Genetic Epidemiology最新文献

{"title":"Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease","authors":"Jin Ren,&nbsp;Yasaman J. Soofi,&nbsp;Md Asad Rahman,&nbsp;Qing Lu,&nbsp;Jinling Liu","doi":"10.1002/gepi.70045","DOIUrl":"https://doi.org/10.1002/gepi.70045","url":null,"abstract":"<div>\u0000 \u0000 <p>Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online study, including 8840 individuals (8585 PD cases and 255 controls). IBI prioritized variants that were not detected or were ranked substantially lower by GWAS, including variants within or near genes with prior PD association. The top 200 IBI SNPs showed stronger predictive performance in ANN models (AUC = 0.79) than the top 200 GWAS SNPs (AUC = 0.72), providing complementary support for the utility of IBI-based prioritization in this cohort. Notably, IBI highlighted variants with lower minor allele frequencies that GWAS did not detect. This study demonstrates the utility of IBI as a complementary tool for prioritizing PD-related candidate variants and genes for further investigation.</p></div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data","authors":"James T. Baker,&nbsp;Hung-Hsin Chen,&nbsp;Grahame F. Evans,&nbsp;Alyssa C. Scartozzi,&nbsp;Ryan J. Bohlender,&nbsp;Chad D. Huff,&nbsp;Quinn S. Wells,&nbsp;David C. Samuels,&nbsp;Jennifer E. Below","doi":"10.1002/gepi.70048","DOIUrl":"https://doi.org/10.1002/gepi.70048","url":null,"abstract":"<p>There is a need for genetic analytical methods that integrate multi-individual identity-by-descent (IBD) tools with phenotypic enrichment testing to discover novel shared haplotypes contributing to disease traits. Existing tools are designed to identify IBD sharing and leave interpretation and phenotype association tests to further analyses. Here we present Distant Relatedness for Identification and Variant Evaluation (DRIVE) v3, a python command-line interface tool that identifies networks of participants who share an identical haplotype at a given genomic location. Given phenotypic data, DRIVE additionally estimates significant enrichment of dichotomous traits within networks. DRIVE is designed for efficient use across large-scale genetic data resources, featuring a versatile application programming interface and a backend structure designed for flexible integration into existing analytical pipelines. In this work, we describe the implementation of DRIVE v3 and illustrate two applications of the tool to an autosomal dominant condition and to an autosomal recessive condition, cardiomyopathy and cystic fibrosis, respectively. These applications highlight the substantial performance improvements between v1 and v3 and demonstrate practically how the newer features of DRIVE such as the enrichment test can be used in the interpretation of the identified networks.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics","authors":"Yu Shi,&nbsp;Wei Xu,&nbsp;Pingzhao Hu","doi":"10.1002/gepi.70044","DOIUrl":"10.1002/gepi.70044","url":null,"abstract":"<p>The Cancer dependency maps (DepMap) identify genetic dependencies in cancer cells using large-scale loss-of-function screens, providing a foundation for cancer-specific treatment strategies. However, discrepancies exist between cancer cell line models (CCLs) and patient-derived tumor models, particularly in translating findings to clinical settings. To bridge this gap, computational approaches such as artificial intelligence–based domain adaptation can assist in aligning laboratory and patient-derived molecular data, thereby improving the translation of preclinical findings into personalized treatment strategies. We developed a deep unsupervised domain adaptation (UDA) algorithm to align features between source and target domains. It was trained on labeled CCLs data from the source domain and unseen, unlabeled CCL data from the target domain. The trained model was applied to predict the dependency map of breast cancer (BC) patients in The Cancer Genome Atlas (TCGA). To validate its performance, we used the predicted BC dependency map to classify ER + /HER2 + BC subtype statuses and identify synthetic lethality (SL) gene pairs for drug discovery. Our model demonstrated high accuracy in predicting cancer dependency maps for patient-derived tumors. The generated maps showed excellent performance in predicting ER + /HER2+ subtype statuses, with an area under the curve of the receiver operating characteristic (AUC-ROC) of 0.99. Notably, our analysis also identified two potential synthetic lethality gene pairs: PBRM1-NF2 and PBRM1-CTNND2, which can be potentially used for developing precision therapies for ER + /HER2+ breast cancer. Domain adaptation is a promising approach for transferring biological knowledge between different cancer models and improving patient-specific treatment strategies.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148307946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on: A Novel Mendelian Randomization Method With Binary Risk Factor and Outcome","authors":"Sandeep Chowdary Vejandla,&nbsp;Mengchen Ding,&nbsp;Hemant K. Tiwari","doi":"10.1002/gepi.70049","DOIUrl":"https://doi.org/10.1002/gepi.70049","url":null,"abstract":"&lt;p&gt;We write regarding the article by Allman et al., “A Novel Mendelian Randomization Method With Binary Risk Factor and Outcome,” published in &lt;i&gt;Genetic Epidemiology&lt;/i&gt;, 2021:45(5):540–560. The paper addressed an important Mendelian randomization setting in which both the risk factor and outcome are binary. In continuing this methodological work, we re-examined the mathematical formulation and computational implementation of the methods. We have identified and rectified three methodological issues: an optimization issue in the generalized method of moments (GMM) implementation, a derivation issue in the Instrumental Variable–Multivariate Bernoulli (IV-MVB) formulation, and a simulation-evaluation issue in the definition of bias and coverage. These corrections improve numerical stability, clarify the mathematical presentation of the methods, and provide a more appropriate evaluation of simulation performance.