Fig. 4: Cross-modal autoencoders capture genotype–phenotype associations for cardiovascular data.
From: Cross-modal autoencoder framework learns holistic representations of cardiovascular state
a Manhattan plots for GWAS of BMI and RVEF derived from cross-modal embeddings identify lead SNPs associated with these traits. For BMI, such GWAS identifies SNPs associated with FTO, which is known to have an effect on BMI and obesity risk. For RVEF, such GWAS identifies SNPs associated with BAG3, HMGA2, and MLF1, which have been previously associated with RVEF. b To more generally capture genetic associations with the heart, a GWAS can be performed in the cross-modal ECG and MRI latent space even in the absence of labelled data. The Manhattan plots of such unsupervised GWAS identify lead SNPs including those associated with NOS1AP, TTN, SCN10A, SLC35F1, KCNQ1, which have been previously associated with cardiovascular phenotypes. c The corresponding QQ plots and λGC factors indicate that there is minimal inflation in the unsupervised GWAS of cross-modal ECG and cardiac MRI embeddings. d Clustering SNPs by the vector from the mean embedding of homozygous reference samples to the mean embedding of heterozygous and homozygous alternate samples in order to group SNPs by similar phenotypic effect results in clusters of SNPs corresponding to those associated with the QT interval (NOS1AP and KCNQ1), those related to the P-wave (SCN10A and ALPK3), as well as SNPs that affect multiple cardiac traits (e.g., BAG3, SLC35F1, and KCND3).
