Polygenic Risk Score Models Research Articles

Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder

Objectives Major depressive disorder (MDD) is a psychiatric disorder with pathogenesis influenced by both genetic and environmental factors. To date, the molecular-level understanding of its aetiology remains unclear. Thus, we aimed to identify genetic variants and susceptibility genes for MDD with a genome-wide association study (GWAS) approach. Methods We performed a meta-analysis of GWASs and a gene-based analysis on two Northern Italy isolated populations (cases/controls n = 166/472 and 33/320), followed by replication and polygenic risk score (PRS) analyses in Italian independent samples (cases n = 464, controls n = 339). Results We identified two novel MDD-associated genes, KCNQ5 (lead SNP rs867262, p = 3.82 × 10−9) and CTNNA2 (rs6729523, p = 1.25 × 10−8). The gene-based analysis revealed another six genes (p < 2.703 × 10−6): GRM7, CTNT4, SNRK, SRGAP3, TRAPPC9, and FHIT. No replication of the genome-wide significant SNPs was found in the independent cohort, even if 14 SNPs around CTNNA2 showed association with MDD and related phenotypes at the nominal level of p (<0.05). Furthermore, the PRS model developed in the discovery cohort discriminated cases and controls in the replication cohort. Conclusions Our work suggests new possible genes associated with MDD, and the PRS analysis confirms the polygenic nature of this disorder. Future studies are required to better understand the role of these findings in MDD.

Mitochondrial and autophagy-lysosomal pathway polygenic risk scores predict Parkinson's disease

Polygenic Risk Scores (PRS), which allow assessing an individuals' genetic risk for a complex disease, are calculated as the weighted number of genetic risk alleles in an individual's genome, with the risk alleles and their weights typically derived from the results of genome-wide association studies (GWAS). Among a wide range of applications, PRS can be used to identify at-risk individuals and select them for further clinical follow-up. Pathway PRS are genetic scores based on single nucleotide polymorphisms (SNPs) assigned to genes involved in major disease pathways. The aim of this study is to assess the predictive utility of PRS models constructed based on SNPs corresponding to two cardinal pathways in Parkinson's disease (PD) including mitochondrial PRS (Mito PRS) and autophagy-lysosomal PRS (ALP PRS). PRS models were constructed using the clumping-and-thresholding method in a German population as prediction dataset that included 371 cases and 249 controls, using SNPs discovered by the most recent PD-GWAS.We showed that these pathway PRS significantly predict the PD status. Furthermore, we demonstrated that Mito PRS are significantly associated with later age of onset in PD patients.Our results may add to the accumulating evidence for the contribution of mitochondrial and autophagy-lysosomal pathways to PD risk and facilitate biologically relevant risk stratification of PD patients.

Are polygenic risk scores ready for the cancer clinic?-a perspective.

To realize the goals of precision medicine in complex disease, discriminative clinical risk models are needed. One approach that has been proposed is polygenic risk scores (PRSs). PRSs incorporate information about inherited genetic risk for cancer, specifically those genetic variants that are common in the population. While PRSs are clearly associated with risk of cancer, there is an on-going debate on whether integrating PRSs into clinical practice have utility. Here, we present this important discussion to the cancer clinic. We argue that in cancer, the clinical utility of PRSs will depend on their actionability, or how such a score may guide clinical practice. In turn, the actionability depends on several factors. First, actionability depends on the discriminative power of the score, or how well it predicts who is at risk of the disease. Second, it depends on their comparative performance with respect to existing practice, as a score with good discriminative power will not be useful if there are better predictors used in the clinic. Finally, for a PRS to be useful there must also be available preventive actions. We discuss the strengths and challenges of utilizing a PRS in the context of each of these criteria, and provide insights on what is needed towards moving forward in translating PRSs into the cancer clinic. We further argue that in future studies, beyond predicting cancer risk, similarly developed PRS models may be of utility in predicting prognosis or treatment resistance.

