Polygenic Scores Research Articles

Association of sarcopenia with the long-term risk of atrial fibrillation: A prospective cohort study.

The relationship between sarcopenia and the long-term risk of atrial fibrillation (AF) remains unclear. This study recruited a large prospective Caucasian cohort from the UK Biobank. Participants were assessed at baseline with handgrip strength and muscle mass and were categorized into groups of non-sarcopenia, probable sarcopenia, and confirmed sarcopenia. Kaplan-Meier method and Cox proportional hazards model were used to explore the association between sarcopenia and the incidence of AF. The genetic predisposition of AF was assessed by polygenic risk score. Sensitivity analyses were performed to validate the results. A total of 384,433 participants with a median age of 58 years and 54.3% women were enrolled in this study. There were 24,007 cases of new-onset AF over a median follow-up of 12.56 years. The groups of non-sarcopenia, probable sarcopenia, and confirmed sarcopenia accounted for 22,290 (6.1%), 1665 (9.2%), and 52 (11.9%) cases, respectively. Compared with the non-sarcopenia group, participants with probable sarcopenia or confirmed sarcopenia had an 8% (95% CI, 1.03-1.14) or 61% (95% CI, 1.23-2.12) higher risk of AF incidence. The findings remained robust in multiple sensitivity analyses, such as subgroup analysis and further adjustment of genetic predisposition. Notably, the association between sarcopenia and a high AF risk was more pronounced in younger participants, women, and those with valvular heart disease. In conclusion, sarcopenia was associated with a high long-term risk of AF in Caucasians, supporting sarcopenia as a new independent risk factor of AF.

Peripheral GFAP and NfL as early biomarkers for dementia: longitudinal insights from the UK Biobank

BackgroundPeripheral glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are sensitive markers of neuroinflammation and neuronal damage. Previous studies with highly selected participants have shown that peripheral GFAP and NfL levels are elevated in the pre-clinical phase of Alzheimer’s disease (AD) and dementia. However, the predictive value of GFAP and NfL for dementia requires more evidence from population-based cohorts.MethodsThis was a prospective cohort study to evaluate UK Biobank participants enrolled from 2006 to 2010 using plasma GFAP and NfL measurements measured by Olink Target Platform and prospectively followed up for dementia diagnosis. Primary outcome was the risk of clinical diagnosed dementia. Secondary outcomes were cognition. Linear regression was used to assess the associations between peripheral GFAP and NfL with cognition. Cox proportional hazard models with cross-validations were used to estimate associations between elevated GFAP and NfL with risk of dementia. All models were adjusted for covariates.ResultsA subsample of 48,542 participants in the UK Biobank with peripheral GFAP and NfL measurements were evaluated. With an average follow-up of 13.18 ± 2.42 years, 1312 new all-cause dementia cases were identified. Peripheral GFAP and NfL increased up to 15 years before dementia diagnosis was made. After strictly adjusting for confounders, increment in NfL was found to be associated with decreased numeric memory and prolonged reaction time. A greater annualized rate of change in GFAP was significantly associated with faster global cognitive decline. Elevation of GFAP (hazard ratio (HR) ranges from 2.25 to 3.15) and NfL (HR ranges from 1.98 to 4.23) increased the risk for several types of dementia. GFAP and NfL significantly improved the predictive values for dementia using previous models (area under the curve (AUC) ranges from 0.80 to 0.89, C-index ranges from 0.86 to 0.91). The AD genetic risk score and number of APOE*E4 alleles strongly correlated with GFAP and NfL levels.ConclusionsThese results suggest that peripheral GFAP and NfL are potential biomarkers for the early diagnosis of dementia. In addition, anti-inflammatory therapies in the initial stages of dementia may have potential benefits.

Polygenic Risk Score Modifies the Association of HbA1c With Hearing Loss in Middle-Aged and Older Chinese: The Dongfeng-Tongji Cohort.

