EHR-Based Genomic Medicine Implementation
A major focus of the Atherosclerosis and Lipid Genomics Laboratory is electronic health record (EHR)-based implementation of genomic medicine.
A genome-enabled EHR is needed to:
- Receive, store and present complex genomic information for clinical use
- Incorporate clinical decision support to help providers practice individualized medicine
- Provide links to relevant knowledge resources
Although adoption of electronic health records is increasing, the majority of EHR systems are not configured to manage genetic data, particularly data from whole-genome/exome sequences.
The Atherosclerosis and Lipid Genomics Lab is addressing the challenges in implementing genomic medicine using the EHR.
- Kullo IJ, Jarvik GP, Manolio TA, Williams MS, Roden DM. Leveraging the electronic health record to implement genomic medicine. Genet Med. 2013;15(4):270-1. [PMID: 23018749; PMC4206937].
- Gallego CJ, Burt A, Sundaresan AS, Ye Z, Shaw C, Crosslin DR, Crane PK, Fullerton SM, Hansen K, Carrell D, Kuivaniemi H, Derr K, de Andrade M, McCarty CA, Kitchner TE, Ragon BK, Stallings SC, Papa G, Bochenek J, Smith ME, Aufox SA, Pacheco JA, Patel V, Friesema EM, Erwin AL, Gottesman O, Gerhard GS, Ritchie M, Motulsky AG, Kullo IJ, Larson EB, Tromp G, Brilliant MH, Bottinger E, Denny JC, Roden DM, Williams MS, Jarvik GP. Penetrance of hemochromatosis in HFE genotypes resulting in p.Cys282Tyr and p.[Cys282Tyr];[His63Asp] in the eMERGE Network. Am J Hum Genet. 2015 Sep 9. 97(4):512-20. [PMID: 26365338; PMC4596892].
Study 1: Returning results from sequencing of FH genes to individuals with elevated lipid levels
As part of the Mayo eMERGE III project, clinically actionable results relevant to familial hypercholesterolemia (FH) and hypertriglyceridemia will be returned to patients and physicians at the point of care.
This process will be followed by assessment of the subsequent patient outcomes, costs and utilization of health care resources, and behavioral and psychosocial effects.
By building on methods we developed in the eMERGE II project, our research team in the Atherosclerosis and Lipid Genomics Lab will evaluate:
- The impact of genomic results on patient outcomes, including diagnostic and therapeutic interventions, and new-case detection in families
- Costs and utilization of health care resources after return of results
- Perception of genomic results, concerns about including genomic data in the EHR, worries about future employability or insurance coverage, and views regarding sharing genomic results with family members and others
- Kullo IJ, de Andrade M, Kardia SL, Boerwinkle E, Turner ST. Pleiotropic genetic effects account for the correlation between HDL cholesterol, triglycerides, and LDL particle size in hypertensive sibships. Am J Hypertens. 2005;18:99-103. [PMID: 15691623].
- Cassidy A, Bielak L, Kullo IJ, Klee G, Turner ST, Sheedy PF, Peyser PA. Sex-specific associations of lipoprotein(a) with presence and quantity of coronary artery calcification in an asymptomatic population. Med Sci Monitor. 2004;10:CR493-503. [PMID: 15328481].
- Kullo IJ, Bailey KR, McConnell JP, Peyser PA, Bielak LF, Kardia SL, Sheedy PF II, Boerwinkle E, Turner ST. Low-density lipoprotein particle size and coronary atherosclerosis in subjects belonging to hypertensive sibships. Am J Hypertens. 2004;17:845-851. [PMID: 15363830].
- Klos KL, Kullo IJ. Genetic determinants of HDL: Monogenic disorders and contributions to variation. Curr Opin Cardiol. 2007;22:344-351. [PMID: 17556888].
- Ding K, Kullo IJ. Molecular population genetics of PCSK9: a signature of recent positive selection. Pharmacogenet Genomics. 2008;18(3):169-179. [PMID: 18300938; PMC2842919].
- Ding K, McDonough SJ, Kullo IJ. Evidence for positive selection in the c-terminal domain of the cholesterol metabolism gene PCSK9 based on phylogenetic analysis in 14 primate species. PLoS One. 2007;2(10):e1098. [PMID:17971861 PMCID: 2034530].
