Introduction

Translating findings from genome-wide association studies (GWAS) to clinical utility in terms of complex trait prediction is a major milestone in genetics research [1]. This is especially important for traits whose estimated heritability was reported to be high. However, the identified common single nucleotide polymorphisms (SNPs) seldom have deterministic consequences. While each identified common risk SNP contributes to the overall disease risk, by itself it is unlikely to predict a large degree of variation in a disease outcome and thus usually represents a poor predictor by itself. The combination of all risk SNPs into a polygenic risk score (PRS) is a popular approach to improve predictive power and can be valuable for risk stratification, i.e., the identification of a small subset of a population with extreme PRS values that is at higher risk to develop a disease [1].

The discovery of risk SNPs through GWAS often depends on very large sample sizes of genotyped data (hundreds of thousands of tag SNPs or more) especially if one aims to capture a large fraction of the SNP heritability [2–4]. Until recently, GWAS of this scale were either exclusively or predominantly based on European populations, trailed by Asian populations, while all other ancestry groups comprised less than 5% [5]. The resulting bias in published GWAS results [6] is passed on to the development and application of PRS for many complex traits and despite current efforts to increase diversity in genetics research will likely continue in the foreseeable future [6].

The lack of portability of PRS across populations with different ancestry compositions is known and usually attributed to differences in causal variants, linkage disequilibrium (LD) patterns, allele frequencies, and effect sizes [7,8]. In addition, genotyping or imputation methods that were originally developed for European ancestry (EUR) studies can amplify such differences [7,8].

There are several examples of studies that explore PRS constructed using GWAS results from different ancestry groups. Belsky et al. [9] constructed an obesity PRS based on EUR-GWAS and found that it performed poorly individuals of African American compared to those of EA [9]. Grinde et al. [10] assessed the performance of PRS based on EUR GWAS in a Hispanic/Latino population for three groups of traits: anthropometric measures, blood pressure, and blood count. The EUR-based PRS performed well for anthropometric and blood count traits but performed poorly for blood pressure traits [10]. EUR-based PRS for these quantitative traits also showed on average a 3.3-fold decrease in predictive performance in East Asian population when compared to the European population [11]. Others have demonstrated an association between PRS and genetic ancestry [12,13]. Simply put, the literature cautions against the transferability of EUR-based GWAS to other populations [5,8]. Recently we have provided a catalog of more than 500 PRS for various cancer using EUR-based GWAS [14]. However, there are little or no reports on the transferability of cancer PRS or whether these PRS can be used for other ancestries.

The UK Biobank Study (UKB) offers detailed questionnaire, electronic health record (EHR) and genetic data representing an excellent resource to study the influence of genetic risk factors on common complex disease. While predominantly European ancestry, it also includes over 20,000 participants of self-reported non-EUR ancestry (reported as “ethnic groups”) [15] that can, together with genetically inferred ancestry information, be stratified into the four main ancestry groups: African, East Asian, European or South Asian ancestry (S1 Table). Thus, UKB offers the opportunity to evaluate the performance of PRS across various ancestry groups and to assess the transferability of EUR-based cancer PRS.

To increase power for such an evaluation, we focus on two common cancer traits, breast and prostate cancer. Both of these traits offer several advantages for PRS explorations: high disease prevalence, large fraction of heritability already explained through known risk variants, low chance of phenotype misclassification, and available full summary statistics from very large, EUR-based GWAS [16,17].

Results

We constructed cancer PRS specifically for the European subgroup of UKB individuals using two different approaches for each cancer trait: GPRS is an effect-size weighted PRS based on a sparse set of GWAS hits (independent risk SNPs with P-value below 5x10^-8) and CSPRS, a PRS based on the Bayesian-regression method CS-PRS that uses continuous shrinkage (CS) priors [18]. Relatively sparse sets of 334 and 377 SNPs were incorporated in the GPRS for breast cancer and prostate cancer, respectively. By contrast the CSPRS constructs integrated over 1.1 million SNPs for each of the two cancers.

What can be clearly seen in Fig 1 are the different distributions of PRS across the European, South Asian, African and East Asian ancestry groups that were statistically significantly different in group means by one-way ANOVA (P < 2.31x10^-141; S2 Table). Both breast cancer PRS were on average higher in non-EUR groups, whereas prostate cancer PRS were higher in African and lower in East and South Asian ancestry groups (Fig 1 and S2 Table). These differences were pronounced for the CSPRS. This is likely a result of the summation of hundreds of thousands allele-frequency differences between ancestry groups compared to a few hundred for the GPRS. Overall, this suggests that these PRS are not directly transferable, e.g., a high breast cancer PRS in EUR individuals might fall into the lower PRS distribution of Africans ancestry individuals. Fig 1 also shows that most of the EAS and AFR and half of the SAS female control individuals would have a breast cancer CSPRS above the 10% population threshold and thus might be considered at increased risk, when using the EUR-based scale as reference. And conversely, most if not all the EAS males would not reach the 10% population threshold of the prostate cancer CSPRS, and males with genetic profiles that would place them at risk within the EAS ancestry group would not be considered at risk on a EUR-centric scale.