Evidence regarding the modifying effect of the polygenic risk score (PRS) on the associations between glycemic traits and hearing loss (HL) was lacking. We aimed to examine whether these associations can be influenced by genetic susceptibility. This cross-sectional study included 13,275 participants aged 64.9 years from the Dongfeng-Tongji cohort. HL was defined according to a pure tone average >25 dB in the better ear and further classified by severity. Prediabetes and type 2 diabetes (T2D) were defined based on the 2013 criteria from the American Diabetes Association. A PRS was derived from 37 single-nucleotide polymorphisms associated with HL. Multivariable logistic regression models were fitted to estimate the associations of PRS and glycemic traits with HL and its severity. Elevated fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), and T2D were positively associated with higher HL risks and its severity, with odds ratios (ORs) ranging from 1.04 (95% CI 1.00, 1.08) to 1.25 (95% CI 1.06, 1.46). We also found significant interaction between HbA1c and PRS on risks of overall HL and its severity (P for multiplicative interaction <0.05), and the effects of HbA1c on HL risks were significant only in high PRS. Additionally, compared with normoglycemiain the low PRS, T2D was respectively associated with an OR of up to 2.00 and 2.40 for overall HL and moderate to severe HL in the high PRS (P for additive interaction <0.05). PRS modifies the association of HbA1c with HL prevalence among middle-aged and older Chinese.

Cerebral Amyloidosis in Individuals with Subjective Cognitive Decline: From Genetic Predisposition to Actual Cerebrospinal Fluid Measurements

The possible relationship between Subjective Cognitive Decline (SCD) and dementia needs further investigation. In the present study, we explored the association between specific biomarkers of Alzheimer’s Disease (AD), amyloid-beta 42 (Aβ42) and Tau with the odds of SCD using data from two ongoing studies. In total, 849 cognitively normal (CN) individuals were included in our analyses. Among the participants, 107 had available results regarding cerebrospinal fluid (CSF) Aβ42 and Tau, while 742 had available genetic data to construct polygenic risk scores (PRSs) reflecting their genetic predisposition for CSF Aβ42 and plasma total Tau levels. The associations between AD biomarkers and SCD were tested using logistic regression models adjusted for possible confounders such as age, sex, education, depression, and baseline cognitive test scores. Abnormal values of CSF Aβ42 were related to 2.5-fold higher odds of SCD, while higher polygenic loading for Aβ42 was associated with 1.6-fold higher odds of SCD. CSF Tau, as well as polygenic loading for total Tau, were not associated with SCD. Thus, only cerebral amyloidosis appears to be related to SCD status, either in the form of polygenic risk or actual CSF measurements. The temporal sequence of amyloidosis being followed by tauopathy may partially explain our findings.

Alcohol milestones and internalizing, externalizing, and executive function: longitudinal and polygenic score associations.

Although the link between alcohol involvement and behavioral phenotypes (e.g. impulsivity, negative affect, executive function [EF]) is well-established, the directionality of these associations, specificity to stages of alcohol involvement, and extent of shared genetic liability remain unclear. We estimate longitudinal associations between transitions among alcohol milestones, behavioral phenotypes, and indices of genetic risk. Data came from the Collaborative Study on the Genetics of Alcoholism (n = 3681; ages 11-36). Alcohol transitions (first: drink, intoxication, alcohol use disorder [AUD] symptom, AUD diagnosis), internalizing, and externalizing phenotypes came from the Semi-Structured Assessment for the Genetics of Alcoholism. EF was measured with the Tower of London and Visual Span Tasks. Polygenic scores (PGS) were computed for alcohol-related and behavioral phenotypes. Cox models estimated associations among PGS, behavior, and alcohol milestones. Externalizing phenotypes (e.g. conduct disorder symptoms) were associated with future initiation and drinking problems (hazard ratio (HR)⩾1.16). Internalizing (e.g. social anxiety) was associated with hazards for progression from first drink to severe AUD (HR⩾1.55). Initiation and AUD were associated with increased hazards for later depressive symptoms and suicidal ideation (HR⩾1.38), and initiation was associated with increased hazards for future conduct symptoms (HR = 1.60). EF was not associated with alcohol transitions. Drinks per week PGS was linked with increased hazards for alcohol transitions (HR⩾1.06). Problematic alcohol use PGS increased hazards for suicidal ideation (HR = 1.20). Behavioral markers of addiction vulnerability precede and follow alcohol transitions, highlighting dynamic, bidirectional relationships between behavior and emerging addiction.

COSGAP: COntainerized statistical genetics analysis pipelines

Abstract The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: “COntainerized Statistical Genetics Analysis Pipelines”, consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements. Availability COSGAP is freely available on GitHub (https://github.com/comorment/containers) under the GPLv3 license.

