To see the other types of publications on this topic, follow the link: Polygenic risk modeling.
Journal articles on the topic 'Polygenic risk modeling'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Polygenic risk modeling.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Dareng, Eileen O., Jonathan P. Tyrer, Daniel R. Barnes, et al. "Polygenic risk modeling for prediction of epithelial ovarian cancer risk." European Journal of Human Genetics 30, no. 3 (2022): 349–62. http://dx.doi.org/10.1038/s41431-021-00987-7.
Full textAbstract:
AbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.
2
Thomas, Minta, Lori C. Sakoda, Michael Hoffmeister, et al. "Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk." American Journal of Human Genetics 107, no. 3 (2020): 432–44. http://dx.doi.org/10.1016/j.ajhg.2020.07.006.
Full text
3
Aguirre, Matthew, Yosuke Tanigawa, Guhan Ram Venkataraman, Rob Tibshirani, Trevor Hastie, and Manuel A. Rivas. "Polygenic risk modeling with latent trait-related genetic components." European Journal of Human Genetics 29, no. 7 (2021): 1071–81. http://dx.doi.org/10.1038/s41431-021-00813-0.
Full text
4
Vilhjálmsson, Bjarni J., Jian Yang, Hilary K. Finucane, et al. "Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores." American Journal of Human Genetics 97, no. 4 (2015): 576–92. http://dx.doi.org/10.1016/j.ajhg.2015.09.001.
Full text
5
Li, Jiang, Durgesh P. Chaudhary, Ayesha Khan, et al. "Polygenic Risk Scores Augment Stroke Subtyping." Neurology Genetics 7, no. 2 (2021): e560. http://dx.doi.org/10.1212/nxg.0000000000000560.
Full textAbstract:
ObjectiveTo determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.MethodsControls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.ResultsA moderate heritability (10%–20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the R2 increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling (R2 = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.ConclusionsWe provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.
6
Chen, Chia-Yen, Jiali Han, David J. Hunter, Peter Kraft, and Alkes L. Price. "Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction." Genetic Epidemiology 39, no. 6 (2015): 427–38. http://dx.doi.org/10.1002/gepi.21906.
Full text
7
Faucon, Annika, Julian Samaroo, Tian Ge, et al. "Improving the computation efficiency of polygenic risk score modeling: faster in Julia." Life Science Alliance 5, no. 12 (2022): e202201382. http://dx.doi.org/10.26508/lsa.202201382.
Full textAbstract:
To enable large-scale application of polygenic risk scores (PRSs) in a computationally efficient manner, we translate a widely used PRS construction method, PRS–continuous shrinkage, to the Julia programming language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average runtime by 5.5×. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes usage of PRSs by lowering the computational burden of the PRS–continuous shrinkage method.
8
Massi, Michela C., Nicola R. Franco, Andrea Manzoni, et al. "Learning high-order interactions for polygenic risk prediction." PLOS ONE 18, no. 2 (2023): e0281618. http://dx.doi.org/10.1371/journal.pone.0281618.
Full textAbstract:
Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.
9
Chen, Siting, Corey L. Nagel, Jodi Lapidus, and Ana Quiñones. "LONGEVITY POLYGENIC RISK IN MULTIDOMAIN AGING AND LONG-TERM SURVIVAL AMONG OLDER WHITE AND BLACK ADULTS." Innovation in Aging 8, Supplement_1 (2024): 869. https://doi.org/10.1093/geroni/igae098.2810.
Full textAbstract:
Abstract This study aims to identify genetic characteristics of multi-dimensional aging and longevity by examining the association of longevity polygenic risk scores with multi-domain aging trajectories and long-term survival in older Non-Hispanic White and Non-Hispanic Black adults. We studied 7,287 older adults (≥ 65 years, 14.0% Non-Hispanic Black) from the Health and Retirement Study (HRS, 1998-2016). Group-based trajectory modeling was performed to identify distinct groups of older adults following similar joint trajectories of aging across four domains (multimorbidity burden, functional status, cognitive performance, and depressive symptomatology). We identified four distinct multi-domain trajectory groups: minimal impairment with low multimorbidity (34.1%), minimal impairment with high multimorbidity (37.1%), multidomain impairment with intermediate multimorbidity (16.7%), and multidomain impairment with high multimorbidity (12.1%). Longevity polygenic risk scores (categorized as low, medium, medium-high, and high) were compared between long-term survival groups (long-lived group and controls) across the identified trajectory groups in Non-Hispanic White and Black adults. The proportion of long-lived group was significantly higher in minimal impairment with low multimorbidity group in both Non-Hispanic White (23.3%,p< 0.01) and Non-Hispanic Black adults (19.6%,p< 0.01). The long-lived group had greater proportions of Non-Hispanic White participants with high longevity polygenic risk scores in all trajectory groups. The long-lived group had lower proportions of Non-Hispanic Black participants with low longevity polygenic risk scores in all trajectory groups except for the multi-domain impairment with high multimorbidity group. Our study highlights the need to examine the constellation of genetic and socioeconomic factors on aging-related health domains and longevity among diverse groups of older adults.
10
Hu, Yiming, Qiongshi Lu, Wei Liu, Yuhua Zhang, Mo Li, and Hongyu Zhao. "Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction." PLOS Genetics 13, no. 6 (2017): e1006836. http://dx.doi.org/10.1371/journal.pgen.1006836.
Full text
11
Elsheikh, *Samar Salah Mohamedahmed, Victoria Marshe, James Kennedy, et al. "UTILIZING STRUCTURAL EQUATION MODELING TO ANALYZE POLYGENIC RISK AND ENVIRONMENTAL INFLUENCES ON LATE-LIFE DEPRESSION." International Journal of Neuropsychopharmacology 28, Supplement_1 (2025): i329. https://doi.org/10.1093/ijnp/pyae059.588.
