Polygenic risk prediction models for colorectal cancer: a systematic review

Challenges around diagnosis of early onset colorectal cancer, and the case for screening

BACKGROUND

Colorectal cancer (CRC) is the third most diagnosed cancer in the world, with an estimated 1.93 million cases diagnosed in 2020. While the overall CRC incidence in many countries is falling there has been a dramatic increase in CRC in those aged under 50 (early onset colorectal cancer, EOCRC). The reason for this increase in EOCRC is unknown. As the best predictor of survival is stage at diagnosis, early diagnosis is likely to be beneficial and population screening may facilitate this.

METHODS

A narrative review of the literature was undertaken.

RESULTS

Improving time to diagnosis in symptomatic patients is beneficial. However, by the time symptoms develop, over a third of patients already have metastatic disease. Screening asymptomatic patients (with Faecal Immunochemical test (FIT) and colonoscopy) has been proved to be effective in older patients (>60 years). In younger populations, the decreasing incidence rates of CRC previously made cost effectiveness, compliance and therefore benefit questionable. Now, with the increasing incidence of CRC in those under 50 years of age, modelling suggests screening with FIT and colonoscopy is cost effective from 40 years of age. There is evidence that some countries screening below 50 have prevented the rise in EOCRC incidence. Additionally the use of new and novel non-invasive biomarkers may also be able to improve the accuracy of screening asymptomatic patients.

CONCLUSION

Diagnosis of EOCRC once symptoms develop is often too late, and screening patients from age 40 is the best way to improve outcomes in this group.

Colorectal cancer risk stratification using a polygenic risk score in symptomatic primary care patients-a UK Biobank retrospective cohort study

Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide. Accurate cancer risk assessment approaches could increase rates of early CRC diagnosis, improve health outcomes for patients and reduce pressure on diagnostic services. The faecal immunochemical test (FIT) for blood in stool is widely used in primary care to identify symptomatic patients with likely CRC. However, there is a 6-16% noncompliance rate with FIT in clinic and ~90% of patients over the symptomatic 10 µg/g test threshold do not have CRC. A polygenic risk score (PRS) quantifies an individual's genetic risk of a condition based on many common variants. Existing PRS for CRC have so far been used to stratify asymptomatic populations. We conducted a retrospective cohort study of 50,387 UK Biobank participants with a CRC symptom in their primary care record at age 40+. A PRS based on 201 variants, 5 genetic principal components and 22 other risk factors and markers for CRC were assessed for association with CRC diagnosis within 2 years of first symptom presentation using logistic regression. Associated variables were included in an integrated risk model and trained in 80% of the cohort to predict CRC diagnosis within 2 years. An integrated risk model combining PRS, age, sex, and patient-reported symptoms was predictive of CRC development in a testing cohort (receiver operating characteristic area under the curve, ROCAUC: 0.76, 95% confidence interval: 0.71-0.81). This model has the potential to improve early diagnosis of CRC, particularly in cases of patient noncompliance with FIT.

Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study

As the pandemic continues to pose challenges to global public health, developing effective predictive models has become an urgent research topic. This study aims to explore the application of multi-objective optimization methods in selecting infectious disease prediction models and evaluate their impact on improving prediction accuracy, generalizability, and computational efficiency. In this study, the NSGA-II algorithm was used to compare models selected by multi-objective optimization with those selected by traditional single-objective optimization. The results indicate that decision tree (DT) and extreme gradient boosting regressor (XGBoost) models selected through multi-objective optimization methods outperform those selected by other methods in terms of accuracy, generalizability, and computational efficiency. Compared to the ridge regression model selected through single-objective optimization methods, the decision tree (DT) and XGBoost models demonstrate significantly lower root mean square error (RMSE) on real datasets. This finding highlights the potential advantages of multi-objective optimization in balancing multiple evaluation metrics. However, this study's limitations suggest future research directions, including algorithm improvements, expanded evaluation metrics, and the use of more diverse datasets. The conclusions of this study emphasize the theoretical and practical significance of multi-objective optimization methods in public health decision support systems, indicating their wide-ranging potential applications in selecting predictive models.