Population-specific validation and comparison of the performance of 77- and 313-variant polygenic risk scores for breast cancer risk prediction.

The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low-penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low-penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population. In a retrospective case-control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS. The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2×10-16) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an ORof3.05 (95% CI,1.66-5.89; p=1.76×10-4). The continuous risk was estimated as an HRper SDof1.64 (95% CI,1.49-1.81; p<2.0×10-16), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%). Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.

Polygenic liability for anxiety in association with comorbid anxiety in multiple sclerosis.

Comorbid anxiety occurs often in MS and is associated with disability progression. Polygenic scores offer a possible means of anxiety risk prediction but often have not been validated outside the original discovery population. We aimed to investigate the association between the Generalized Anxiety Disorder 2-item scale polygenic score with anxiety in MS. Using a case-control design, participants from Canadian, UK Biobank, and United States cohorts were grouped into cases (MS/comorbid anxiety) or controls (MS/no anxiety, anxiety/no immune disease or healthy). We used multiple anxiety measures: current symptoms, lifetime interview-diagnosed, and lifetime self-report physician-diagnosed. The polygenic score was computed for current anxiety symptoms using summary statistics from a previous genome-wide association study and was tested using regression. A total of 71,343 individuals of European genetic ancestry were used: Canada (n = 334; 212 MS), UK Biobank (n = 70,431; 1,390 MS), and the USA (n = 578 MS). Meta-analyses identified that in MS, each 1-SD increase in the polygenic score was associated with ~50% increased odds of comorbid moderate anxious symptoms compared to those with less than moderate anxious symptoms (OR: 1.47, 95% CI: 1.09-1.99). We found a similar direction of effects in the other measures. MS had a similar anxiety genetic burden compared to people with anxiety as the index disease. Higher genetic burden for anxiety was associated with significantly increased odds of moderate anxious symptoms in MS of European genetic ancestry which did not differ from those with anxiety and no comorbid immune disease. This study suggests a genetic basis for anxiety in MS.

Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits.

Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits. We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture. The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.

European and US Guideline-Based Statin Eligibility, Genetically Predicted Coronary Artery Disease, and the Risk of Major Coronary Events.

A study was designed to investigate whether the coronary artery disease polygenic risk score (CAD-PRS) may guide lipid-lowering treatment initiation as well as deferral in primary prevention beyond established clinical risk scores. Participants were 311 799 individuals from the UK Biobank free of atherosclerotic cardiovascular disease, diabetes, chronic kidney disease, and lipid-lowering treatment at baseline. Participants were categorized as statin indicated, statin indication unclear, or statin not indicated as defined by the European and US guidelines on statin use. For a median of 11.9 (11.2-12.6) years, 8196 major coronary events developed. CAD-PRS added to European-Systematic Coronary Risk Evaluation 2 (European-SCORE2) and US-Pooled Cohort Equation (US-PCE) identified 18% and 12% of statin-indication-unclear individuals whose risk of major coronary events were the same as or higher than the average risk of statin-indicated individuals and 16% and 12% of statin-indicated individuals whose major coronary event risks were the same as or lower than the average risk of statin-indication-unclear individuals. For major coronary and atherosclerotic cardiovascular disease events, CAD-PRS improved C-statistics greater among statin-indicated or statin-indication-unclear than statin-not-indicated individuals. For atherosclerotic cardiovascular disease events, CAD-PRS added to the European evaluation and US equation resulted in a net reclassification improvement of 13.6% (95% CI, 11.8-15.5) and 14.7% (95% CI, 13.1-16.3) among statin-indicated, 10.8% (95% CI, 9.6-12.0) and 15.3% (95% CI, 13.2-17.5) among statin-indication-unclear, and 0.9% (95% CI, 0.6-1.3) and 3.6% (95% CI, 3.0-4.2) among statin-not-indicated individuals. CAD-PRS may guide statin initiation as well as deferral among statin-indication-unclear or statin-indicated individuals as defined by the European and US guidelines. CAD-PRS had little clinical utility among statin-not-indicated individuals.

Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers.

