Why is cancer polygenic

Integrating clinical and polygenic factors to predict breast cancer risk in women undergoing genetic testing. PubMed Google Scholar.

Association of a polygenic risk score with breast cancer among women carriers of high- and moderate-risk breast cancer genes. Risk of breast cancer among carriers of pathogenic variants in breast cancer predisposition genes varies by polygenic risk score. Limit characters. Limit 25 characters.

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Customize your interests. Create a personal account or sign in to:. Privacy Policy. Make a comment. Sample sizes for each sub-set can be found in Table 1. Still, what is striking is the consistent right shift of the PRS distributions in cases compared to controls with each ancestry group Fig 1. Especially for the breast cancer, the CSPRS showed consistent effect sizes across the ancestry groups 1.

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.

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. 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.

However, there are limitations in regard to the generalizability of this approach. 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. 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. 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. 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.

In our examples, the proportion of cases by PRS risk decile was informative within the studies ancestry group, i. 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.

Duncan et al. However, a restriction to global risk variants, e. 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 ].

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 ].

All participants gave full informed written consent. The UK Biobank UKB is a population-based cohort collected from multiple sites across the United Kingdom and includes over , participants aged between 40 and 69 years when recruited in — [ 15 ]. The open-access UK Biobank data used in this study included questionnaire data, electronic health record data, and genotype and genotyped derived data.

The present analyses were conducted under UK Biobank data application number We excluded 2, samples which were flagged by the UK Biobank quality control documentation as 1 het. For the current study we included self-reported ethnic group field: , sex fields: 31, , income field: , education field: , diet fields: , , , , , , , , , year of birth field: We used both principal component-based ancestry prediction and self-reported ethnic information to define ancestry groups.

Full list of weights can be downloaded from our web site see Web resources. Sequencing data was filtered to have a minimum depth of 10, to be polymorphic and located on chromosomes 1—22, X.

For comparability of association effect sizes corresponding to the continuous PRS across cancer traits and PRS construction methods, we centered PRS values to their mean and scaled them to have a standard deviation of 1. To study the ability of the PRS to identify high risk patients, we fit the above model Eq 1 by replacing the PRS with an indicator for whether the PRS value was in the top decile or not.

To test if the PRS means between the ancestry groups are equal we used ANOVA adjusting for genotyping array, birthyear and the first 10 principal components. ANOVA test was adjusted using birth year, genotyping array and first ten principal components.

This research has been conducted using the UK Biobank Resource under application number 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.

Abstract Polygenic risk scores PRS can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. Author summary The translation of results from genome-wide association studies GWAS into polygenic risk scores PRS to predict disease risk or outcomes is a major aspiration in the field of statistical genetics.

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 ]. Download: PPT. Fig 1. Violin plots of the breast and prostate cancer PRS distributions. Table 1. Association and evaluation of cancer PRS across ancestry groups. Fig 2. Table 2. Phenotype and covariate data For the current study we included self-reported ethnic group field: , sex fields: 31, , income field: , education field: , diet fields: , , , , , , , , , year of birth field: Supporting information.

S1 Fig. S2 Fig. S3 Fig. S4 Fig. The pairs of first-degree relatives were inferred with KING v2. To analyse the impact of family history in first-degree relatives, we randomly sampled one female relative for each woman who had at least one first-degree relative in the dataset.

For mother—daughter pairs, the mother was assigned as the index relative. For sisters, we randomly assigned one to be the index relative, irrespective of age. If both women in the pair were breast cancer cases, we used the year of diagnosis to assign the woman diagnosed earlier as the index.

Some individuals appeared several times as non-index individuals, which may occur when, for instance, a woman is the daughter of one index individual and the sister of another — we therefore randomly sampled the data to contain each non-index individual only once.

We then inferred the risk of breast cancer in these unique non-index individuals. Start of follow-up was set at birth, and follow-up ended at the first record of the endpoint of interest, death or at the end of follow-up on 31 December , whichever came first.

All tests were two-tailed. In all survival analyses, we used age as the time scale, with 63 batches and the first 10 principal components as covariates. The only exception was the analysis on contralateral breast cancer, where follow-up started from the diagnosis, and age was included as a covariate.

In line with previous studies on breast cancer susceptibility genes, we assessed lifetime risk cumulative incidence without competing risks by age 80 14 , The adjusted survival curves were plotted with the R package survminer. This presents the expected survival curves separately for subgroups, based on the Cox model. To estimate the covariate-adjusted cumulative incidence functions in the presence of competing risks, we used the Stata module stcompadj The competing event was non-breast cancer causes of death and covariates were assumed to have similar effects the main and competing event.

Interactions between the PRS and the pathogenic mutations were assessed 1 by comparing the PRS effect sizes in pooled and non-pooled mutation carriers and non-carriers with the PRS scaled to zero mean and unit variance within the whole dataset , and 2 formally by introducing an interaction term for the mutation and the continuous PRS. For statistical analyses, we used R 3. Cromwell and WOMtool were used for workflow handling.

Patients and control subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older research cohorts, collected prior the start of FinnGen in August , were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Valvira, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Valvira. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

The remaining data are available within the Article, Supplementary Information or available from the authors upon request. Bray, F. CA Cancer J. Article Google Scholar.

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D; to J. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.