Association of Novel Loci With Keratoconus Susceptibility in a Multitrait Genome-Wide Association Study of the UK Biobank Database and Canadian Longitudinal Study on Aging

Keratoconus can be a debilitating corneal ectasia in which the cornea thins, bulges, and steepens into a conical shape. Early features of keratoconus include myopia and irregular astigmatism, which affect vision and can be treated with contact lenses, collagen cross-linking, or, in advanced cases, corneal transplant. Recent estimates of the prevalence of keratoconus based on results of Scheimpflug imaging in young adults are as high as 1.2%. However, obtaining very large keratoconus data sets for a genome-wide association study (GWAS) is problematic because few population studies include Scheimpflug imaging and because severe keratoconus is relatively rare. To identify novel keratoconus loci using corneal resistance factor (CRF) and central corneal thickness (CCT). This multitrait GWAS used European ancestry CRF data from UK Biobank (UKB) (n = 105 427) and the Canadian Longitudinal Study on Aging (CLSA) (n = 18 307) and European ancestry CCT data from the International Glaucoma Genetics Consortium (IGGC) (n = 17 803). The CRF and CCT variants in published keratoconus data sets (4669 cases and 116 547 controls) were compared. The data set from UKB was compiled March 24, 2020; data were released from the CLSA in July 2020; and IGGC data were available from May 1, 2018. Association of CRF and CCT variants with keratoconus risk. The GWAS included 4 cohorts: 105 427 UKB European ancestry (56 134 women [53.2%] and 49 293 men [46.7%]; mean [SD] age, 57 [8] years), 5029 UKB South Asian ancestry (2368 women [47.1%] and 2661 men [52.9%]; mean [SD] age, 54 [8] years), 902 UKB East Asian ancestry (622 women [68.9%] and 280 men [31.0%]; mean [SD] age, 53 [8] years), and 18 307 CLSA European ancestry (9260 women [50.6%] and 9047 men [49.4%]; mean [SD] age, 63 [10] years) participants. A total of 369 CRF and 233 CCT loci were identified, including 36 novel CRF loci and 114 novel CCT loci. Twenty-nine CRF loci and 24 CCT loci were associated with keratoconus. Polygenic risk scores (PRS) were constructed using CRF- and CCT-associated variants and published keratoconus variants. The PRS result showed that adding a CRF- or CCT-based PRS to the keratoconus PRS from previously published variants improved the prediction area under the receiver operating characteristic curve (from 0.705 to 0.756 for CRF and from 0.715 to 0.755 for CCT). These findings support the use of multitrait modeling of corneal parameters in a relatively large data set to identify new keratoconus risk loci and enhance polygenic risk score models.

Genome-wide polygenic risk impact on intracranial aneurysms and acute ischemic stroke.

Polygenic risk scores (PRSs) have an important relevance to approaches for clinical usage in intracranial aneurysm (IA) patients. Hence, we aimed to develop IA-predicting PRS models including the genetic basis shared with acute ischemic stroke (AIS) in Korean populations. We applied a weighted PRS (wPRS) model based on a previous genome-wide association study (GWAS) of 250 IA patients in a hospital-based multicenter cohort, 222 AIS patients in a validation study, and 296 shared controls. Risk predictability was analyzed by the area under the receiver operating characteristic curve (AUROC). The best-fitting risk models based on wPRSs were stratified into tertiles representing the lowest, middle, and highest risk groups. The weighted PRS, which included 29 GWASs (p < 5×10-8) and two reported genetic variants (p < 0.01), showed a high predictability in IA patients (AUROC = 0.949, 95% CI: 0.933-0.966). This wPRS was significantly validated in AIS patients (AUROC = 0.842, 95% CI: 0.808-0.876; p < 0.001). Two-stage risk models stratified into tertiles showed an increased risk for IA (OR = 691.25, 95% CI: 241.77-1976.35; p = 3.1×10-34; sensitivity/specificity = 0.728/0.963), which was replicated in AIS development (OR = 39.76, 95% CI: 16.91-93.49; p = 3.1×10-17; sensitivity/specificity = 0.284/0.963). A higher wPRS for IA may be associated with an increased risk of AIS in the Korean population. These findings suggest that IA and AIS may have a shared genetic architecture and should be studied further to generate a precision medicine model for use in personalized diagnosis and treatment.

