Jarvik, Gail P.

AbstractObjectiveHigh BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome‐ and phenome‐wide approach.MethodsThis study performed a phenome‐wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank.ResultsClass 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome‐wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux.ConclusionsThis combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Purpose To summarise the clinical, molecular and biochemical phenotype of mannosyl-oligosaccharide glucosidase-related congenital disorders of glycosylation (MOGS-CDG), which presents with variable clinical manifestations, and to analyse which clinical biochemical assay consistently supports diagnosis in individuals with bi-allelic variants in MOGS. Methods Phenotypic characterisation was performed through an international and multicentre collaboration. Genetic testing was done by exome sequencing and targeted arrays. Biochemical assays on serum and urine were performed to delineate the biochemical signature of MOGS-CDG. Results Clinical phenotyping revealed heterogeneity in MOGS-CDG, including neurological, immunological and skeletal phenotypes. Bi-allelic variants in MOGS were identified in 12 individuals from 11 families. The severity in each organ system was variable, without definite genotype correlation. Urine oligosaccharide analysis was consistently abnormal for all affected probands, whereas other biochemical analyses such as serum transferrin analysis was not consistently abnormal. Conclusion The clinical phenotype of MOGS-CDG includes multisystemic involvement with variable severity. Molecular analysis, combined with biochemical testing, is important for diagnosis. In MOGS-CDG, urine oligosaccharide analysis via matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry can be used as a reliable biochemical test for screening and confirmation of disease.

Abstract Objective The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled “Developing a Clinical Genomic Informatics Research Agenda”. The meeting’s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. Materials and Methods Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. Results Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. Discussion Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Background: Sequencing Mendelian arrhythmia genes in individuals without an indication for arrhythmia genetic testing can identify carriers of pathogenic or likely pathogenic (P/LP) variants. However, the extent to which these variants are associated with clinically meaningful phenotypes before or after return of variant results is unclear. In addition, the majority of discovered variants are currently classified as variants of uncertain significance, limiting clinical actionability. Methods: The eMERGE-III study (Electronic Medical Records and Genomics Phase III) is a multicenter prospective cohort that included 21 846 participants without previous indication for cardiac genetic testing. Participants were sequenced for 109 Mendelian disease genes, including 10 linked to arrhythmia syndromes. Variant carriers were assessed with electronic health record–derived phenotypes and follow-up clinical examination. Selected variants of uncertain significance (n=50) were characterized in vitro with automated electrophysiology experiments in HEK293 cells. Results: As previously reported, 3.0% of participants had P/LP variants in the 109 genes. Herein, we report 120 participants (0.6%) with P/LP arrhythmia variants. Compared with noncarriers, arrhythmia P/LP carriers had a significantly higher burden of arrhythmia phenotypes in their electronic health records. Fifty-four participants had variant results returned. Nineteen of these 54 participants had inherited arrhythmia syndrome diagnoses (primarily long-QT syndrome), and 12 of these 19 diagnoses were made only after variant results were returned (0.05%). After in vitro functional evaluation of 50 variants of uncertain significance, we reclassified 11 variants: 3 to likely benign and 8 to P/LP. Conclusions: Genome sequencing in a large population without indication for arrhythmia genetic testing identified phenotype-positive carriers of variants in congenital arrhythmia syndrome disease genes. As the genomes of large numbers of people are sequenced, the disease risk from rare variants in arrhythmia genes can be assessed by integrating genomic screening, electronic health record phenotypes, and in vitro functional studies. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier; NCT03394859.

Background: Lp(a) (lipoprotein [a]) levels are higher in individuals of African ancestry (AA) than in individuals of European ancestry (EA). We examined associations of genetically predicted Lp(a) levels with (1) atherosclerotic cardiovascular disease subtypes: coronary heart disease, cerebrovascular disease, peripheral artery disease, and abdominal aortic aneurysm and (2) nonatherosclerotic cardiovascular disease phenotypes, stratified by ancestry. Methods: We performed (1) Mendelian randomization analyses for previously reported cardiovascular associations and (2) Mendelian randomization–phenome-wide association analyses for novel associations. Analyses were stratified by ancestry in electronic Medical Records and Genomics, United Kingdom Biobank, and Million Veteran Program cohorts separately and in a combined cohort of 804 507 EA and 103 580 AA participants. Results: In Mendelian randomization analyses using the combined cohort, a 1-SD genetic increase in Lp(a) level was associated with atherosclerotic cardiovascular disease subtypes in EA—odds ratio and 95% CI for coronary heart disease 1.28 (1.16–1.41); cerebrovascular disease 1.14 (1.07–1.21); peripheral artery disease 1.22 (1.11–1.34); abdominal aortic aneurysm 1.28 (1.17–1.40); in AA, the effect estimate was lower than in EA and nonsignificant for coronary heart disease 1.11 (0.99–1.24) and cerebrovascular disease 1.06 (0.99–1.14) but similar for peripheral artery disease 1.16 (1.01–1.33) and abdominal aortic aneurysm 1.34 (1.11–1.62). In EA, a 1-SD genetic increase in Lp(a) level was associated with aortic valve disorders 1.34 (1.10–1.62), mitral valve disorders 1.18 (1.09–1.27), congestive heart failure 1.12 (1.05–1.19), and chronic kidney disease 1.07 (1.01–1.14). In AA, no significant associations were noted for aortic valve disorders 1.08 (0.94–1.25), mitral valve disorders 1.02 (0.89–1.16), congestive heart failure 1.02 (0.95–1.10), or chronic kidney disease 1.05 (0.99–1.12). Mendelian randomization–phenome-wide association analyses identified novel associations in EA with arterial thromboembolic disease, nonaortic aneurysmal disease, atrial fibrillation, cardiac conduction disorders, and hypertension. Conclusions: Many cardiovascular associations of genetically increased Lp(a) that were significant in EA were not significant in AA. Lp(a) was associated with atherosclerotic cardiovascular disease in four major arterial beds in EA but only with peripheral artery disease and abdominal aortic aneurysm in AA. Additionally, novel cardiovascular associations were detected in EA.