- Kullo IJ, Ding K, Boerwinkle E, Turner ST, Kardia SLR, de Andrade M. Quantitative trait loci influencing low density lipoprotein particle size in African Americans. J Lipid Res. 2006;47:1457-1462. [PMID: 16625024].
- Kullo IJ, Turner ST, Kardia SL, Eric Boerwinkle, de Andrade M. A novel quantitative trait locus on chromosome 1 with pleiotropic effects on HDL-cholesterol and LDL particle size in hypertensive sibships. Am J Hypertens. 2005;18:1084-1090. [PMID: 16109322].
Study 2: Developing a framework to assign pathogenicity to LDLR variants
Determining potentially disease-relevant DNA variants from genome sequencing is important since false assignments of pathogenicity can have adverse consequences in clinical practice.
One of the aims of the Mayo eMERGE project is to determine which genetic variants are most likely to contribute to familial hypercholesterolemia and which should be discussed with patients and families.
Our research team is currently assessing variability in assigning pathogenicity to rare putatively functional variants identified by sequencing of LDLR.
- Safarova MS, Klee EW, Baudhuin LM, Winkler EM, Kluge ML, Bielinski SJ, Olson JE, Kullo IJ. Variability in Assigning Pathogenicity to Incidental Findings: Insights from LDLR Sequence Linked to the Electronic Health Record in 1013 Individuals. In review.
Study 3: Clinical trials in genomic medicine
As genetic testing becomes widely available, its use for estimating risk of common diseases is becoming of increasing scientific and public health interest.
Genome-wide association studies (GWAS) have identified multiple loci associated with coronary heart disease (CHD). The majority of these loci are associated with CHD independent of conventional risk factors and could potentially improve the accuracy of CHD risk estimates.
Several studies have investigated the association of a genetic risk score (GRS) based on multiple CHD susceptibility single-nucleotide polymorphisms (SNPs) with incident CHD events. Most of the studies reported that a GRS is associated with adverse CHD events.
Incorporating CHD genetic risk information in clinical practice may refine risk estimates and aid in prevention of CHD, concordant with recent calls to promote the practice of precision medicine. Whether disclosing genetic risk of CHD influences health-related outcomes remains unknown and clinical trials are needed to address this gap in knowledge.
Study 4: Myocardial Infarction Genes (MI-Genes) Study: Using genomic data to refine risk assessment for heart attack
Coronary Heart Disease Genomic Decision Aid. A special decision aid for CHD disclosure was used for the MI-Genes Study. CHD risk estimates were based on the conventional risk score (panel A) vs. conventional as well as genetic risk (panel B), which can be obtained by activating the genetic risk score (GRS) button (arrow).
Our research team conducted a clinical trial to investigate whether disclosing a genetic risk score (GRS) for coronary heart disease (CHD) leads to lowering of low-density lipoprotein cholesterol (LDL-C) levels.
The genetic risk score was incorporated into CHD risk estimates based on a conventional risk score (CRS), yielding a genetically informed risk score (+GRS). We assessed whether disclosure of genetic risk of CHD affects LDL-C levels and whether any differences were due to changes in dietary fat intake, physical activity levels or statin initiation.
We tested two hypotheses:
- In patients randomized to receive +GRS, LDL-C levels at the end of the study period would be lower than in participants randomized to receive CRS alone
- +GRS participants with a high GRS would have lower LDL-C levels than +GRS participants with average/low GRS and those randomized to receive CRS alone
The MI-Genes Study was conducted as an eMERGE Network genomic medicine pilot. The study investigated patient response to disclosure of genetic risk of myocardial infarction in comparison with patient response to conventional risk factors (Framingham risk score).
Genotyping results for 28 variants from 27 genes for coronary heart disease were performed and documented in the electronic medical record. Genetic counselors provided education and support for patients in the MI-Genes Study. The MI-Genes Study team developed a state-of-the art web tool for disclosure of genetic risk.
Patients participated in a series of questionnaires to determine the impact of genetic risk on social and lifestyle behaviors. Changes in health were measured through laboratory tests for lipids and self-disclosed lifestyle changes.
For more information about the MI-Genes Study, see:
- Ding K, Bailey KR, Kullo IJ. Genotype-informed calculation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record. BMC Cardiovascular Disorders. 2011;11:66. [PMID: 22151179; PMC3269823].
- Robinson CL, Jouni H, Kruisselbrink TM, Austin EE, Christensen KD, Green RC, Kullo IJ. Disclosing genetic risk for coronary heart disease: effects on perceived personal control and genetic counseling satisfaction. Clin Genet. 2015 Feb 23;89(2):251-7. [PMID: 25708169].