Significant sparse polygenic risk scores across 813 traits in UK Biobank.

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).

Concurrent IgA Nephropathy and Membranous Nephropathy, Is It an Overlap Syndrome?

IgA nephropathy (IgAN) and membranous nephropathy (MN) are common glomerulonephritis, the presence of which in the same patient– concurrent of IgAN and MN (cIgAN/MN) has been described occasionally. This study aims to show clinical-pathological features of cIgAN/MN and attempts to suggest underlying pathogenesis using disease-specific biomarkers and a genomics approach. This retrospective cohort study described the clinical and pathological data from 137 patients with cIgAN/MN diagnosed in Peking University First Hospital from 2005 to 2019. One hundred primary IgAN and 100 MN cases were randomly selected as disease controls between the same time interval. Moreover, disease-specific biomarkers and polygenic risk score models were conducted to reveal the underlying pathogenesis. The median age of the cIgAN/MN cases was 45-year-old, and 46% were women. Compared to IgAN, patients with cIgAN/MN had a higher level of 24-hour proteinuria excretion but lower microscopic hematuria. They had a lower median level of galactose-deficient IgA1 (Gd-IgA1, 4.00 versus 5.45 μg/ml, P=0.002) as well as the standardized genetic risk scores of developing IgAN (GRSs: 0.05 versus 0.68, P<0.001). Compared to MN, patients with cIgAN/MN had a lower proportion of nephrotic syndrome and a lower level of albumin-to-creatinine ratio. However, the 24-hour proteinuria levels, serum lipid profiles, proportion of hypertension, and pathology classification were similar. Patients with cIgAN/MN had lower levels of plasma autoantibodies against the M-type transmembrane phospholipase A2 receptor (PLA2R) (11.23 versus 36.59 U/ml, P=0.005). Intriguingly, there were no statistical differences in standardized GRSs of developing MN between them (2.77 versus 3.02, P=0.326). Compared to IgAN, cIgAN/MN may lean towards MN more according to clinical-pathological features, disease-specific biomarker levels, and disease-specific genetic risk scores.

Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer's Disease Based on Major Depressive Disorder Risk Variants.

IntroductionDepression is a common, though heterogenous, comorbidity in late-onset Alzheimer’s Disease (LOAD) patients. In addition, individuals with depression are at greater risk to develop LOAD. In previous work, we demonstrated shared genetic etiology between depression and LOAD. Collectively, these previous studies suggested interactions between depression and LOAD. However, the underpinning genetic heterogeneity of depression co-occurrence with LOAD, and the various genetic etiologies predisposing depression in LOAD, are largely unknown.MethodsMajor Depressive Disorder (MDD) genome-wide association study (GWAS) summary statistics were used to create polygenic risk scores (PRS). The Religious Orders Society and Rush Memory and Aging Project (ROSMAP, n = 1,708) and National Alzheimer’s Coordinating Center (NACC, n = 10,256) datasets served as discovery and validation cohorts, respectively, to assess the PRS performance in predicting depression onset in LOAD patients.ResultsThe PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and APOEε4 allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.581). In time-to-event analysis, standalone PRS models showed significant effects in ROSMAP (P = 0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (P < 0.001 for all).ConclusionThis study provided new insights into the genetic factors contributing to depression onset in LOAD and advanced our knowledge of the genetics underlying the heterogeneity of depression in LOAD. The developed PRS accurately predicted LOAD patients with depressive symptoms, thus, has clinical implications including, diagnosis of LOAD patients at high-risk to develop depression for early anti-depressant treatment.

