Discovering weaker genetic associations guided by known associations
在已知关联的指导下发现较弱的遗传关联
作者: Haohan WangMichael M. VanyukovEric P. XingWei Wu
作者单位: 1Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
2Department of Pharmaceutical Sciences, Departments of Psychiatry, and Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
3Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
4Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
刊名: BMC Medical Genomics, 2020, Vol.13 (5), pp.747-53
来源数据库: Springer Nature Journal
DOI: 10.1186/s12920-020-0667-4
关键词: Weak associationLinear mixed modelGWAS
英文摘要: Abstract(#br)Background(#br)The current understanding of the genetic basis of complex human diseases is that they are caused and affected by many common and rare genetic variants. A considerable number of the disease-associated variants have been identified by Genome Wide Association Studies, however, they can explain only a small proportion of heritability. One of the possible reasons for the missing heritability is that many undiscovered disease-causing variants are weakly associated with the disease. This can pose serious challenges to many statistical methods, which seems to be only capable of identifying disease-associated variants with relatively stronger coefficients. Results(#br)In order to help identify weaker variants, we propose a novel statistical method, Constrained Sparse...
全文获取路径: Springer Nature  (合作)
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影响因子:3.466 (2012)

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