Sex-Stratified Polygenic Risk Score Identifies Individuals at Increased Risk of Basal Cell Carcinoma

Considering strategies for SNP selection in genetic and polygenic risk scores

Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.

Polygenic risk score as a determinant of risk of keratinocyte cancer in an Australian population-based cohort

BACKGROUND

Keratinocyte cancer (KC) risk is determined by genetic and environmental factors. Genetic risk can be quantified by polygenic risk scores (PRS), which sum the combined effects of single nucleotide polymorphisms (SNPs).

OBJECTIVES

Our objective here was to evaluate the contribution of the summed genetic score to predict the KC risk in the phenotypically well-characterised Nambour population.

METHODS

We used PLINK v1.90 to calculate PRS for 432 cases, 566 controls, using 78 genome-wide independent SNPs that are associated with KC risk. We assessed the association between PRS and KC using logistic regression, stratifying the cohort into 3 risk groups (high 20%, intermediate 60%, low 20%).

RESULTS

The fully adjusted model including traditional risk factors (phenotypic and sun exposure-related), showed a significant 50% increase in odds of KC per standard deviation of PRS (odds ratio (OR) =1.51; 95% confidence interval (CI) =1.30-1.76, P=5.75 × 10^-8 ). Those in the top 20% PRS had over three times the risk of KC of those in the lowest 20% (OR=3.45; 95% CI=2.18-5.50, P=1.5×10^-7 ) and higher absolute risk of KC per 100 person-years of 2.96 compared with 1.34. Area under the ROC curve increased from 0.72 to 0.74 on adding PRS to the fully adjusted model.

CONCLUSIONS

These results show that PRS can enhance the prediction of KC above traditional risk factors.

MFAP4-Mediated Effects in Elastic Fiber Homeostasis, Integrin Signaling and Cancer, and Its Role in Teleost Fish

Microfibrillar-associated protein 4 (MFAP4) is an extracellular matrix (ECM) protein belonging to the fibrinogen-related domain superfamily. MFAP4 is highly expressed in elastin-rich tissues such as lung, blood vessels and skin. MFAP4 is involved in organization of the ECM, regulating proper elastic fiber assembly. On the other hand, during pathology MFAP4 actively contributes to disease development and progression due to its interactions with RGD-dependent integrin receptors. Both tissue expression and circulating MFAP4 levels are associated with various disorders, including liver fibrosis and cancer. In other experimental models, such as teleost fish, MFAP4 appears to participate in host defense as a macrophage-specific innate immune molecule. The aim of this review is to summarize the accumulating evidence that indicates the importance of MFAP4 in homeostasis as well as pathological conditions, discuss its known biological functions with special focus on elastic fiber assembly, integrin signaling and cancer, as well as describe the reported functions of non-mammalian MFAP4 in fish. Overall, our work provides a comprehensive overview on the role of MFAP4 in health and disease.

Comparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics

The polygenic risk score (PRS) is calculated as the weighted sum of an individual's genotypes and their estimated effect sizes, which is often used to estimate an individual's genetic susceptibility to complex traits and disorders. It is well known that some complex human traits or disorders have sex differences in trait distributions, disease onset, progression, and treatment response, although the underlying mechanisms causing these sex differences remain largely unknown. PRSs for these traits are often based on Genome-Wide Association Studies (GWAS) data with both male and female samples included, ignoring sex differences. In this study, we present a benchmark study using both simulations with various combinations of genetic correlation and sample size ratios between sexes and real data to investigate whether combining sex-specific PRSs can outperform sex-agnostic PRSs on traits showing sex differences. We consider two types of PRS models in our study: single-population PRS models (PRScs, LDpred2) and multiple-population PRS models (PRScsx). For each trait or disorder, the candidate PRSs were calculated based on sex-specific GWAS data and sex-agnostic GWAS data. The simulation results show that applying LDpred2 or PRScsx to sex-specific GWAS data and then combining sex-specific PRSs leads to the highest prediction accuracy when the genetic correlation between sexes is low and the sample sizes for both sexes are balanced and large. Otherwise, the PRS generated by applying LDpred2 or PRScs to sex-agnostic GWAS data is more appropriate. If the sample sizes between sexes are not too small and very unbalanced, combining LDpred2-based sex-specific PRSs to predict on the sex with a larger sample size and combining PRScsx-based sex-specific PRSs to predict on the sex with a smaller size are the preferred strategies. For real data, we considered 19 traits from Genetic Investigation of ANthropometric Traits (GIANT) consortium studies and UK Biobank with both sex-specific GWAS data and sex-agnostic GWAS data. We found that for waist-to-hip ratio (WHR) related traits, accounting for sex differences and incorporating information from the opposite sex could help improve PRS prediction accuracy. Taken together, our findings in this study provide guidance on how to calculate the best PRS for sex-differentiated traits or disorders, especially as the sample size of GWASs grows in the future.

