Taking genetic background into account improves the performance of GWAS. Manhattan plots for a simulated trait, in which each data point represents a genotyped SNP, ordered across the five chromosomes of Arabidopsis. Five SNPs (indicated by vertical dashed lines) were randomly chosen to be ‘causative’ and account for up to 10% of the phenotypic variance each. GWAS using a) a linear model, and b) a mixed model that accounts for population structure and other background genomic factors. The simple linear model leads to heavily inflated p-values and the five causative markers are not the strongest associations. The mixed model is superior, but still leads to one false negative and one false positive. A dashed horizontal line denotes the 5% Bonferroni threshold.