Full textAbstract:
Abstract Background Our understanding of the interplay between genetic and environmental factors (Gene x Environment Interaction, or GxE) determining mental health disorders has improved through the proliferation of genome-wide interaction association studies (GWIAS) and targeted GxE analyses. Moreover, multivariate modelling approaches, such as structural equation modelling (SEM) and polygenic risk scores (PRS), offer opportunities for the integration of clinical and genome-wide genotype data in building improved biopsychosocial models of mental illness aetiology and their response to treatment. Aims & Objectives We propose to construct a SEM framework to uncover the inter-correlation and directed structure of mental health phenotypes by leveraging the joint predictive capacity of PRS for comorbid traits that share underlying biological and environmental risk pathways. The proposed model will be capable of linking latent constructs to their observed measurements; these will include disease severity, comorbidities and clinical histories, and behaviours and lifestyle factors such as physical and social activity. Method Our gene-by-environment SEM (GESEM) will be initially developed and tested using four well- characterized clinical cohorts for older adults diagnosed with late-life depression and treated with antidepressants (CAN-BIND, IRL-GREY, STOP-PD II and IMPACT; n =1,238). The primary outcome will be antidepressant remission. Multiple PRS will be calculated to capture underlying genetic risk across vulnerable pathways which contribute to comorbidities. This selection will be made based on new, largely unpublished work from our group on the impact of PRS and targeted GxE studies on psychiatric outcomes across the lifespan. Each PRS will be calculated using both clumping and thresholding (PRSice-2) and continuous shrinkage (PRS-CS-auto) methods across selected cohorts using well-powered publicly available GWAS summary statistics. The multilevel GESEM model will include interactions between symptoms and comorbidities (i.e., observed measurements), which are caused by unobserved factors (i.e.,latent constructs), and are subject to modification by background PRS. We will compare our GESEM model against existing SEM-based approaches to GxE, including local SEM (LOSEM). Discussion & Conclusion An open-source R package of the analytical code will be created and shared with the research community. This work has the potential to improve upon existing PRS-based predictive models in a clinical setting.
12
Li, Jiang, Vida Abedi, and Ramin Zand. "Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine." Journal of Clinical Medicine 11, no. 20 (2022): 5980. http://dx.doi.org/10.3390/jcm11205980.
Full textAbstract:
Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.
13
Shah, Yajas, Scott Kulm, Jones T. Nauseef, et al. "Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort." PLOS Computational Biology 20, no. 4 (2024): e1011990. http://dx.doi.org/10.1371/journal.pcbi.1011990.
Full textAbstract:
Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.
14
Janssens, A. Cecile J. W. "Validity of polygenic risk scores: are we measuring what we think we are?" Human Molecular Genetics 28, R2 (2019): R143—R150. http://dx.doi.org/10.1093/hmg/ddz205.
Full textAbstract:
Abstract Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.
15
Braun, Hayden, Charles Brunette, Kurt Christensen, et al. "Polygenic risk score-guided prostate cancer screening among white and Black US men: a Markov modeling study." Molecular Genetics and Metabolism 132 (April 2021): S328—S329. http://dx.doi.org/10.1016/s1096-7192(21)00586-2.
Full text
16
Bae, Ji Hyun, and Hyunju Kang. "Identification of Sweetness Preference-Related Single-Nucleotide Polymorphisms for Polygenic Risk Scores Associated with Obesity." Nutrients 16, no. 17 (2024): 2972. http://dx.doi.org/10.3390/nu16172972.
Full textAbstract:
Our study aimed to identify sweetness preference-associated single-nucleotide polymorphisms (SNPs), characterize the related genetic loci, and develop SNP-based polygenic risk scores (PRS) to analyze their associations with obesity. For genotyping, we utilized a pooled genome-wide association study (GWAS) dataset of 18,499 females and 10,878 males. We conducted genome-wide association analyses, functional annotation, and employed the weighted method to calculate the levels of PRS from 677 sweetness preference-related SNPs. We used Cox proportional hazards modeling with time-varying covariates to estimate age-adjusted and multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for obesity incidence. We also tested the correlation between PRS and environmental factors, including smoking and dietary components, on obesity. Our results showed that in males, the TT genotype of rs4861982 significantly increased obesity risk compared to the GG genotype in the Health Professionals Follow-up Study (HPFS) cohort (HR = 1.565; 95% CI, 1.122–2.184; p = 0.008) and in the pooled analysis (HR = 1.259; 95% CI, 1.030–1.540; p = 0.025). Protein tyrosine phosphatase receptor type O (PTPRO) was identified as strongly associated with sweetness preference, indicating a positive correlation between sweetness preference and obesity risk. Moreover, each 10 pack-year increment in smoking was significantly associated with an increased risk of obesity in the HPFS cohort (HR = 1.024; 95% CI, 1.000–1.048) in males but not in females. In conclusion, significant associations between rs4861982, sweetness preference, and obesity were identified, particularly among males, where environmental factors like smoking are also correlated with obesity risk.
17
Vivian-Griffiths, Timothy, Emily Baker, Karl M. Schmidt, et al. "Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach." American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 180, no. 1 (2018): 80–85. http://dx.doi.org/10.1002/ajmg.b.32705.
Full text
18
Trendowski, Matthew R., Chrissy Lusk, Angie Wenzlaff, et al. "Polygenic risk scores in assessing lung cancer susceptibility in non-Hispanic White and Black populations." Journal of Clinical Oncology 41, no. 16_suppl (2023): 10548. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.10548.
