Disease risk-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies have the potential to be used for disease risk prediction. An important feature of these risk-associated SNPs is their weak individual effect but stronger cumulative effect on disease risk. Several approaches are commonly used to model the combined effect in risk prediction, but their performance is unclear. We compared two methods to model the combined effect of 14 prostate cancer risk-associated SNPs and family history for the estimation of absolute risk for prostate cancer in a population-based case-control study in Sweden (2,899 cases and 1,722 controls). Method 1 weighs each risk allele equally using a simple method of counting the number of risk alleles, whereas method 2 weighs each risk SNP differently based on its odds ratio. We found considerable differences between the two methods. Absolute risk estimates from method 1 were generally higher than those of method 2, especially among men at higher risk. The difference in the overall discriminative performance, measured by area under the curve of the receiver operating characteristic, was small between method 1 (0.614) and method 2 (0.618), P = 0.20. However, the performance of these two methods in identifying high-risk individuals (2- or 3-fold higher than average risk), measured by positive predictive values, was higher for method 2 than for method 1. These results suggest that method 2 is superior to method 1 in estimating absolute risk if the purpose of risk prediction is to identify high-risk individuals.