M2-phenotype tumour-associated macrophages upregulate the expression of prognostic predictors MMP14 and INHBA in pancreatic cancer.

Pancreatic cancer is one of the most lethal gastrointestinal tumours, the most common pathological type is pancreatic adenocarcinoma (PAAD). In recent year, immune imbalanced in tumour microenvironment has been shown to play an important role in the evolution of tumours progression, and the efficacy of immunotherapy has been gradually demonstrated in clinical practice. In this study, we propose to construct an immune‐related prognostic risk model based on immune‐related genes MMP14 and INHBA expression that can assess the prognosis of pancreatic cancer patients and identify potential therapeutic targets for pancreatic cancer, to provide new ideas for the treatment of pancreatic cancer. We also investigate the correlation between macrophage infiltration and MMP14 and INHBA expression. First, the gene expression data of pancreatic cancer and normal pancreatic tissue were obtained from The Cancer Genome Atlas Program (TCGA) and The Genotype‐Tissue Expression public database (GTEx). The differentially expressed immune‐related genes between pancreatic cancer samples and normal sample were screened by R software. Secondly, univariate Cox regression analysis were used to evaluate the relationship between immune‐related genes and the prognosis of pancreatic cancer patients. A polygenic risk score model was constructed by Cox regression analysis. The prognostic nomogram was constructed, and its performance was evaluated comprehensively by internal calibration curve and C‐index. Using the risk model, each patient gets a risk score, and was divided into high‐ or low‐ risk groups. The proportion of 22 types of immune cells infiltration in pancreatic cancer samples was inferred by CIBERSOFT algorithm, correlation analysis (Pearson method) was used to analyse the correlation between the immune‐related genes and immunes cells. Then, we applied macrophage conditioned medium to culture pancreatic cancer cell line PANC1, detected the expression of MMP14 and INHBA by qRT‐PCR and Western blot methods. Knock‐down MMP14 and INHBA in PANC1 cells by transfected with shRNA lentiviruses. Detection of migration ability of pancreatic cells was done by trans‐well cell migration assay. A subcutaneous xenograft tumour model of human pancreatic cancer in nude mice was constructed. In conclusion, an immune‐related gene prognostic model was constructed, patients with high‐risk scores have poorer survival status, M2‐phenotype tumour‐associated macrophages (TAMs) up‐regulate two immune‐related genes, MMP14 and INHBA, which were used to establish the prognostic model. Knock‐down of MMP14 and INHBA inhibited invasion of pancreatic cancer.

Predicting Pancreatic Cancer in the UK Biobank Cohort Using Polygenic Risk Scores and Diabetes Mellitus

Diabetes mellitus (DM) is known to be associated with pancreatic ductal adenocarcinoma (PDAC), particularly new-onset DM (NODM). Others have developed polygenic risk scores (PRS) associated with PDAC risk. We aimed to compare the performance of these PRS in an independent cohort to determine if they can discriminate between NODM and long-standing DM patients with PDAC. Cases (1042) and matched cancer-free controls (10,420) were drawn from the UK Biobank. Five PRS models were calculated using single nucleotide polymorphisms (SNPs) from previous studies (Nakatochi, Galeotti, Molina, Jia, and Rashkin) and a combination of these. Regression models were used to assess the association between PDAC and PRS adjusted for ancestry, smoking, DM, waist circumference, and family history of digestive cancer. Receiver operator characteristic curves and area under the curve metrics (AUC) were used to assess the performance of each PRS for classifying PDAC risk. The combined PRS model achieved the highest AUC (0.605), and significantly improved a clinical risk model in this cohort (AUC= 0.83; P= .0002). Individuals within the fifth quintile have a 2.74-fold increased risk of developing PDAC vs those in the first quintile (P < .001), and have a 3.05-fold increased risk of developing PDAC if they have DM vs those without DM (P < .001). The positive predictive value was 11.9% in participants without DM, 23.9% with long-standing DM, and 86.7% with NODM. The PDAC-related common genetic variants are more strongly associated with DM. This PRS has the potential for targeting individuals with NODM for PDAC secondary screening measures.