Full textAbstract:
10548 Background: Polygenic risk scores (PRS) have become an increasingly popular approach to evaluate cancer susceptibility, but have not adequately represented Black patients in model development. We used previously identified single nucleotide polymorphisms (SNPs) and annotated SNPs in associated gene regions to develop PRS in non-Hispanic Whites and Blacks using the INHALE dataset. Methods: Using the Multi-Ethnic Genotype Array, 1,204 SNPs for non-Hispanic Whites and 1,515 SNPs for Blacks were evaluated for their association with lung cancer risk adjusting for age, sex, total pack-years, family history of lung cancer, history of COPD and the top five PCs for genetic ancestry. Gene region-specific significant SNPs (p < 0.05) were used to develop race-specific PRS. Results: The race-specific PRS included different sets of significant SNPs and were highly associated with lung cancer risk in both non-Hispanic Whites (OR = 1.07, 95% CI: 1.05-1.09, p = 3.44x10-9) and Blacks (OR = 1.12, 95% CI: 1.08-1.17, p = 9.14x10-8). These models remained significant for both Whites (OR = 1.05, 95% CI: 1.03-1.09, p = 0.0004) and Blacks (OR = 1.08, 95% CI: 1.01-1.15, p = 0.01) who currently do not meet USPSTF screening guidelines. AUC analysis demonstrated the Black-specific model (AUC = 0.68) outperformed the White-specific model (AUC = 0.57) (p = 0.03) when examined exclusively in the Black cohort. Conclusions: Using previously validated SNPs and gene regions, we developed race-specific PRS in non-Hispanic White and Black INHALE participants. Further validation of PRS could enable the incorporation of genetic risk modeling into lung cancer screening to identify patients who do not have traditional risk factors for lung cancer, as well as stratify patients into different levels of risk based on their genetic profile. Through the development of a reliable genetic risk factor prediction model, clinicians will have another method by which to evaluate lung cancer susceptibility, potentially leading to earlier diagnoses that portend more favorable treatment outcomes.
19
Trentham-Dietz, Amy, Oguzhan Alagoz, Christina Chapman, et al. "Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts." PLOS Computational Biology 17, no. 6 (2021): e1009020. http://dx.doi.org/10.1371/journal.pcbi.1009020.
Full textAbstract:
Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.
20
Masip, Guiomar, Karri Silventoinen, Anna Keski-Rahkonen, et al. "The genetic architecture of the association between eating behaviors and obesity: combining genetic twin modeling and polygenic risk scores." American Journal of Clinical Nutrition 112, no. 4 (2020): 956–66. http://dx.doi.org/10.1093/ajcn/nqaa181.
Full textAbstract:
ABSTRACT Background Obesity susceptibility genes are highly expressed in the brain suggesting that they might exert their influence on body weight through eating-related behaviors. Objectives To examine whether the genetic susceptibility to obesity is mediated by eating behavior patterns. Methods Participants were 3977 twins (33% monozygotic, 56% females), aged 31–37 y, from wave 5 of the FinnTwin16 study. They self-reported their height and weight, eating behaviors (15 items), diet quality, and self-measured their waist circumference (WC). For 1055 twins with genome-wide data, we constructed a polygenic risk score for BMI (PRSBMI) using almost 1 million single nucleotide polymorphisms. We used principal component analyses to identify eating behavior patterns, twin modeling to decompose correlations into genetic and environmental components, and structural equation modeling to test mediation models between the PRSBMI, eating behavior patterns, and obesity measures. Results We identified 4 moderately heritable (h2 = 36–48%) eating behavior patterns labeled “snacking,” “infrequent and unhealthy eating,” “avoidant eating,” and “emotional and external eating.” The highest phenotypic correlation with obesity measures was found for the snacking behavior pattern (r = 0.35 for BMI and r = 0.32 for WC; P &lt; 0.001 for both), largely due to genetic factors in common (bivariate h2 &gt; 70%). The snacking behavior pattern partially mediated the association between the PRSBMI and obesity measures (βindirect = 0.06; 95% CI: 0.02, 0.09; P = 0.002 for BMI; and βindirect = 0.05; 95% CI: 0.02, 0.08; P = 0.003 for WC). Conclusions Eating behavior patterns share a common genetic liability with obesity measures and are moderately heritable. Genetic susceptibility to obesity can be partly mediated by an eating pattern characterized by frequent snacking. Obesity prevention efforts might therefore benefit from focusing on eating behavior change, particularly in genetically susceptible individuals.
21
White, Bethan L., Lorenzo Ficorella, Xin Yang, Douglas F. Easton, and Antonis C. Antoniou. "Abstract 4932: Modeling uncertainty in personalised breast cancer risk prediction." Cancer Research 85, no. 8_Supplement_1 (2025): 4932. https://doi.org/10.1158/1538-7445.am2025-4932.
Full textAbstract:
Abstract Breast cancer risk prediction models use a range of predictors to estimate an individual’s chance of developing breast cancer in a given timeframe. These can facilitate risk stratification, to identify individuals who would benefit most from screening or preventive options. The BOADICEA breast cancer risk model, implemented in the CanRisk tool (www.canrisk.org), uses genetic, lifestyle, hormonal, family history and anthropometric data to estimate an individual’s risk. When implementing risk prediction models, risk predictor data are often incomplete. Point-estimates calculated when some risk factor data are missing can hide considerable uncertainty. We developed a methodological approach for quantifying uncertainty and the probability of risk-reclassification in the presence of missing data. We employed Monte-Carlo simulation methods to estimate the distribution of breast cancer risk for individuals with missing data. Multiple imputation by chained equations (MICE) with UK Biobank as a reference dataset was used to sample missing covariates. We developed a framework for estimating uncertainty, that can be applied to any given individual with missing risk factor data. We used exemplar cases to assess the probability that collecting missing data would result in a change in risk categorisation, on the basis of the 10-year predicted risk from age 40, using the UK National Institute for Health and Care Excellence (NICE) guidelines. For example, a woman known to have a pathogenic variant in CHEK2, but with all other information unmeasured, will be categorised as at “moderate risk” by the BOADICEA model. Measuring lifestyle and hormonal risk factors would result in reclassification to either the “population-” or “high-risk” group in around 24% of cases (respectively 23% and 1%). Measuring only a polygenic score (PGS) would reclassify the individual about 43% of the time into either the “population-” or “high risk” group (respectively 34% and 9%); measuring a PGS and lifestyle and hormonal risk factors would reclassify the individual to the “population-” or “high-risk” group in around 51% of cases (respectively 43% and 8%). The results demonstrate that there may be a considerable likelihood of reclassification into a different risk category after collecting missing data. The methodology presented here can identify situations where it would be most beneficial to collect additional patient information, and enable better informed clinical decision making. Citation Format: Bethan L. White, Lorenzo Ficorella, Xin Yang, Douglas F. Easton, Antonis C. Antoniou. Modeling uncertainty in personalised breast cancer risk prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4932.
