic similarity). We represent the genetic similarity using a weighted gram matrix of genotypes, where a data-dependent weight matrix is used to disentangle the heterogeneous variant effects from the genotype distribution. SMILE includes the classical random-effects model as a special case and improves the fixed-effects model by not requiring accurate estimation of the precision matrix or the regression coefficients. We develop a scalable implementation for efficient analysis of large biobank GWAS data. Extensive simulations and the analysis of the UK biobank data demonstrate the robustness of the proposed method over the existing methods across a range of genetic architectures, and show that SMILE provides a versatile approach for heritability estimation.