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Fig 1. Violin plots of the breast and prostate cancer PRS distributions.

Breast cancer (left) and prostate cancer (right) GPRS (GWAS hit-based PRS, top) and CSPRS (PRS-CS-based PRS, bottom) stratified by ancestry group are shown. Black vertical lines indicate 25, 50, and 75% quantiles within the ancestry-specific case (orange) and control (green) distributions. Red lines indicate 10% quantiles of the corresponding UKB PRS distribution in all controls. Sample sizes for each sub-set can be found in Table 1.

https://doi.org/10.1371/journal.pgen.1009670.g001

Still, what is striking is the consistent right shift of the PRS distributions in cases compared to controls with each ancestry group (Fig 1). With exception of the small sample of East Asian prostate cancer cases (n = 7), all PRS were significantly associated with increased continuous ORs for their corresponding cancers when standardized to one standard deviation (S.D.) within each ancestry group (OR [per unit S.D.] ≥ 1.44, Table 1). Furthermore, all PRS also indicated satisfactory discriminative performance within each ancestry group (covariate-adjusted AUC [AAUC] > 0.589).

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Table 1. Association and evaluation of cancer PRS across ancestry groups.

https://doi.org/10.1371/journal.pgen.1009670.t001

The CSPRSs usually outperformed the GPRSs in terms of association strength, accuracy and discrimination (Table 1). Especially for the breast cancer, the CSPRS showed consistent effect sizes across the ancestry groups (1.66 ≤ OR [per unit S.D.] ≤ 1.77) and good discriminatory ability (0.64 ≤ AAUC ≤ 0.66)

To evaluate if the increased risk is observable with increasing score or only present in the tails of the distribution, we stratified the PRS, again standardized within each ancestry group, and detected a trend of increasing number of cases within the increasing CSPRS deciles. This trend was strikingly monotonous in the substantially larger sample of European ancestry and, except for the small sample of prostate cancer cases of East Asian ancestry, noticeable though more capricious in non-EUR groups (Cochran-Armitage P < 0.00297; Fig 2 and S3 and S4 Tables). We saw similar trends for the GPRS (S1 Fig and S3 and S4 Tables).

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Fig 2. Observed case proportion across CSPRS (PRS-CS-based PRS) risk deciles.

Proportions of breast cancer cases (A) and prostate cancer cases (B) stratified by ancestry groups are shown. Total case counts per ancestry group are given in parentheses. Underlying sample counts and corresponding Cochran-Armitage Test for Trend P-values are reported in S3 and S4 Tables.

https://doi.org/10.1371/journal.pgen.1009670.g002

Finally, we quantified the PRS’s ability to enrich cases in the top 10% of the PRS distribution (defined in controls within each ancestry group) when compared to the bottom 90%. We observed an enrichment for breast cancer cases in the tail of the PRS distribution when we defined the top 10% within each ancestry group (breast cancer: OR Top10% > 2.18; prostate cancer: OR Top10% > 1.41). The enrichment was particularly sizable for breast cancer CSPRS for cases in European and African ancestry females (OR Top10%: 2.81 [95% CI: 2.69, 2.93] and 2.88 [95% CI: 1.85, 4.48], respectively) as well as for the prostate cancer CSPRS for cases in European, South Asian and East Asian ancestry males (OR Top10%: 4.00 [95% CI: 3.78, 4.23]; 4.41 [95% CI: 2.43, 8.04] and 6.53 [95% CI: 1.71, 25.0], respectively; Table 2).

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Table 2. Case enrichment in breast and prostate cancer PRS top 10% versus bottom 90%.

https://doi.org/10.1371/journal.pgen.1009670.t002

One may be concerned regarding the unbalanced case: control ratio used in our analysis and potential distortion of the asymptotic properties of the test statistics. For our ancestry specific calibration and reference group selection, we need to retain the maximal number of controls. We conducted a sensitivity analysis by using a matched sample that does not use all of the controls and we noted consistent point estimates but wider confidence intervals due to loss of sample size (S1 Text and S9 and S10 Tables).

Discussion

Overall, our findings in the UKB data are encouraging and suggest that cancer PRS derived from large EUR-based GWAS can, to a certain degree, be useful for risk stratification within EUR or within non-EUR individuals even though their distributions are dissimilar. However, there are limitations in regard to the generalizability of this approach.

First, a matching ancestry group with sufficiently large control sample sizes is needed to adequately place a person’s PRS within its reference PRS distributions. In this study, we obtained more homogenous groups by combining self-reported ethnic groups with genetically inferred ancestry groups. However, even within such groups an adjustment for any remaining population stratification, e.g., by including the first ten principal components, should be considered.