22
Alloza-Moral, Iraide, Ane Aldekoa-Etxabe, Raquel Tulloch-Navarro, et al. "Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort." Biomolecules 15, no. 3 (2025): 393. https://doi.org/10.3390/biom15030393.
Full textAbstract:
The COVID-19 pandemic has had a devastating impact, with more than 7 million deaths worldwide. Advanced age and comorbidities partially explain severe cases of the disease, but genetic factors also play a significant role. Genome-wide association studies (GWASs) have been instrumental in identifying loci associated with SARS-CoV-2 infection. Here, we report the results from a >820 K variant GWAS in a COVID-19 patient cohort from the hospitals associated with IIS Biobizkaia. We compared intensive care unit (ICU)-hospitalized patients with non-ICU-hospitalized patients. The GWAS was complemented with an integrated phenotype and genetic modeling analysis using HLA genotypes, a previously identified COVID-19 polygenic risk score (PRS) and clinical data. We identified four variants associated with COVID-19 severity with genome-wide significance (rs58027632 in KIF19; rs736962 in HTRA1; rs77927946 in DMBT1; and rs115020813 in LINC01283). In addition, we designed a multivariate predictive model including HLA, PRS and clinical data which displayed an area under the curve (AUC) value of 0.79. Our results combining human genetic information with clinical data may help to improve risk assessment for the development of a severe outcome of COVID-19.
23
Klie, Adam, James Talwar, Meghana Pagadala, and Hannah Carter. "Abstract 1942: Interpretation of machine learning methods for the prediction of breast and prostate cancer risk." Cancer Research 82, no. 12_Supplement (2022): 1942. http://dx.doi.org/10.1158/1538-7445.am2022-1942.
Full textAbstract:
Abstract Neural networks are powerful tools for modeling genetic contributions to cancer risk that can theoretically capture nonlinear, epistatic interactions between disease-associated loci in heritable cancers. Recent studies applying neural networks and other machine learning methods to complex trait risk prediction from single nucleotide polymorphism (SNP) array data have shown promise in improving risk stratification. However, current performance gains for neural networks when compared to traditional polygenic risk scoring (PRS) approaches and other nonlinear and linear machine learning methods have been modest. Moreover, there remains substantial debate as to the effect of capturing epistatic interactions between SNPs in risk modeling. Central to the debate has been the difficulty in interpreting the complex, nonlinear mapping learned by neural networks and other nonlinear modeling approaches. To decipher the importance of capturing nonlinear interactions in cancer risk modeling, we first applied several PRS approaches to the prediction of breast and prostate cancer status for individuals in the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) and Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) datasets respectively. Consistent with previous studies of complex disease prediction using machine learning, we noted greater predictive capability upon inclusion of more loci in our modeling but only small performance gains when nonlinearity was captured in both cancer types. We then applied machine learning interpretation methods to derive a score for each variant per method, including several neural network interpretation methods which, to our knowledge, have not been applied in this context. We noted varying degrees of concordance between the scores assigned by each method. Finally, we performed pairwise in silico perturbations on salient features and techniques from network biology to identify epistatic interactions between loci captured by each model. Our work represents a comprehensive study of methods for inferring both variant level and epistatic interaction contributions to cancer risk. Citation Format: Adam Klie, James Talwar, Meghana Pagadala, Hannah Carter. Interpretation of machine learning methods for the prediction of breast and prostate cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1942.
24
González-Garrido, Antonia, Omar López-Ramírez, Abel Cerda-Mireles, et al. "KCNQ1 p.D446E Variant as a Risk Allele for Arrhythmogenic Phenotypes: Electrophysiological Characterization Reveals a Complex Phenotype Affecting the Slow Delayed Rectifier Potassium Current (IKs) Voltage Dependence by Causing a Hyperpolarizing Shift and a Lack of Response to Protein Kinase A Activation." International Journal of Molecular Sciences 25, no. 2 (2024): 953. http://dx.doi.org/10.3390/ijms25020953.
Full textAbstract:
Genetic testing is crucial in inherited arrhythmogenic channelopathies; however, the clinical interpretation of genetic variants remains challenging. Incomplete penetrance, oligogenic, polygenic or multifactorial forms of channelopathies further complicate variant interpretation. We identified the KCNQ1/p.D446E variant in 2/63 patients with long QT syndrome, 30-fold more frequent than in public databases. We thus characterized the biophysical phenotypes of wildtype and mutant IKs co-expressing these alleles with the β-subunit minK in HEK293 cells. KCNQ1 p.446E homozygosity significantly shifted IKs voltage dependence to hyperpolarizing potentials in basal conditions (gain of function) but failed to shift voltage dependence to hyperpolarizing potentials (loss of function) in the presence of 8Br-cAMP, a protein kinase A activator. Basal IKs activation kinetics did not differ among genotypes, but in response to 8Br-cAMP, IKs 446 E/E (homozygous) activation kinetics were slower at the most positive potentials. Protein modeling predicted a slower transition of the 446E Kv7.1 tetrameric channel to the stabilized open state. In conclusion, biophysical and modelling evidence shows that the KCNQ1 p.D446E variant has complex functional consequences including both gain and loss of function, suggesting a contribution to the pathogenesis of arrhythmogenic phenotypes as a functional risk allele.
25
Shi, Jianxin, Ju-Hyun Park, Jubao Duan, et al. "Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data." PLOS Genetics 12, no. 12 (2016): e1006493. http://dx.doi.org/10.1371/journal.pgen.1006493.
Full text
26
Zhou, Xiaopu, Yu Chen, Fanny C. F. Ip, et al. "Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction." Communications Medicine 3, no. 1 (2023). http://dx.doi.org/10.1038/s43856-023-00269-x.
Full textAbstract:
Abstract Background The polygenic nature of Alzheimer’s disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual’s genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. Methods We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. Results The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. Conclusion Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.
27
Surakka, Ida, Brooke N. Wolford, Scott C. Ritchie, et al. "Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction." Circulation: Genomic and Precision Medicine, December 29, 2022. http://dx.doi.org/10.1161/circgen.121.003542.
