Genome-wide Polygenic Risk Scores Research Articles

Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization.

Schizophrenia (SCZ) is a complex psychiatric disorder presenting challenges for characterization. The current study aimed to identify and evaluate disease-responsive essential genes (DREGs) to enhance the molecular characterization of SCZ. RNA-sequencing data from PsychENCODE (536 SCZ patients, 832 controls) and peripheral blood transcriptome data from 144 recruited subjects (59 SCZ patients, 6 non-SCZ psychiatric patients, 79 controls) are analyzed. Shared differential expression genes are obtained using three algorithms. Support vector machine (SVM)-based recursive feature elimination is employed to identify DREGs. The biological relevance of these DREGs is examined through protein-protein interaction network, pathway enrichment, polygenic scoring, and brain tissue expression. Key DREGs are validated in SCZ animal models. A DREGs-based machine-learning model for SCZ characterization is developed and its performance is assessed using multiple datasets. The analysis identified 184 DREGs forming an interconnected network involved in synaptic plasticity, inflammation, neuronal development, and neurotransmission. DREGs exhibited distinct expression in SCZ-related brain regions and animal models. Their genetic contributions are comparable to genome-wide polygenic risk scores. The DREG-based SVM model demonstrated high performance (AUC 85% for SCZ characterization, 79% for specificity). These findings provide new insights into the molecular mechanisms underlying SCZ and emphasize the potential of DREGs in improving SCZ characterization.

Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score.

Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors. We evaluated the performance of this metaPRS and benchmarked it against existing genome-wide PRS in 620,059 participants and 50,572 T2D cases amongst six diverse genetic ancestries from UK Biobank, INTERVAL, the All of Us Research Program, and the Singapore Multi-Ethnic Cohort. We show that our metaPRS was the most powerful PRS for predicting T2D in European population-based cohorts and had comparable performance to the top ancestry-specific PRS, highlighting its transferability. In UK Biobank, we show the metaPRS had stronger predictive power for 10-year risk than all individual risk factors apart from BMI and biomarkers of dysglycemia. The metaPRS modestly improved T2D risk stratification of QDiabetes risk scores for 10-year risk prediction, particularly when prioritising individuals for blood tests of dysglycemia. Overall, we present a highly predictive and transferrable PRS for T2D and demonstrate that the potential for PRS to incrementally improve T2D risk prediction when incorporated into UK guideline-recommended screening and risk prediction with a clinical risk score.

Polygenic Interactions With Environmental Exposures in Blood Pressure Regulation: The HUNT Study.

The essential hypertension phenotype results from an interplay between genetic and environmental factors. The influence of lifestyle exposures such as excess adiposity, alcohol consumption, tobacco use, diet, and activity patterns on blood pressure (BP) is well established. Additionally, polygenic risk scores for BP traits are associated with clinically significant phenotypic variation. However, interactions between genetic and environmental risk factors in hypertension morbidity and mortality are poorly characterized. We used genotype and phenotype data from up to 49 234 participants from the HUNT (Trøndelag Health Study) to model gene-environment interactions between genome-wide polygenic risk scores for systolic BP and diastolic BP and 125 environmental exposures. Among the 125 environmental exposures assessed, 108 and 100 were independently associated with SBP and DBP, respectively. Of these, 12 interactions were identified for genome-wide PRSs for systolic BP and 4 for genome-wide polygenic risk scores for diastolic BP, 2 of which were overlapping (P < 2 × 10-4). We found evidence for gene-dependent influence of lifestyle factors such as cardiorespiratory fitness, dietary patterns, and tobacco exposure, as well as biomarkers such as serum cholesterol, creatinine, and alkaline phosphatase on BP. Individuals that are genetically susceptible to high BP may be more vulnerable to common acquired risk factors for hypertension, but these effects appear to be modifiable. The gene-dependent influence of several common acquired risk factors indicates the potential of genetic data combined with lifestyle assessments in risk stratification, and gene-environment-informed risk modeling in the prevention and management of hypertension.

Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes.

Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data. We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 × 10-8). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts. PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range = 11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 × 10-54 vs. OR = 1.83, P = 9.4 × 10-50) and higher explained variance (R2 = 2.82% vs. R2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 × 10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, PΔAUC = 6.8 × 10-9). Genome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.

Influence of Polygenic Background on the Clinical Presentation of Familial Hypercholesterolemia.