Secondly, overall breast and prostate cancer were selected because they offered several advantages compared to other traits: their estimated heritability is relatively high [17,19,20], they are common across all ancestry groups (breast cancer 3.1–6.2%; prostate cancer 1.2–5.1%; S1 Table) and each had summary statistics publicly available from large EUR-based GWAS meta-analyses.

Thirdly, the UKB study individuals were recruited from the same country, the UK, where healthcare coverage and non-genetic risk factors might be more similar compared to diverse ancestries from geographically separate populations. Though we recognize that lifestyle, health disparities and socioeconomic factors (e.g., education and income, S1 Table) might vary between ethnic groups of the UKB study.

While a fraction of risk variants is likely population-specific, our observation of a decent predictive PRS performance across ancestry groups indicated that, for the two analyzed cancers, a fraction of the cancer risk variants obtained from an EUR-based GWAS is shared with non-EUR groups. So, while PRS that rely on EUR-based GWAS were reported to be not ideal for non-EUR groups, they can be useful for risk stratification also in non-EUR groups. In our examples, the proportion of cases by PRS risk decile was informative within the studies ancestry group, i.e., an increasing PRS was associated with increased proportion of cases also among non-EUR groups. However, we noted that the EUR-based prostate cancer PRS performed particularly poor in AFR males indicating ancestry-specific diversity for prostate cancer as previously reported [21]. This also suggested that transferability of PRS across ancestries needs to be carefully evaluated by cancer and by ancestry group.

In our manuscript we focused on the two primary methods GPRS and CSPRS representing a simple and complex method of the spectrum of PRS methods. However, we also compared the performance of three alternative methods C+T PRS, Lassosum PRS, and LDpred PRS. For breast and prostate cancer these additional methods performed better than GPRS but poorer than CSPRS (S1 Text and S6–S10 Figs and S5 and S6 Tables). Furthermore, we repeated the same approach in another genetics linked biorepository at the University of Michigan, the Michigan Genomics Initiative (MGI) and though much limited in sample size, we saw similar performance behavior of GPRS and CSPRS in relative terms and the improved performance in terms of risk stratification by using ancestry-specific reference groups (S1 Text and S11 and S12 Figs and S7 and S8 Tables).

We recommend that PRS be constructed using GWAS based on the same ancestry group, if large diverse GWAS and their summary statistics are available. In the absence of large-scale GWAS for non-EUR groups, several groups are developing methods to improve PRS performance in non-EUR groups. These methods may leverage evidence that SNP selection based on EUR-based GWAS is generally appropriate while the use of EUR-based GWAS effect sizes in ethnically mismatched groups might not [22]. Duncan et al. [5] highlight the need for improved understanding and consideration of LD and variant frequencies when applying European ancestry based GWAS to non-EUR groups, while at the same time calling for large-scale GWAS in diverse populations [5]. Modelling ancestry into polygenic risk predictors or focusing on global risk variants might allow the retention of comparable predictive power across ancestries [8] and allow risk stratification also in understudies populations as shown for Hispanics/Latinos [10]. However, a restriction to global risk variants, e.g., defined by similar frequencies across all ancestry groups, might lead to the exclusion of true causal risk variants. When we applied such a global risk variant approach to the current dataset through simple frequency filtering, we made PRS distributions more similar across ancestry groups but also observed markedly reduced predictive power (S2–S5 Figs). While efforts are underway to contribute more diverse samples to genetic studies, their sample sizes will trail behind sample sizes of European ancestry GWAS for a long time [6]. Multiethnic PRS that combine larger EUR-based GWAS with smaller GWAS of the target ancestry group were recently proposed and might alleviate the discrepancies in sample sizes for the time being [23]. Until we obtain comparable/reasonable sized discovery cohorts, we need alternative approaches that make the best use of the data we already have, so that diverse ancestry groups can benefit from PRS research, too.

Taken together, our findings suggest that cross-ancestry cancer PRS can be useful for risk stratification, especially when there is a lack of well-powered diverse cancer GWAS. However, caution needs to be applied to the interpretation and application of such genetic risk predictors as they can be prone to multiple sources of bias [8].

Materials and methods

Supporting information

Acknowledgments

This research has been conducted using the UK Biobank Resource under application number 24460. The authors also acknowledge the Michigan Genomics Initiative participants, Precision Health at the University of Michigan, and the University of Michigan Medical School Data Office for Clinical and Translational Research, the University of Michigan Medical School Central Biorepository, and the University of Michigan Advanced Genomics Core for providing data storage, management, processing, and distribution services, and the Center for Statistical Genetics in the Department of Biostatistics at the School of Public Health for genotype data curation, imputation, and management in support of the research reported in this publication. Additional acknowledgements of GWAS sources are listed in S2 Text.