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Abstract:
:Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.
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最新影响因子:2.344 | 期刊ISSN:0741-0395 | CiteScore:2.24 |
出版周期:Bimonthly | 是否OA:YES | 出版年份:0 |
期刊官方网址:http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291098-2272
自引率:10.10% | 研究方向:医学-公共卫生、环境卫生与职业卫生 |
出版地区:UNITED STATES |
SCI期刊coverage:Science Citation Index Expanded(科学引文索引扩展)
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Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations. Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.
《遗传流行病学》是一份同行评议的期刊,旨在探讨人类特征在家庭和人口中分布的遗传原因。重点放在遗传和环境因素对人类疾病的相对贡献,揭示了遗传,流行病学和生物学调查。 遗传流行病学主要发表统计遗传学方面的论文,这是一个主要涉及发展统计、生物信息学和计算模型来分析遗传数据的研究领域。将基础生物学和群体遗传学结合到概念模型中是受欢迎的。该杂志寻求包括应用研究或创新的统计、数学、计算或基因组方法学的原创文章,以促进遗传流行病学的研究。鼓励其他类型的报告,如给编辑的信、主题评论和来自其他研究领域的观点,这些可能会丰富遗传流行病学领域。
大类(学科) | 小类(学科) | 学科排名 |
生物 |
GENETICS & HEREDITY (遗传学) 4区 MATHEMATICAL & COMPUTATIONAL BIOLOGY (数学与计算生物学) 2区 |
90/171 9/59 |
年度总发文量 | 年度论文发表量 | 年度综述发表量 |
68 | 68 | 0 |
引文计数(2018)
文献(2015-2017)
477次引用
213篇文献
序号 | 类别 | 排名 | 百分位 |
1 |
大类(学科):Medicine
小类(学科):Epidemiology
|
#39/87
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|
2 |
大类(学科):Medicine
小类(学科):Genetics (clinical)
|
#46/89
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研究方向:生命科学 遗传学 遗传学研究新技术与方法 生物信息学
审稿时间: 3个月内
影响因子:2.655
ISSN:1520-9512
研究方向:医学-呼吸系统
影响因子:3.427
ISSN:1027-3719
研究方向:医学-传染病学
影响因子:4.09
ISSN:8755-6863
研究方向:医学-呼吸系统
影响因子:5.526
ISSN:1526-0542
研究方向:医学-呼吸系统
影响因子:2.973
ISSN:1472-9792
研究方向:医学-呼吸系统
影响因子:3.289
ISSN:2055-1010
研究方向:PRIMARY HEALTH CARE-RESPIRATORY SYSTEM
影响因子:10.262
ISSN:0012-3692
研究方向:医学-呼吸系统
影响因子:2.339
ISSN:0020-1324
研究方向:医学-呼吸系统
影响因子:102.642
ISSN:2213-2600
研究方向:RESPIRATORY SYSTEM-RESPIRATORY SYSTEM
发表一篇学和医学成像类SCI论文
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