小柯机器人

英国剑桥大学Michael Inouye等研究人员合作绘制出一个预测多基因组性状的遗传分数图谱。2023年3月29日，国际知名学术期刊《自然》在线发表了这一成果。

研究人员调查了一个大型队列（INTERVAL研究；n=50000名参与者），其血浆蛋白组学有大量的多组学数据（SomaScan，n=3175；Olink，n=4822）、血浆代谢组学（Metabolon HD4，n=8153）、血清代谢组学（Nightingale，n=37359）和全血Illumina RNA测序（n=4136），并使用机器学习来训练17227个分子性状的遗传评分，包括达到Bonferroni调整后显著性的10521个分子形状。研究人员通过对欧洲、亚洲和非裔美国人血统的队列进行外部验证来评估了基因分数的性能。

此外，研究人员通过量化生物途径的遗传控制，以及通过生成英国生物库的合成多组数据集，使用全表型扫描来识别疾病关联，并显示了这些多组基因分数的效用。研究人员强调了一系列关于新陈代谢的遗传机制和与疾病相关的典型途径的生物学见解；例如，JAK-STAT信号和冠状动脉硬化。最后，研究人员开发了一个门户网站（https://www.omicspred.org/），以方便公众访问所有的基因评分和验证结果，并作为未来扩展和加强多组学基因评分的平台。

据悉，使用组学模式来剖析常见疾病和性状的分子基础正变得越来越普遍。但是多组学性状可以通过基因预测，这使得没有多组学的研究可以进行高性价比和强大的分析。

附：英文原文

Title: An atlas of genetic scores to predict multi-omic traits

Author: Xu, Yu, Ritchie, Scott C., Liang, Yujian, Timmers, Paul R. H. J., Pietzner, Maik, Lannelongue, Loc, Lambert, Samuel A., Tahir, Usman A., May-Wilson, Sebastian, Foguet, Carles, Johansson, sa, Surendran, Praveen, Nath, Artika P., Persyn, Elodie, Peters, James E., Oliver-Williams, Clare, Deng, Shuliang, Prins, Bram, Luan, Jianan, Bomba, Lorenzo, Soranzo, Nicole, Di Angelantonio, Emanuele, Pirastu, Nicola, Tai, E. Shyong, van Dam, Rob M., Parkinson, Helen, Davenport, Emma E., Paul, Dirk S., Yau, Christopher, Gerszten, Robert E., Mlarstig, Anders, Danesh, John, Sim, Xueling, Langenberg, Claudia, Wilson, James F., Butterworth, Adam S., Inouye, Michael

Issue&Volume: 2023-03-29

Abstract: The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n=3,175; Olink, n=4,822), plasma metabolomics (Metabolon HD4, n=8,153), serum metabolomics (Nightingale, n=37,359) and whole-blood Illumina RNA sequencing (n=4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK–STAT signalling and coronary atherosclerosis. Finally, we develop a portal (https://www.omicspred.org/) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

DOI: 10.1038/s41586-023-05844-9

Source: https://www.nature.com/articles/s41586-023-05844-9

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html