&lt;/p&gt;&lt;p&gt;Below, we outline these issues using the notation from the published manuscript, followed by our proposed corrections.&lt;/p&gt;&lt;p&gt;&lt;b&gt;The Issue:&lt;/b&gt;&lt;/p&gt;&lt;p&gt;The manuscript specifies the covariance matrix as &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Ω&lt;/mi&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;Z&lt;/mi&gt;\u0000 &lt;mo&gt;′&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;mo&gt;⋅&lt;/mo&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;I&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mi&gt;Z&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;, where &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt; is introduced as a nuisance parameter.&lt;/p&gt;&lt;p&gt;Consequently, the optimizer is artificially driven to increase &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;mo&gt;→&lt;/mo&gt;\u0000 &lt;mi&gt;∞&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt; to reduce the objective function, rather than optimize solely over the meaningful causal parameters. This introduces an ill-posed optimization problem with an unidentified scale direction, leading to severe convergence failures in the GMM method, as reflected in the published simulation results in Supporting Information S1: Tables 2.1 and 2.2. Furthermore, the standard errors become contaminated because the unidentified &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;θ&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt; is subsequently plugged into the sandwich variance estimator in Equation (11).&lt;/p&gt;&lt;p&gt;&lt;b&gt;The Proposed Correction:&lt;/b&gt;&lt;/p&gt;&lt;p&gt;This preserves the GMM estimating princi","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data","authors":"Maxwell Salvatore,&nbsp;Ritoban Kundu,&nbsp;Jiacong Du,&nbsp;Christopher R. Friese,&nbsp;Alison M. Mondul,&nbsp;David Hanauer,&nbsp;Haidong Lu,&nbsp;Celeste Leigh Pearce,&nbsp;Bhramar Mukherjee","doi":"10.1002/gepi.70047","DOIUrl":"https://doi.org/10.1002/gepi.70047","url":null,"abstract":"<div>\u0000 \u0000 <p>Electronic health records (EHRs) are valuable sources of data but are susceptible to biases from missing data and sample selection, often due to clinically informative visiting processes and non-probability sampling. This research explores whether genetic data, typically measured on nearly all participants in EHR-linked biobanks, can be used to mitigate these biases. Simulations were performed under conditions of missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) within random and biased sampling frameworks. We evaluated PRS-informed imputation, PRS-uninformed imputation, and complete case analysis across these scenarios in terms of bias, coverage, and root mean square error (RMSE) of the regression coefficient estimates. A real-world example using data from the Michigan Genomics Initiative (MGI, <i>n</i> = 68,063) compared the effectiveness of these methods against national benchmark estimates. PRS-informed imputation generally reduced bias and RMSE and improved coverage, particularly under MAR conditions in random samples. In analyses of biased samples of <i>n</i> = 10,000 with MAR exposure-only missingness, weighted, PRS-informed imputation analyses showed substantially lower percent bias (0.6%) and closer to nominal coverage (89.1%) compared to weighted, complete case analyses (9.4%; 74.3%). The MGI estimates showed that PRS-informed approaches aligned more closely with national benchmarks than unweighted complete case analysis. Leveraging genetic data with sample weighting can help reduce bias in outcome-exposure association estimates derived from biobank data. When available, researchers should consider including PRS for imputation and survey methods for sample weighting when estimating outcome-exposure association coefficients in a target population of interest, recognizing that benefits may vary by outcome and data structure.</p></div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis","authors":"Diane Xue,&nbsp;Elizabeth E. Blue,&nbsp;Tamar Sofer,&nbsp;Timothy M. Hughes,&nbsp;Jerome I. Rotter,&nbsp;Wendy S. Post,&nbsp;Alison E. Fohner","doi":"10.1002/gepi.70046","DOIUrl":"https://doi.org/10.1002/gepi.70046","url":null,"abstract":"<p>Over 75 Alzheimer's disease (AD) and dementia-associated variants have been identified through genome-wide association studies, but the utility of polygenic risk scores (PRS) for predicting AD and dementia in diverse and admixed populations remains unclear. We compared how PRS approaches differing in <i>p</i>-value thresholds, variant weights, and source ancestry perform in predicting dementia in 6338 African American, Chinese, Hispanic, and White individuals from the Multi-Ethnic Study of Atherosclerosis. We tested clumping and thresholding (C+T) methods with varying