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We would like to inform you about an incident involving UK Biobank data. We apologise to our participants for the concern this will cause, and we hope to provide reassurance by outlining the serious a...

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Analyses of snRNA genes in a French cohort of people with rare disorders, with validation through international collaboration, identify monoallelic and biallelic variants in RNU2-2 as frequent causes ...

Accurate estimates of allele frequencies aid in genetic discovery, including rare disease diagnosis, common disease investigations, and population genetics. Here, we present the Genome Aggregation Dat...

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Population-scale WGS reveals genetic determinants of persistent EBV DNA, linking immune regulation—especially antigen processing and MHC class II variation—to EBV persistence and heterogeneous di...

A longstanding goal in human genetics is to prioritize noncoding loci that, when disrupted, lead to developmental disorders and other Mendelian traits. In pursuit of this goal, multiple metrics have been developed to distinguish neutrally evolving sequences from those subjected to purifying selection. These metrics are commonly evaluated genome-wide, e.g., by computing a precision-recall curve on windows tiling the entire noncoding genome. Here, we identify parts of the noncoding genome where these metrics significantly underperform relative to their genome-wide performance due to "bias" in the underlying models of neutral genetic variation and/or a low "signal-to-noise ratio" in the genetic data. The most extreme effects are found for Gnocchi (Chen et al. 2024), the performance of which declines as GC content increases. We suggest annotating constraint scores of noncoding genomic intervals with robust measures of the bias of the corresponding model, allowing users to gauge confidence in those scores. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, R01HG012252