Genome-based, mechanism-driven computational modeling of risks of ionizing radiation: The next frontier in genetic risk estimation?

Mutat Res Rev Mutat Res. 2015 Apr-Jun:764:1-15. doi: 10.1016/j.mrrev.2014.12.003. Epub 2014 Dec 31.

Abstract

Research activity in the field of estimation of genetic risks of ionizing radiation to human populations started in the late 1940s and now appears to be passing through a plateau phase. This paper provides a background to the concepts, findings and methods of risk estimation that guided the field through the period of its growth to the beginning of the 21st century. It draws attention to several key facts: (a) thus far, genetic risk estimates have been made indirectly using mutation data collected in mouse radiation studies; (b) important uncertainties and unsolved problems remain, one notable example being that we still do not know the sensitivity of human female germ cells to radiation-induced mutations; and (c) the concept that dominated the field thus far, namely, that radiation exposures to germ cells can result in single gene diseases in the descendants of those exposed has been replaced by the concept that radiation exposure can cause DNA deletions, often involving more than one gene. Genetic risk estimation now encompasses work devoted to studies on DNA deletions induced in human germ cells, their expected frequencies, and phenotypes and associated clinical consequences in the progeny. We argue that the time is ripe to embark on a human genome-based, mechanism-driven, computational modeling of genetic risks of ionizing radiation, and we present a provisional framework for catalyzing research in the field in the 21st century.

Keywords: Biophysical models; DNA damage; DNA repair; Radiation risk.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • DNA / radiation effects*
  • Female
  • Genetic Predisposition to Disease
  • Genome, Human
  • Humans
  • Mice
  • Models, Genetic*
  • Radiation Injuries / genetics*
  • Radiation, Ionizing
  • Risk Assessment

Substances

  • DNA