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Development and Validation of a Breast Cancer Polygenic Risk Score on the Basis of Genetic Ancestry Composition

PURPOSE Polygenic risk scores (PRSs) for breast cancer (BC) risk stratification have been developed primarily in women of European ancestry. Their application to women of non-European ancestry has lagged because of the lack of a formal approach to incorporate genetic ancestry and ancestry-dependent variant frequencies and effect sizes. Here, we propose a multiple-ancestry PRS (MA-PRS) that addresses these issues and may be useful in the development of equitable PRSs across other cancers and common diseases. MATERIALS AND METHODS Women referred for hereditary cancer testing were divided into consecutive cohorts for development (n = 189,230) and for independent validation (n = 89,126). Individual genetic composition as fractions of three reference ancestries (African, East Asian, and European) was determined from ancestry-informative single-nucleotide polymorphisms. The MA-PRS is a combination of three ancestry-specific PRSs on the basis of genetic ancestral composition. Stratification of risk was evaluated by multivariable logistic regression models controlling for family cancer history. Goodness-of-fit analysis compared expected with observed relative risks by quantiles of the MA-PRS distribution. RESULTS In independent validation, the MA-PRS was significantly associated with BC risk in the full cohort (odds ratio, 1.43; 95% CI, 1.40 to 1.46; P = 8.6 × 10–308) and within each major ancestry. The top decile of the MA-PRS consistently identified patients with two-fold increased risk of developing BC. Goodness-of-fit tests showed that the MA-PRS was well calibrated and predicted BC risk accurately in the tails of the distribution for both European and non-European women. CONCLUSION The MA-PRS uses genetic ancestral composition to expand the utility of polygenic risk prediction to non-European women. Inclusion of genetic ancestry in polygenic risk prediction presents an opportunity for more personalized treatment decisions for women of varying and mixed ancestries.


INTRODUCTION
Breast cancer (BC) is a common disease with a substantial hereditary component. 1,2 Heritable risk of BC is defined primarily by rare causal variants with a highly significant (BRCA1, BRCA2, and TP53) and moderate (CHEK2 and ATM) influence. 3 Polygenic risk scores (PRSs) integrate small effects from tens to thousands of common genetic markers, providing potentially actionable BC risk stratification when combined with clinical and biologic risk factors in patients without highly penetrant or moderately penetrant gene mutations. [4][5][6][7][8] Initial development of PRSs for BC was largely limited to genome-wide association studies (GWASs) in women of self-reported European and Ashkenazi Jewish ancestry. 9 Examination of common variation underlying BC risk in non-European women has centered around fine-mapping of loci discovered in European populations or the utility of European-derived PRSs in these groups. [10][11][12][13][14][15][16][17][18][19] European-derived PRSs do not stratify risk effectively in different ancestral populations. 10,12,17 Improvements could be achieved by addressing allele frequency variations and differential locus contributions. 20 Unique population-specific variations, such as a previously identified protective single-nucleotide polymorphism (SNP) in women of Amerindian ancestry, 13 must also be considered.
Although ancestral homogeneity is necessary for GWAS discovery efforts, human populations are complex and clinical risk stratification tools should ASSOCIATED CONTENT

Data Supplement
Author affiliations and support information (if applicable) appear at the end of this article.
Accepted on September 8, 2022 and published at ascopubs.org/journal/ po on November 3, 2022: DOI https://doi. org/10.1200/PO. 22. 00084 ideally be applicable to any individual, regardless of ancestry. 21,22 Frequencies and risks associated with individual SNPs have been shown to differ by ancestry. 16 A universal PRS must therefore consider an individual's unique ancestry profile at the genetic level. 23,24 PRS-based risk can then be estimated according to an individual's ancestral composition.
Here, we describe development and validation of the first PRS to incorporate genetic ancestry. We termed this the multipleancestry PRS (MA-PRS) since it is designed to enable BC risk prediction for women from diverse ancestral backgrounds. The framework described here may serve as a basis for the development of multiple-ancestry PRSs for other conditions as well, such as colon cancer, heart disease, and diabetes.

