Lead Genetic Counselor, Stanford Center for Inherited Cardiovascular Disease (2010 - Present)
This study is designed to determine whether meditation is beneficial for genetic counselors and genetic counseling students. The main goal is to see if meditation can help with professional well-being (burnout for genetic counselors, stress for genetic counseling students). The investigators will also explore whether meditation has other benefits for the genetic counseling profession.
Stanford is currently not accepting patients for this trial. For more information, please contact Colleen Caleshu, 650.725.6273.
We sought to delineate the genetic test review and interpretation practices of clinical cardiovascular genetic counselors. A one-time anonymous online survey was taken by 46 clinical cardiovascular genetic counselors recruited through the National Society of Genetic Counselors Cardiovascular Special Interest Group. Nearly all (95.7%) gather additional information on variants reported on clinical genetic test reports and most (81.4%) assess the classification of such variants. Clinical cardiovascular genetic counselors typically (81.0%) classify variants in collaboration with cardiologist and/or geneticist colleagues, with the genetic counselor as the team member who is primarily responsible. Variant classification is a relatively recent (mean 3.2 years) addition to practice. Most genetic counselors learned classification skills on the job from clinical and laboratory colleagues. Recent graduates were more likely to have learned this in graduate school (p < 0.001). Genetic counselors are motivated to take responsibility for the classification of variants because of prior experiences with variant reclassification, inconsistencies between laboratories, and incomplete laboratory reports. They are also driven by a sense of professional duty and their proximity to the clinical context. This practice represents a broadening of the skill set of clinical cardiovascular genetic counselors and a unique expertise that they contribute to the interdisciplinary teams in which they work.
View details for DOI 10.1007/s10897-017-0175-7
View details for Web of Science ID 000438282000002
View details for PubMedID 29234989
Genetic testing is a valuable tool for managing inherited cardiovascular disease in patients and families, including hypertrophic, dilated, and arrhythmogenic cardiomyopathies and inherited arrhythmias. By identifying the molecular etiology of disease, genetic testing can improve diagnostic accuracy and refine family management. However, unique features associated with genetic testing affect the interpretation and application of results and differentiate it from traditional laboratory-based diagnostics. Clinicians and patients must have accurate and realistic expectations about the yield of genetic testing and its role in management. Familiarity with the rationale, implications, benefits, and limitations of genetic testing is essential to achieve the best possible outcomes.Successfully incorporating genetic testing into clinical practice requires (1) recognizing when inherited cardiovascular disease may be present, (2) identifying appropriate individuals in the family for testing, (3) selecting the appropriate genetic test, (4) understanding the complexities of result interpretation, and (5) effectively communicating the results and implications to the patient and family. Obtaining a detailed family history is critical to identify families who will benefit from genetic testing, determine the best strategy, and interpret results. Instead of focusing on an individual patient, genetic testing requires consideration of the family as a unit. Consolidation of care in centers with a high level of expertise is recommended. Clinicians without expertise in genetic testing will benefit from establishing referral or consultative networks with experienced clinicans in specialized multidisciplinary clinics.Genetic testing provides a foundation for transitioning to more precise and individualized management. By distinguishing phenotypic subgroups, identifying disease mechanisms, and focusing family care, gene-based diagnosis can improve management. Successful integration of genetic testing into clinical practice requires understanding of the complexities of testing and effective communication of the implications to patients and families.
View details for DOI 10.1001/jamacardio.2017.2352
View details for Web of Science ID 000413247700021
View details for PubMedID 28793145
Psychotherapeutic genetic counseling is an increasingly relevant practice description. In this paper we aim to demonstrate how psychotherapeutic genetic counseling can be achieved by using psychological theories to guide one's approach to working with clients. We describe two illustrative examples, fuzzy trace theory and cognitive behavior theory, and apply them to two challenging cases. The theories were partially derived from evidence of beneficial client outcomes using a psychotherapeutic approach to patient care in other settings. We aim to demonstrate how these two specific theories can inform psychotherapeutic genetic counseling practice, and use them as examples of how to take a psychological theory and effectively apply it to genetic counseling.
View details for DOI 10.1007/s10897-016-0023-1
View details for Web of Science ID 000399163800016
View details for PubMedID 27812918
View details for PubMedCentralID PMC5383519
Whether genetic counseling is a form of psychotherapy is open for debate. Early practicioners in genetic counseling described it as such, and this claim has been replicated in recent publications. This commentary is a rebuttal to the claim that genetic counseling is distinct from psychotherapty. We argue that it is a a form of psychoterapy that aims to help clients manage a health threat that affects their psychological wellbeing, paralleling the goals of psychotherapy.
