多组学不能取代全基因组关联研究中的样本量

IF 2.4 4区 心理学 Q2 BEHAVIORAL SCIENCES
David A. A. Baranger, Alexander S. Hatoum, Renato Polimanti, Joel Gelernter, Howard J. Edenberg, Ryan Bogdan, Arpana Agrawal
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引用次数: 0

摘要

整合多组学信息(如表观遗传学和转录组学)有助于解释全基因组关联研究(GWAS)的结果。有人认为,多组学可以避免或大大减少为发现新变异而增加全基因组关联研究样本量的需要。我们测试了在早期较小规模的全基因组关联研究中纳入多组学信息是否能促进基因的真正阳性发现,而这些基因后来在相同/相似性状的较大型全基因组关联研究中被发现。我们采用了 10 种不同的分析方法来整合来自 12 个来源(如基因型-组织表达项目)的多组学数据,以检验较早且规模较小的 4 种脑相关性状(酒精使用障碍/问题性酒精使用、重度抑郁/抑郁症、精神分裂症和颅内容积/脑容积)的 GWAS 是否能检测到较晚且规模较大的 GWAS 所揭示的基因。多组学数据并不能可靠地识别早期较弱的全球基因组研究中的新基因(PPV <0.2;80%的假阳性关联)。机器学习预测略微增加了已识别新基因的数量,又正确识别了1-8个基因,但仅适用于高遗传性状(如颅内容积和精神分裂症)的强效早期GWAS。虽然多组学,特别是定位图谱(即 fastBAT、MAGMA 和 H-MAGMA),可以帮助确定全基因组显著位点(PPV=0.5-1.0)内基因的优先次序,并将其转化为疾病生物学信息,但它并不能可靠地提高脑相关 GWAS 的新基因发现率。要提高发现新基因和新位点的能力,就必须扩大样本量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multi-omics cannot replace sample size in genome-wide association studies

Multi-omics cannot replace sample size in genome-wide association studies

Multi-omics cannot replace sample size in genome-wide association studies

The integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has been suggested that multi-omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi-omics information in earlier and smaller-sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi-omics data did not reliably identify novel genes in earlier less-powered GWAS (PPV <0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1–8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), can help to prioritize genes within genome-wide significant loci (PPVs = 0.5–1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required.

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来源期刊
Genes Brain and Behavior
Genes Brain and Behavior 医学-行为科学
CiteScore
6.80
自引率
4.00%
发文量
62
审稿时长
4-8 weeks
期刊介绍: Genes, Brain and Behavior was launched in 2002 with the aim of publishing top quality research in behavioral and neural genetics in their broadest sense. The emphasis is on the analysis of the behavioral and neural phenotypes under consideration, the unifying theme being the genetic approach as a tool to increase our understanding of these phenotypes. Genes Brain and Behavior is pleased to offer the following features: 8 issues per year online submissions with first editorial decisions within 3-4 weeks and fast publication at Wiley-Blackwells High visibility through its coverage by PubMed/Medline, Current Contents and other major abstracting and indexing services Inclusion in the Wiley-Blackwell consortial license, extending readership to thousands of international libraries and institutions A large and varied editorial board comprising of international specialists.
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