Heterozygous familial hypercholesterolemia (FH) is among the most common genetic conditions worldwide that affects ≈ 1 in 300 individuals. FH is characterized by increased levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of coronary artery disease (CAD), but there is a wide spectrum of severity within the FH population. This variability in expression is incompletely explained by known risk factors. We hypothesized that genome-wide genetic influences, as represented by polygenic risk scores (PRSs) for cardiometabolic traits, would influence the phenotypic severity of FH. We studied individuals with clinically diagnosed FH (n=1123) from the FH Canada National Registry, as well as individuals with genetically identified FH from the UK Biobank (n=723). For all individuals, we used genome-wide gene array data to calculate PRSs for CAD, LDL-C, lipoprotein(a), and other cardiometabolic traits. We compared the distribution of PRSs in individuals with clinically diagnosed FH, genetically diagnosed FH, and non-FH controls and examined the association of the PRSs with the risk of atherosclerotic cardiovascular disease. Individuals with clinically diagnosed FH had higher levels of LDL-C, and the incidence of atherosclerotic cardiovascular disease was higher in individuals with clinically diagnosed compared with genetically identified FH. Individuals with clinically diagnosed FH displayed enrichment for higher PRSs for CAD, LDL-C, and lipoprotein(a) but not for other cardiometabolic risk factors. The CAD PRS was associated with a risk of atherosclerotic cardiovascular disease among individuals with an FH-causing genetic variant. Genetic background, as expressed by genome-wide PRSs for CAD, LDL-C, and lipoprotein(a), influences the phenotypic severity of FH, expanding our understanding of the determinants that contribute to the variable expressivity of FH. A PRS for CAD may aid in risk prediction among individuals with FH.

Integrating Genome-Wide Polygenic Risk Scores With Nongenetic Models to Predict Surgical Site Infection After Total Knee Arthroplasty Using United Kingdom Biobank Data

BackgroundPrediction of the risk of developing surgical site infection (SSI) in patients following total knee arthroplasty (TKA) is of clinical importance. Genetic susceptibility is involved in developing TKA-related SSI. Previously reported models for predicting SSI were constructed using nongenetic risk factors without incorporating genetic risk factors. To address this issue, we performed a genome-wide association study (GWAS) using the UK Biobank database. MethodsAdult patients who underwent primary TKA (n = 19,767) were analyzed and divided into SSI (n = 269) and non-SSI (n = 19,498) cohorts. Nongenetic covariates, including demographic data and preoperative comorbidities, were recorded. Genetic variants associated with SSI were identified by GWAS and included to obtain standardized polygenic risk scores (zPRS, an estimate of genetic risk). Prediction models were established through analyses of multivariable logistic regression and the receiver operating characteristic curve. ResultsThere were 4 variants (rs117896641, rs111686424, rs8101598, and rs74648298) achieving genome-wide significance that were identified. The logistic regression analysis revealed 7 significant risk factors: increasing zPRS, decreasing age, men, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, and peripheral vascular disease. The areas under the receiver operating characteristic curve were 0.628 and 0.708 when zPRS (model 1) and nongenetic covariates (model 2) were used as predictors, respectively. The areas under the receiver operating characteristic curve increased to 0.76 when both zPRS and nongenetic covariates (model 3) were used as predictors. A risk-prediction nomogram was constructed based on model 3 to visualize the relative effect of statistically significant covariates on the risk of SSI and predict the probability of developing SSI. Age and zPRS were the top 2 covariates that contributed to the risk, with younger age and higher zPRS associated with higher risks. ConclusionsOur GWAS identified 4 novel variants that were significantly associated with susceptibility to SSI following TKA. Integrating genome-wide zPRS with nongenetic risk factors improved the performance of the model in predicting SSI.

Abstract 38: Prediction of Coronary Artery Disease Subtypes With Polygenic Risk Scores