View details for DOI 10.1007/s10897-016-0025-z
View details for Web of Science ID 000399163800018
View details for PubMedID 27804046
View details for PubMedCentralID PMC5383505
Inherited cardiovascular diseases pose unique and complex psychosocial challenges for families, including coming to terms with life-long cardiac disease, risk of sudden death, grief related to the sudden death of a loved one, activity restrictions, and inheritance risk to other family members. Psychosocial factors impact not only mental health but also physical health and cooperation with clinical recommendations. We describe an interdisciplinary approach to the care of families with inherited cardiovascular disease, in which psychological care provided by specialized cardiac genetic counselors, nurses, and psychologists is embedded within the cardiovascular care team. We report illustrative cases and the supporting literature to demonstrate common scenarios, as well as practical guidance for clinicians working in the inherited cardiovascular disease setting.
View details for DOI 10.1016/j.tcm.2016.04.010
View details for PubMedID 27256036
Advances in sequencing technology have taught us much about the human genome, including how difficult it is to interpret rare variation. Improvements in genetic test interpretation are likely to come through data sharing, international collaborative efforts to develop disease-gene specific guidelines, and computational analyses using big data.
View details for DOI 10.1186/s13073-016-0325-9
View details for Web of Science ID 000378592700002
View details for PubMedID 27324065
View details for PubMedCentralID PMC4915179
Myosin motors are the fundamental force-generating elements of muscle contraction. Variation in the human β-cardiac myosin heavy chain gene (MYH7) can lead to hypertrophic cardiomyopathy (HCM), a heritable disease characterized by cardiac hypertrophy, heart failure, and sudden cardiac death. How specific myosin variants alter motor function or clinical expression of disease remains incompletely understood. Here, we combine structural models of myosin from multiple stages of its chemomechanical cycle, exome sequencing data from two population cohorts of 60,706 and 42,930 individuals, and genetic and phenotypic data from 2,913 patients with HCM to identify regions of disease enrichment within β-cardiac myosin. We first developed computational models of the human β-cardiac myosin protein before and after the myosin power stroke. Then, using a spatial scan statistic modified to analyze genetic variation in protein 3D space, we found significant enrichment of disease-associated variants in the converter, a kinetic domain that transduces force from the catalytic domain to the lever arm to accomplish the power stroke. Focusing our analysis on surface-exposed residues, we identified a larger region significantly enriched for disease-associated variants that contains both the converter domain and residues on a single flat surface on the myosin head described as the myosin mesa. Notably, patients with HCM with variants in the enriched regions have earlier disease onset than patients who have HCM with variants elsewhere. Our study provides a model for integrating protein structure, large-scale genetic sequencing, and detailed phenotypic data to reveal insight into time-shifted protein structures and genetic disease.
View details for DOI 10.1073/pnas.1606950113
View details for Web of Science ID 000377948800046
View details for PubMedID 27247418
View details for PubMedCentralID PMC4914177
Recent advances in genetic testing for heritable cardiac diseases have led to an increasing involvement of the genetic counselor in cardiology practice. We present a series of cases collected from a nationwide query of genetics professionals regarding issues related to cost and utilization of genetic testing. Three themes emerged across cases: (1) choosing the most appropriate genetic test, (2) choosing the best person to test, and (3) interpreting results accurately. These cases demonstrate that involvement of a genetic counselor throughout the evaluation, diagnosis, and continuing management of individuals and families with inherited cardiovascular conditions helps to promote the efficient use of healthcare dollars.