- Kullo IJ, Jouni H, Olson JE, Montori VM, Bailey KR. Design of a randomized controlled trial of disclosing genomic risk of coronary heart disease: the Myocardial Infarction Genes (MI-GENES) study. BMC Med Genomics. 2015 Aug 15;8:51. [PMID: 26271327; PMC4536729].
- Kullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN, Haddad RA, Marroush TS, Shameer K, Olson JE, Broeckel U, Green RC, Schaid DJ, Montori VM, Bailey KR. Incorporating a Genetic Risk Score into Coronary Heart Disease Risk Estimates: Effect on LDL Cholesterol Levels (the MIGENES Clinical Trial). Circulation. 2016 Feb 25. pii: CIRCULATIONAHA.115.020109. [Epub ahead of print]. [PMID: 26915630; PMC4803581].
- Safarova MS, Bailey KR, Kullo IJ. Association of a Family History of Coronary Heart Disease With Initiation of Statin Therapy in Individuals at Intermediate Risk. Post Hoc Analysis of a Randomized Clinical Trial. JAMA Cardiol. JAMA Cardiol. 2016;1(3):364-366.
Study 5: Genetic risk score for abdominal aortic aneurysm
Abdominal aortic aneurysm (AAA) is relatively prevalent in older adults. It is often asymptomatic and associated with a high mortality, up to 80 percent, due to aneurysm rupture, with poor long-term survival due to concomitant atherosclerotic cardiovascular disease (ASCVD). Previous studies also indicate a significant sex difference in terms of the prevalence, progression and outcome of the disease.
Given the important genetic component in disease development reported by previous GWAS and family-based studies, our research team in the Atherosclerosis and Lipid Genomics Lab utilized available genomic information to construct a genetic risk score of AAA to:
- Predict aneurysm expansion
- Assess the effect of this genetic risk score on survival in older adults with ASCVD
- Investigate sex differences in the impact of genetic markers on aneurysm expansion
Study 6: Pharmacogenomics eMERGE PGx Project (RIGHT PROTOCOL)
Clinical decision support for commonly used cardiovascular drugs
The eMERGE PGx Project involved implementation of pre-emptive pharmacogenomics across the network sites.
At Mayo Clinic, 1,013 patients underwent sequencing of 84 pharmacogenes in a Clinical Laboratory Improvement Amendments (CLIA) environment. Dr. Kullo and other investigators led the development of physician education content (AskMayoExpert) and clinical decision support (CDS) logic for three cardiovascular drugs: clopidogrel, warfarin and simvastatin.
A multidisciplinary team was involved in the creation of CDS modules. Additional and specific online PGx education was provided to physicians using web links embedded in the alerts and inboxes, as well as specific drug-gene information.
Ten CDS rules (HLA-B*57:01-abacavir, HLA-B*15:02-carbamazepine, TPMT-thiopurines, CYP2D6-codeine/-tramadol/-tamoxifen, SLCO1B1-simvastatin, CYP2C19-clopidogrel, CYP2C9/VKORC-warfarin and IL28B-PEG-IFN alfa) have been approved by the Mayo Clinic PGx Task Force.
- Barajas MR, Formea CM, McCormick JB, Abdalrhim AD, Han LC, McBane RD, Fiksdal AS, Kullo IJ. A patient-centered approach to the development and pilot of a warfarin pharmacogenomics patient education tool for health professionals. Curr Pharm Teach Learn. 2015;7(2):249-255. [PMID: 25729462; PMC4339072].
- Crosslin DR, Robertson PD, Carrell DS, Gordon AS, Hanna DS, Burt A, Fullerton SM, Scrol A, Ralston J, Leppig K, Hartzler A, Baldwin E, Andrade MD, Kullo IJ, Tromp G, Doheny KF, Ritchie MD, Crane PK, Nickerson DA, Larson EB, Jarvik GP. Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network. Genome Med. 2015;7(1):67. [PMID: 26221186; PMC4517371].
- Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Denny J, Freimuth RR, Hartzler A, Kannry J, Kohane IS, Kullo IJ, Lin S, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden D, Tromp G, Williams MS, Starren J. A conceptual model for translating omic data into clinical action. J Pathol Inform. 2015;6:46. [PMID: 26430534; PMC4584438].