parameters against Bayesian approaches (PRS-CS, PRS-CSx). We compared the ability of each method to predict incident dementia in all participants and in groups stratified by self-reported race/ethnicity. We additionally analyzed performance across groups stratified by estimated proportion of non-Finnish European (NFE)-like ancestry. Including more variants does not improve performance. We found comparable associations between dementia and PRS when comparing a C+T method with only 15 SNPs and PRS derived from Bayesian models that include &gt; 800,000 SNPs (HR_5e-08 = 1.18, 95% CI: 1.08–1.28; HR_CSx = 1.17, 95% CI: 1.07−1.27). The <i>p</i> &lt; 5e-08 C+T method was more strongly associated with incident dementia in populations genetically dissimilar from the source data (HR_lowNFE__5e-08 = 1.27, 95% CI: 1.08−1.50; HR_lowNFE__CSx = 1.12, 95% CI: 0.94−1.33). More selective PRS models using genome-wide significant SNPs may be preferable for dementia prediction in diverse populations.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shared and Distinct Genetic Factors Underlying Bile Acid Regulation and Intrahepatic Cholestasis of Pregnancy.","authors":"Xinyi Zhang, Junwei Li, Huanhuan Zhu, Linxuan Li, Yu Wang, Meng Yang, Lin Wang, Xianmei Lan, Yingying Wang, Ying Lin, Jingyu Zeng, Panhong Liu, Aifen Zhou, Han Xiao, Xin Jin","doi":"10.1002/gepi.70040","DOIUrl":"10.1002/gepi.70040","url":null,"abstract":"<p><p>Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disorder characterized by elevated total bile acid (TBA) levels, leading to adverse maternal and fetal outcomes. While genetic factors contribute to ICP and bile acid metabolism, the underlying mechanisms remain incompletely understood, particularly in East Asian populations. We conducted a genome-wide association study (GWAS) in 13,357 pregnant women from a Chinese cohort to investigate genetic determinants of TBA levels and ICP. Meta-analysis was performed by combining our data with the Shenzhen cohort. Post-GWAS analyses included pathway enrichment, integrative analysis with liver single-cell RNA sequencing (scRNA-seq) data, and Mendelian randomization (MR). We identified genome-wide significant associations at CYP7A1 (rs4738680, p = 1.08 × 10^-10) and SLC39A9 (rs17107007, p = 3.46 × 10^-28) for TBA, and at SLC39A9 (rs17107007, p = 3.34 × 10^-16) for ICP. Pathway analysis highlighted bile acid synthesis and metabolism pathways for TBA and immune-related pathways for ICP. Integrative scRNA-seq analysis revealed enrichment of TBA-associated genes in hepatocytes and ICP-associated genes in neutrophils. MR analysis suggested a potential causal effect of estrone on TBA levels. Our findings provide novel insights into the genetic architecture of TBA and ICP, emphasizing the roles of bile acid metabolism in hepatocytes and immune dysregulation in ICP pathogenesis. The identified genetic loci and pathways may inform future research on risk prediction and targeted therapies for ICP.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 4","pages":"e70040"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147814216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 4","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148063306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 4","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148063305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating a Mendelian Risk Prediction Model That Aggregates Across Genes and Cancers","authors":"Jane W. Liang,&nbsp;Gregory E. Idos,&nbsp;Christine Hong,&nbsp;Kristen M. Shannon,&nbsp;Lauren M. Bear,&nbsp;Jennifer Morales Pichardo,&nbsp;Zoe Guan,&nbsp;Anne Marie McCarthy,&nbsp;James M. Ford,&nbsp;Allison W. Kurian,&nbsp;Stephen B. Gruber,&nbsp;Danielle Braun,&nbsp;Giovanni Parmigiani","doi":"10.1002/gepi.70038","DOIUrl":"10.1002/gepi.70038","url":null,"abstract":"<div>\u0000 \u0000 <p>Using principles of Mendelian genetics, probability theory, and mutation-specific knowledge, Mendelian risk prediction models identify those at high risk of carrying a heritable cancer susceptibility variant and assess future risk of cancer. Our previously-validated Fam3PRO model is a generalizable and computationally efficient Mendelian risk prediction framework that incorporates an arbitrary number of gene-cancer associations. In practice, from a model training perspective, there may be uncertainty in estimating the population-level model parameters necessary for rare gene-cancer associations. From a clinical perspective, it may be infeasible to obtain a detailed patient family history for many cancers. Motivated by the context of pre-screening for germline testing of a broad hereditary cancer gene panel, we propose a Mendelian model that aggregates information across genes and cancers, reducing patient burden and bypassing the need for robust parameter estimation for rare genes and syndromes. We evaluated this aggregate model through simulations and applied it to two independent clinical cohorts. We show that when the clinical goal is to assess patient risk of carrying a pathogenic variant for any cancer susceptibility gene, the aggregate model can give results comparable to a Mendelian model that considers many genes and cancers individually, while greatly simplifying model assumptions and user input.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 3","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147372927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}