Background: The pathophysiology of lipoprotein (a) [Lp(a)] remains obscure. We conducted a phenome wide analysis study (PheWAS) to identify pleiotropic effects of LPA variants. Methods: Among 51,843 European-ancestry participants (age 58±16 years, 54% female) of the electronic MEdical Records and Genomics (eMERGE) network we assessed whether LPA variants exhibited pleiotropic affects through an electronic health record-based PheWAS. We adjusted significant associations by presence of atherosclerotic cardiovascular disease (ASCVD; defined as a composite of coronary heart disease, cerebrovascular disease, and peripheral artery disease) to determine whether those associations were independent of ASCVD. Results: In PheWAS analysis, we tested 864 phecodes with 36 independent LPA variants. We identified 21 significant associations with non-ASCVD phenotypes including heart failure, aortic valve stenosis, and chronic kidney disease of which 14 were replicated across independent cohorts. The strength of associations between the LPA variant rs10455872 and both heart failure and aortic valve stenosis related phecodes were significantly attenuated after adjustment for ASCVD. However, the association with chronic kidney disease phecode remained independent following adjustment for ASCVD without any attenuation in the estimated odds ratio (Table). Conclusions: LPA genetic variants were associated with aortic valve stenosis, heart failure and chronic kidney disease. The observed association of LPA variant rs10455872 with aortic stenosis and heart failure appear to be partially mediated by ASCVD, while the association with chronic kidney disease appears to be independent of ASCVD. Additional studies are needed to elucidate potential mechanisms by which Lp(a) may contribute to the pathogenesis of non-ASCVD disease phenotypes.

Background: Return of incidental genetic findings is recommended for pathogenic/likely pathogenic (P/LP) variants in Mendelian arrhythmia genes. The extent to which these variants are associated with arrhythmia phenotypes in unselected populations is unknown. Objective: Assess the impact of return of results (RoR) of P/LP variants in 4 arrhythmia genes in research participants not selected for cardiovascular disease. Methods: The cohort included 24,410 participants from the Electronic Medical Records and Genomics Network. Rare variants (minor allele frequency <1%) in KCNE1 , KCNH2 , KCNQ1 , and SCN5A were identified and classified according to ACMG guidelines. Arrhythmia phenotypes extracted from electronic health records (EHRs) included atrial fibrillation/flutter (AF/AFL), conduction system disease, long QT syndrome (LQTS), ventricular tachycardia/fibrillation (VT/VF), and premature ventricular contractions (PVCs). P/LP carriers’ phenotypes were compared to non-carriers using logistic regression adjusted for demographic covariates and site. Participants with P/LP variants were informed of their results and the impact of this RoR was assessed after 1 year of follow-up clinical care. Results: The participants included 53.7% females and 72.5% white individuals, with a median age of 57 years. 71 participants had a heterozygous P/LP variant in one of the 4 genes. LQTS diagnoses were associated with P/LP carrier status for all 4 genes ( KCNE1 OR 22.8, p=1.0e-4; KCNH2 OR 35.1, p=1.8e-7; KCNQ1 OR 27.3, p=3.6e-18; SCN5A OR 5.6, p=0.02). Carriers of SCN5A P/LP variants were also more likely to have PVCs (OR 4.3, p=0.003). Sensitivity analyses showed the associations predated the RoR. EHRs included a diagnosis of LQTS or Brugada Syndrome in 11/71 (16%) pre-RoR, and 16 more were diagnosed after RoR (27/71, 38%). A total of 47/71 (66%) participants had a documented arrhythmia phenotype 1 year after RoR, although 20 of these participants were not diagnosed with an inherited arrhythmia syndrome. Conclusion: Inherited arrhythmia diagnoses could be established in nearly half of participants with P/LP variants. These data indicate that return of incidental P/LP variants in these genes may facilitate the early diagnosis of arrhythmias.

ABSTRACTBackgroundCurrently available structural variant (SV) detection methods do not span the complete spectrum of disease‐causing SVs. Optical genome mapping (OGM), an emerging technology with the potential to resolve diagnostic dilemmas, was performed to investigate clinically‐relevant SVs in a 4‐year‐old male with an epileptic encephalopathy of undiagnosed molecular origin.MethodsOGM was utilized to image long, megabase‐size DNA molecules, fluorescently labeled at specific sequence motifs throughout the genome with high sensitivity for detection of SVs greater than 500 bp in size. OGM results were confirmed in a CLIA‐certified laboratory via mate‐pair sequencing.ResultsOGM identified a mosaic, de novo 90 kb deletion and inversion on the X chromosome disrupting the CDKL5 gene. Detection of the mosaic deletion, which had been previously undetected by chromosomal microarray, an infantile epilepsy panel including exon‐level microarray for CDKL5, exome sequencing as well as genome sequencing, resulted in a diagnosis of X‐linked dominant early infantile epileptic encephalopathy‐2.ConclusionOGM affords an effective technology for the detection of SVs, especially those that are mosaic, since these remain difficult to detect with current NGS technologies and with conventional chromosomal microarrays. Further research in undiagnosed populations with OGM is warranted.