Evaluating Somatic Mutational Contamination in Large-Scale Germline Genomic Studies

Le résumé fourni par la source

Large-scale genomic initiatives like the UK Biobank have revolutionized our understanding of human disease. These studies typically assume that blood-derived DNA faithfully reflects an individual's germline genome. However, this assumption is challenged by somatic mutations arising from processes like clonal hematopoiesis. Although standard bioinformatics pipelines employ variant allele frequency (VAF)-based filtering to mitigate such contamination, the efficacy of these approaches requires systematic evaluation. By systematically analyzing germline genome data from large cohorts through applications of mutational signatures, we revealed critical limitations in current filtering methodologies. We found that the mutational spectrum of rare "germline" variants is highly similar to that of somatic mutations. Furthermore, we uncovered that these variants show significant associations with phenotypes such as age, sex, and smoking status, established drivers of somatic mutagenesis. Notably, our multivariable regression models estimated that these somatic artifacts contribute to a substantial excess burden, such as 4.73 mutations per megabase (mut/Mb) in males compared to females, a magnitude exceeding the mutation burden of many cancers. Although the precise absolute size of this contamination may vary depending on specific pathologies and individual environmental exposures, this persistent somatic contamination introduces substantial confounder effects, posing a risk of spurious associations and reverse causality in genetic studies. Our work underscores the urgent reconsideration of two fundamental aspects of genomic research: (1) refinement of variant filtering strategies to better distinguish true germline variants from somatic contaminants, and (2) incorporation of somatic mutagenesis factors as essential covariates in study design. Our findings provide basic guidance for improving the accuracy and interpretability of large-scale genomic studies.

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