Patients
Data from consecutive cohorts of women referred for hereditary cancer testing (Myriad Genetic Laboratories Inc, Salt Lake City, UT) between July 2016 and March 2021 were used for this study. All patients were evaluated for the presence of germline mutations in 11 BC-related genes. 25,26 Details are provided in the Data Supplement. Independent cohorts were used to inform ancestry-specific weights of BCassociated SNPs, for development of the MA-PRS and for validation. Details of each study cohort are given in Figure 1. Clinical characteristics of patients included in MA-PRS development and validation are shown in Table 1. This study was approved by the Advarra Institutional Review Board with a waiver of informed consent (Pro00036775).

SNP Selection
Two classes of SNPs were selected: BC-associated SNPs (Data Supplement) and ancestry-informative SNPs (Data Supplement). Ninety-two BC-associated SNPs from previously published sources were genotyped from June 2016 to March 2021. 6,27 One BC-associated variant (rs140068132), prevalent in women with Amerindian ancestry, was genotyped beginning in March 2018. 13 Fifty-six ancestryinformative markers were selected on the basis of the greatest ability to discern between ancestries and for genotyping reliability. 23 Details of genotyping and marker selection are provided in the Data Supplement.

Statistical Methods
The Data Supplement describes the main steps in development and validation of MA-PRS. Briefly, we first determined a small number of reference ancestries (African, East Asian, and European). A set of ancestral markers was selected to enable representation of genetic ancestry, for each individual, in terms of fractions attributable to each reference ancestry (Data Supplement). For each reference ancestry, we developed a PRS using ancestry-specific weights and frequencies of BC-associated SNPs. MA-PRS was defined as a combination of ancestry-specific PRSs on the basis of genetic ancestral composition. Finally, the association of MA-PRS with BC risk was validated in a large, independent cohort of patients of diverse ancestry.
Ancestry-specific PRSs. We estimated SNP weights (or effect sizes), as log odds ratios (ORs) for association with BC, for each reference ancestry on the basis of the largest accessible data set. African SNP weights were based on meta-analysis of 31,126 patients with hereditary cancer of self-reported Black/African ancestry and European SNP weights (Data Supplement). East Asian weights were based on the literature 15 where available (76 SNPs) and European weights for the remaining SNPs. European SNP weights were determined as previously described. 4 For one Amerindian SNP (rs140068132), which is prevalent only among Hispanics, we estimated a weight, denoted by β Am , on the basis of 9,321 self-reported Hispanic patients referred for hereditary cancer testing.
For each reference ancestry, an ancestry-specific PRS was defined as the sum of BC risk alleles, centered by the CONTEXT Key Objective Breast cancer (BC) risk assessment is essential to inform personalized screening and prevention. For women of European ancestry, incorporation of polygenic risk scores (PRSs) into BC risk assessment leads to substantial improvement over traditional methods. However, PRSs have shown poor performance among non-European populations. This work describes a multiple-ancestry PRS (MA-PRS) designed to achieve high accuracy for women of all ancestries through incorporation of genetic ancestry and ancestry-dependent variant frequencies and effect sizes.

Knowledge Generated
In independent validation, the MA-PRS was well calibrated and predicted BC risk accurately for both European and non-European populations. The top decile of MA-PRS consistently identified women with two-fold increased risk of developing BC.