Polygenic scores (PGS) enable the prediction of genetic predisposition for a wide range of traits and diseases by calculating the weighted sum of allele dosages for genetic variants associated with the trait or disease in question. Present approaches for calculating PGS from genotypes are often inefficient and labor-intensive, limiting transferability into clinical applications. Here, we present 'Imputation Server PGS', an extension of the Michigan Imputation Server designed to automate a standardized calculation of polygenic scores based on imputed genotypes. This extends the widely used Michigan Imputation Server with new functionality, bringing the simplicity and efficiency of modern imputation to the PGS field. The service currently supports over 4489 published polygenic scores from publicly available repositories and provides extensive quality control, including ancestry estimation to report population stratification. An interactive report empowers users to screen and compare thousands of scores in a fast and intuitive way. Imputation Server PGS provides a user-friendly web service, facilitating the application of polygenic scores to a wide range of genetic studies and is freely available at https://imputationserver.sph.umich.edu.

Polygenic risk score for autoimmune Addison's disease combined with whole-genome sequencing identifies patients with undiagnosed monogenic primary adrenal insufficiency

Polygenic risk score for autoimmune Addison's disease combined with whole-genome sequencing identifies patients with undiagnosed monogenic primary adrenal insufficiency

Sex-Specific Association Between Childhood Adversity and Accelerated Biological Aging.

Is childhood adversity associated with biological aging, and if so, does sex modify the association, and do lifestyle and mental health mediate the association? A lifespan analysis is conducted using data on 142872 participants from the UK Biobank to address these questions. Childhood adversity is assessed through the online mental health questionnaire (2016), including physical neglect, physical abuse, emotional neglect, emotional abuse, sexual abuse, and a cumulative score. Biological aging is indicated by telomere length (TL) measured from leukocyte DNA using qPCR, and the shorter TL indicates accelerated biological aging; a lifestyle score is constructed using body mass index, physical activity, drinking, smoking, and diet; mental disorder is assessed using depression, anxiety, and insomnia at the baseline survey. The results reveal a sex-specific association such that childhood adversity is associated with shorter TL in women after adjusting for covariates including polygenic risk score for TL, but not in men. Unhealthy lifestyle and mental disorder partially mediate the association in women. The proportions of indirect effects are largest for sexual and physical abuse. These findings highlight the importance of behavioral and psychological interventions in promoting healthy aging among women who experienced childhood adversity, particularly sexual and physical abuse.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Excess weight, polygenic risk score and findings of colorectal neoplasms at screening colonoscopy.

Excess weight is an established risk factor for colorectal cancer (CRC). However, evidence is lacking on how its impact varies by polygenic risk at different stages of colorectal carcinogenesis. We assessed the individual and joint associations of body mass index (BMI) and polygenic risk scores (PRSs) with findings of colorectal neoplasms among 4,784 participants of screening colonoscopy. Adjusted odds ratios (aORs) for excess weight derived by multiple logistic regression were converted to genetic risk equivalents (GREs) to quantify the impact of excess weight compared to genetic predisposition. Overweight and obesity (BMI 25-<30 and ≥30 kg/m2) were associated with increased risk of any colorectal neoplasm (aOR [95% CI] 1.26 [1.09-1.45] and 1.47 [1.24-1.75]). Obesity was associated with increased risk of advanced colorectal neoplasm (aOR [95% CI] 1.46 [1.16-1.84]). Dose-response relationships were seen for the PRS (stronger for advanced neoplasms than any neoplasms), with no interaction with BMI, suggesting multiplicative effects of both factors. Obese participants with a PRS in the highest tertile had a 2.3-fold (95% CI 1.7-3.1) and 2.9-fold (95%CI 1.9-4.3) increased risk of any colorectal neoplasm and advanced colorectal neoplasm, respectively. The aOR of obesity translated into a GRE of 38, meaning that its impact was estimated to be equivalent to the risk caused by 38 percentiles higher PRS for colorectal neoplasm. Excess weight and polygenic risk are associated with increased risk of colorectal neoplasms in a multiplicative manner. Maintaining normal weight is estimated to have an equivalent effect as having 38 percentiles lower PRS.

The role of environmental sensitivity in the mental health of Syrian refugee children: a multi-level analysis.

