Genome-based prediction of common diseases: methodological considerations for future research

Review Genome-based prediction of common diseases: methodological considerations for future research A Cecile JW Janssens* and Cornelia M van Duijn* Address: *Department of Epidemiology, Erasmus University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands. Correspondence: A Cecile JW Janssens. Email: . Published: 18 February 2009 Genome Medicine 2009, 1:N (doi:10.1186/gm20) The electronic version of this article is the complete one and can be found online at http://genomemedicine.com/content/1/2/20 © 2009 BioMed Central Ltd Abstract The translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our understanding of the genetic basis of multifactorial diseases is improving, but the currently identified susceptibility variants contribute only marginally to the development of disease. At the same time, an increasing number of companies are offering personalized lifestyle and health recommendations on the basis of individual genetic profiles. This discrepancy between the limited predictive value and the commercial availability of genetic profiles highlights the need for a critical appraisal of the usefulness of genome-based applications in clinical and public health care. Anticipating the discovery of a large number of genetic variants in the near future, we need to prepare a framework for the design and analysis of studies aiming to evaluate the clinical validity and utility of genetic tests. In this article, we review recent studies on the predictive value of genetic profiling from a methodological perspective and address issues around the choice of the study population, the construction of genetic profiles, the measurement of the predictive value, calibration and validation of prediction models, and assessment of clinical utility. Careful consideration of these issues will contribute to the knowledge base that is needed to identify useful genome-based applications for implementation in clinical and public health practice. Introduction The past decade has seen rapid developments in our understanding of the genetic etiology of various common multifactorial diseases, such as age-related macular degeneration (AMD), type 1 and type 2 diabetes, cardiovascular diseases, Crohn’s disease and various cancers [1]. Further developments in genomic research, such as the growing number of genome-wide association studies, the large-scale consortia that are pooling data from various studies, and the advances in statistical genomics and genotype technology, are drastically improving the chances of identifying common low risk variants and rare high risk variants. It is beyond doubt that many more genetic susceptibility variants will be discovered in the next few years. Expectations are high that increasing knowledge of the genetic bases of disease will eventually lead to personalized medicine, that is, to preventive and therapeutic interventions for complex diseases that are tailored to individuals on the basis of their genetic profiles [2,3]. Genome-based personalized medicine already exists for monogenic disorders. For example, female carriers of BRCA1 or BRCA2 mutations are offered biannual mammography screening or provided the opportunity of preventive surgery. Potential Genome Medicine 2009, 1:20 Genome Medicine 2009, http://genomemedicine.com/content/1/2/20 Volume 1, Issue 2, Article 20 Janssens and van Duijn 20.2 Table 1 AUC and effect estimates of susceptibility variants for the prediction of three diseases Disease Coronary heart disease Systemic lupus erythematosus Hypertriglyceridemia AUC* 0.55 0.67 0.80 Reference [35] [28] [12] Genes and effect estimates AGT 1.28 (1.02, 1.61) HLA 2.36 (2.11, 2.64)‡ APOA5 7.36 (3.98, 13.6) ACE 1.18 (0.97, 1.44) ITGAM 1.62 (1.47,1.78)‡ APOA5 5.57 (3.13, 9.90) TBL2 2.81 (1.46, 5.24) APOE 2.14 (1.31, 3.49) AGTR1 1.21 (1.00, 1.45) IRF5/TNPO3 1.54 (1.40, 1.70)‡ CYP11B2 1.22 (1.01, 1.48) KIAA1542 0.78 (0.73, 0.85) ADD1 1.22 (1.01, 1.47) PXK 1.25 (1.16, 1.35) GCKR 2.11 (1.21, 3.67) GNB3 0.72 (0.52, 1.01) rs10798269 0.82 (0.76, 0.88) GALNT2 2.10 (1.15, 3.81) TRIB1 2.02 (1.24, 3.30) *AUC, area under the receiver operating characteristic curve. Values are hazard ratios [35] or odds ratios with 95% confidence intervals. ‡The original paper mentions several polymorphisms per gene and that one for each gene was included to assess the combined predictive value of six variants. The polymorphisms that had the highest odds ratios are reported here. applications of genetic profiling in multifactorial diseases include tailoring of prevention programs to at-risk individuals, determining the starting age of participation in screening programs [4] and, when profiles predict treatment success, tailoring treatment modalities and starting doses. As we have reviewed recently [5], the predictive value of genetic profiling is still limited at present, with a few promising exceptions. The area under the receiver operating characteristic curve (AUC) gives an assessment of the discriminative accuracy of a prediction model, that is, the degree to which the test results can discriminate between persons who will develop the disease and those who will not. AUC ranges from 0.50 (equal to tossing a coin) to 1.00 (perfect prediction). We found that the AUC was low for the genetic prediction of type 2 diabetes and coronary heart disease and high for the prediction of hypertriglyceridemia and AMD [5]. Table 1 illustrates that the high AUC of 0.80 for hypertriglyceridemia resulted from very strong individual genetic factors, with odds ratios ranging from 2.0 to 7.4, and the low AUC of 0.55 for coronary heart disease from genetic variants with low odds ratios. Note that the strongest genetic predictor by far for coronary heart disease had a weaker effect than the weakest predictor for hypertriglyceridemia. In order to achieve appreciable predictive value, genetic profiles need to include a few strong genetic risk factors or a large number of weak susceptibility variants [6]. Although the predictive value of genetic profiling is still limited, an increasing number of companies already offer personalized lifestyle health recommendations and nutritional supplements on the basis of clients’ genetic profiles [7]. Despite the limited predictive value of genetic testing in multifactorial diseases, these commercial developments will yield ongoing interest from consumers, from health care professionals confronted with questions from patients who underwent testing, and from policy makers who search for novel strategies to improve health care and population health. These developments ask for a solid evidence base for genomics applications. One of the major challenges for the coming decades will be to inve (...truncated)