Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives

Abstract

Reliable risk assessment of frequent, but treatable diseases and disorders has considerable clinical and socio-economic relevance. However, as these conditions usually originate from a complex interplay between genetic and environmental factors, precise prediction remains a considerable challenge. The current progress in genotyping technology has resulted in a substantial increase of knowledge regarding the genetic basis of such diseases and disorders. Consequently, common genetic risk variants are increasingly being included in epidemiological models to improve risk prediction. This work reviews recent high-quality publications targeting the prediction of common complex diseases. To be included in this review, articles had to report both, numerical measures of prediction performance based on traditional (non-genetic) risk factors, as well as measures of prediction performance when adding common genetic variants to the model. Systematic PubMed-based search finally identified 55 eligible studies. These studies were compared with respect to the chosen approach and methodology as well as results and clinical impact. Phenotypes analysed included tumours, diabetes mellitus, and cardiovascular diseases. All studies applied one or more statistical measures reporting on calibration, discrimination, or reclassification to quantify the benefit of including SNPs, but differed substantially regarding the methodological details that were reported. Several examples for improved risk assessments by considering disease-related SNPs were identified. Although the add-on benefit of including SNP genotyping data was mostly moderate, the strategy can be of clinical relevance and may, when being paralleled by an even deeper understanding of disease-related genetics, further explain the development of enhanced predictive and diagnostic strategies for complex diseases.

Fig. 1

Search strategy for the inclusion of studies in the analysis. This figure provides an overview of the search strategy and the numbers of eligible studies meeting the inclusion criteria

Fig. 2

Overview of methods as to how genetic data were included in the prediction model. “Sum score”: from all SNPs a single predictor reflecting the genetic burden was created and used as single parameter in the prediction model, “individual SNPs”: SNPs were included as individual covariates in the model used for prediction, “weighted”: risk alleles of SNPs were weighted according to the respective odds ratio, and “unweighted”: risk alleles of SNPs were counted without weighting. Note that Brautbar et al. (2012), Everett et al. (2013) and Talmud et al. (2010) used weighted as well as unweighted sum scores in their analyses and thus appear in both categories in the figure

Fig. 3

Overview of the discrimination improvement due to inclusion of genetic data across all included 100 analyses. An AUC of 1.0 indicates perfect discrimination between cases and controls, 0.5 is equivalent to random guessing. Studies are stratified according to their predicted phenotype. Each reported analysis is depicted in form of an arrow with the arrow start indicating the AUC when using traditional risk factors only and the arrowhead indicating the AUC of the model including genetic data. The colour of the arrow illustrates significance of reclassification measures with blue statistically significant, orange not statistically significant, and grey not tested. Solid lines indicate GWAS-derived SNPs and dashed lines all other SNPs. The figure clearly illustrates it is generally harder to improve discrimination of a prediction model by including the genetic data in cases where the baseline model already performs well. Nevertheless, in some cases significant reclassification can be observed even for high baseline AUC values. For numbers and additional details on studies, please also refer to Supplemental Table 1

Fig. 4

Take-home messages for predicting complex diseases with common genetic markers