Relevance
The MA-PRS uses genetic ancestral composition to expand polygenic risk assessment to women of diverse ancestries. The methodology presented here may lead to the development of equitable PRSs across other common diseases.
ancestry-specific allele frequency and weighted by the ancestry-specific effect size multiplied by the probability of the allele having been inherited from that ancestry (Data Supplement).
Development of the MA-PRS. The development set of 189,230 women was used to combine African-, East Asian-and European-specific PRSs (PRS Af , PRS EA , and PRS Eu , respectively) and the Amerindian SNP genotype (x Am = 0, 1, or 2) into a final MA-PRS. Weights for the African-, East Asian-, and European-specific PRSs, denoted by B Af , B EA , and B Eu , respectively, were estimated as log ORs from a multivariable logistic regression model with BC status as the dependent variable and age, personal and family cancer history, self-reported ancestry, genetic ancestry, and interaction between self-reported and genetic ancestry as independent variables. Variables were coded as described in the Data Supplement.
The final MA-PRS was defined as Independent validation of the MA-PRS. BC risk discrimination of the MA-PRS was evaluated in terms of ORs and P values from multivariable logistic regression models. ORs were normalized to the standard deviation of the MA-PRS distribution in women unaffected by BC and reported with 95% Wald CIs. Additional details are provided in the Data Supplement.
The primary analysis evaluated the association of MA-PRS with BC after accounting for the same independent variables used in logistic regression models for MA-PRS development.
This study included two prespecified secondary analyses. First, the MA-PRS was evaluated for improved discrimination compared with a previously published PRS-86, which is based on a subset of 93 BC-associated SNPs used in the MA-PRS. 2 Second, the primary and first secondary analyses were repeated within subcohorts defined by self-reported ancestry.
We evaluated goodness of fit of the relative risks associated with the MA-PRS after accounting for clinical factors. The observed versus predicted effect of the MA-PRS on BC risk was assessed by comparing ORs from the continuous MA-PRS with those obtained from analysis of patients binned in categories according to MA-PRS percentiles.
All analyses were conducted using R version 3.5.3 or higher. 28 P values were calculated from likelihood ratio

Genetic Ancestry
Three reference ancestries (African, East Asian, and European) were selected to meet the following criteria.
First, reference ancestries combined should represent a significant portion of human genetic diversity. Second, each reference ancestry should be relatively genetically homogeneous. Third, to enable development of an ancestry-specific PRS, data in each ancestry on the association of polygenic variants with BC should be sufficient. Several studies have shown, through principal component analysis, that the first two principal components separate human populations into three distinct clusters corresponding to sub-Saharan Africa, East Asia, and Europe. 30,31 African, East Asian, and European are therefore the most suitable ancestries to represent the majority of human genetic diversity. Furthermore, these ancestries are each relatively genetically homogeneous. In addition, available meta-analyses of BC association data were similarly grouped and served as sources of ancestryspecific effect sizes for BC-associated SNPs. 12,15 Consequently, a panel of 56 SNP markers was developed to represent the ancestral composition of each patient from the three reference ancestries. The Data Supplement shows the result of applying this approximation to the development cohort. The most homogenous representatives of each of these three populations occupy the apices of the two-dimensional plot. Patients reporting White/non-Hispanic ancestry were genetically homogeneous, whereas all other ancestries showed a high level of genetic diversity. This implies that a PRS on the basis of self-reported ancestry, rather than genetic ancestry, would be accurate for Europeans but inaccurate for non-Europeans.
The Data Supplement shows the estimated average genetic ancestral composition within self-reported ancestries in the development cohort. As expected, there is a high correlation between genetic and self-reported ancestry. The observed admixtures in the major ancestral groups are consistent with previous reports. 31 Two or more ancestries including Others, but excluding the following: White/non-Hispanic and Ashkenazi Jewish, White/non-Hispanic and Black, White/non-Hispanic and Hispanic, and White/non-Hispanic and Native American.  Table 1.

