REPRODUCIBILITY OF RESULTS IN THE GENETICS OF PREDISPOSITIONS AND THEIR PREDICTIVE VALUES

A.V. Rubanovich (1), N.N. Khromov-Borisov (2,3)
1 -Vavilov Institute of General Genetics of Russian Academy of Sciences, Russian Federation, 119991, Moscow, Gubkina Str., 3,
2 -Pavlov First State Medical University of Saint Petersburg; Russian Federation, 197022, Saint Petersburg, L, va Tolstogo Str., 6–8,
3 -Russian R.R. Vreden Research Institute of Traumatology and Orthopedy, Russian Federation, 195427, Saint Petersburg, Akademika Baikova Str., 8

Poor reproducibility and low predictive values of the results in the genetics of predispositions become a systemic problem. Results of the statistical quality control of genetic tests in the study should be supported with not only integral indices such as odds ratios (OR), but with the post-test (posterior) predictive probabilities (PPV and NPV) and likelihood ratios (LR[+] and LR[-]). Usefulness of predictiveness graphs for visualization of the relationships between the prevalence as a pretest (prior) probability of disease and predictive valuesPPV and NPV as posttest (posterior) probabilities is demonstrated. Predictive capabilities of widely used genetic, observational, instrumentaland immunological diagnostic tests are discussed. Several examples of such tests are presented and it is shown that despite of their high statistical significance they are not able to provide clinically important association between the disease and biomarker. The predictive power of the vast majority of genetic markers (given very wide confidence intervals due to small sample sizes) differs little from the population prevalence of the disease. Extremely rare the odds ratios in the studies on the genetics of dispositions exceed practically critical OR = 5. As a result, in most cases, recommendations of medical geneticists are based on clinically negligible (though statistically significant) recognizablity and predictability of genetic markers.
Keywords: 
genetics of predisposition, genetic association, reproducibility, predictive values, Bayesian graphs