PRS could be useful to identify individuals at risk of disease development, however, the accuracy of current methods for the distribution as a whole, precludes the use of PRS in the clinic (too many false positives and false negatives). Results of this and other studies17 confirm that identification based on having a PRS above/below a certain threshold provides much better prediction accuracy than attempting to classify all individuals in a dataset.
In this study we provide ample evidence that AD should be modelled as a polygenic disease. In fact, risk of AD is not different from other diseases where liability to disease is continuous, and disease becomes evident after a threshold has been passed (the liability threshold model). In the threshold model, liability for a genetic disorder is (normally) distributed across the population and polygenic risk scores are a measure of disease liability39. The relative contributions of alleles of various effect sizes and frequencies are not fully resolved; while common alleles of small risk, captured by genome-wide association study arrays, capture between a third and a half of the genetic variance in liability, APOE alone substantially increases risk for the disorder40. A major problem with AD and using PRS to categorise people at risk, is the age of the study participants. Here we show that APOE-ε4 carriers have a lower burden of common AD risk alleles of small effect, implying that under the liability threshold model, the APOE risk is substantial enough to develop the disease with a lesser burden of common risk alleles with small effects. Since allelic variation at the APOE locus impacts survival altering the age at onset of AD and risk of other conditions (hyperlipidaemia, atherosclerosis, cardiovascular disease41–46), the frequency of APOE-ε4 goes down with age whereas genetic liability to AD measured by the PRS increases. In other polygenic diseases like schizophrenia, penetrance of the phenotype is mostly complete at 40 years of age47, while for AD even at 80 there are still individuals at risk but who have not yet developed AD. Looking at the means of the oligogenic risk score and the polygenic risk score across age groups, we found that following the pattern of APOE-ε4 frequency, the ORS decreased with age in cases but was on average higher than in controls. Conversely, PRS increased with age in cases, but decreased in controls. This can be explained if APOE and most of the GWAS significant SNPs used to calculate oligogenic scores point to genes which are in the same or overlapping pathways48–52. This would also explain why adding the oligogenic scores to the calculation do not improve prediction very much compared to APOE genotype alone (see Table 2). An important point here is that ORS is likely not very suitable to identify genes that provide protection, while PRS becomes lower in controls.
Since 1) age is the major confounding factor, 2) APOE is strongly associated with the age at onset, and 3) it is difficult to disentangle the aging and disease pathogenic components, we suggest to model APOE and PRS.no.APOE as two independent predictors or to use PRS.no.APOE as a predictor in subsamples stratified by the APOE genotype. In this study, the results show that the prediction accuracy of the oligogenic risk score was not better than using the effect of the APOE gene alone. The best performance overall was found here (and in our earlier study3) using the model with two variables (i.e. PRS.AD), APOE and PRS at pT ≤ 0.1, which excludes the APOE region (PRS.no.APOE). These differences in prediction modelling also explain why different optimal pTs may have been observed in other studies5,7,8.
We also looked at individuals at the extremes of the PRS distributions (above and below 2SDs) and found that both OR and AUC are very high in the whole sample (OR = 124, 95%CI=[6, 2707]) and for the ε3-homozygous individuals (OR = 95, 95%CI=[3, 2683]) using the proposed approach. The confidence intervals for the ORs are of course broad, as the sample size is small when looking at the extremes, but the accuracy remains high. The ORs for the extremes identified by ORS were smaller (OR = 10, 95%CI=[1, 75]) and the ORs had narrower CIs, suggesting that this model identifies a greater number of extremes than the polygenic model, but with poorer accuracy. The oligogenic score was not suitable to identify the extremes in the ε33 individuals with OR = 0.6, i.e. misclassifying high ORS cases as controls and vice versa.
Notably, the prediction accuracies using p-value thresholds of 0.1 and 0.5 (the latter reported in earlier work by us and others36) were similar. The reduction of the optimal pT from 0.5 to 0.1 is likely due to the improved estimation of SNP effect sizes, imputation quality and increased GWAS sample size in the latest GWAS2 in comparison to the earlier GWAS study53. Similar findings have been observed for other polygenic disorders, e.g. in Schizophrenia and Bipolar datasets of the Psychiatric Genetic Consortium54.
Comparing six PRS calculation methods, we conclude that the prediction accuracy in the whole sample is very similar, however, the individuals’ scores differ. The choice of the individuals at the extremes of the PRS distribution were concordant with PRSice, LDpred-inf, PRS-CS and PRS(P + T). There were more differences shown between LDAK and SBayesR. Due to lack of transparency of the Bayesian approaches, it is difficult to explain why certain individuals are at high polygenic risk whereas others are not, compared to PRS (P + T), where the SNP effect sizes and the LD pruning parameters are traceable. All these reasons allow us to conclude that for AD, PRS(P + T) is the method of choice.
An interesting and important conclusion of our study is that projecting a relatively small case/control sample onto the general population, results in a much better representation of risk in the study. Since case-control samples are enriched for cases as compared to the general population, the PRS distribution of the former is a mixture of two distributions (cases and controls) with distinct means. The PRS distribution for a population sample is likely to have a mean between the means of cases and controls, and a smaller variance (and hence, standard deviation) than that of the combined case-control sample. Standardising the case-control sample to the population sample will result in the shift of the individual scores in the case-control samples to the positive or negative side of the population mean. This makes the detection of more patients at high risk or with high protection possible. Increasing the size of the population sample will provide better estimates of the population PRS mean and SD (since the standard errors of these estimates will decrease as N increases). Note that including a larger population sample will proportionally increase the total number of people in the above and below 2SD categories in the joint (population plus case-control) sample, but that this will not necessarily be enriched by the individuals from the case-control sample. Hence the use of the 1000G population is an easy and straight-forward way to obtain this beneficial effect.
In conclusion, identifying individuals at high and low polygenic risk is very important for further work to understand how genetic risk translates into mechanisms of disease40. It might also become very relevant for drug development efforts targeting precise mechanisms of disease, as the PRS scores could be used to select small samples of patients in which proof of concept for the treatment can be obtained before testing the drug in larger cohorts. We show here that for AD, the optimal p-value threshold is pT ≤ 0.1, and the PRS calculation should account for the age-related genetic differences in cases and controls either by modelling APOE separately to the PRS or matching cases and controls for age and APOE status. This adjustment will be refined when we have a better idea of which genes are contributing to the disease aetiology via aging and which are directly on the pathology pathway.