Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags.

IF 8.1 1区 生物学 Q1 GENETICS & HEREDITY American journal of human genetics Pub Date : 2024-10-21 DOI:10.1016/j.ajhg.2024.09.008
Yosuke Tanigawa,Manolis Kellis
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Abstract

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n = 227,469 UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies.
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超计量遗传学:纳入质量控制标志,提高基因发现的能力。
在表型数据的数量和质量之间权衡利弊,是omics 研究的关键。低于定量限(BLQ)的测量值通常会被标记在质量控制字段中,但这些标记目前在人类遗传学研究中还未得到充分利用。极端表型取样有利于绘制罕见变异效应图。我们假设,遗传驱动因素以及环境和技术因素是导致出现 BLQ 标志的原因。在这里,我们引入了 "低计量遗传学"(hMG)分析,发现了 BLQ 标志的遗传基础,为遗传发现提供了额外的遗传信号源,尤其是来自极端表型的信号源。我们对 n = 227,469 名英国生物库中具有代谢组学特征的个体采用了 hMG 方法,发现 BLQ 标志的遗传率超过 5%,并报告了与生物学相关的关联,例如在 APOC3、APOA5 和 PDE3B 位点上的关联。对于常见变异,仅针对 BLQ 标志训练的多基因评分预测相应数量性状的准确率为 91%,验证了遗传基础。对于罕见编码变异的关联,我们发现 BLQ 标志的代谢物降低关联的富集度不对称地高达 65.4%,突出了对表型极端影响较大的假定功能缺失变异的影响。对二进制 BLQ 标志和相应的定量代谢物测量结果进行联合分析,提高了贝叶斯稀有变异聚合测试的能力,使优先考虑的基因平均增加了 181%。我们的方法广泛适用于 omics 图谱分析。总之,我们的研究结果强调了整合质量控制标志和定量测量的好处,并突出了在人类遗传学研究中联合分析基于人群的样本和表型极端的优势。
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来源期刊
CiteScore
14.70
自引率
4.10%
发文量
185
审稿时长
1 months
期刊介绍: The American Journal of Human Genetics (AJHG) is a monthly journal published by Cell Press, chosen by The American Society of Human Genetics (ASHG) as its premier publication starting from January 2008. AJHG represents Cell Press's first society-owned journal, and both ASHG and Cell Press anticipate significant synergies between AJHG content and that of other Cell Press titles.
期刊最新文献
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