A Deep Learning-based Genome-wide Polygenic Risk Score for Common Diseases Identifies Individuals with Risk

Peng, Jiajie; Li, Jingyi; Han, Ruijiang; Wang, Yuxian; Han, Lu; Wang, Tao; Hao, Jiaoye; Shang, Xuequn; Wei, Zhongyu

doi:10.1101/2021.11.17.21265352

Cited by 11 publications

(8 citation statements)

References 46 publications

(39 reference statements)

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“…Besides, deep learning may be a feasible approach to incorporate different factors due to its powerful ability of data representation. DL-PRS [36] and DeepPRS [37] are two deep learning models proposed recently, utilizing fully connected neural networks and recurrent neural networks to calculate PRS. Their works proved that deep learning models could be applied to the genetic variants for risk prediction but required stringent SNP selection.…”

Section: Discussionmentioning

confidence: 99%

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

Wang

et al. 2022

Preprint

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Polygenic risk score (PRS) has been widely used to identify the high-risk individuals from the general population, which would be helpful for disease prevention and early treatment. Many methods have been developed to calculate PRS by weighted aggregating the phenotype-associated risk alleles from genome-wide association studies. However, only considering genetic effects may not be sufficient for risk prediction because the disease risk is not only related to genetic factors but also non-genetic factors, e.g., diet, physical exercise et al. But it is still a challenge to integrate these genetic and non-genetic factors into a unified machine learning framework for disease risk prediction. In this paper, we proposed PRSIMD (PRS Integrating Multi-source Data), a machine learning model that applies posterior regularization to integrate genetic and non-genetic factors to improve disease risk prediction. Also, we applied Mendelian Randomization analysis to identify the causal non-genetic risk factors for the selected diseases. We applied PRSIMD to predict type 2 diabetes and coronary artery disease from UK Biobank and observed that PRSIMD was significantly better than the methods to calculate PRS including p-value threshold (P+T), PRSice2, SBLUP, DMSLMM, and LDpred2. In addition, we observed that PRSIMD achieved the better predictive power than the composite risk score.

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Section: Discussionmentioning

confidence: 99%

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

Wang

et al. 2022

Preprint

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“…However, the degree of improvement offered by ML methods may be partly dependent on the complexity and inter‐individual heterogeneity of the genetic architecture underlying the disease of interest. For instance, DeepPRS, 40 a novel DL‐based model that does not only rely on the additive effect of risk SNPs, outperformed more traditional PRS models across a variety of disease phenotypes, including AD. Thus, we anticipate further improvements in these approaches will unlock some of the unexplained heritability observed in prior GWAS, enhancing future research, trials, and clinical practice.…”

Section: Key Challengesmentioning

confidence: 99%

Artificial intelligence for dementia genetics and omics

Bettencourt,

Skene,

Bandres‐Ciga

et al. 2023

Alzheimer's & Dementia

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Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine.Highlights We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

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“…This emphasizes the signi cance of AI in cancer classi cation, potentially aiding in precision oncology. Peng et al [144] identi ed high-risk individuals for various diseases, including Alzheimer's Disease (AD), In ammatory Bowel Disease (IBD), Type 2 Diabetes (T2D), and Breast Cancer (BRCA) using Bidirectional Long Short-Term Memory (BiLSTM). The model achieved promising performance with AUC values ranging from 0.6585 to 0.8624, outperforming traditional methods.…”

Section: Cardiovascular Diseasesmentioning

confidence: 99%

Personalized Medicine for Cardiovascular Disease Risk in Artificial Intelligence Framework

Singh,

Kumar,

Khanna

et al. 2023

Preprint

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Background & Motivation: The field of personalized medicine endeavors to transform the healthcare industry by advancing individualized strategies for diagnosis, treatment modalities, and prognostic assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Medical practitioners can use this strategy to tailor early interventions for each patient's explicit treatment or preventative requirements. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: In this comprehensive analysis, we conducted a detailed examination of the term "personalized medicine," delving into its fundamental principles, the obstacles it encounters as an emerging subject, and its potentially revolutionary implications in the domain of CVD. A total of 228 studies were selected using the PRISMA methodology. Findings and Conclusions: Herein, we provide a scoping review highlighting the role of AI, particularly DL, in personalized risk assessment for CVDs. It underscores the prospect for AI-driven personalized medicine to significantly improve the accuracy and efficiency of controlling CVD, revolutionizing patient outcomes. The article also presents examples from real-world case studies and outlines potential areas for future research.

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A Deep Learning-based Genome-wide Polygenic Risk Score for Common Diseases Identifies Individuals with Risk

Cited by 11 publications

References 46 publications

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

Artificial intelligence for dementia genetics and omics

Personalized Medicine for Cardiovascular Disease Risk in Artificial Intelligence Framework

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