Identifying Genomic Signatures of Positive Selection to Predict Protective Genomic Loci in the Cohort of Lithuanian Clean-Up Workers of the Chornobyl Nuclear Disaster

Some people resist or recover from health challenges better than others. We studied Lithuanian clean-up workers of the Chornobyl nuclear disaster (LCWC) who worked in the harshest conditions and, despite high ionising radiation doses as well as other factors, continue ageing relatively healthily. Thus, we hypothesised that there might be individual features encoded by the genome which act protectively for better adaptiveness and health that depend on unique positive selection signatures. Whole-genome sequencing was performed for 40 LCWC and a control group composed of 25 men from the general Lithuanian population (LTU). Selective sweep analysis was performed to identify genomic regions which may be under recent positive selection and determine better adaptiveness. Twenty-two autosomal loci with the highest positive selection signature values were identified. Most important, unique loci under positive selection have been identified in the genomes of the LCWC, which may influence the survival and adaptive qualities to extreme conditions, and the disaster itself. Characterising these loci provide a better understanding of the interaction between ongoing microevolutionary processes, multifactorial traits, and diseases. Studying unique groups of disease-resistant individuals could help create new insights for better, more individualised, disease diagnostics and prevention strategies.

Automated summary

Through studying Lithuanian clean-up workers of the Chornobyl nuclear disaster, it has been found that some individuals are more resistant or recover better from health challenges. This may be due to individual genomic features that have been positively selected for better adaptiveness and health. The identification of 22 unique loci under positive selection in the genomes of these workers provides insights into the interaction between ongoing microevolutionary processes, multifactorial traits, and diseases.