Background: Coronary Artery Disease (CAD) is a complex heterogeneous disease with multiple known etiological mechanisms. Consequently, CAD could be considered to have multiple subtypes, reflecting alternative driving etiologies that may warrant distinct therapeutic approaches. Motivated by this, we developed and optimized statistical models to predict CAD subtypes informed by genetic and clinical risk factors. Methods: We developed three models, each using logistic regression: (a) clinical risk factors only, (b) clinical risk factors plus genome-wide polygenic risk scores (PRS), and (c) clinical risk factors and pathway-based PRSs. We defined 5 different CAD subtypes on the basis of the literature: (1) stable vs. unstable, (2) occlusive vs non-occlusive, (3) high vs normal LDL, (4) high vs normal Lp(a), and (5) high vs normal ASCVD score. Clinical risk factors included: ASCVD score, ApoA, ApoB, LDL, HDL, triglycerides, and C-reactive protein. We calculated 4,402 pathway PRSs obtained from six genomic pathway databases (e.g. KEGG). We optimized the models using 80% of the samples as training, and then performed model evaluation (Bonferroni-corrected P-values) in an unseen 20% of the samples as validation. Results: Among 20,935 UK Biobank participants with CAD (mean age 63 years, 58% female), addition of pathway-based PRSs improved prediction of high LDL (P=1e-19) and high Lp(a) subtypes (P=1.8e-78). Using a lasso dimension reduction approach, the pathways most associated with the high Lp(a) subtype of CAD were: Fibronectin Binding (P=6xe-260), Apolipoprotein Binding (P=6.2e-252) and Serine-Type Endopeptidase Activity (P=1.7e-203). Conclusions: We demonstrate that prediction models distinguish CAD subtypes and that a novel pathway PRS approach can increase predictive power and provide insights into the genetic underpinnings of CAD subtypes. Pathway PRSs hold promise for the targeted therapeutic management for subgroups of CAD patients.

A high-resolution PheWAS approach to alcohol-related polygenic risk scores reveals mechanistic influences of alcohol reinforcing value and drinking motives.

This study uses a high-resolution phenome-wide approach to evaluate the motivational mechanisms of polygenic risk scores (PRSs) that have been robustly associated with coarse alcohol phenotypes in large-scale studies. In a community-based sample of 1534 Europeans, we examined genome-wide PRSs for the Alcohol Use Disorders Identification Test (AUDIT), drinks per week, alcohol use disorder (AUD), problematic alcohol use (PAU), and general addiction, in relation to 42 curated phenotypes. The curated phenotypes were in seven categories: alcohol consumption, alcohol reinforcing value, drinking motives, other addictive behaviors, commonly comorbid psychiatric syndromes, impulsivity, and personality traits. The PRS for each alcohol phenotype was validated via its within-sample association with the corresponding phenotype (adjusted R2s = 0.35-1.68%, Ps = 0.012-3.6 × 10-7) with the exception of AUD. All PRSs were positively associated with alcohol reinforcing value and drinking motives, with the strongest effects from AUDIT-consumption (adjusted R2s = 0.45-1.33%, Ps = 0.006-3.6 × 10-5) and drinks per week PRSs (adjusted R2s = 0.52-2.28%, Ps = 0.004-6.6 × 10-9). Furthermore, the PAU and drinks per week PRSs were positively associated with adverse childhood experiences (adjusted R2s = 0.6-0.7%, Ps = 0.0001-4.8 × 10-4). These results implicate alcohol reinforcing value and drinking motives as genetically-influenced mechanisms using PRSs for the first time. The findings also highlight the value of dissecting genetic influence on alcohol involvement through diverse phenotypic risk pathways but also the need for future studies with both phenotypic richness and larger samples.

High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases

Formalin-fixed paraffin-embedded (FFPE) tissues stored in biobanks and pathology archives are a vast but underutilized source for molecular studies on different diseases. Beyond being the “gold standard” for preservation of diagnostic human tissues, FFPE samples retain similar genetic information as matching blood samples, which could make FFPE samples an ideal resource for genomic analysis. However, research on this resource has been hindered by the perception that DNA extracted from FFPE samples is of poor quality. Here, we show that germline disease-predisposing variants and polygenic risk scores (PRS) can be identified from FFPE normal tissue (FFPE-NT) DNA with high accuracy. We optimized the performance of FFPE-NT DNA on a genome-wide array containing 657,675 variants. Via a series of testing and validation phases, we established a protocol for FFPE-NT genotyping with results comparable with blood genotyping. The median call rate of FFPE-NT samples in the validation phase was 99.85% (range 98.26%-99.94%) and median concordance with matching blood samples was 99.79% (range 98.85%-99.9%). We also demonstrated that a rare pathogenic PALB2 genetic variant predisposing to cancer can be correctly identified in FFPE-NT samples. We further imputed the FFPE-NT genotype data and calculated the FFPE-NT genome-wide PRS in 3 diseases and 4 disease risk variables. In all cases, FFPE-NT and matching blood PRS were highly concordant (all Pearson’s r > 0.95). The ability to precisely genotype FFPE-NT on a genome-wide array enables translational genomics applications of archived FFPE-NT samples with the possibility to link to corresponding phenotypes and longitudinal health data.