Individuals with high environmental sensitivity have nervous systems that are disproportionately receptive to both the protective and imperilling aspects of the environment, suggesting their mental health is strongly context-dependent. However, there have been few consolidated attempts to examine putative markers of sensitivity, across different levels of analysis, within a single cohort of individuals with high-priority mental health needs. Here, we examine psychological (self-report), physiological (hair hormones) and genetic (polygenic scores) markers of sensitivity in a large cohort of 1591 Syrian refugee children across two waves of data. Child-caregiver dyads were recruited from informal tented settlements in Lebanon, and completed a battery of psychological instruments at baseline and follow-up (12 months apart). Univariate and multivariate Bayesian linear mixed models were used to examine a) the interrelationships between markers of sensitivity and b) the ability of sensitivity markers to predict anxiety, depression, post-traumatic stress disorder, and externalising behaviour. Self-reported sensitivity (using the Highly Sensitive Child Scale) significantly predicted a higher burden of all forms of mental illness across both waves, however, there were no significant cross-lagged pathways. Physiological and genetic markers were not stably predictive of self-reported sensitivity, and failed to similarly predict mental health outcomes. The measurement of environmental sensitivity may have significant implications for identifying and treating mental illness, especially amongst vulnerable populations, but clinical utility is currently limited to self-report assessment.

A comprehensive genome-wide cross-trait analysis of sexual factors and uterine leiomyoma.

Age at first sexual intercourse (AFS) and lifetime number of sexual partners (NSP) may influence the pathogenesis of uterine leiomyoma (UL) through their associations with hormonal concentrations and uterine infections. Leveraging summary statistics from large-scale genome-wide association studies conducted in European ancestry for each trait (NAFS = 214,547; NNSP = 370,711; NUL = 302,979), we observed a significant negative genomic correlation for UL with AFS (rg = -0.11, P = 7.83×10-4), but not with NSP (rg = 0.01, P = 0.62). Four specific genomic regions were identified as contributing significant local genetic correlations to AFS and UL, including one genomic region further identified for NSP and UL. Partitioning SNP-heritability with cell-type-specific annotations, a close clustering of UL with both AFS and NSP was identified in immune and blood-related components. Cross-trait meta-analysis revealed 15 loci shared between AFS/NSP and UL, including 7 novel SNPs. Univariable two-sample Mendelian randomization (MR) analysis suggested no evidence for a causal association between genetically predicted AFS/NSP and risk of UL, nor vice versa. Multivariable MR adjusting for age at menarche or/and age at natural menopause revealed a significant causal effect of genetically predicted higher AFS on a lower risk of UL. Such effect attenuated to null when age at first birth was further included. Utilizing participant-level data from the UK Biobank, one-sample MR based on genetic risk scores yielded consistent null findings among both pre-menopausal and post-menopausal females. From a genetic perspective, our study demonstrates an intrinsic link underlying sexual factors (AFS and NSP) and UL, highlighting shared biological mechanisms rather than direct causal effects. Future studies are needed to elucidate the specific mechanisms involved in the shared genetic influences and their potential impact on UL development.