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.

Lifestyle predictors of colorectal cancer in European populations: a systematic review

Background

Colorectal cancer (CRC) is the second most prevalent cancer in Europe, with one-fifth of cases attributable to unhealthy lifestyles. Risk prediction models for quantifying CRC risk and identifying high-risk groups have been developed or validated across European populations, some considering lifestyle as a predictor.

Purpose

To identify lifestyle predictors considered in existing risk prediction models applicable for European populations and characterise their corresponding parameter values for an improved understanding of their relative contribution to prediction across different models.

Methods

A systematic review was conducted in PubMed and Web of Science from January 2000 to August 2021. Risk prediction models were included if (1) developed and/or validated in an adult asymptomatic European population, (2) based on non-invasively measured predictors and (3) reported mean estimates and uncertainty for predictors included. To facilitate comparison, model-specific lifestyle predictors were visualised using forest plots.

Results

A total of 21 risk prediction models for CRC (reported in 16 studies) were eligible, of which 11 were validated in a European adult population but developed elsewhere, mostly USA. All models but two reported at least one lifestyle factor as predictor. Of the lifestyle factors, the most common predictors were body mass index (BMI) and smoking (each present in 13 models), followed by alcohol (11), and physical activity (7), while diet-related factors were less considered with the most commonly present meat (9), vegetables (5) or dairy (2). The independent predictive contribution was generally greater when they were collected with greater detail, although a noticeable variation in effect size estimates for BMI, smoking and alcohol.

Conclusions

Early identification of high-risk groups based on lifestyle data offers the potential to encourage participation in lifestyle change and screening programmes, hence reduce CRC burden. We propose the commonly shared lifestyle predictors to be further used in public health prediction modelling for improved uptake of the model.

Personalized Multilevel Intervention for Improving Appropriate Use of Colorectal Cancer Screening in Older Adults: A Cluster Randomized Clinical Trial

Importance

Despite guideline recommendations, clinicians do not systematically use prior screening or health history to guide colorectal cancer (CRC) screening decisions in older adults.

Objective

To evaluate the effect of a personalized multilevel intervention on screening orders in older adults due for average-risk CRC screening.

Design, Setting, and Participants

Interventional 2-group parallel unmasked cluster randomized clinical trial conducted from November 2015 to February 2019 at 2 US Department of Veterans Affairs (VA) facilities: 1 academic VA medical center and 1 of its connected outpatient clinics. Randomization at the primary care physician/clinician (PCP) level, stratified by study site and clinical full-time equivalency. Participants were 431 average-risk, screen-due US veterans aged 70 to 75 years attending a primary care visit. Data analysis was performed from August 2018 to August 2023.

Intervention

The intervention group received a multilevel intervention including a decision-aid booklet with detailed information on screening benefits and harms, personalized for each participant based on age, sex, prior screening, and comorbidity. The control group received a multilevel intervention including a screening informational booklet. All participants received PCP education and system-level modifications to support personalized screening.

Main Outcomes and Measures

The primary outcome was whether screening was ordered within 2 weeks of clinic visit. Secondary outcomes were concordance between screening orders and screening benefit and screening utilization within 6 months.

Results

A total of 436 patients were consented, and 431 were analyzed across 67 PCPs. Patients had a mean (SD) age of 71.5 (1.7) years; 424 were male (98.4%); 374 were White (86.8%); 89 were college graduates (21.5%); and 351 (81.4%) had undergone prior screening. A total of 258 (59.9%) were randomized to intervention, and 173 (40.1%) to control. Screening orders were placed for 162 of 258 intervention patients (62.8%) vs 114 of 173 control patients (65.9%) (adjusted difference, -4.0 percentage points [pp]; 95% CI, -15.4 to 7.4 pp). In a prespecified interaction analysis, the proportion receiving orders was lower in the intervention group than in the control group for those in the lowest benefit quartile (59.4% vs 71.1%). In contrast, the proportion receiving orders was higher in the intervention group than in the control group for those in the highest benefit quartile (67.6% vs 52.2%) (interaction P = .049). Fewer intervention patients (106 of 256 [41.4%]) utilized screening overall at 6 months than controls (96 of 173 [55.9%]) (adjusted difference, -13.4 pp; 95% CI, -25.3 to -1.6 pp).

