Genetic Epidemiology最新文献

{"title":"Interpreting disease genome-wide association studies and polygenetic risk scores given eligibility and study design considerations","authors":"Catherine Mary Schooling,&nbsp;Mary Beth Terry","doi":"10.1002/gepi.22567","DOIUrl":"10.1002/gepi.22567","url":null,"abstract":"<p>Genome-wide association studies (GWAS) have been helpful in identifying genetic variants predicting cancer risk and providing new insights into cancer biology. Increasing use of genetically informed care, as well as genetically informed prevention and treatment strategies, have also drawn attention to some of the inherent limitations of cancer genetic data. Specifically, genetic endowment is lifelong. However, those recruited into cancer studies tend to be middle-aged or older people, meaning the exposure most likely starts before recruitment, as opposed to exposure and recruitment aligning, as in a trial or a target trial. Studies in survivors can be biased as a result of depletion of the susceptibles, here specifically due to genetic vulnerability and the cancer of interest or a competing risk. In addition, including prevalent cases in a case-control study will make the genetics of survival with cancer look harmful (Neyman bias). Here, we describe ways of designing GWAS to maximize explanatory power and predictive utility, by reducing selection bias due to only recruiting survivors and reducing Neyman bias due to including prevalent cases alongside using other techniques, such as selection diagrams, age-stratification, and Mendelian randomization, to facilitate GWAS interpretability and utility.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 8","pages":"468-472"},"PeriodicalIF":1.7,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155102","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":"Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis—An application in renal clear cell carcinoma","authors":"Diptavo Dutta,&nbsp;Ananda Sen,&nbsp;Jaya M. Satagopan","doi":"10.1002/gepi.22566","DOIUrl":"10.1002/gepi.22566","url":null,"abstract":"<p>Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA), an extension of sCCA, to effectively identify an ensemble of CNAs, methylation sites and gene (expression) components in the context of disease endpoints, especially tumor characteristics. Our approach detects potentially orthogonal gene components that are highly correlated with sets of methylation sites which in turn are correlated with sets of CNA sites. It then identifies the genes within these components that are associated with the outcome. Further, we aggregate the effect of each gene expression set on tumor stage by constructing “gene component scores” and test its interaction with traditional risk factors. Analyzing clinical and genomic data on 515 renal clear cell carcinoma (ccRCC) patients from the TCGA-KIRC, we found eight gene components to be associated with methylation sites, regulated by groups of proximally located CNA sites. Association analysis with tumor stage at diagnosis identified a novel association of expression of <i>ASAH1</i> gene trans-regulated by methylation of several genes including <i>SIX5</i> and by CNAs in the 10q25 region including <i>TCF7L2</i>. Further analysis to quantify the overall effect of gene sets on tumor stage, revealed that two of the eight gene components have significant interaction with smoking in relation to tumor stage. These gene components represent distinct biological functions including immune function, inflammatory responses, and hypoxia-regulated pathways. Our findings suggest that jsCCA analysis can identify interpretable and important genes, regulatory structures, and clinically consequential pathways. Such methods are warranted for comprehensive analysis of multimodal data especially in cancer genomics.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 8","pages":"414-432"},"PeriodicalIF":1.7,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140943393","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":"Identifying somatic fingerprints of cancers defined by germline and environmental risk factors","authors":"Saptarshi Chakraborty,&nbsp;Zoe Guan,&nbsp;Caroline E. Kostrzewa,&nbsp;Ronglai Shen,&nbsp;Colin B. Begg","doi":"10.1002/gepi.22565","DOIUrl":"10.1002/gepi.22565","url":null,"abstract":"<p>Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline <i>BRCA1/2</i> mutations and in head and neck cancer patients exposed to human papillomavirus.