Although humans contain the same genes, the sequence within these DNA sites can vary from person to person. These small variations, also known as genetic variants, can increase the risk of developing certain diseases. While each variant will only have a weak effect, if multiple variations are present the odds of developing the disease becomes significantly higher.

To determine which variants are linked to a disease, researchers carry out genome-wide association studies which involve analyzing the genomes of individuals with and without the condition and comparing their genetic codes. This data is then used to calculate how different combinations of variants impact a person’s chance of getting the disease, also known as a polygenic risk score.

Currently, most genome-wide association studies only incorporate genetic data from people with European ancestry. Consequently, polygenic risk scores performed using this information may not accurately predict the risk of developing the disease for individuals with other ethnicities, such as people with Asian ancestry.

Here, Ho et al. evaluated how well previously calculated polygenic risk scores for the four most common cancers (breast, colorectal, prostate and lung) worked on individuals of East Asian descent. The scores were tested on a dataset containing the genetic sequence, medical history, diet and activity levels of over 21,000 people living in Singapore in the 1990s. Ho et al. found that the polygenic risk scores for breast, prostate and colorectal cancer were able to predict disease risk. However, the score for lung cancer did not perform as well. The polygenic risk score for breast cancer was the most accurate, and was able to stratify individuals into distinct risk bands at an earlier age than other scores.

These findings shed light on which existing polygenic risk scores will be effective at assessing cancer risk in individuals with East Asian ancestry. Indeed, Ho et al. have already incorporated the polygenic risk score for breast cancer into a pilot study screening individuals in a comparable population in Singapore. However, the polygenic risk scores tested still performed better on individuals with European ancestry, highlighting the need to address the lack of Asian representation in genome-wide association studies.

Polygenic risk scores (PRSs) for a range of health traits and conditions have been developed in recent years. These scores, which are based on summary statistics from genome-wide association studies (GWAS), can be used to stratify people depending on their genetic risk of acquiring various diseases, to improve screening and preventative interventions, as well as patient care (Polygenic Risk Score Task Force of the International Common Disease Alliance, 2021; Lambert et al., 2019). Precision risk assessment may help develop tailored screening strategies targeting individuals at higher risk of disease of interest (Clift et al., 2022).

The contributions of heritable genetic factors are different for different cancers. Twin studies have highlighted statistically significant effects of heritable genetic risk factors for cancers of the prostate, colorectal, and breast (Lichtenstein et al., 2000). The amount of phenotypic variance explained by the common genetic variants found by GWAS is also known to vary (Cano-Gamez and Trynka, 2020), suggesting that PRS derived from GWAS findings may perform to varying degrees for different cancers.

The area under receiver operating characteristic curve (AUC) is an important discrimination index for evaluating the performance of PRS. The greater the AUC, the better the discriminatory ability to separate cases from non-cases. A value of 0.5 suggests that the tool is performing no better than chance, while a value of 1 is obtained when cases and non-cases are perfectly separated. The range of reported AUC associated with published PRS ranged from 0.584 to 0.678 for breast cancer (Mavaddat et al., 2019; Ho et al., 2020; Kachuri et al., 2020; Du et al., 2021; Jia et al., 2020; Lacaze et al., 2021; Zhang et al., 2018), 0.591–0.769 for prostate cancer (Kachuri et al., 2020; Jia et al., 2020; Fritsche et al., 2020), 0.609–0.708 for colorectal cancer (Kachuri et al., 2020; Jia et al., 2020; Gafni et al., 2021; Archambault et al., 2022), and 0.52–0.846 for lung cancer (Kachuri et al., 2020; Jia et al., 2020; Fritsche et al., 2020; Hung et al., 2021). In a study by Jia et al. looking at eight common cancers in the UK Biobank population-based cohort study (n=400,812 participants of European descent), the observed AUC ranged from 0.567 to 0.662 (Jia et al., 2020).

