Genetic And Environmental Associations Of Nonspecific Chromosomal Aberrations

Abstract

Nonspecific structural chromosomal aberrations (CAs) are found in around 1% of circulating lymphocytes from healthy individuals but the frequency may be higher after exposure to carcinogenic chemicals or radiation. CAs have been used in the monitoring of persons exposed to genotoxic agents and radiation. Previous studies on occupationally exposed individuals have shown associations between the frequency of CAs in peripheral blood lymphocytes and subsequent cancer risk. The cause for CA formation are believed to be unrepaired or insufficiently repaired DNA double-strand breaks or other DNA damage, and additionally telomere shortening. CAs include chromosome (CSAs) and chromatid type aberrations (CTAs). In the present review, we first describe the types of CAs, the conventional techniques used for their detection and some aspects of interpreting the results. We then focus on germline genetic variation in the frequency and type of CAs measured in a genome-wide association study (GWAS) in healthy individuals in relation to occupational and smoking-related exposure compared to non-exposed referents. The associations (at p<10-5) on 1473 healthy individuals were broadly classified in candidate genes from functional pathways related to DNA damage response/repair, including PSMA1, UBR5, RRM2B, PMS2P4, STAG3L4, BOD1, COPRS and FTO; another group included genes related to apoptosis, cell proliferation, angiogenesis and tumorigenesis, COPB1, NR2C1, COPRS, RHOT1, ITGB3, SYK, and SEMA6A; a third small group mapped to genes KLF7, SEMA5A and ITGB3 which were related to autistic traits, known to manifest frequent CAs. Dedicated studies on 153 DNA repair genes showed associations for some 30 genes, expression of which could be modified by the implicated variants. We finally point out that monitoring of CAs is so far the only method of assessing cancer risk in healthy human populations, and the use of the technology should be made more attractive by developing automated performance steps and incorporating artificial intelligence methods into the scoring.