Full textAbstract:
Background: The 10-year Atherosclerotic Cardiovascular Disease risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of coronary artery disease (CAD) polygenic scores has been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction. Methods: The population-based Norwegian HUNT2 (Trøndelag Health Study 2) cohort of 51 036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372 410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards, and Harrell concordance index, sensitivity, and specificity were compared. Results: Inclusion of age and sex interactions of CAD polygenic score to the prediction models increased the C-index and sensitivity by accounting for nonadditive effects of CAD polygenic score and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a 2-step approach: (1) Atherosclerotic Cardiovascular Disease risk score (74.1%) and (2) the CAD polygenic score interaction model for those in low clinical risk (additional 8.5%). Conclusions: These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age- and sex-interaction terms with polygenic scores to optimize detection of individuals at high risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.
28
Dareng, Eileen O., Jonathan P. Tyrer, Daniel R. Barnes, et al. "Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk." European Journal of Human Genetics, March 22, 2022. http://dx.doi.org/10.1038/s41431-022-01085-y.
Full text
29
Lu, Tianyuan, Patrícia Pelufo Silveira, and Celia M. T. Greenwood. "Development of risk prediction models for depression combining genetic and early life risk factors." Frontiers in Neuroscience 17 (July 18, 2023). http://dx.doi.org/10.3389/fnins.2023.1143496.
Full textAbstract:
BackgroundBoth genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated.MethodsLeveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression.ResultsIn the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14–1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05–1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively.ConclusionWe have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.
30
Hess, Jonathan L., Manuel Mattheisen, Tiffany A. Greenwood, et al. "A polygenic resilience score moderates the genetic risk for schizophrenia: Replication in 18,090 cases and 28,114 controls from the Psychiatric Genomics Consortium." American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, August 8, 2023. http://dx.doi.org/10.1002/ajmg.b.32957.
Full textAbstract:
AbstractIdentifying heritable factors that moderate the genetic risk for schizophrenia (SCZ) could help clarify why some individuals remain unaffected despite having relatively high genetic liability. Previously, we developed a framework to mine genome‐wide association (GWAS) data for common genetic variants that protect high‐risk unaffected individuals from SCZ, leading to derivation of the first‐ever “polygenic resilience score” for SCZ (resilient controls n = 3786; polygenic risk score‐matched SCZ cases n = 18,619). Here, we performed a replication study to verify the moderating effect of our polygenic resilience score on SCZ risk (OR = 1.09, p = 4.03 × 10−5) using newly released GWAS data from 23 independent case–control studies collated by the Psychiatric Genomics Consortium (PGC) (resilient controls n = 2821; polygenic risk score‐matched SCZ cases n = 5150). Additionally, we sought to optimize our polygenic resilience‐scoring formula to improve subsequent modeling of resilience to SCZ and other complex disorders. We found significant replication of the polygenic resilience score, and found that strict pruning of SNPs based on linkage disequilibrium to known risk SNPs and their linked loci optimizes the performance of the polygenic resilience score.
31
van Rooij, J. "Implementation of polygenic risk scores in secondary prevention of (breast) cancer." European Journal of Public Health 34, Supplement_3 (2024). http://dx.doi.org/10.1093/eurpub/ckae144.777.
Full textAbstract:
Abstract Utilizing polygenic risk scores to inform secondary prevention of breast cancer via population screening programs is one of the pioneering applications of genomics in public health applications. Over the last decade, polygenic risk scores for breast cancer have been developed and validated in various European populations (AUC ± 0.63, OR per Z unit ± 1.6, depending on the population). In some countries, the polygenic risk score has been integrated with decision modeling tools, such as CanRisk in the Netherlands, and low-level implementation into health and care systems has started. Implementation strategies vary, but generally consider initial implementation into high-risk populations, such as breast cancer families, in specialized clinical setting, such as academic medical centres, or specialized cancer clinics, referring relatives of patients to population screening based on comprehensive risk modelling, including polygenic risk scores. This route may be followed by other disease fields as well. The presentations outlines the current evidence and considerations on breast cancer genetic risk modelling, clinical pilot studies and the outline of step-wise implementation of such models to benefit secondary prevention programs. We start from the perspective of a single hospital, to the needed steps to upscale to national populations, both in diversity of these populations, as the throughput of genetic testing and counselling, until the harmonization and recalibration of various European healthcare models. We draw from examples of the Genotyping on all patients (GOALL), building cancer health platforms (CanHeal) and Genome of Europe (GoE) consortia.
32
Zabad, Shadi, Simon Gravel, and Yue Li. "Fast and accurate Bayesian polygenic risk modeling with variational inference." American Journal of Human Genetics, April 2023. http://dx.doi.org/10.1016/j.ajhg.2023.03.009.
Full text
33
Li, Han, Jianyang Zeng, Michael P. Snyder, and Sai Zhang. "Modeling gene interactions in polygenic prediction via geometric deep learning." Genome Research, November 19, 2024, gr.279694.124. http://dx.doi.org/10.1101/gr.279694.124.
Full textAbstract:
Polygenic risk score (PRS) is a widely-used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution, and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared to conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.
34
Nascimben, Mauro, Lia Rimondini, Davide Corà, and Manolo Venturin. "Polygenic risk modeling of tumor stage and survival in bladder cancer." BioData Mining 15, no. 1 (2022). http://dx.doi.org/10.1186/s13040-022-00306-w.
Full textAbstract:
Abstract Introduction Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns. Methods Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions. Results Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction). Conclusions The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.
35
Nascimben, Mauro, Lia Rimondini, Davide Corà, and Manolo Venturin. "Polygenic risk modeling of tumor stage and survival in bladder cancer." September 30, 2022. https://doi.org/10.1186/s13040-022-00306-w.
Full textAbstract:
<strong>Introduction</strong> Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns. <strong>Methods</strong> Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions. <strong>Results</strong> Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction). <strong>Conclusions</strong> The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.
36
Chung, Ryan, Zhe Xu, Matthew Arnold, et al. "Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment." Journal of the American Heart Association, July 25, 2023. http://dx.doi.org/10.1161/jaha.122.029296.
