Genetic Misdiagnoses and the Potential for Health Disparities

By Arjun K. Manrai, Birgit Funke, H. Rehm, M. Olesen, B. Maron, Peter Szolovits, D. Margulies, Joseph Loscalzo, Isaac S. Kohane. 2016

683 citations recorded by the source corpus.

BACKGROUND For more than a decade, risk stratification for hypertrophic cardiomyopathy has been enhanced by targeted genetic testing. Using sequencing results, clinicians routinely assess the risk of hypertrophic cardiomyopathy in a patient’s relatives and diagnose the condition in patients who have ambiguous clinical presentations. However, the benefits of genetic testing come with the risk that variants may be misclassified. METHODS Using publicly accessible exome data, we identified variants that have previously been considered causal in hypertrophic cardiomyopathy and that are overrepresented in the general population. We studied these variants in diverse populations and reevaluated their initial ascertainments in the medical literature. We reviewed patient records at a leading genetic-testing laboratory for occurrences of these variants during the near-decade-long history of the laboratory. RESULTS Multiple patients, all of whom were of African or unspecified ancestry, received positive reports, with variants misclassified as pathogenic on the basis of the understanding at the time of testing. Subsequently, all reported variants were recategorized as benign. The mutations that were most common in the general population were significantly more common among black Americans than among white Americans (P<0.001). Simulations showed that the inclusion of even small numbers of black Americans in control cohorts probably would have prevented these misclassifications. We identified methodologic shortcomings that contributed to these errors in the medical literature. CONCLUSIONS The misclassification of benign variants as pathogenic that we found in our study shows the need for sequencing the genomes of diverse populations, both in asymptomatic controls and the tested patient population. These results expand on current guidelines, which recommend the use of ancestry-matched controls to interpret variants. As additional populations of different ancestry backgrounds are sequenced, we expect variant reclassifications to increase, particularly for ancestry groups that have historically been less well studied. (Funded by the National Institutes of Health.)

Subfields: Cardiology and Cardiovascular Medicine, Genetics, AI Ethics, Fairness & Society, Medical & Health AI

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, background): “ample, in medical domains there are many instances in which the data studied and used are skewed toward certain populations—which can have dangerous consequences for the underrepresented communities. [83] showed how exclusion of African-Americans resulted in their misclassification in clinical studi”
Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, neutral): “, variants of unknown or uncertain significance) (Petrovski and Goldstein, 2016) and false-positive diagnoses (Manrai et al., 2016).”
Large-scale whole-genome sequencing of three diverse Asian populations in Singapore (2018, neutral): “005) and population-specific, highlighting the importance of population diversity in human genomics research.(5,11,15) To implement precision medicine for local populations, many countries have initiated population-based WGS studies.”
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data (2018, methodology): “15 Using simulations, the study demonstrated that the inclusion of African Americans in control groups could have prevented misclassification.”
2019 HRS expert consensus statement on evaluation, risk stratification, and management of arrhythmogenic cardiomyopathy. (2019, background): “(97-100) Ethnicity information is essential for interpreting the data.(101) Within the ACMs, examples of incorrect”
The critical needs and challenges for genetic architecture studies in Africa (2018, background): “For example, while easily avoidable, African American patients were more likely than white Americans to be incorrectly told they have a genetic mutation that increases their risk of hypertrophic cardiomyopathy, an early-onset life-threatening heart disease, at leading genetic testing labs [5].”
Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. (2017, background): “Knowledge of current estimates of mutation prevalence and penetrance has important implications for which variants to pursue experimentally, timing and scope of genetic testing, pharmacogenomics decisions, and for patient counselling.(134)”
A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog (2018, background): “with potentially serious clinical consequences if such data are not available [25].”
Integrative omics for health and disease (2018, background): “A major obstacle remains in building such frameworks for multiple omics profiles, which is likely to face some of the same challenges, such as the difficulty in applying results discovered in one population to individuals in another 79,80 .”
Algorithm fairness in artificial intelligence for medicine and healthcare (2023, background): “…biobanks is well-known in the context of genome-wide association studies, where variations in linkage disequilibrium structures and minor allele frequencies across ancestral populations can contribute to worsening the performance of polygenic-risk models for under-represented populations 172–177 .”