eMJA: Genetic risk estimation by health care professionals

Edwin P Kirk,* Annette Hattam,^† Anne Turner^‡

* Co-ordinator of Advanced Training, ^† Chair, Specialist Advisory Committee in Clinical Genetics, Royal Australasian College of Physicians; and Geneticists, Department of Medical Genetics, Sydney Children’s Hospital, High Street, Randwick, NSW 2031; ^‡ Chairperson, Board of Censors in Genetic Counselling, Human Genetics Society of Australasia. kirkedATsesahs.nsw.gov.au

To the Editor: Genetic risk estimation is a key element of the practice of clinical geneticists and genetic counsellors. Given this, it was with some concern that we read the findings of Bonke and colleagues regarding the performance of (mainly European) geneticists and counsellors in the application of Bayesian analysis to risk estimation.1

Bayesian analysis is taught as part of Australasian training in both clinical genetics and genetic counselling, and has been for as long as there have been formal programs. Thus, most Australian geneticists and counsellors should be familiar with the application of Bayes’ theorem to risk estimation.

In actual clinical practice, it is rare to need to perform this type of analysis. This is partly because of the rapid progress in molecular genetic testing, which often obviates the need for such calculations, and partly because situations in which Bayesian analysis is clinically helpful are uncommon. Pedigrees like those in the study by Bonke et al do not come along often; when they do, the modification of prior risk by Bayesian analysis is not often important. For example, modification of a risk from 50% to 33% or from 25% to 17% (as in two of the examples used by Bonke et al) is unlikely to alter decision-making for the families involved. Specifically, as these examples all involve testing for Huntington’s disease, in which molecular analysis is usually quite straightforward, we would expect very few individuals would decide whether to proceed with testing based on being given information about modification of risk expressed this way.

Moreover, when you are not performing this type of calculation regularly, it is time-consuming to do. It seems possible that many of those who completed the questionnaire would have taken greater care, and achieved greater accuracy, if faced by a real clinical situation. Nonetheless, for those of us who are involved in training clinical geneticists and genetic counsellors, the article is a useful reminder of the importance of this skill, and we will communicate with supervisors to reinforce the importance of teaching Bayesian analysis to our trainees.

Benno Bonke,* Aad Tibben,^† Dick Lindhout,^‡ Angus J Clarke,§ Theo Stijnen¶

* Associate Professor, Department of Medical Psychology and Psychotherapy, Erasmus MC, PO Box 1738, Rotterdam, The Netherlands; ^† Professor, Department of Clinical Genetics and Neurology, Leiden University Medical Centre, Leiden, The Netherlands; ^‡ Professor, Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands; § Professor, Department of Medical Genetics, University of Wales College of Medicine, Cardiff, UK; ¶ Professor, Department of Epidemiology and Biostatistics, Erasmus MC. b.bonkeATerasmusmc.nl

In reply: Geneticists and counsellors must be able to calculate risks according to professional standards, regardless of whether modified risks lead to decision changes. Does training in genetic risk calculation help? Only 21% of our respondents who had had such training recently (< 3 years ago) estimated all target risks correctly. In response to Kirk et al, calculating conditional risks need not be time-consuming in scenarios similar to our target pedigrees,1 and is often helpful when at-risk (grand)parents do not wish to be tested but their offspring do.

Given n children at 25% prior risk tested negative and no other (grand)children tested, the conditional risk for at-risk individuals in generation g (with g = 0 at 50% prior risk, g = 1 at 25% prior risk, etc) is 1/[2g(2n+1)]. Thus, in target #4 (n = 1), the father’s risk (g = 0) equals 1/[20(21+1)] = 0.33. In target #9 (n = 2), the unborn’s risk (g = 2) equals 1/[22(22+1)] = 0.05.

Similar formulas for more complicated scenarios are available upon request. In calculating risks, however, care must be taken that the pedigrees and target individuals are comparable to our scenarios. In target #7, for instance, the risk for the untested aunt does not increase simply because of the decreased risk for her brother (gambler’s fallacy).2