Abstract PS10-07: A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries

Abstract Background: Common genetic variants, mainly single-nucleotide polymorphisms (SNPs) explain substantial genetic susceptibility to BC. PRS have been developed to quantify the combined effects of BC-associated SNPs, providing important information about BC risk. Historically, genome-wide association studies have been conducted in predominantly European populations, resulting in miscalibrated and inaccurate PRS for non-Europeans. We previously described a multiple-ancestry PRS (MA-PRS-149) based on 56 ancestry-informative and 93 BC-associated SNPs. The MA-PRS-149 achieved accuracy for all women by characterizing the genetic ancestry of each BC-associated SNP in terms of three reference ancestries (African, East Asian, and European), applying ancestry-specific SNP risks and frequencies, and combining the results as a weighted sum of three ancestry-specific PRS. Here, we aimed to improve the predictive accuracy of MA-PRS-149, particularly for non-Europeans, through the inclusion of additional BC-associated SNPs. Methods: Women referred for hereditary cancer testing and negative for pathogenic variants in BC-associated genes were divided into consecutive study cohorts to (1) quantify ancestry-specific SNP risks, (2) combine the three ancestry-specific PRS, and (3) pre-specified clinical validation. To select an optimal set of BC-associated SNPs, we developed a novel synthetic stepwise regression methodology that accounts for linkage disequilibrium. Ancestry-specific SNP risks were determined from meta-analyses of literature with clinical cohorts of 57,827 Black/African and 26,992 East Asian women. Ancestry-specific PRS were combined based on a diverse cohort of 157,740 women. Clinical validation was conducted in an independent cohort of 77,774 women. We used multivariable logistic regression adjusted for age, ancestry, and cancer history to test for improved BC risk prediction over clinical factors. We tested for improvement over the MA-PRS-149, and a European PRS, by including additional PRS as covariates. Calibration was assessed through goodness-of-fit tests. All analyses were conducted within the full cohort and ancestral subpopulations. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported per standard deviation within the corresponding population. Results: An optimal set of 383 SNPs (56 ancestry-informative and 327 BC-associated) was included in the final PRS (MA-PRS-383). MA-PRS-383 added significant predictive information to clinical factors in the full cohort and within each ancestry (Table 1). MA-PRS-383 had greater predictive accuracy than MA-PRS-149 or a 383-SNP PRS with European weights. Goodness-of-fit tests showed that MA-PRS-383 was well-calibrated and predicted risk accurately in the tails of the distribution for both European and non-European women. Conclusion: MA-PRS-383 was well-calibrated and substantially improved upon existing PRS in all tested ancestral populations. Incorporation of MA-PRS-383 into BC risk assessment may lead to more accurate identification of women who are most likely to benefit from screening and preventive medications. Citation Format: Timothy Simmons, Elisha Hughes, Dmitry Pruss, Matthew Kucera, Benjamin Roa, Thaddeus Judkins, Thomas Slavin, Victor Abkevich, Ryan Hoff, Srikanth Jammulapati, Susanne Wagner, Dale Muzzey, Jerry Lanchbury, Alexander Gutin. A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-07.

Abstract PO4-19-02: Genetic risk estimation in breast cancer and assessing health disparities

Abstract Background: Breast Cancer (BC) incidence and the distribution of BC risk factors vary between racial and ethnic groups within the United States. It has been demonstrated that providing education on individualized breast cancer risk will improve patient uptake to recommended BC screening and prevention strategies. Most clinical risk assessment tools, including the validated IBIS model were derived from data using non-Hispanic white women. Consortia have also now identified over 300 common genetic susceptibility loci for BC, summarized by the polygenic risk score (PRS). When combined with clinical risk assessment tools, such as the IBIS or BCRAT models, the PRS can further refine risk estimation for patients. To date, most studies on the use of PRS have been done in non-Hispanic white women. While these PRS still perform well in racial minorities, there has been a recognized need to improve racial and ethnic diversity in genomic research cohorts. Herein, we aim to combine clinical risk assessment models that are already used in routine clinical practice with information derived from PRS testing in women of racial minorities to determine if this can improve risk estimation, patient understanding, and uptake to recommended breast cancer screening and prevention strategies. Design: This is a minimal risk prospective study with a single arm incorporating the PRS into a standard breast cancer risk reduction consultation, followed by annual surveys over 10 years to determine if and how the information provided by the PRS influenced patient decisions regarding recommended BC screening and prevention. Patients will have IBIS and BCRAT risks calculated at baseline visit. A survey of patient perceptions/understanding of risk, intention to undergo screening, and use of preventive medicine is completed after baseline visit prior to receiving the PRS results. Blood sample is obtained at baseline and DNA samples are analyzed for approximately 300 SNPs on a custom-designed, targeted SNP panel from ThermoFisher Scientific by the Mayo Clinic Genomics Laboratory. This PRS result is then combined with the clinical risk assessments (IBIS and BCRAT scores) using the R package Individualized Coherent Absolute Risk Estimators (iCare) tool to provide a new estimate of breast cancer risk for 5year, 10year, and lifetime risk. Patients are seen for follow-up to review results and then complete a second survey to assess their understanding of the results, BC risk, and how the PRS impacted their decision to undergo screening/prevention strategies. Eligibility Criteria: Women who self-identify as African American/Black or Hispanic/Latinx between 30-75 years old with any of the following: 1.)IBIS score of ≥5% for the 10 year risk OR BCRAT score of ≥ 3 % for the 5 year risk. 2.) History of biopsy proven atypical hyperplasia 3.) History of biopsy proven lobular carcinoma in situ. Specific Aims: Aim 1: The primary aim of this study is to explore if the addition of PRS to the BCRAT and IBIS score will improve intentions to undergo recommended breast cancer screening strategies such as mammography, MRI, or molecular breast imaging in women of underserved racial minorities Aim 2: To explore if the addition of the PRS to the BCRAT and IBIS risk score will aid women of racial minorities in deciding whether to take preventative endocrine therapy. Aim 3: To understand how individualized risk assessment and information on PRS may alter perceived risk of breast cancer. Statistical Methods Analysis will be mostly descriptive. Continuous variables will be summarized as mean (standard deviation) or median (range) and categorical variables will be reported as frequency (percentage). Kaplan-Meier method will be used to estimate the long-term cumulative risk of cancer. Current Accrual: 3. Target Accrual: 50 Citation Format: Lauren Cornell, Sandhya Pruthi, Linda Hasadsri, Sarah McLaughlin. Genetic risk estimation in breast cancer and assessing health disparities [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-19-02.