While prediction of individual cancer risks through PRS remains moderate, emerging data supports the use of PRS for population-based cancer risk stratification. In previous work, Ho et al. examined the overlap of women identified to be at high risk of developing breast cancer based on family history for the disease, a non-genetic breast cancer risk prediction model, a breast cancer PRS, and carriership of rare pathogenic variants in established breast cancer predisposition genes (Ho et al., 2022). The overlap of individuals found to be at elevated risk of developing breast cancer based on the genetic and non-genetic models was low. PRS was also found to be able to identify high-risk individuals among young women who were not yet eligible to attend mammography screening. The findings suggest that a genetic tool that is feasible to be deployed for population-based screening may complement current screening programs.

Disparities in the genetic risk of cancer among various ancestry populations are poorly understood. Ideally, selected genetic variants that make up PRS should be relevant to the population being screened. The development of training datasets of PRS are dominated by samples of European ancestry, resulting in ancestry bias and issues with transferability to other populations (Lambert et al., 2019; Fritsche et al., 2021). The mismatch between the ancestries of the GWAS samples and the target populations for PRS application is a limiting factor (Fritsche et al., 2021). In this study, we evaluated the utility of common PRS, curated in the Polygenic Score (PGS) Catalog, in predicting the risk of the commonly diagnosed cancers with high genetic predisposition (breast, prostate, colorectal, and lung) in a prospective cohort comprising 21,694 participants of East Asian descent in Singapore. The reporting framework recommended for the interpretation and evaluation of PRS detailed in Wand et al. is used (Wand et al., 2021).

Precision prevention in oncology is based on the idea that an individual’s risk, which is influenced by genetics, environment, and lifestyle factors, is linked to the amount of benefit achieved through cancer screening (Roberts, 2018). Risk stratification for cancer screening can be used in this framework to identify and recommend screening for persons with a high enough cancer risk that the benefits outweigh the risks. Several PRS prediction models have been established for site-specific cancers, each with its own set of strengths and limitations, and different risk models may produce different results for the same individual.

In an increasingly inclusive world, genetic studies fall short on diversity. According to a 2009 study, an overwhelming 96% of people who took part in genome-wide association studies (GWAS) were of European ancestry (Need and Goldstein, 2009). GWAS results are the backbone on which PRS is developed. A concern raised was that, without representation from a broader spectrum of populations, genomic medicine may be limited to benefitting ‘a privileged few’ (Popejoy and Fullerton, 2016).

Genetic studies in 2016 showed that the proportion of people not of European ancestry included in GWAS has increased to approximately 20% (Popejoy and Fullerton, 2016). Most of this rise can be attributed to more research on Asian ancestry communities in Asia (Popejoy and Fullerton, 2016). With increasing interest worldwide in using a risk-based approach to screening programs over the current age-based paradigm, this progress raises questions on whether selected established PRS shown to perform well in European-based populations has equal utility in Asians. Nonetheless, as our results show, most of the populations from which PRS were developed are still predominantly of European ancestry.

In accordance with published Polygenic Risk Score Reporting Standards, we reported PRS distribution, discrimination, absolute risk association, and calibration for each of the four common cancers studied (Wand et al., 2021). Our results show that cancer cases were associated with higher PRS compared to non-cancer controls. In the age-adjusted models, a constant trend between PRS percentile rank and observed cancer risk in our study population supports the validity of PRS for breast, prostate, and colorectal cancers, but not for lung cancer. The best-performing PRS for female breast cancer was able to stratify women into distinct bands of breast cancer risk at an earlier age, and across all ages, suggesting that it could be a useful prediction tool in risk-based breast cancer screening in combination with other risk factors specific to breast cancer (Ho et al., 2022). This PRS has been incorporated into a pilot risk-based breast cancer screening study in a comparable study population (Liu et al., 2022). The best-performing PRS for prostate and male colorectal cancers in this study appeared to exhibit sufficient discriminatory ability and predictive value, especially for older participants.