MA-PRS Development and Validation
Both the allele frequency and the effect size of a SNP can vary between ancestries, potentially leading to biased risk estimates. Differences in allele frequencies cause a shift in the distribution of the PRS. For example, a European PRS applied to non-European women shows a shift to higher values (Fig 2A), an effect that is not related to BC risk but may be misinterpreted as such, as risks of BC are similar in these two populations. 1 Differences in linkage disequilibrium between ancestries can affect the association of individual SNPs with BC risk. This requires a re-estimation of the effect size in each ancestry. In addition, population-specific causal variants can contribute to risk in one ancestry, but not others.
To achieve appropriate PRS calibration in each ancestral group, we built a PRS for each reference ancestry with allele frequencies obtained from the 1000 Genomes Project and effect sizes for each variant obtained from published studies or meta-analysis (Data Supplement). For each patient, the final MA-PRS is a combination of the three ancestry-specific PRSs on the basis of genetic ancestral composition. The improvement in PRS calibration due to ancestry specificity is demonstrated in Figure 2. For a European 86-SNP PRS, the PRS distribution in non-Europeans is not centered, resulting in inflated PRS values (Fig 2A). This effect not only is especially pronounced in women of self-reported Black/African ancestry but also shows significant deviations in women of selfreported Asian and Hispanic ancestry. The MA-PRS, however, is centered for all ancestries, with the sole exception of Hispanic women (Fig 2B). A partially protective variant (rs140068132) contributes to the overall reduced incidence of BC in Amerindian women. 1 Because of the presence of this variant among women of self-reported Hispanic ancestry, the MA-PRS distribution is shifted to lower risk in Hispanic women overall, but remains centered for Hispanic women without the protective variant (Fig 2C).
In independent prespecified validation, the MA-PRS was a highly significant predictor of BC, after accounting for family cancer history, in the overall cohort (OR per standard deviation, 1.43; 95% CI, 1.40 to 1.46; P = 8.6 × 10 -308 ) and within each self-reported ancestry ( Table 2). In the top decile of the MA-PRS distribution, the relative risk of developing BC was approximately twice the population risk (Data Supplement).
In prespecified secondary analyses, we compared risk stratification because of the MA-PRS against that of a European-derived 86-SNP PRS by including both scores in the same multivariable logistic regression model (Data Supplement). In the full cohort, the MA-PRS was a highly significant (P = 8.4 × 10 -24 ) predictor of BC risk after accounting for the 86-SNP PRS. By contrast, the 86-SNP PRS showed only marginal association with BC (P = .023) after accounting for MA-PRS. Similar results were observed Goodness-of-fit tests indicated that MA-PRS relative risk estimates were well calibrated for both European and non-European populations (Fig 3). Importantly, the MA-PRS predicts disease risk accurately in the tails of the distribution. By contrast, the European-derived 86-SNP PRS performed poorly for non-European women at both the lower and higher PRS percentiles (Data Supplement).

