To determine the prevalence, penetrance, and disease expression of cardiomyopathy-related genetic variants in an unselected, richly phenotyped Mayo Clinic population in the setting of preemptive seque Show more
To determine the prevalence, penetrance, and disease expression of cardiomyopathy-related genetic variants in an unselected, richly phenotyped Mayo Clinic population in the setting of preemptive sequencing, with return of incidental findings following the American College of Medical Genetics and Genomics recommendations. We analyzed a quaternary medical center-based biobank cohort (n=983) for reportable variants in 15 cardiomyopathy genes. Prioritization of genetic variants was performed using an internally developed pipeline to identify potentially reportable variants. Prioritized variants were then manually curated. The correlation of likely pathogenic/pathogenic (LP/P) variants with clinical phenotypes and outcomes was established. Artificial intelligence-enabled electrocardiographic predictions of reduced left ventricular ejection fraction and hypertrophic cardiomyopathy were applied to genotype-positive (G+) participants. Of the 983 patients, 11 (1%) were G+, with 11 LP/P variants found in the MYBPC3, DSG2, MYH7, DSP, and PKP2 genes. All G+ participants underwent electrocardiography, and 10 (90%) underwent echocardiography. Most patients (10 [90%]) did not have a prior diagnosis of cardiomyopathy. Definitive disease penetrance (heart failure or cardiomyopathy) was present in 4 (36%), while 3 (27%) had possible penetrance (structural heart disease identified by echocardiography). Arrhythmias and/or cardiac conduction disease was present in 4 of 11 G+ individuals (36%). Artificial intelligence-electrocardiography was positive for hypertrophic cardiomyopathy or reduced left ventricular ejection fraction in 5 of the G+ participants (45%), of whom 4 (80%) had definitive or possible disease penetrance. Cardiomyopathy-associated LP/P variants are present in a small subset of a quaternary medical center population, and disease penetrance in G+ individuals is high in the form of cardiac structural abnormalities and heart failure. Show less
Genome-wide association studies (GWAS) have identified many disease-associated variants, yet mechanisms underlying these associations remain unclear. To understand obesity-associated variants, we gene Show more
Genome-wide association studies (GWAS) have identified many disease-associated variants, yet mechanisms underlying these associations remain unclear. To understand obesity-associated variants, we generate gene regulatory annotations in adipocytes and hypothalamic neurons across cellular differentiation stages. We then test variants in 97 obesity-associated loci using a massively parallel reporter assay and identify putatively causal variants that display cell type specific or cross-tissue enhancer-modulating properties. Integrating these variants with gene regulatory information suggests genes that underlie obesity GWAS associations. We also investigate a complex genomic interval on 16p11.2 where two independent loci exhibit megabase-range, cross-locus chromatin interactions. We demonstrate that variants within these two loci regulate a shared gene set. Together, our data support a model where GWAS loci contain variants that alter enhancer activity across tissues, potentially with temporally restricted effects, to impact the expression of multiple genes. This complex model has broad implications for ongoing efforts to understand GWAS. Show less