Genetic Misdiagnoses and the Potential for Health Disparities

Abstract

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.)

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

“…94 Multiple false positive reports of pathogenic variants causing hypertrophic cardiomyopathy, a disease with relatively simple genomic architecture, have been returned to individuals of African descent or unspecified ancestry that would have been prevented if even a small number of African American samples were included in control cohorts. 9 At the highly complex end of the polygenicity spectrum, we and others have shown that the utility of polygenic risk inferences and the heritable phenotypic variance explained in diverse populations is improved with more diverse cohorts. 92,95 Standard single-ancestry GWASs typically apply linear mixed model approaches and/or incorporate principal components as covariates to control for confounding from population structure with primarily Europeandescent cohorts.…”

“…8 Additionally, multiple individuals with African ancestry have received false positive misdiagnoses of hypertrophic cardiomyopathy that would have been prevented with the inclusion of even small numbers of African Americans in these studies. 9 Further, a previous study finding that 96% of GWAS participants are of European descent 1 has recently been updated; although the non-European proportion of GWAS participants has increased to nearly 20%, this is primarily driven by Asian individuals, and the proportion of individuals with African and Hispanic/Latino ancestry in GWASs has remained essentially unchanged. 4 As GWAS sample sizes grow to hundreds of thousands of samples, they also become better powered to detect rare variant associations.…”

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

“…[1][2][3] Big Data, defined by its volume, variety, velocity, variability, and veracity, is expected to bring significant benefits to health and health care, as it has to other sectors of the economy. 4,5 The improving quantity and quality of data, the changing dynamic and scale of data collection from various sources, and the fast development in measurements, analytic methods, and parallel computing of large amounts of biological and clinical data promise to dramatically transform clinical medicine and biomedical science.…”

UniProt Genomic Mapping for Deciphering Functional Effects of Missense Variants

“…http://dx.doi.org/10.1101/192914 doi: bioRxiv preprint first posted online Sep. 22, 2017; annotation of genetic variants (Richards et al 2015;Amr et al 2016;Manrai et al 2016;O'Daniel et al 2016;Walsh et al 2016) along with increasing amounts of population data (Amr et al 2016;Walsh et al 2016) are leading to the revaluation of previous assertions of pathogenicity.…”