Genome-wide polygenic risk scores for colorectal cancer have implications for risk-based screening.

Hereditary factors, including single genetic variants and family history, can be used for targeting colorectal cancer (CRC) screening, but limited data exist on the impact of polygenic risk scores (PRS) on risk-based CRC screening. Using longitudinal health and genomics data on 453,733 Finnish individuals including 8801 CRC cases, we estimated the impact of a genome-wide CRC PRS on CRC screening initiation age through population-calibrated incidence estimation over the life course in men and women. Compared to the cumulative incidence of CRC at age 60 in Finland (the current age for starting screening in Finland), a comparable cumulative incidence was reached 5 and 11 years earlier in persons with high PRS (80-99% and >99%, respectively), while those with a low PRS (< 20%) reached comparable incidence 7 years later. The PRS was associated with increased risk of post-colonoscopy CRC after negative colonoscopy (hazard ratio 1.76 per PRS SD, 95% CI 1.54-2.01). Moreover, the PRS predicted colorectal adenoma incidence and improved incident CRC risk prediction over non-genetic risk factors. Our findings demonstrate that a CRC PRS can be used for risk stratification of CRC, with further research needed to optimally integrate the PRS into risk-based screening.

Risk Assessment for Gastrointestinal Diseases via Clinical Dimension and Genome-Wide Polygenic Risk Scores of Type 2 Diabetes: A Population-Based Cohort Study.

We aimed to evaluate whether individuals with type 2 diabetes (T2D) were at higher risk of developing a wide range of gastrointestinal diseases based on a population-based cohort study. This study included 374,125 participants free of gastrointestinal disorders at baseline; of them, 19,719 (5.27%) with T2D were followed-up by linking to multiple medical records to record gastrointestinal disease diagnoses. Multivariable Cox models were used to estimate the hazard ratios (HRs) and CIs. Logistic models were used to examine the associations between polygenic risk scores (PRS) and clinical gastrointestinal phenotypes. During a median follow-up of 12.0 years, we observed the new onset of 15 gastrointestinal diseases. Compared with nondiabetes, participants with T2D had an increased risk of gastritis and duodenitis (HR 1.58, 95% CI 1.51-1.65), peptic ulcer (HR 1.56, 95% CI 1.43-1.71), diverticular disease (HR 1.19, 95% CI 1.14-1.24), pancreatitis (HR 1.45, 95% CI 1.24-1.71), nonalcoholic fatty liver disease (HR 2.46, 95% CI 2.25-2.69), liver cirrhosis (HR 2.92, 95% CI 2.58-3.30), biliary disease (HR 1.18, 95% CI 1.10-1.26), gastrointestinal tract cancers (HR 1.28, 95% CI 1.17-1.40), and hepatobiliary and pancreatic cancer (HR 2.32, 95% CI 2.01-2.67). Positive associations of PRS of T2D with gastritis, duodenitis, and nonalcoholic fatty liver disease were also observed. In this large cohort study, we found that T2D was associated with increased risks of a wide range of gastrointestinal outcomes. We suggest the importance of early detection and prevention of gastrointestinal disorders among patients with T2D.

Developing Topics.

Alzheimer's disease (AD) is associated with the accumulation of neuropathological beta-amyloid (Ab) plaques, which is thought to be caused by an imbalance between Ab overproduction and dysfunctional Ab clearance. Both animal and human studies have shown that increased cerebrospinal fluid (CSF) levels of Ab peptides, especially Ab-40 and Ab-38 due to their high solubility, may be indicative of overall Ab dysregulation in preclinical AD, years before pathological Ab plaques begin to aggregate. The apolipoprotein E (APOE) e4 allele, which is the strongest genetic risk factor for AD, has been linked to impaired Ab clearance and disruption of the amyloid precursor protein (APP) pathway that is responsible for Ab production. However, the association between early Ab dysregulation and other genetic risk variants remains understudied. Thus, we aimed to explore whether genetic risk for AD is related to early markers of Ab overproduction by examining associations between polygenic risk scores (PRS) and CSF Ab-40 and Ab-38 in an AD-vulnerable population. We selected 88 non-demented Veterans (Age: M = 68.22, SD = 3.75 years) from the Alzheimer's Disease Neuroimaging Initiative-Department of Defense (ADNI-DOD) database with available genetic markers and CSF measures for Ab-40 and Ab-38. Genetic propensity for AD was assessed using genome-wide PRS, both with and without the APOE region, employing a p-value threshold < 0.01. CSF Ab concentrations were measured using a 2D-UPLC-tandem mass spectrometry method outlined by ADNI-DOD. Partial spearman's correlations related PRS to CSF Ab-38 and Ab-40 levels adjusting for age. CSF Ab-40 levels were positively associated with PRS for AD (rhopartial = 0.22, p = 0.037). When variants in the APOE region were excluded from the score, the relationship between CSF Ab-40 and PRS remained significant (rhopartial = 0.23, p = 0.035). The relationship between PRS for AD and CSF Ab-38 levels was not significant, regardless of whether APOE regions were included in the score (all p's > 0.1). Genetic risk for AD may lead to early Ab overproduction, as evidenced by elevated CSF Ab levels, particularly Ab-40. Other genetic variants, in addition to APOE, may play an integral role in disrupting the APP pathway and dysregulating Ab production.