PRS may be of limited use in predicting female colorectal and female/male lung cancer. The least predictive value was in lung cancer, which could be related to the higher prevalence of EGFR mutant lung cancer which has an Asian predilection, thus less amenable to PRS developed in Caucasian population (Shigematsu et al., 2005). It is reassuring to see tobacco smoking is a strong risk factor for lung cancer in our dataset. However, smoking appeared to be associated with a protective effect for prostate cancer. While smoking is a well-known risk factor for many cancers (Jacob et al., 2018), in particular lung cancer, observational studies frequently show that smokers are associated with a lower incidence of prostate cancer (Rohrmann et al., 2013; Watters et al., 2009; Adami et al., 1996; Lund Nilsen et al., 2000; Engeland et al., 1996; Islami et al., 2014; Ordóñez-Mena et al., 2016; Giovannucci et al., 2007). However, a Mendelian randomisation study did not support the association (Larsson et al., 2020).

There is room for improvement in the discriminatory ability of PRS (Lewis and Green, 2021). As noted by Lambert et al. in a review, a wider divergence between the average scores of cases and non-cases (quantified by AUC) and associated effect sizes (odds ratio and SD) is expected when PRS explains more of the heredity for each trait (Lambert et al., 2019). Larger GWAS sample sizes of appropriate ancestries and the inclusion of rarer genetic variants, obtained through other methods such as whole-genome sequencing, would likely be required to boost explained heritability (Lambert et al., 2019). In addition, group-wise estimates, which arbitrarily classify the top 10%, 5%, or 1% of samples as the at-risk group, are not optimal for decisions at the individual level (Lewis and Green, 2021). Emerging new methodologies that estimate probability values for hypothetically assigning an individual as at risk or not at risk, thus providing individuals with more clarity, may help to overcome this limitation (Sun et al., 2021). At this point, PRS may not have yet reached the standards as a clinical tool by itself. However, it is still helpful in guiding screening decisions and supplementing established protocols (Polygenic Risk Score Task Force of the International Common Disease Alliance, 2021).

As highlighted by Wei et al., the reliability of score values is necessary for application at the individual level (Wei et al., 2022). Even when a PRS has adequate discrimination, estimated risks can be unreliable (Van Calster et al., 2019). Our results show that cancer risk estimates based on PRS developed using populations of European ancestry are not optimally calibrated for our Asian study population. However, the CI associated with the calibration slopes for all cancers except for female and male lung included the value of 1, suggesting that the overall calibration is not poor. For female and male lung cancers associated with low AUC values, poor calibration is not unexpected. All PRSs except PGS000662 (prostate cancer) passed the formal Hosmer-Lemeshow goodness-of-fit test. Males in the first 5 deciles of PGS000662 did not develop prostate cancer, suggesting that a linear fit may not be appropriate. A hard threshold beginning from the 6th decile may perform better at identifying males at elevated risk of developing prostate cancer.

Poorly calibrated PRS can be misleading and have clinical repercussions (Van Calster et al., 2019; Van Calster and Vickers, 2015). Underestimation of risk may result in a false sense of security. Overestimation of risk may cause unnecessary anxiety, misguided interventions, and overtreatment. In a population-wide screening setting, however, where the return of PRS results can be designed such that only high-risk individuals are highlighted, underestimation of risk may be less of an issue. Arguably, with parallel input from other risk factors and evaluation by healthcare specialists, the overestimation of risk that results in a higher number of at-risk individuals identified may increase the number of cancers potentially detected early. Nonetheless, suitable correction factors will be required to ensure the reliability of PRS prior to clinical implementation.

