Context-adjusted proportion of singletons (CAPS): a novel metric for assessing negative selection in the human genome

Abstract

Abstract Interpretation of genetic variants remains challenging, partly due to the lack of well-established ways of determining the potential pathogenicity of genetic variation, especially for understudied classes of variants. Addressing this, population genetics methods offer a practical solution by evaluating variant effects through human population distributions. Negative selection influences the ratio of singleton variants and can serve as a proxy for deleteriousness, as exemplified by the Mutability-Adjusted Proportion of Singletons (MAPS) metric. However, MAPS is sensitive to the calibration of the singletons-by-mutability linear model, which results in biased estimates for certain variant classes. Building up on the methodology used in MAPS, we introduce the Context-Adjusted Proportion of Singletons (CAPS) metric for assessing negative selection in the human genome. CAPS produces corrected estimates with more accurate confidence intervals by eliminating the mutability layer in the model. Retaining the advantageous features of MAPS, CAPS emerges as a robust and reliable tool. We believe that CAPS has the potential to enhance the identification of new disease-variant associations in clinical and research settings, offering improved accuracy in assessing negative selection for diverse SNV classes.