The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data.

Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer. The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients. Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.

Five-year absolute risk estimates of colorectal cancer based on CCRAT model and polygenic risk scores: A validation study using the Quebec population-based cohort CARTaGENE

The objective was to evaluate the predictive performance of the Colorectal Cancer Risk Assessment Tool (CCRAT) and three polygenic risk scores (Hsu et al., 2015; Law et al., 2019, Archambault et al., 2020) to predict the occurrence of colorectal cancer at five years in a Quebec population-based cohort. By using the CARTaGENE cohort, we computed the absolute risk of colorectal cancer with the CCRAT model, the polygenic risk scores (PRS) and combined clinico-genetic models (CCRAT + PRS). We also tailored the CCRAT model by using the marginal age-specific colorectal incidence rates in Canada and the risk score distribution. We reported the calibration and the discrimination. Performances of the PRSs, combined and tailored CCRAT models were compared to the original CCRAT model. The expected-to-observed ratio of the original CCRAT model was 0.54 [0.43–0.68]. The c-index was 74.79 [68.3–80.5]. The tailored CCRAT model improved the expected-to-observed ratio (0.74 [0.59–0.94]) and c-index (76.39 [69.7–82.1]). All PRS improved the expected-to-observed ratios (around 0.83, confidence intervals including one). PRSs’ c-indexes were not significantly different from CCRAT models. Results from the combined models were close to those from the PRS models, Archambault combined model’s c-index being significantly higher than the original and tailored CCRAT models (78.67 [70.8–86.5]; p < 0.001 and p = 0.028, respectively). In this Quebec cohort, CCRAT model has a good discrimination with a poor calibration. While the tailored CCRAT provides some gain in calibration, clinico-genetic models improved both calibration and discrimination. However, better calibrations must be obtained before a practical use among the inhabitants of Quebec province.

Predictive model incorporating polygenic risk score for Alzheimer's disease predicts MMSE decline in APOE4 carriers and noncarriers.

Heterogeneity in the progression of cognitive impairment in later life is especially challenging to predict in pre-symptomatic and prodromal AD populations and can negatively impact the efficiency and statistical power of clinical trials. Genetic risk estimation as the earliest measurable contributor to AD is one of the most promising solutions. Polygenic risk scores (PRS) calculated based on multiple common genetic variants identified in large genome-wide association studies (GWAS) take the comprehensive genetic effect into consideration beyond APOE genotype. Here we present the PRS model results comparing prediction for decline among APOE4 carriers and non-carriers among pre-symptomatic individuals. A PRS was calculated using GWAS summary statistics for clinical AD diagnosis. The samples used in modeling were amyloid-PET positive confirmed individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Logistic regression models assessed the association between PRS and MMSE decline covarying for age, sex, education, APOE-ε4, and baseline MMSE score. Feature importance rank was estimated by the absolute value of the corresponding coefficients of all predictors in the model. The PRS model outcompeted the base model for MMSE decline, which included age, sex, and education as predictors, and exhibited the best performance (AUC = 0.82). Additionally, the PRS model revealed the highest classification performance in both APOE4 carriers (AUC = 0.81) and noncarriers (AUC=0.82) of the pre-symptomatic individuals. Meanwhile, the feature importance analysis showed that PRS is ranked as the second most important feature in both subgroups. The proposed model including PRS explains the heterogeneity of cognitive decline above and beyond the APOE4 allele and increases the model performance over traditional methods to predict cognitive decline in individuals at risk for AD. Utilization of additional genomic factors beyond APOE in PRS models could enhance clinical trial recruitment and stratification strategies for trial analyses, such that high polygenic risk APOE3 carriers are selected for probable cognitive decline, in addition to APOE4 carriers.

Polygenic risk scores for Alzheimer's disease predict MMSE decline in APOE4 carriers and noncarriers and the impact of sample overlap with GWAS summary statistics.

