Unraveling phenotypic variance in metabolic syndrome through multi-omics

IF 3.8 2区生物学 Q2 GENETICS & HEREDITY Human Genetics Pub Date : 2023-12-14 DOI:10.1007/s00439-023-02619-0

{"title":"Unraveling phenotypic variance in metabolic syndrome through multi-omics","authors":"","doi":"10.1007/s00439-023-02619-0","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics’ roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s00439-023-02619-0","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}

引用次数: 0

Abstract

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics’ roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

通过多组学揭示代谢综合征的表型差异

摘要复杂的多组学效应推动了心脏代谢风险因素的聚类，凸显了理解个体和组合组学如何形成表型变异的必要性。我们的研究通过基因组、转录组、代谢组和暴露组（即生活方式暴露组）分析，对代谢综合征（MetS）、血糖（GLU）、甘油三酯（TG）、高密度脂蛋白胆固醇（HDL-C）和血压的表型变异进行了分区。我们的分析对象包括来自英国生物库的 62,822 名无血缘关系的英国白人。我们采用线性混合模型，利用 MTG2（v2.22）中实施的限制性最大似然法（REML）划分表型方差。在分析开始时，我们先单独建立omics模型，然后在联合模型中整合成对的omics，该模型还考虑了omics层之间的协方差和相互作用。最后，我们使用双变量 REML 估算了表型之间各种全微观效应的相关性。MetS 变异的很大一部分归因于不同的数据源：基因组（9.47%）、转录组（4.24%）、代谢组（14.34%）和暴露组（3.77%）。基因组、转录组、代谢组和暴露组所解释的表型变异范围分别为：GLU 为 3.28%，HDL-C 为 25.35%；GLU 为 0%，HDL-C 为 19.34%；收缩压 (SBP) 为 4.29%，TG 为 35.75%；GLU 为 0.89%，HDL-C 为 10.17%。在 TG 和 HDL-C 的基因组效应和转录组效应之间发现了显著的相关性。此外，在 MetS 及其成分中还发现了 omics 数据之间的显着交互效应。有趣的是，表型之间的全局数据效应也存在明显的相关性。这项研究强调了 omics 在揭示多组学领域表型变异方面的作用、交互效应和随机效应协方差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Human Genetics 生物-遗传学

CiteScore

10.80

自引率

3.80%

发文量

审稿时长

1 months

期刊介绍： Human Genetics is a monthly journal publishing original and timely articles on all aspects of human genetics. The Journal particularly welcomes articles in the areas of Behavioral genetics, Bioinformatics, Cancer genetics and genomics, Cytogenetics, Developmental genetics, Disease association studies, Dysmorphology, ELSI (ethical, legal and social issues), Evolutionary genetics, Gene expression, Gene structure and organization, Genetics of complex diseases and epistatic interactions, Genetic epidemiology, Genome biology, Genome structure and organization, Genotype-phenotype relationships, Human Genomics, Immunogenetics and genomics, Linkage analysis and genetic mapping, Methods in Statistical Genetics, Molecular diagnostics, Mutation detection and analysis, Neurogenetics, Physical mapping and Population Genetics. Articles reporting animal models relevant to human biology or disease are also welcome. Preference will be given to those articles which address clinically relevant questions or which provide new insights into human biology. Unless reporting entirely novel and unusual aspects of a topic, clinical case reports, cytogenetic case reports, papers on descriptive population genetics, articles dealing with the frequency of polymorphisms or additional mutations within genes in which numerous lesions have already been described, and papers that report meta-analyses of previously published datasets will normally not be accepted. The Journal typically will not consider for publication manuscripts that report merely the isolation, map position, structure, and tissue expression profile of a gene of unknown function unless the gene is of particular interest or is a candidate gene involved in a human trait or disorder.