Full textAbstract:
Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex‐specific Cox models. We modeled the implications of initiating guideline‐recommended statin therapy after prioritizing individuals for invitation to a formal CVD risk assessment. If primary care records were used to prioritize individuals for formal risk assessment using age‐ and sex‐specific thresholds corresponding to 5% false‐negative rates, then the numbers of men and women needed to be screened to prevent 1 CVD event are 149 and 280, respectively. In contrast, adding polygenic risk scores to both prioritization and formal assessments, and selecting thresholds to capture the same number of events, resulted in a number needed to screen of 116 for men and 180 for women. Conclusions Using both polygenic risk scores and primary care records to prioritize individuals at highest risk of a CVD event for a formal CVD risk assessment can efficiently prioritize those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of risk assessments in primary care while still preventing the same number of CVD events.
37
Rehbach Dobrinth, Kristina, Hanwen Zhang, Debamitra Das, et al. "Publicly available hiPSC lines with extreme polygenic risk scores for modeling schizophrenia." Complex Psychiatry, November 2, 2020. http://dx.doi.org/10.1159/000512716.
Full textAbstract:
Schizophrenia (SZ) is a common and debilitating psychiatric disorder with limited effective treatment options. Although highly heritable, risk for this polygenic disorder depends on the complex interplay of hundreds of common and rare variants. Translating the growing list of genetic loci significantly associated with disease into medically actionable information remains an important challenge. Thus, establishing platforms with which to validate the impact of risk variants in cell-type-specific and donor-dependent contexts is critical. Towards this, we selected and characterize a collection of twelve human induced pluripotent stem cell (hiPSC) lines derived from control donors with extremely low and high SZ polygenic risk scores (PRS). These hiPSC lines are publicly available at the California Institute for Regenerative Medicine (CIRM). The suitability of these extreme PRS hiPSCs for CRISPR-based isogenic comparisons of neurons and glia was evaluated across three independent laboratories, identifying 9 out of 12 meeting our criteria. We report a standardized resource of publicly available hiPSCs, with which we collectively commit to conducting future CRISPR-engineering, in order to facilitate comparison and integration of functional validation studies across the field of psychiatric genetics.
38
Kalia, Sarah S., Nicholas J. Boddicker, Siddhartha Yadav, et al. "Development of a breast cancer risk prediction model integrating monogenic, polygenic, and epidemiologic risk." Cancer Epidemiology, Biomarkers & Prevention, September 11, 2024. http://dx.doi.org/10.1158/1055-9965.epi-24-0594.
Full textAbstract:
Abstract Background: Breast cancer has been associated with monogenic, polygenic, and epidemiologic (clinical, reproductive and lifestyle) risk factors, but studies evaluating the combined effects of these factors have been limited. Methods: We extended previous work in breast cancer risk modeling, incorporating pathogenic variants (PV) in six breast cancer predisposition genes and a 105-SNP polygenic risk score (PRS), to include an epidemiologic risk score (ERS) in a sample of non-Hispanic White women drawn from prospective cohorts and population-based case-control studies, with 23,518 cases and 22,832 controls, from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium. Results: The model predicts 4.4-fold higher risk of breast cancer for postmenopausal women with no predisposition PV and median PRS, but with the highest versus lowest ERS. Overall, women with CHEK2 PVs had &gt;20% lifetime risk of breast cancer. However, 15.6% of women with CHEK2 PVs and a family history of breast cancer, and 45.1% of women with CHEK2 PVs but without a family history of breast cancer, had low (&lt;20%) predicted lifetime risk and thus were below the threshold for MRI screening. CHEK2 PV carriers at the 10th percentile of the joint distribution of ERS and PRS, without a family history of breast cancer, had a predicted lifetime risk similar to the general population. Conclusions: These results illustrate that an ERS, alone and combined with the PRS, can contribute to clinically relevant risk stratification. Impact: Integrating monogenic, polygenic, and epidemiologic risk factors in breast cancer risk prediction models may inform personalized screening and prevention efforts.
39
Zavlis, Orestis, Sam Parsons, Elaine Fox, Charlotte Booth, Annabel Songco, and John Paul Vincent. "The effects of life experiences and polygenic risk for depression on the development of positive and negative cognitive biases across adolescence: The CogBIAS hypothesis." Development and Psychopathology, January 22, 2024, 1–10. http://dx.doi.org/10.1017/s0954579423001645.
Full textAbstract:
Abstract The Cognitive Bias (CogBIAS) hypothesis proposes that cognitive biases develop as a function of environmental influences (which determine the valence of biases) and the genetic susceptibility to those influences (which determines the potency of biases). The current study employed a longitudinal, polygenic-by-environment approach to examine the CogBIAS hypothesis. To this end, measures of life experiences and polygenic scores for depression were used to assess the development of memory and interpretation biases in a three-wave sample of adolescents (12–16 years) (N = 337). Using mixed effects modeling, three patterns were revealed. First, positive life experiences (PLEs) were found to diminish negative and enhance positive forms of memory and social interpretation biases. Second, and against expectation, negative life experiences and depression polygenic scores were not associated with any cognitive outcomes, upon adjusting for psychopathology. Finally, and most importantly, the interaction between high polygenic risk and greater PLEs was associated with a stronger positive interpretation bias for social situations. These results provide the first line of polygenic evidence in support of the CogBIAS hypothesis, but also extend this hypothesis by highlighting positive genetic and nuanced environmental influences on the development of cognitive biases across adolescence.
40
Aldisi, Rana, Emadeldin Hassanin, Sugirthan Sivalingam, et al. "GenRisk: a tool for comprehensive genetic risk modeling." Bioinformatics, March 10, 2022. http://dx.doi.org/10.1093/bioinformatics/btac152.
Full textAbstract:
Abstract Summary The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats. Availability and implementation GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk). Supplementary information Supplementary data are available at Bioinformatics online.
41
Fu, Mingzhou, Leopoldo Valiente-Banuet, Satpal S. Wadhwa, Bogdan Pasaniuc, Keith Vossel, and Timothy S. Chang. "Improving genetic risk modeling of dementia from real-world data in underrepresented populations." Communications Biology 7, no. 1 (2024). http://dx.doi.org/10.1038/s42003-024-06742-0.