Heterogeneity in the progression of cognitive impairment, which is common in sporadic Alzheimer's disease (AD) trials, is especially challenging to predict in pre-symptomatic populations, and has a negative impact on clinical trial power. Heritability of AD extends beyond the APOE genotype, with multiple common genetic variants identified in large genome wide association studies (GWAS). Incorporating APOE and additional genetic variants into models has the potential to improve the prediction of disease progression. The limited volume of genetic cohorts in AD and the sample overlap in GWAS summary statistics poses some concern for polygenic risk score (PRS) overfitting. A PRS was calculated using genome-wide association study (GWAS) summary statistics for clinical AD diagnosis. PRS derived from this GWAS study was computed for participants drawn from two aging studies. Logistic regression models assessed the association between PRS and Mini Mental State Exam (MMSE) decline covarying for age, sex, education, APOE-ε4, and baseline MMSE score. Age and sex interactions with PRS were also assessed. Samples assumed to be used in the GWAS calculations were then removed from the test set and the impact to performance was analyzed. Participants in the training and test set showed similar baseline ages, years of education and baseline MMSE scores in the whole sample and after stratification by APOE4 carrier status. The PRS model was a significant predictor of MMSE decline, as well as in APOE4 carriers and noncarriers. Model performance was compared to the test set excluding the GWAS overlap samples and no significant difference was observed. The proposed model including PRS explains heterogeneity in cognitive decline above and beyond the APOE4 allele. Testing the model in a dataset excluding GWAS overlap samples did not result in a significant difference in performance and potential overfitting does not appear to be an impact. Utilization of demographic and genomic factors beyond APOE in PRS models could enhance clinical trial recruitment and stratification strategies for trial analyses, such that APOE4 carriers are selected for probable cognitive decline, in addition to APOE3 carriers that are high on polygenic risk.

VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.

Single nucleotide polymorphism (SNP) set analysis aggregates both common and rare variants and tests for association between phenotype(s) of interest and a set. However, multiple SNP-sets, such as genes, pathways, or sliding windows are usually investigated across the whole genome in which all groups are tested separately, followed by multiple testing adjustments. We propose a novel method to prioritize SNP-sets in a joint multivariate variance component model. Each SNP-set corresponds to a variance component (or kernel), and model selection is achieved by incorporating either convex or nonconvex penalties. The uniqueness of this variance component selection framework, which we call VCSEL, is that it naturally encompasses multivariate traits (VCSEL-M) and SNP-set-treatment or -environment interactions (VCSEL-I). We devise an optimization algorithm scalable to many variance components, based on the majorization-minimization (MM) principle. Simulation studies demonstrate the superiority of our methods in model selection performance, as measured by the area under the precision-recall (PR) curve, compared to the commonly used marginal testing and group penalization methods. Finally, we apply our methods to a real pharmacogenomics study and a real whole exome sequencing study. Some top ranked genes by VCSEL are detected as insignificant by the marginal test methods which emphasizes formal inference of individual genes with a strict significance threshold. This provides alternative insights for biologists to prioritize follow-up studies and develop polygenic risk score models.

Anti-Irritant Strategy against Retinol Based on the Genetic Analysis of Korean Population: A Genetically Guided Top-Down Approach.

Retinol, one of the most powerful cosmetic materials for anti-aging supported by a solid scientific background, exhibits a wide range of type and severity of irritation while showing limited user compliance. The lack of understanding of the mechanism of retinol-induced irritation has been the main hurdle in the development of anti-irritation strategies. Here, we identified 30 genetic markers related to the susceptibility to retinol-induced irritation in the Korean population. Based on the genetic analysis, a novel formula against retinol-induced irritation was developed, which mitigated the molecular pathogenesis—as indicated by the genetic markers—of the retinol-induced irritation. In human tests, this formula effectively decreased retinol-induced irritation. Furthermore, a polygenic risk score model for irritation was constructed and validated. Our comprehensive approach for the analysis of retinol-induced irritation will not only aid the development of anti-irritation strategies to ensure higher user compliance but also contribute to improving the current knowledge about the biological effects of retinoids.