Conclusions and Relevance

In this cluster randomized clinical trial, patients who were presented with personalized information about screening benefits and harms in the context of a multilevel intervention were more likely to receive screening orders concordant with benefit and were less likely to utilize screening.

Trial Registration

ClinicalTrials.gov Identifier: NCT02027545.

AGA Clinical Practice Update on Risk Stratification for Colorectal Cancer Screening and Post-Polypectomy Surveillance: Expert Review

DESCRIPTION

Since the early 2000s, there has been a rapid decline in colorectal cancer (CRC) mortality, due in large part to screening and removal of precancerous polyps. Despite these improvements, CRC remains the second leading cause of cancer deaths in the United States, with approximately 53,000 deaths projected in 2023. The aim of this American Gastroenterological Association (AGA) Clinical Practice Update Expert Review was to describe how individuals should be risk-stratified for CRC screening and post-polypectomy surveillance and to highlight opportunities for future research to fill gaps in the existing literature.

METHODS

This Expert Review was commissioned and approved by the American Gastroenterological Association (AGA) Institute Clinical Practice Updates Committee (CPUC) and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership, and underwent internal peer review by the CPUC and external peer review through standard procedures of Gastroenterology. These Best Practice Advice statements were drawn from a review of the published literature and from expert opinion. Because systematic reviews were not performed, these Best Practice Advice statements do not carry formal ratings regarding the quality of evidence or strength of the presented considerations. Best Practice Advice Statements BEST PRACTICE ADVICE 1: All individuals with a first-degree relative (defined as a parent, sibling, or child) who was diagnosed with CRC, particularly before the age of 50 years, should be considered at increased risk for CRC. BEST PRACTICE ADVICE 2: All individuals without a personal history of CRC, inflammatory bowel disease, hereditary CRC syndromes, other CRC predisposing conditions, or a family history of CRC should be considered at average risk for CRC. BEST PRACTICE ADVICE 3: Individuals at average risk for CRC should initiate screening at age 45 years and individuals at increased risk for CRC due to having a first-degree relative with CRC should initiate screening 10 years before the age at diagnosis of the youngest affected relative or age 40 years, whichever is earlier. BEST PRACTICE ADVICE 4: Risk stratification for initiation of CRC screening should be based on an individual's age, a known or suspected predisposing hereditary CRC syndrome, and/or a family history of CRC. BEST PRACTICE ADVICE 5: The decision to continue CRC screening in individuals older than 75 years should be individualized, based on an assessment of risks, benefits, screening history, and comorbidities. BEST PRACTICE ADVICE 6: Screening options for individuals at average risk for CRC should include colonoscopy, fecal immunochemical test, flexible sigmoidoscopy plus fecal immunochemical test, multitarget stool DNA fecal immunochemical test, and computed tomography colonography, based on availability and individual preference. BEST PRACTICE ADVICE 7: Colonoscopy should be the screening strategy used for individuals at increased CRC risk. BEST PRACTICE ADVICE 8: The decision to continue post-polypectomy surveillance for individuals older than 75 years should be individualized, based on an assessment of risks, benefits, and comorbidities. BEST PRACTICE ADVICE 9: Risk-stratification tools for CRC screening and post-polypectomy surveillance that emerge from research should be examined for real-world effectiveness and cost-effectiveness in diverse populations (eg, by race, ethnicity, sex, and other sociodemographic factors associated with disparities in CRC outcomes) before widespread implementation.