Full textAbstract:
AbstractGenetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31–84% (Wilcoxon signed-rank test p-value <0.05) and the area-under-the-receiver-operating characteristic by 11–17% (DeLong test p-value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
42
Irvin, Marguerite R., Tian Ge, Amit Patki, et al. "Polygenic Risk for Type 2 Diabetes in African Americans." Diabetes, March 12, 2024. http://dx.doi.org/10.2337/db23-0232.
Full textAbstract:
African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Herein, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA focused T2D PRS (PRSAA), using a Bayesian polygenic modeling method (PRS-CS). We further tested the score in three independent studies with a total of 7,275 AAs. We then compared the PRSAA to other published scores. Results show that a 1 standard deviation increase in the PRSAA was associated with 40%-60% increase in the odds of T2D (OR=1.60, 95% CI 1.37-1.88; OR=1.40, 95% CI 1.16-1.70; and OR=1.45, 95% CI 1.30-1.62) across three testing cohorts. These models captured 1.0%-2.6% of the variance (R2) in T2D on the liability scale. The positive predictive values (PPV) for three calculated score thresholds (the top 2%, 5% 10%) ranged from 14% to 35%. The PRSAA, in general, performed similarly to existing T2D PRS. Larger datasets remain needed to continue to evaluate the utility of within-ancestry scores in the AA population.
43
Gerlikhman, Lazer, and Dipak K. Sarkar. "Exploring the intersection of polygenic risk scores and prenatal alcohol exposure: Unraveling the mental health equation." Alcohol, Clinical and Experimental Research, September 29, 2024. http://dx.doi.org/10.1111/acer.15456.
Full textAbstract:
AbstractBackgroundPrenatal alcohol exposure poses significant risks to offspring mental health. However, the interplay between genetic predispositions to mental health disorders and prenatal alcohol exposure remains incompletely understood, limiting our ability to develop effective interventions for these conditions.MethodsData from the Adolescent Brain and Cognitive Development (ABCD) Study were analyzed to explore associations between polygenic risk scores (PRS) for mental disorders and maternal alcohol consumption during pregnancy. Logistic regression and structural equation modeling were utilized to assess these relationships.ResultsMaternal alcohol consumption after pregnancy awareness was significantly associated with an increased genetic risk for specific mental health disorders, particularly bipolar disorder in offspring. The relationship between maternal alcohol consumption and mental health outcomes was influenced by polygenic risk scores, with both externalizing and internalizing problems being affected.ConclusionsOur findings highlight the specific interaction between increased genetic risk for bipolar disorder and prenatal alcohol exposure in shaping offspring mental health outcomes. The significant associations we observed underscore the importance of considering both polygenic risk scores and prenatal alcohol exposure when assessing mental health risks in children. These insights emphasize the need for targeted interventions that address both genetic predispositions and environmental exposures to better understand and mitigate the impact on offspring mental health.
44
Taha, A., J. Matt McCrary, J. van Rooij, et al. "Clinical recommendations for polygenic risk score-enhanced breast cancer screening: Can.Heal project." European Journal of Public Health 34, Supplement_3 (2024). http://dx.doi.org/10.1093/eurpub/ckae144.706.
Full textAbstract:
Abstract Background The application of polygenic risk score (PRS) in breast cancer (BC) screening presents promising opportunities. Developing recommendations for future use and research on this topic is a key focus of the EU4Health project: Building the EU cancer and public health genomics platform (Can.Heal). We aim to provide these recommendations based on the analysis of available evidence through a transparent and rigorous development process. Methods The recommendations adopt the GRADE evidence to decision methodology, leveraging an evidence review team and a multidisciplinary panel of nine experts. A systematic review is being conducted to evaluate the evidence for PRS in BC screening, in the domains of benefits and harms, acceptability, feasibility, equity and cost-effectiveness. Results Regarding benefits and harms of adding PRS to BC standard screening, we identified 63 relevant articles. Forty-five (71%) discussed benefits, while 14 (22%) addressed harms. Forty-two (67%) were observational studies, 18 (28%) modeling studies and 1 non-randomised control trial (2%) that examined the diagnostic accuracy of PRS-enhanced screening for relative BC risk prediction using measures such as net reclassification index and area under the curve. Lastly, two modeling studies (3%) assessed the clinical utility of PRS-enhanced screening in terms of life years gained, BC deaths averted. The other domains are under examination. The panel convened to redefine the research question and outcomes of interest and will reconvene to assess the certainty of evidence collected, and subsequently to draft recommendations. Conclusions The integration of PRS into BC screening shows potential benefits in improving risk prediction. Ongoing trials, such as Wisdom and MyPEBS, are studying the clinical utility of integrating PRS in BC screening. The approach taken by the Can.Heal aims to ensure that the recommendations are based on a thorough and balanced evaluation of the available evidence. Key messages • The Can.Heal project aims to formulate policy and research recommendations for breast cancer screening using PRS through a thorough evidence review. • Upcoming PRS recommendations rely on expert opinion and modeling studies.
45
Zapata, Isain, Luis E. Moraes, Elise M. Fiala, et al. "Risk-modeling of dog osteosarcoma genome scans shows individuals with Mendelian-level polygenic risk are common." BMC Genomics 20, no. 1 (2019). http://dx.doi.org/10.1186/s12864-019-5531-6.
Full text
46
"Functional neuroimaging and schizophrenia: a view towards effective connectivity modeling and polygenic risk." Static and Dynamic Imaging: Clinical and Therapeutic Implications 15, no. 3 (2013): 279–89. http://dx.doi.org/10.31887/dcns.2013.15.3/rbirnbaum.