Metabolomic profiles, polygenic risk scores and risk of rheumatoid arthritis: a population-based cohort study in the UK Biobank

ObjectiveTo investigate the relationship between metabolomic profiles, genome-wide polygenic risk scores (PRSs) and risk of rheumatoid arthritis (RA).Methods143 nuclear magnetic resonance-based plasma metabolic biomarkers were measured among 93 800 participants...

28 Traumatic Brain Injury and Genetic Risk for Alzheimer's Disease Influence ß-Amyloid Levels

Objective:Traumatic brain injuries (TBIs) are a common occurrence among Veterans and may increase risk for neurodegenerative diseases, such as Alzheimer's disease (AD). Neuropathological correlates of AD, including buildup of ß-amyloid (Aß) plaques, formation of neurofibrillary tangles, and cortical atrophy, begin years before the onset of noticeable clinical and cognitive symptoms, emphasizing the importance of identifying early risk factors that could be targeted to prevent the development of AD. Of note, Aß ratios (e.g., Aß 42/40) have been shown to efficiently capture brain amyloid accumulation in prodromal AD, and thus may serve as a useful biological marker of preclinical AD. The present study investigates the mechanism by which TBI is associated with AD by examining the synergistic effects of TBI and genetic risk for AD on Aß among aging Veterans without dementia.Participants and Methods:Participants included 88 White, Non-Hispanic/Latino male Vietnam War Veterans (Mage = 68.3 years) from the Alzheimer's Disease Neuroimaging Initiative Department of Defense (ADNI DoD) cohort, 49 of whom reported a history of at least one mild, moderate, or severe TBI. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Aß levels were extracted from cerebrospinal fluid and Aß 42/40 ratios were calculated as an index of Aß deposition in the brain. Linear regression models were run to determine if TBI history and polygenic risk influence Aß 42/40 levels. An ANCOVA was implemented to examine the interaction between TBI severity and polygenic risk. Covariates in all models included age, education, and posttraumatic stress disorder symptoms.Results:Results demonstrated a significant interaction between TBI and genetic risk on Aß 42/40 (B = -0.45, Puncorrected = 0.029, Pcorrected = 0.0495). Specifically, higher polygenic risk was associated with lower Aß 42/40 ratio, suggesting greater Aß burden in the brain, among those with a history of TBI (pr = -0.33, P = 0.024) compared to individuals without a history of TBI (pr = 0.17, P = 0.308). This relationship trended towards being stronger as a function of increasing TBI severity (F(2, 77) = 3.01, P = 0.055).Conclusions:These results show that, in the context of TBI, higher genetic risk for AD is associated with greater AD-related pathology, particularly with more severe injuries. TBI and polygenic risk may implicate similar biological pathways, notably amyloid precursor protein processing, to increase Aß burden in the brain and likelihood of progression to AD in future years. These findings could inform early intervention techniques to delay or preclude conversion to AD.

Alcohol use polygenic risk score, social support, and alcohol use among European American and African American adults.