Findings from the Hispanic Community Health Study/Study of Latinos on the Importance of Sociocultural Environmental Interactors: Polygenic Risk Score-by-Immigration and Dietary Interactions

Introduction: Hispanic/Latinos experience a disproportionate burden of obesity. Acculturation to US obesogenic diet and practices may lead to an exacerbation of innate genetic susceptibility. We examined the role of gene-environment interactions to better characterize the sociocultural environmental determinants and their genome-scale interactions, which may contribute to missing heritability of obesity. We utilized polygenic risk scores (PRSs) for body mass index (BMI) to perform analyses of PRS-by-acculturation and other environmental interactors among self-identified Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Methods: PRSs were derived using genome-wide association study (GWAS) weights from a publicly available, large meta-analysis of European ancestry samples. Generalized linear models were run using a set of a priori acculturation-related and environmental factors measured at visit 1 (2008-2011) and visit 2 (2014-2016) in an analytic subsample of 8,109 unrelated individuals with genotypic, phenotypic, and complete case data at both visits. We evaluated continuous measures of BMI and waist-to-hip ratio. All models were weighted for complex sampling design, combined, and sex-stratified. Results: Overall, we observed a consistent increase of BMI with greater PRS across both visits. We found the best-fitting model adjusted for top five principal components of ancestry, sex, age, study site, Hispanic/Latino background genetic ancestry group, sociocultural factors and PRS interactions with age at immigration, years since first arrival to the United States (p < 0.0104), and healthy diet (p < 0.0036) and explained 16% of the variation in BMI. For every 1-SD increase in PRS, there was a corresponding 1.10 kg/m² increase in BMI (p < 0.001). When these results were stratified by sex, we observed that this 1-SD effect of PRS on BMI was greater for women than men (1.45 vs. 0.79 kg/m², p < 0.001). Discussion: We observe that age at immigration and the adoption of certain dietary patterns may play a significant role in modifying the effect of genetic risk on obesity. Careful consideration of sociocultural and immigration-related factors should be evaluated. The role of nongenetic factors, including the social environment, should not be overlooked when describing the performance of PRS or for promoting population health in understudied populations in genomics.

Polygenic Risk Scores Stratify Keratinocyte Cancer Risk among Solid Organ Transplant Recipients with Chronic Immunosuppression in a High Ultraviolet Radiation Environment

Solid organ transplant recipients (SOTRs) have elevated risks for basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), especially in high UVR environments. We assessed whether polygenic risk scores can improve the prediction of BCC and SCC risks and multiplicity over and above the traditional risk factors in SOTRs in a high UV setting. We built polygenic risk scores for BCC (n = 594,881) and SCC (n = 581,431) using UK Biobank and 23andMe datasets, validated them in the Australian QSkin Sun and Health Study cohort (n > 6,300), and applied them in SOTRs in the skin tumor in allograft recipients cohort from Queensland, Australia, a high UV environment. About half of the SOTRs with a high genetic risk developed BCC (absolute risk = 45.45%, 95% confidence interval = 33.14-58.19%) and SCC (absolute risk = 44.12%, 95% confidence interval = 32.08-56.68%). For both cancers, SOTRs in the top quintile were at >3-fold increased risk relative to those in the bottom quintile. The respective polygenic risk scores improved risk predictions by 2% for BCC (area under the curve = 0.77 vs. 0.75, P = 0.0691) and SCC (area under the curve = 0.84 vs. 0.82, P = 0.0260), over and above the established risk factors, and 19.03% (for BCC) and 18.10% (for SCC) of the SOTRs were reclassified in a high/medium/low risk scenario. The polygenic risk scores also added predictive accuracy for tumor multiplicity (BCC R2 = 0.21 vs. 0.19, P = 3.2 × 10-3; SCC R2 = 0.30 vs. 0.27, P = 4.6 × 10-4).