Refining fine-mapping: Effect sizes and regional heritability.

IF 4 2区 生物学 Q1 GENETICS & HEREDITY
PLoS Genetics Pub Date : 2025-01-09 eCollection Date: 2025-01-01 DOI:10.1371/journal.pgen.1011480
Christian Benner, Anubha Mahajan, Matti Pirinen
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引用次数: 0

Abstract

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per region. Using the UK Biobank (UKB) data to simulate genomic regions, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS, particularly when there are only a few causal variants in the fine-mapped region. Using data from 2,940 plasma proteins from the UKB study, we observed that on average FINEMAP identified 2.5 causal variants at an association signal and captured 36% more regional heritability than the variant with the lowest P-value. We estimate that in genomic regions with notable contribution to the total heritability, FINEMAP captures on average 13% and 40% more heritability than BOLT and HESS respectively. Our analysis shows how FINEMAP, BOLT and HESS relate to each other in cases where inference of a variant-level picture of the regional genetic architecture is possible.

细化精细映射:效应大小和区域遗传性。
最近的统计方法表明,与与这些表型密切相关的单个变异相比,所有可用遗传变异的集合解释了复杂性状和疾病的更多表型变异。然而,快速增加的样本量不断提高了个体变异的检测和优先级,推动了基因组区域和表型之间的关联。因此,考虑到变异的相关结构及其因果状态的不确定性,常规估计检测到的变异在每个区域解释了多少表型变异是有用的。在这里,我们扩展了FINEMAP软件,在假设每个地区有少量因果变量的概率模型下估计效应大小和区域遗传度。使用UK Biobank (UKB)数据模拟基因组区域,我们证明FINEMAP提供了更高的精度,并且能够比BOLT中实施的方差成分模型或HESS中实施的固定效应模型更详细地将区域遗传力分解为单个变异,特别是当精细映射区域中只有少数因果变异时。使用来自UKB研究的2940个血浆蛋白的数据,我们观察到FINEMAP平均在关联信号中识别出2.5个因果变异,并且比具有最低p值的变异捕获了36%的区域遗传率。我们估计,在对总遗传率有显著贡献的基因组区域,FINEMAP的遗传率比BOLT和HESS分别高出13%和40%。我们的分析显示了FINEMAP、BOLT和HESS在推断区域遗传结构的变异水平图是可能的情况下是如何相互关联的。
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来源期刊
PLoS Genetics
PLoS Genetics GENETICS & HEREDITY-
自引率
2.20%
发文量
438
期刊介绍: PLOS Genetics is run by an international Editorial Board, headed by the Editors-in-Chief, Greg Barsh (HudsonAlpha Institute of Biotechnology, and Stanford University School of Medicine) and Greg Copenhaver (The University of North Carolina at Chapel Hill). Articles published in PLOS Genetics are archived in PubMed Central and cited in PubMed.
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