View details for DOI 10.1097/CRD.0000000000000081
View details for PubMedID 26186385
View details for PubMedCentralID PMC4715801
This study sought to discover the key determinants of exercise capacity, maximal oxygen consumption (oxygen uptake [Vo2]), and ventilatory efficiency (ventilation/carbon dioxide output [VE/Vco2] slope) and assess the prognostic potential of metabolic exercise testing in hypertrophic cardiomyopathy (HCM).The intrinsic mechanisms leading to reduced functional tolerance in HCM are unclear.The study sample included 156 HCM patients consecutively enrolled from January 1, 2007 to January 1, 2012 with a complete clinical assessment, including rest and stress echocardiography and cardiopulmonary exercise test (CPET) with impedance cardiography. Patients were also followed for the composite outcome of cardiac-related death, heart transplant, and functional deterioration leading to septal reduction therapy (myectomy or septal alcohol ablation).Abnormalities in CPET responses were frequent, with 39% (n = 61) of the sample showing a reduced exercise tolerance (Vo2 max <80% of predicted) and 19% (n = 30) characterized by impaired ventilatory efficiency (VE/Vco2 slope >34). The variables most strongly associated with exercise capacity (expressed in metabolic equivalents), were peak cardiac index (r = 0.51, p < 0.001), age (r = -0.25, p < 0.01), male sex (r = 0.24, p = 0.02), and indexed right ventricular end-diastolic area (r = 0.31, p = 0.002), resulting in an R(2) of 0.51, p < 0.001. Peak cardiac index was the main predictor of peak Vo2 (r = 0.61, p < 0.001). The variables most strongly related to VE/VCO2 slope were E/E' (r = 0.23, p = 0.021) and indexed left atrial volume index (LAVI) (r = 0.34, p = 0.005) (model R(2) = 0.15). The composite endpoint occurred in 21 (13%) patients. In an exploratory analysis, 3 variables were independently associated with the composite outcome (mean follow-up 27 ± 11 months): peak Vo2 <80% of predicted (hazard ratio: 4.11; 95% confidence interval [CI]: 1.46 to 11.59; p = 0.008), VE/Vco2 slope >34 (hazard ratio: 3.14; 95% CI: 1.26 to 7.87; p = 0.014), and LAVI >40 ml/m(2) (hazard ratio: 3.32; 95% CI: 1.08 to 10.16; p = 0.036).In HCM, peak cardiac index is the main determinant of exercise capacity, but it is not significantly related to ventilatory efficiency. Peak Vo2, ventilatory inefficiency, and LAVI are associated with an increased risk of major events in the short-term follow-up.
View details for DOI 10.1016/j.jchf.2014.11.011
View details for PubMedID 25863972
The clinical significance of variants in genes associated with inherited cardiomyopathies can be difficult to determine because of uncertainty regarding population genetic variation and a surprising amount of tolerance of the genome even to loss-of-function variants. We hypothesized that genes associated with cardiomyopathy might be particularly resistant to the accumulation of genetic variation.We analyzed the rates of single nucleotide genetic variation in all known genes from the exomes of >5000 individuals from the National Heart, Lung, and Blood Institute's Exome Sequencing Project, as well as the rates of structural variation from the Database of Genomic Variants. Most variants were rare, with over half unique to 1 individual. Cardiomyopathy-associated genes exhibited a rate of nonsense variants, about 96.1% lower than other Mendelian disease genes. We tested the ability of in silico algorithms to distinguish between a set of variants in MYBPC3, MYH7, and TNNT2 with strong evidence for pathogenicity and variants from the Exome Sequencing Project data. Algorithms based on conservation at the nucleotide level (genomic evolutionary rate profiling, PhastCons) did not perform as well as amino acid-level prediction algorithms (Polyphen-2, SIFT). Variants with strong evidence for disease causality were found in the Exome Sequencing Project data at prevalence higher than expected.Genes associated with cardiomyopathy carry very low rates of population variation. The existence in population data of variants with strong evidence for pathogenicity suggests that even for Mendelian disease genetics, a probabilistic weighting of multiple variants may be preferred over the single gene causality model.
View details for DOI 10.1161/CIRCGENETICS.112.963421
View details for Web of Science ID 000312774800008
View details for PubMedID 23074333
View details for PubMedCentralID PMC3526690
Mutations in genes that encode components of the sarcomere are well established as the cause of hypertrophic and dilated cardiomyopathies. Sarcomere genes, however, are increasingly being associated with other cardiomyopathies. One phenotype more recently recognized as a disease of the sarcomere is restrictive cardiomyopathy (RCM). We report on two patients with RCM associated with multiple mutations in sarcomere genes not previously associated with RCM. Patient 1 presented with NYHA Class III/IV heart failure at 22 years of age. She was diagnosed with RCM and advanced heart failure requiring heart transplantation. Sequencing of sarcomere genes revealed previously reported homozygous p.Glu143Lys mutations in MYL3, and a novel heterozygous p.Gly57Glu mutation in MYL2. The patient's mother is a double heterozygote for these mutations, with no evidence of cardiomyopathy. Patient 2 presented at 35 years of age with volume overload while hospitalized for oophorectomy. She was diagnosed with RCM and is being evaluated for heart transplantation. Sarcomere gene sequencing identified homozygous p.Asn279His mutations in TPM1. The patient's parents are consanguineous and confirmed heterozygotes. Her father was diagnosed with HCM at 42 years of age. This is the first report of mutations in TPM1, MYL3, and MYL2 associated with primary, non-hypertrophied RCM. The association of more sarcomere genes with RCM provides further evidence that mutations in the various sarcomere genes can cause different cardiomyopathy phenotypes. These cases also contribute to the growing body of evidence that multiple mutations have an additive effect on the severity of cardiomyopathies.
View details for DOI 10.1002/ajmg.a.34097
View details for Web of Science ID 000294182500030
View details for PubMedID 21823217
View details for PubMedCentralID PMC3158811