Genetic Variants in the Mitochondrial Thymidylate Biosynthesis Pathway Increase Colorectal Cancer Risk

We assess the contributions of genetic variants for the enzymes involved in capecitabine metabolism to colorectal cancer (CRC) development risk. In this case-control study, DNA samples were collected from 66 patients (King Abdulaziz University Hospital) and 65 controls (King Fahad General Hospital) between April and November 2022 to be used in PCR-RFLP. The chi-square (χ2) test at a significance level of p ˂ 0.05 was used to estimate genotype and allele frequencies. The Lys27Gln variant of cytidine deaminase (CDA) showed a risk ratio (RR) of 1.47 for heterozygous (AC) carriers, with genotype distributions for patients (χ2 = 1.97) and controls (χ2 = 14.7). Homozygous (AA) Ala70Thr carriers demonstrated a three-fold higher risk, with genotype distributions for patients (χ2 = 3.85) and controls (χ2 = 4.23). Genotype distributions of the 5,10-methylenetetrahydrofolate reductase (MTHFR) C677T variant for patients were (χ2 = 22.43) and for controls were (χ2 = 0.07); for the MTHFR A1298C variant, they were (χ2 = 54.44) for patients and (χ2 = 4.58) for controls. Heterozygous (AC) carriers of the A1298C variant demonstrated highly significant protection against CRC development (RR = 0.2, p = 0.001), while a two-fold higher risk for CRC was estimated for homozygous genotype (CC) carriers. In conclusion, the heterozygous genotype of CDA Lys27Gln, the homozygous genotype of CDA Ala70Thr, and the homozygous genotype of MTHFR A1298C were associated with CRC development risk. The heterozygous genotype of MTHFR A1298C variant provided highly significant protection against CRC development. Further examinations using a larger population size are needed to reliably confirm our findings.

Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms

We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

Detection of germline variants with pathogenic potential in 48 patients with familial colorectal cancer by using whole exome sequencing

BACKGROUND

Hereditary genetic mutations causing predisposition to colorectal cancer are accountable for approximately 30% of all colorectal cancer cases. However, only a small fraction of these are high penetrant mutations occurring in DNA mismatch repair genes, causing one of several types of familial colorectal cancer (CRC) syndromes. Most of the mutations are low-penetrant variants, contributing to an increased risk of familial colorectal cancer, and they are often found in additional genes and pathways not previously associated with CRC. The aim of this study was to identify such variants, both high-penetrant and low-penetrant ones.

METHODS

We performed whole exome sequencing on constitutional DNA extracted from blood of 48 patients suspected of familial colorectal cancer and used multiple in silico prediction tools and available literature-based evidence to detect and investigate genetic variants.

RESULTS

We identified several causative and some potentially causative germline variants in genes known for their association with colorectal cancer. In addition, we identified several variants in genes not typically included in relevant gene panels for colorectal cancer, including CFTR, PABPC1 and TYRO3, which may be associated with an increased risk for cancer.

CONCLUSIONS

Identification of variants in additional genes that potentially can be associated with familial colorectal cancer indicates a larger genetic spectrum of this disease, not limited only to mismatch repair genes. Usage of multiple in silico tools based on different methods and combined through a consensus approach increases the sensitivity of predictions and narrows down a large list of variants to the ones that are most likely to be significant.

Translation of nutrigenomic research for personalised and precision nutrition for cancer prevention and for cancer survivors

Personalised and precision nutrition uses information on individual characteristics and responses to nutrients, foods and dietary patterns to develop targeted nutritional advice that is more effective in improving the diet and health of each individual. Moving away from the conventional 'one size fits all', such targeted intervention approaches may pave the way to better population health, including lower burden of non-communicable diseases. To date, most personalised and precision nutrition approaches have been focussed on tackling obesity and cardiometabolic diseases with limited efforts directed to cancer prevention and for cancer survivors. Advances in understanding the biological basis of cancer and of the role played by diet in cancer prevention and in survival after cancer diagnosis, mean that it is timely to test and to apply such personalised and precision nutrition approaches in the cancer area. This endeavour can take advantage of the enhanced understanding of interactions between dietary factors, individual genotype and the gut microbiome that impact on risk of, and survival after, cancer diagnosis. Translation of these basic research into public health action should include real-time acquisition of nutrigenomic and related data and use of AI-based data integration methods in systems approaches that can be scaled up using mobile devices.