Alcohol use is influenced by genetic and environmental factors. We examined the interactive effects between genome-wide polygenic risk scores for alcohol use (alc-PRS) and social support in relation to alcohol use among European American (EA) and African American (AA) adults across sex and developmental stages (emerging adulthood, young adulthood, and middle adulthood). Data were drawn from 4,011 EA and 1,274 AA adults from the Collaborative Study on the Genetics of Alcoholism who were between ages 18-65 and had ever used alcohol. Participants completed the Semi-Structured Assessment for the Genetics of Alcoholism and provided saliva or blood samples for genotyping. Results indicated that social support from friends, but not family, moderated the association between alc-PRS and alcohol use among EAs and AAs (only in middle adulthood for AAs); alc-PRS was associated with higher levels of alcohol use when friend support was low, but not when friend support was high. Associations were similar across sex but differed across developmental stages. Findings support the important role of social support from friends in buffering genetic risk for alcohol use among EA and AA adults and highlight the need to consider developmental changes in the role of social support in relation to alcohol use.

Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis.

Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.

Genome-wide polygenic risk score for type 2 diabetes in Indian population

Genome-wide polygenic risk scores (PRS) for lifestyle disorders, like Type 2 Diabetes (T2D), are useful in identifying at-risk individuals early on in life, and to guide them towards healthier lifestyles. The current study was aimed at developing PRS for the Indian population using imputed genotype data from UK Biobank and testing the developed PRS on data from GenomegaDB of Indians living in India. 959 T2D cases and 2,818 controls were selected from Indian participants of UK Biobank to develop the PRS. Summary statistics available for South Asians, from the DIAMANTE consortium, were used to weigh genetic variants. LDpred2 algorithm was used to adjust the effect of linkage disequilibrium among the variants. The association of PRS with T2D, after adjusting for age, sex and top ten genetic principal components, was found to be very significant (AUC = 0.7953, OR = 2.9856 [95% CI: 2.7044–3.2961]). When participants were divided into four PRS quartile groups, the odds of developing T2D increased sequentially with the higher PRS groups. The highest PRS group (top 25%) showed 5.79 fold increased risk compared to the rest of the participants (75%). The PRS derived using the same set of variants was found to be significantly associated with T2D in the test dataset of 445 Indians (AUC = 0.7781, OR = 1.6656 [95%CI = 0.6127–4.5278]). Our study demonstrates a framework to derive Indian-specific PRS for T2D. The accuracy of the derived PRS shows it’s potential to be used as a prognostic metric to stratify individuals, and to recommend personalized preventive strategies.

1477-P: Improved Diabetes Risk Prediction Using Ancestry-Specific Polygenic Score—Results from the Asian Indian Diabetic Heart Study/Sikh Diabetes Study

Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, PRS-driven information on other ethnic groups is sparse, including Asian Indians (AI), who have a heightened risk for T2D. We examined the predictive efficacy of ancestry-derived (PRSAI) and European-derived (PRSEU) with a clinical risk score (CRS) of T2D. Methods: Weighted PRSs were computed using 2921 variants (common and rare) from (Punjabi/Sikh) genome-wide association studies and 432 variants from European meta-analyses studies in 4,602 individuals (2,574 cases and 2,028 controls) of the Asian Indian Diabetes Heart Study/Sikh Diabetes Study. Both Ancestry-specific PRSAI and European PRSEU were validated on 9372 UK Biobank South Asian individuals (1943 cases/7429 controls). Results: Ancestry-specific PRSAI showed 36.2% improved efficacy than PRSEU in Punjabi Asian Indians. Similarly, Ancestry-specific PRSAI was 18.6% superior to PRSEU in Asian Indians from UKBB based on Nagelkerke’s R2. Sensitivity analysis explained enhanced sensitivity (area under the curve-AUC) of ancestry-specific PRS over PRSEU in predicting T2D risk. Conclusions: Our data suggest expanding genetic studies in diverse ethnic groups to exploit the full potential of PRS to improve health outcomes. More genetic evaluations, specifically in understudied/underrepresented populations, would help increase the transferability of genome-wide polygenic scores across racial and ethnic groups to make their integration into clinical practice easier. Disclosure G.K.Tung: None. M.Rout: None. D.K.Sanghera: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK082766, R01DK118427); Presbyterian Health Foundation

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. Inthe testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI= 0.635-0.677) in African and 0.844 (95%CI=0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI= 1.67-2.00) and 2.19 (95% CI= 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC= 0.679, 95% CI= 0.659-0.700 and AUC= 0.845, 95% CI= 0.841-0.849, respectively) and comparable prostate cancer ORs (OR= 2.05, 95% CI= 1.87-2.26 and OR= 2.21, 95% CI= 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Polygenic prediction of preeclampsia and gestational hypertension.

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.