Figure 2. A scenario where single cell probabilistic trait loci (scPTL) mapping has greater potential than a classical approach.

(A) A single cell trait, such as the expression level of an oncogene, is pathogenic at high values. Graphs represent distributions of the trait value (x-axis) among isogenic cells for four individuals having genotype g_ref, g₁, g₂, and g₃, respectively, at a given locus. This locus is a scPTL because the distributions are significantly different, but it is not a QTL because the mean trait value (orange broken line) is the same in all four individuals. Genotypes g₁, g₂, and g₃ generate more pathogenic cells than genotype g_ref, thereby increasing disease risk. (B) Corresponding data set used if a classical approach is applied. Brown and red symbols represent healthy and diseased individuals, respectively. Due to incomplete penetrance, only few individuals carrying the g₁ genotype at the locus display the disease. Therefore, the correlation between the macroscopic phenotype (disease versus healthy) and the genotype (g_ref versus g₁) is weak. (C) Data set used for scPTL mapping. Symbols represent the same individuals as in (B). This time, the phenotype on the y-axis is the variance of the distributions shown in (A). All g₁ individuals display greater phenotypic values compared with g_ref controls. This greater correlation between the ‘microscopic’ phenotype and the genotype enables detection of the locus.