Colorectal Cancer Risk Assessment and Precision Approaches to Screening: Brave New World or Worlds Apart?

Current colorectal cancer (CRC) screening recommendations take a "one-size-fits-all" approach using age as the major criterion to initiate screening. Precision screening that incorporates factors beyond age to risk stratify individuals could improve on current approaches and optimally use available resources with benefits for patients, providers, and health care systems. Prediction models could identify high-risk groups who would benefit from more intensive screening, while low-risk groups could be recommended less intensive screening incorporating noninvasive screening modalities. In addition to age, prediction models incorporate well-established risk factors such as genetics (eg, family CRC history, germline, and polygenic risk scores), lifestyle (eg, smoking, alcohol, diet, and physical inactivity), sex, and race and ethnicity among others. Although several risk prediction models have been validated, few have been systematically studied for risk-adapted population CRC screening. In order to envisage clinical implementation of precision screening in the future, it will be critical to develop reliable and accurate prediction models that apply to all individuals in a population; prospectively study risk-adapted CRC screening on the population level; garner acceptance from patients and providers; and assess feasibility, resources, cost, and cost-effectiveness of these new paradigms. This review evaluates the current state of risk prediction modeling and provides a roadmap for future implementation of precision CRC screening.

Evaluation of polygenic risk score for risk prediction of gastric cancer

Genetic variations are associated with individual susceptibility to gastric cancer. Recently, polygenic risk score (PRS) models have been established based on genetic variants to predict the risk of gastric cancer. To assess the accuracy of current PRS models in the risk prediction, a systematic review was conducted. A total of eight eligible studies consisted of 544842 participants were included for evaluation of the performance of PRS models. The overall accuracy was moderate with Area under the curve values ranging from 0.5600 to 0.7823. Incorporation of epidemiological factors or Helicobacter pylori (H. pylori) status increased the accuracy for risk prediction, while selection of single nucleotide polymorphism (SNP) and number of SNPs appeared to have little impact on the model performance. To further improve the accuracy of PRS models for risk prediction of gastric cancer, we summarized the association between gastric cancer risk and H. pylori genomic variations, cancer associated bacteria members in the gastric microbiome, discussed the potentials for performance improvement of PRS models with these microbial factors. Future studies on comprehensive PRS models established with human SNPs, epidemiological factors and microbial factors are indicated.

Learning high-order interactions for polygenic risk prediction

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.

Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies

Simple Summary

The rising incidence of cutaneous melanoma over recent decades, combined with a general interest in cancer risk prediction, has led to a high number of published melanoma risk prediction models. The aim of our work was to assess the validity of these models in order to discuss the current state of knowledge about how to predict incident cutaneous melanoma. To assess the risk of bias, we used a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). Only one of the 42 studies identified was rated as having a low risk of bias. However, it was encouraging to observe a recent reduction of problematic statistical methods used in the analyses. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.

Abstract

Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall ROB rating. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether, 42 studies were assessed, of which the vast majority (n = 34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our temporal analysis results showed a significant reduction in the number of studies with high ROB for the domain “analysis”. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.

Polygenic risk, population structure and ongoing difficulties with race in human genetics

‘The Apportionment of Human Diversity’ stands as a noteworthy intervention, both for the field of human population genetics as well as in the annals of public communication of science. Despite the widespread uptake of Lewontin's conclusion that racial classification is of ‘virtually no genetic or taxonomic significance’, the biomedical research community continues to grapple with whether and how best to account for race in its work. Nowhere is this struggle more apparent than in the latest attempts to translate genetic associations with complex disease risk to clinical use in the form of polygenic risk scores, or PRS. In this perspective piece, we trace current challenges surrounding the appropriate development and clinical application of PRS in diverse patient cohorts to ongoing difficulties deciding which facets of population structure matter, and for what reasons, to human health. Despite numerous analytical innovations, there are reasons that emerge from Lewontin's work to remain sceptical that accounting for population structure in the context of polygenic risk estimation will allow us to more effectively identify and intervene on the significant health disparities which plague marginalized populations around the world.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.