While the study population used in this analysis comprises less than a thousand cases of the most common cancers examined, the SCHS, established between April 1993 and December 1998, is one of the largest population-based Asian cohorts in the world with high-quality prospective data on exposure and comprehensive capture of morbidity and mortality. All cancer cases are incident cases diagnosed over three decades of follow-up. Blood samples were collected from a subset of SCHS participants who were alive and contactable between 1999 and 2004 (after the recruitment period 1993–1998). While we attempted to adjust associations between PRSs and incident cancers in the study by including multiple related risk factors as covariates in Cox proportional hazards models, we acknowledge the potential of survival biases in the study. This is one of the best resources to evaluate the utility of PRS in a prospective manner. The findings open a window in our current understanding of which PRS is relevant and ready to be deployed in risk-based cancer screening studies. Nonetheless, it should be noted that among the PRS interrogated from the PGS Catalog, the ‘best’ PRS selected in this study may be only superficially superior in terms of AUC (i.e. trailing decimal places) over the ‘next best’ PRS. We further tested the sensitivity of the PRS selection using a time-to-event metric for AUC (at 5 years), and the differences found were non-informative from the logistic regression (Supplementary file 1f). To increase the number of events, we combined the males and females for lung and colorectal cancers. The resulting AUCs (from the logistic regression) were not appreciably different from the sex-specific analysis (Supplementary file 1g).

Ethnic representation in PRS model development, PRS validation, limited discriminative ability in the general population, ill calibration, insufficient healthcare professional and patient education, and healthcare system integration are all hurdles that must be crossed before PRS can be implemented responsibly as a public health instrument (Lewis and Vassos, 2020; Slunecka et al., 2021). While nationwide screening programs have helped to raise cancer awareness, there is still a need to improve the effectiveness and efficiency of cancer screening in Asian countries such as Singapore, given the steadily rising incidence rates. Despite the challenges, a risk-based screening strategy that includes the use of PRS should be actively examined for research and implementation.

All polygenic risk scores used in this study are publicly available in the PGS Catalog (https://www.pgscatalog.org; Lambert et al., 2021). The data that support the findings of our study are available from the corresponding authors of the study upon reasonable request (Dr Rajkumar s/o Dorajoo, dorajoor@gis.a-star.edu.sg and Dr Jingmei Li, lijm1@gis.a-star.edu.sg). More information regarding the data access to SCHS can be found at: https://sph.nus.edu.sg/research/cohort-schs/. The data are not publicly available due to Singapore laws. Figure 1—source data 1 contains the numerical data used to generate Figure 1. The code for the study is uploaded as Source code 1.

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Author details

1. Peh Joo Ho

   1. Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
   2. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
   3. Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Conceptualization, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3017-4023

2. Iain BeeHuat Tan

   1. Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
   2. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
   3. Program in Cancer and Stem Cell Biology, Duke-National University of Singapore Medical School, Singapore, Singapore
   4. Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore

   Contribution

   Writing – review and editing, Interpretation of data

   Competing interests

   No competing interests declared

3. Dawn Qingqing Chong

   1. Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
   2. Duke-NUS Medical School Singapore, Singapore, Singapore

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

4. Chiea Chuen Khor

   Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

   Contribution

   Data curation, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

5. Jian-Min Yuan

   1. UPMC Hillman Cancer Center, Pittsburgh, United States
   2. Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, United States

   Contribution

   Resources, Data curation, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

6. Woon-Puay Koh

   1. Healthy Longevity Translational Research Programme; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
   2. Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore

   Contribution

   Resources, Data curation, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

7. Rajkumar Dorajoo

   Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

   Contribution

   Conceptualization, Resources, Data curation, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   dorajoor@gis.a-star.edu.sg

   Competing interests

   received a grant from the Agency for Science, Technology and Research Career Development Award (A*STAR CDA - 202D8090), and from Ministry of Health Healthy Longevity Catalyst Award (HLCA20Jan-0022). The author has no other competing interests to declare

8. Jingmei Li

   1. Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
   2. Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   lijm1@gis.a-star.edu.sg

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8587-7511

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