Genome-Wide Polygenic Risk Score for Predicting High Risk Glaucoma Individuals of Han Chinese Ancestry.

Glaucoma is a progressive and irreversible blindness-causing disease. However, the underlying genetic factors and molecular mechanisms remain poorly understood. Previous genome-wide association studies (GWAS) have made tremendous progress on the SNP-based disease association and characterization. However, most of them were conducted for Europeans. Since differential genetic characteristics among ethnic groups were evident in glaucoma, it is worthwhile to complete its genetic landscape from the larger cohorts of Asian individuals. Here, we present a GWAS based on the Taiwan Biobank. Among 1013 glaucoma patients and 36,562 controls, we identified a total of 138 independent glaucoma-associated SNPs at the significance level of p < 1 × 10−5. After clumping genetically linked SNPs (LD clumping), 134 independent SNPs with p < 10−4 were recruited to construct a Polygenic Risk Score (PRS). The model achieved an area under the receiver operating characteristic curve (AUC) of 0.8387 (95% CI = [0.8269–0.8506]), and those within the top PRS quantile had a 45.48-fold increased risk of glaucoma compared with those within the lowest quantile. The PRS model was validated with an independent cohort that achieved an AUC of 0.7283, thereby showing the effectiveness of our polygenic risk score in predicting individuals in the Han Chinese population with higher glaucoma risks.

PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics

Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.

Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson\u2019s disease

BackgroundLow-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.ResultsUsing eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered.ConclusionsOverall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.

Generalizability of Polygenic Risk Scores for Breast Cancer Among Women With European, African, and Latinx Ancestry

Multiple polygenic risk scores (PRSs) for breast cancer have been developed from large research consortia; however, their generalizability to diverse clinical settings is unknown. To examine the performance of previously developed breast cancer PRSs in a clinical setting for women of European, African, and Latinx ancestry. This cohort study using the Electronic Medical Records and Genomics (eMERGE) network data set included 39 591 women from 9 contributing medical centers in the US that had electronic medical records (EMR) linked to genotype data. Breast cancer cases and controls were identified through a validated EMR phenotyping algorithm. Multivariable logistic regression was used to assess the association between breast cancer risk and 7 previously developed PRSs, adjusting for age, study site, breast cancer family history, and first 3 ancestry informative principal components. This study included 39 591 women: 33 594 with European, 3801 with African, and 2196 with Latinx ancestry. The mean (SD) age at breast cancer diagnosis was 60.7 (13.0), 58.8 (12.5), and 60.1 (13.0) years for women with European, African, and Latinx ancestry, respectively. PRSs derived from women with European ancestry were associated with breast cancer risk in women with European ancestry (highest odds ratio [OR] per 1-SD increase, 1.46; 95% CI, 1.41-1.51), women with Latinx ancestry (highest OR, 1.31; 95% CI, 1.09-1.58), and women with African ancestry (OR, 1.19; 95% CI, 1.05-1.35). For women with European ancestry, this association with breast cancer risk was largest in the extremes of the PRS distribution, with ORs ranging from 2.19 (95% CI, 1.84-2.53) to 2.48 (95% CI, 1.89-3.25) for the 3 different PRSs examined for those in the highest 1% of the PRS compared with those in the middle quantile. Among women with Latinx and African ancestries at the extremes of the PRS distribution, there were no statistically significant associations. This cohort study found that PRS models derived from women with European ancestry for breast cancer risk generalized well for women with European, Latinx, and African ancestries across different clinical settings, although the effect sizes for women with African ancestry were smaller, likely because of differences in risk allele frequencies and linkage disequilibrium patterns. These results highlight the need to improve representation of diverse population groups, particularly women with African ancestry, in genomic research cohorts.