DISCUSSION
In this report, we describe development and validation of a multiple-ancestry BC PRS (MA-PRS), composed of 93 BCassociated SNPs and 56 ancestry markers, applicable to individuals found in the diverse contemporary US population. The top decile of the MA-PRS identifies women who have BC risks comparable with carriers of pathogenic variants in moderate-risk genes (eg, ATM and CHEK2). 32 Thus, a substantial fraction of women in each ancestry would qualify for enhanced surveillance measures on the basis of their polygenic risk, including annual contrastenhanced MRI and consideration of risk-reducing medications that are recommended for those with 20% or greater lifetime risk of BC. 33 Work in progress aims to create a model that combines clinical and genetic factors, further enhancing the ability to identify women at the highest risk levels who are candidates for both enhanced surveillance that detects cancer earlier and risk-reducing medication that can prevent the onset of cancer.
The MA-PRS is unique in two aspects. First, it assumes that, for the purpose of BC risk prediction, admixed human populations can be described in terms of a genetic mix of three reference ancestries (African, East Asian, and European), 23 consistent with an out-of-Africa model of ancient human dispersion. 34 A second novel aspect of the MA-PRS is the inclusion of PRSs that were optimized for each of the three reference ancestries. The MA-PRS outperforms a PRS built on European weights alone (Data Supplement) and compares favorably with previously published European-derived PRS applied to non-European populations. For example, a study from the Asian BC consortium used European weights to PRSs of 229 and 287 SNPs. 6,21 Despite the larger number of variants, risk stratification by the PRS287 or PRS229 was similar to that by MA-PRS in Asian women. Importantly, substantial shifts in PRS distribution were observed for the PRS229/PRS287 in Asians relative to European women, indicating poor PRS calibration. In self-reported Hispanic women, the MA-PRS performs comparably with a larger 180-SNP PRS built on European weights. 35 Finally, in a recent meta-analysis in African American women, a 313-SNP PRS and a 179-SNP PRS, on the basis of European weights, performed similar to the 93-SNP MA-PRS, highlighting how ancestry specificity enhances performance. 6,12 The preceding also spotlights that all BC PRSs thus far have performed less effectively for individuals of African ancestry. 20 Despite many fine-mapping studies, few African-specific variants have been verified, largely because of limited statistical power (Data Supplement). 11,14,19 This deficiency is being addressed in a number of current studies (CONFLUENCE and BRIDGES) with the intention to bridge the gap in statistical GWAS power between European and non-European ancestries. 36,37 Strengths of this study include the use of genetic ancestry composition as part of the MA-PRS. This eliminates inaccuracy inherent in self-reported ancestry and enables expansion of polygenic risk assessment to individuals of mixed ancestries. The large overall and subgroup sample sizes in this study allowed for precise risk estimates, even in the tails of the MA-PRS distribution. The fact that MA-PRS was developed and validated in a high-risk population, with detailed information regarding family cancer history, allowed for estimation of risk attributable to MA-PRS after accounting for family history; these results may inform personalized assessments that combine genetic risks with familial, environmental, and lifestyle risk factors.
The methodology underlying MA-PRS has some limitations. First, we approximate human ancestry to a three-order contribution from African, East Asian, and European population sources. Although an approximation, principal component analysis has shown that these are the three most suitable ancestries to represent the majority of human genetic diversity. 30,31 Second, our methodology evaluates the ancestral origin of each BC risk allele on the basis of ancestry-specific allele frequencies and global ancestral composition of an individual. Future improvements may include more extensive evaluation of local genetic ancestry at each BC-associated SNP. Finally, the MA-PRS comprised 93 BC SNPs, and although applicable across ancestries, larger SNP sets have been described for European women. 5,6,20,35 Although expansion of SNP numbers provides only incremental improvements in risk stratification in a European population, 6 expansion of the MA-PRS is being explored. Notably, recent examinations of European PRSs containing large numbers of SNPs in US populations have either failed to match the performance of the MA-PRS model 20 or matched it despite containing approximately 50 times more SNPs. 38 One possible explanation is that previous PRS models might have been overfitted.
A possible limitation of the current study is that family history information was collected from the provided completed test request forms and may therefore be incomplete or inaccurate. However, we consider this a minor limitation: we previously published a sensitivity analysis showing that, for the purpose of quantifying associations of genetic  markers with disease risk, the multivariable regression methodology used here is robust to inaccuracies in family history reporting. 32 Furthermore, a recent study by Kurian et al 39 evaluated the accuracy of laboratory reported family history data and found high (95%) concordance between the self-report and the laboratory report.
Another limitation of the current study is potential bias from a study population that met clinical criteria for genetic testing. By including factors related to clinical ascertainment in multivariable regression models (specifically personal/family cancer history, age, and ancestry), we obtain estimates of genetic risk that should be unbiased. 32,40,41 More specifically, the PRS ORs presented in our work should be consistent with those that would be obtained from a population-based study using either multivariable regression or matched case-control analysis to adjust for personal/family cancer history, age, and ancestry.
Overall, the MA-PRS model described here is an important step forward in the accurate prediction of BC risk in the contemporary US population, irrespective of genetic ancestry and without requiring potentially biased or inaccurate self-or health care provider-reported ancestry. Although a PRS is currently not incorporated into eligibility guidelines for enhanced surveillance, 33 a combination of this PRS model with clinical and biologic factors may provide an opportunity to further enhance risk stratification and appropriate allocation of preventive and screening resources. Future research will apply such a combined model in real-world clinical settings to characterize and quantify its utility. The model created herein may also serve as a framework for developing equitable PRS for other cancers and common health conditions. No other potential conflicts of interest were reported.