BackgroundRecent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses.MethodsLeft ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses.ResultsEven within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies.ConclusionsImproving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.
Quantification of chamber size and systolic function is a fundamental component of cardiac imaging, as these measurements provide a basis for establishing both diagnosis and appropriate treatment for a spectrum of cardiomyopathies. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Characterizing variation in cardiac shape and morphology can add to our ability to understand and classify cardiovascular risk and pathophysiology. We describe deep learning enabled measurement of left ventricle (LV) sphericity using cardiac magnetic resonance imaging data from the UK Biobank and show that among adults with normal LV volumes and systolic function, increased sphericity is associated with increased risk for incident atrial fibrillation (HR 1.31 per SD, 95% CI 1.23-1.38), cardiomyopathy (HR 1.62 per SD, 95% CI 1.29-2.02), and heart failure (HR 1.24, 95% CI 1.11-1.39), independent of traditional risk factors including age, sex, hypertension, and body mass index. Using genome-wide association studies, we identify four loci associated with sphericity at genome-wide significance. These loci harbor known and suspected cardiomyopathy genes. Through genetic correlation and Mendelian randomization, we provide evidence that sphericity may represent a subclinical manifestation of non-ischemic cardiomyopathy.
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