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 8","pages":"455-467"},"PeriodicalIF":1.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840250","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":"Meta-analysis of breast cancer risk for individuals with PALB2 pathogenic variants","authors":"Thanthirige L. M. Ruberu,&nbsp;Danielle Braun,&nbsp;Giovanni Parmigiani,&nbsp;Swati Biswas","doi":"10.1002/gepi.22561","DOIUrl":"10.1002/gepi.22561","url":null,"abstract":"<p>Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these estimates is necessary to accurately counsel patients with this mutation. However, this is not trivial due to heterogeneity of studies in terms of study design and risk measure. We utilized a recently proposed Bayesian random-effects meta-analysis method that can synthesize estimates from such heterogeneous studies. We applied this method to combine estimates from 12 studies on BC risk for carriers of pathogenic PALB2 mutations. The estimated overall (meta-analysis-based) risk of BC is 12.80% (6.11%−22.59%) by age 50 and 48.47% (36.05%−61.74%) by age 80. Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 8","pages":"448-454"},"PeriodicalIF":1.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140666320","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":"A novel application of data-consistent inversion to overcome spurious inference in genome-wide association studies","authors":"Negar Janani,&nbsp;Kendra A. Young,&nbsp;Greg Kinney,&nbsp;Matthew Strand,&nbsp;John E. Hokanson,&nbsp;Yaning Liu,&nbsp;Troy Butler,&nbsp;Erin Austin","doi":"10.1002/gepi.22563","DOIUrl":"10.1002/gepi.22563","url":null,"abstract":"<p>The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption-free model built upon data-consistent inversion (DCI), which is a recently developed measure-theoretic framework utilized for uncertainty quantification. This proposed DCI-derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI-derived model to cover all genetic variants without emphasizing on additivity of the classic-GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type-I error rate at least as well as the classic-GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 6","pages":"270-288"},"PeriodicalIF":1.7,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140636418","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":"Shared genetic risk between major orofacial cleft phenotypes in an African population","authors":"Azeez Alade,&nbsp;Tabitha Peter,&nbsp;Tamara Busch,&nbsp;Waheed Awotoye,&nbsp;Deepti Anand,&nbsp;Oladayo Abimbola,&nbsp;Emmanuel Aladenika,&nbsp;Mojisola Olujitan,&nbsp;Oscar Rysavy,&nbsp;Phuong Fawng Nguyen,&nbsp;Thirona Naicker,&nbsp;Peter A. Mossey,&nbsp;Lord J. J. Gowans,&nbsp;Mekonen A. Eshete,&nbsp;Wasiu L. Adeyemo,&nbsp;Erliang Zeng,&nbsp;Eric Van Otterloo,&nbsp;Michael O'Rorke,&nbsp;Adebowale Adeyemo,&nbsp;Jeffrey C. Murray,&nbsp;Salil A. Lachke,&nbsp;Paul A. Romitti,&nbsp;Azeez Butali","doi":"10.1002/gepi.22564","DOIUrl":"10.1002/gepi.22564","url":null,"abstract":"<p>Nonsyndromic orofacial clefts (NSOFCs) represent a large proportion (70%–80%) of all OFCs. They can be broadly categorized into nonsyndromic cleft lip with or without cleft palate (NSCL/P) and nonsyndromic cleft palate only (NSCPO). Although NSCL/P and NSCPO are considered etiologically distinct, recent evidence suggests the presence of shared genetic risks. Thus, we investigated the genetic overlap between NSCL/P and NSCPO using African genome-wide association study (GWAS) data on NSOFCs. These data consist of 814 NSCL/P, 205 NSCPO cases, and 2159 unrelated controls. We generated common single-nucleotide variants (SNVs) association summary statistics separately for each phenotype (NSCL/P and NSCPO) under an additive genetic model. Subsequently, we employed the pleiotropic analysis under the composite null (PLACO) method to test for genetic overlap. Our analysis identified two loci with genome-wide significance (rs181737795 [<i>p</i> = 2.58E−08] and rs2221169 [<i>p</i> = 4.5E−08]) and one locus with marginal significance (rs187523265 [<i>p</i> = 5.22E−08]). Using mouse transcriptomics data and information from genetic phenotype databases, we identified <i>MDN1, MAP3k7, KMT2A, ARCN1</i>, and <i>VADC2</i> as top candidate genes for the associated SNVs. These findings enhance our understanding of genetic variants associated with NSOFCs and identify potential candidate genes for further exploration.