A statistical test for detecting parent-of-origin effects when parental information is missing.

IF 0.8 4区 数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-09-26 DOI:10.1515/sagmb-2017-0007
Chiara Sacco, Cinzia Viroli, Mario Falchi
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Abstract

Genomic imprinting is an epigenetic mechanism that leads to differential contributions of maternal and paternal alleles to offspring gene expression in a parent-of-origin manner. We propose a novel test for detecting the parent-of-origin effects (POEs) in genome wide genotype data from related individuals (twins) when the parental origin cannot be inferred. The proposed method exploits a finite mixture of linear mixed models: the key idea is that in the case of POEs the population can be clustered in two different groups in which the reference allele is inherited by a different parent. A further advantage of this approach is the possibility to obtain an estimation of parental effect when the parental information is missing. We will also show that the approach is flexible enough to be applicable to the general scenario of independent data. The performance of the proposed test is evaluated through a wide simulation study. The method is finally applied to known imprinted genes of the MuTHER twin study data.

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当父母的信息缺失时,用于检测父母起源效应的统计检验。
基因组印记是一种表观遗传机制,导致母本和父本等位基因以亲本起源方式对后代基因表达的差异贡献。我们提出了一种新的测试,用于检测来自相关个体(双胞胎)的全基因组基因型数据中亲本起源效应(POEs),当亲本起源无法推断。所提出的方法利用线性混合模型的有限混合:关键思想是,在poe的情况下,种群可以聚集在两个不同的群体中,其中参考等位基因由不同的亲本遗传。这种方法的另一个优点是,当亲代信息缺失时,可以获得亲代效应的估计。我们还将展示该方法足够灵活,可以适用于独立数据的一般场景。通过广泛的仿真研究对所提出的测试的性能进行了评估。最后将该方法应用于已知的MuTHER双胞胎的印迹基因研究数据。
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来源期刊
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
自引率
11.10%
发文量
8
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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