Full textAbstract:
We review critical trends in imaging genetics as applied to schizophrenia research, and then discuss some future directions of the field. A plethora of imaging genetics studies have investigated the impact of genetic variation on brain function, since the paradigm of a neuroimaging intermediate phenotype for schizophrenia first emerged. It was initially posited that the effects of schizophrenia susceptibility genes would be more penetrant at the level of biologically based neuroimaging intermediate phenotypes than at the level of a complex and phenotypically heterogeneous psychiatric syndrome. The results of many studies support this assumption, most of which show single genetic variants to be associated with changes in activity of localized brain regions, as determined by select cognitive controlled tasks. From these basic studies, functional neuroimaging analysis of intermediate phenotypes has progressed to more complex and realistic models of brain dysfunction, incorporating models of functional and effective connectivity, including the modalities of psycho-physiological interaction, dynamic causal modeling, and graph theory metrics. The genetic association approaches applied to imaging genetics have also progressed to more sophisticated multivariate effects, including incorporation of two-way and three-way epistatic interactions, and most recently polygenic risk models. Imaging genetics is a unique and powerful strategy for understanding the neural mechanisms of genetic risk for complex CNS disorders at the human brain level.
47
Elam, Kit K., Sierra Clifford, Ariana Ruof, Daniel S. Shaw, Melvin N. Wilson, and Kathryn Lemery-Chalfant. "Genotype–environment correlation by intervention effects underlying middle childhood peer rejection and associations with adolescent marijuana use." Development and Psychopathology, December 22, 2020, 1–12. http://dx.doi.org/10.1017/s0954579420001066.
Full textAbstract:
Abstract Aggressive behavior in middle childhood can contribute to peer rejection, subsequently increasing risk for substance use in adolescence. However, the quality of peer relationships a child experiences can be associated with his or her genetic predisposition, a genotype–environment correlation (rGE). In addition, recent evidence indicates that psychosocial preventive interventions can buffer genetic predispositions for negative behavior. The current study examined associations between polygenic risk for aggression, aggressive behavior, and peer rejection from 8.5 to 10.5 years, and the subsequent influence of peer rejection on marijuana use in adolescence (n = 515; 256 control, 259 intervention). Associations were examined separately in control and intervention groups for children of families who participated in a randomized controlled trial of the family-based preventive intervention, the Family Check-Up . Using time-varying effect modeling (TVEM), polygenic risk for aggression was associated with peer rejection from approximately age 8.50 to 9.50 in the control group but no associations were present in the intervention group. Subsequent analyses showed peer rejection mediated the association between polygenic risk for aggression and adolescent marijuana use in the control group. The role of rGEs in middle childhood peer processes and implications for preventive intervention programs for adolescent substance use are discussed.
48
Yusuf, Irawan, Bens Pardamean, James W. Baurley, et al. "Genetic risk factors for colorectal cancer in multiethnic Indonesians." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-88805-4.
Full textAbstract:
AbstractColorectal cancer is a common cancer in Indonesia, yet it has been understudied in this resource-constrained setting. We conducted a genome-wide association study focused on evaluation and preliminary discovery of colorectal cancer risk factors in Indonesians. We administered detailed questionnaires and collecting blood samples from 162 colorectal cancer cases throughout Makassar, Indonesia. We also established a control set of 193 healthy individuals frequency matched by age, sex, and ethnicity. A genome-wide association analysis was performed on 84 cases and 89 controls passing quality control. We evaluated known colorectal cancer genetic variants using logistic regression and established a genome-wide polygenic risk model using a Bayesian variable selection technique. We replicate associations for rs9497673, rs6936461 and rs7758229 on chromosome 6; rs11255841 on chromosome 10; and rs4779584, rs11632715, and rs73376930 on chromosome 15. Polygenic modeling identified 10 SNP associated with colorectal cancer risk. This work helps characterize the relationship between variants in the SCL22A3, SCG5, GREM1, and STXBP5-AS1 genes and colorectal cancer in a diverse Indonesian population. With further biobanking and international research collaborations, variants specific to colorectal cancer risk in Indonesians will be identified.
49
Morys, Filip, Eric Yu, Mari Shishikura, et al. "Neuroanatomical correlates of genetic risk for obesity in children." Translational Psychiatry 13, no. 1 (2023). http://dx.doi.org/10.1038/s41398-022-02301-5.
Full textAbstract:
AbstractObesity has a strong genetic component, with up to 20% of variance in body mass index (BMI) being accounted for by common polygenic variation. Most genetic polymorphisms associated with BMI are related to genes expressed in the central nervous system. At the same time, higher BMI is associated with neurocognitive changes. However, the direct link between genetics of obesity and neurobehavioral mechanisms related to weight gain is missing. Here, we use a large sample of participants (n > 4000) from the Adolescent Brain Cognitive Development cohort to investigate how genetic risk for obesity, expressed as polygenic risk score for BMI (BMI-PRS), is related to brain and behavioral measures in adolescents. In a series of analyses, we show that BMI-PRS is related to lower cortical volume and thickness in the frontal and temporal areas, relative to age-expected values. Relatedly, using structural equation modeling, we find that lower overall cortical volume is associated with higher impulsivity, which in turn is related to an increase in BMI 1 year later. In sum, our study shows that obesity might partially stem from genetic risk as expressed in brain changes in the frontal and temporal brain areas, and changes in impulsivity.
50
Gusakova, Mariya S., Mikhail V. Ivanov, Daria A. Kashtanova, et al. "GWAS reveals genetic basis of a predisposition to severe COVID-19 through in silico modeling of the FYCO1 protein." Frontiers in Medicine 10 (July 20, 2023). http://dx.doi.org/10.3389/fmed.2023.1178939.
Full textAbstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is heavily reliant on its natural ability to “hack” the host’s genetic and biological pathways. The genetic susceptibility of the host is a key factor underlying the severity of the disease. Polygenic risk scores are essential for risk assessment, risk stratification, and the prevention of adverse outcomes. In this study, we aimed to assess and analyze the genetic predisposition to severe COVID-19 in a large representative sample of the Russian population as well as to build a reliable but simple polygenic risk score model with a lower margin of error. Another important goal was to learn more about the pathogenesis of severe COVID-19. We examined the tertiary structure of the FYCO1 protein, the only gene with mutations in its coding region and discovered changes in the coiled-coil domain. Our findings suggest that FYCO1 may accelerate viral intracellular replication and excessive exocytosis and may contribute to an increased risk of severe COVID-19. We found significant associations between COVID-19 and LZTFL1, FYCO1, XCR1, CCR9, TMLHE-AS1, and SCYL2 at 3p21.31. Our findings further demonstrate the polymorphic nature of the severe COVID-19 phenotype.