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 6","pages":"258-269"},"PeriodicalIF":1.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623743","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":"Structured testing of genetic association with mixed clinical outcomes","authors":"Meiling Liu,&nbsp;Yu-Ru Su,&nbsp;Yang Liu,&nbsp;Li Hsu,&nbsp;Qianchuan He","doi":"10.1002/gepi.22560","DOIUrl":"10.1002/gepi.22560","url":null,"abstract":"<p>Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we proposed a structured test statistic for testing genetic association with mixed types of survival, binary, and continuous outcomes. The structured testing incorporates known biological information of variants while allowing for their heterogeneous effects and is a powerful strategy for analyzing infrequent genetic factors. Simulation studies show that the proposed test statistic has correct type I error and is highly effective in detecting significant genetic variants. We applied our approach to a uterine corpus endometrial carcinoma study and identified several genetic pathways associated with the clinical outcomes.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 5","pages":"226-237"},"PeriodicalIF":1.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596715","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":"Hierarchical joint analysis of marginal summary statistics—Part I: Multipopulation fine mapping and credible set construction","authors":"Jiayi Shen,&nbsp;Lai Jiang,&nbsp;Kan Wang,&nbsp;Anqi Wang,&nbsp;Fei Chen,&nbsp;Paul J. Newcombe,&nbsp;Christopher A. Haiman,&nbsp;David V. Conti","doi":"10.1002/gepi.22562","DOIUrl":"10.1002/gepi.22562","url":null,"abstract":"<p>Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 6","pages":"241-257"},"PeriodicalIF":1.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603455","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":"OSCAA: A two-dimensional Gaussian mixture model for copy number variation association analysis","authors":"Xuanxuan Yu,&nbsp;Xizhi Luo,&nbsp;Guoshuai Cai,&nbsp;Feifei Xiao","doi":"10.1002/gepi.22558","DOIUrl":"10.1002/gepi.22558","url":null,"abstract":"<p>Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome-wide assessment of such variation. In this article, we developed One-Stage CNV–disease Association Analysis (OSCAA), a flexible algorithm to discover disease-associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the PCs from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV–disease association, especially for short CNVs or CNVs with weak signals. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 5","pages":"214-225"},"PeriodicalIF":1.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140293348","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":"Breast and bowel cancers diagnosed in people ‘too young to have cancer’: A blueprint for research using family and twin studies","authors":"John L. Hopper,&nbsp;Shuai Li,&nbsp;Robert J. MacInnis,&nbsp;James G. Dowty,&nbsp;Tuong L. Nguyen,&nbsp;Minh Bui,&nbsp;Gillian S. Dite,&nbsp;Vivienne F. C. Esser,&nbsp;Zhoufeng Ye,&nbsp;Enes Makalic,&nbsp;Daniel F. Schmidt,&nbsp;Benjamin Goudey,&nbsp;Karen Alpen,&nbsp;Miroslaw Kapuscinski,&nbsp;Aung Ko Win,&nbsp;Pierre-Antoine Dugué,&nbsp;Roger L. Milne,&nbsp;Harindra Jayasekara,&nbsp;Jennifer D. Brooks,&nbsp;Sue Malta,&nbsp;Lucas Calais-Ferreira,&nbsp;Alexander C. Campbell,&nbsp;Jesse T. Young,&nbsp;Tu Nguyen-Dumont,&nbsp;Joohon Sung,&nbsp;Graham G. Giles,&nbsp;Daniel Buchanan,&nbsp;Ingrid Winship,&nbsp;Mary Beth Terry,&nbsp;Melissa C. Southey,&nbsp;Mark A. Jenkins","doi":"10.1002/gepi.22555","DOIUrl":"10.1002/gepi.22555","url":null,"abstract":"<p>Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 8","pages":"433-447"},"PeriodicalIF":1.7,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169744","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}