Abstract PO5-08-10: Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study

Abstract Introduction: Breast cancer is the most common cancer among women in Taiwan and the incidence rate of breast cancer has been increasing steadily over the past 30 years. To address this, a nationwide screening program with biennial mammography for women aged 40-69 was implemented in 2004, which has contributed to a significant reduction in breast cancer mortality. Asian women are more likely to have dense breast tissue, and studies have shown a correlation between breast density and the risk of developing breast cancer, with higher breast density values associated with increased risk. Polygenic risk scores (PRS) are calculated based on an individual's genetic information and can estimate their risk of developing a particular disease. Although PRS can identify low-penetrance risk variants, its use in breast cancer risk estimation is typically limited to gene-only models, and integration with breast cancer screening databases is rare. Hence, the aim of this specific study was to evaluate the predictive capacity of PRS in women with dense breast tissue by exploring potential genetic markers and constructing a PRS to investigate the genetic factors associated with clinical characteristics and the risk of developing breast cancer. Methods: The PRS was developed using genetic information from the Taiwan Precision Medicine Initiative genotyping data. A total of 6335 patients were enrolled, and 101 single nucleotide polymorphisms (SNPs) were selected as candidate markers based on the PGS Catalog (PGS000001). Clinical characteristics of the study cohort were summarised using median and range, or frequency and percentage. The distribution of characteristics between breast cancer and controls was estimated using an independent two-sample t-test, chi-square, and Fisher’s exact test. The association of the PRS and related characteristics with breast cancer risk in the study cohort was estimated using univariate and multivariate binomial logistic regression. Harrel’s C-index was reported to demonstrate the predictive performance of PRS in both univariate and multivariate models. All p-values were two-sided, and a p less than .05 is considered statistically significant. All analyses were performed using R 4.1.2 (R core team, 2023) Results: The results showed that breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs 24.8% in controls). The model's predictive performance for breast cancer risk increased from 0.565 (95% CI = 0.520-0.611) to 0.699 (95% CI = 0.644-0.755) by adding related clinical characteristics compared to the PRS-only model. Patients characterized with benign breast disease (OR = 0.50, 95% CI = 0.31-0.79, P = 0.004) showed a decreased breast cancer risk, and patients with mastalgia or palpable breast lesion (OR = 6.87, 95% CI = 4.29-10.8, P &amp;lt; 0.001) predicted significant greater breast cancer risk. Subgroup analysis of patients without breast symptoms was conducted as they may be overlooked or underestimated for breast cancer screening. For dense breast patients without symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI = 1.43-3.50, P &amp;lt; 0.001) and multivariate analyses (OR = 2.22, 95% CI = 1.41-3.46, P &amp;lt; 0.001). Conclusion: Breast cancer screening has undergone a shift from a general approach to a personalized, risk-based approach. Besides breast cancer screening using family history as the only risk factor, our proposed model identifies both genetic and clinical risk factors using big data analyses including the EHR, cancer screening, and registry from a single institute. The study suggests that integrating PRS into personalized screening strategies could improve risk prediction for Taiwanese females with dense breasts without prominent symptoms. Table 1. Clinical characteristics of study cohort (n=6335). Table 2. Association of the PRS quartiles in breast cancer risk. Table 3. Predictive performance of PRS for breast cancer risk. Citation Format: Chih Yean Lum, Chih Chiang Hung, Chi-Cheng Huang, Tzu-Hung Hsiao, Sin-Hua Moi. Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-08-10.