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<p>Blood metabolites are critical biomarkers linking genetic variation to diverse health outcomes, exhibiting significant variability across individuals. Here, we analyze and integrate whole-genome sequencing with plasma metabolomics to investigate the genetic architecture of 313 metabolic biomarkers in up to 199,138 UK Biobank participants. Through single-variant analyses, we identify 36,105 independent signals, with 22.20% being novel. Furthermore, we pinpoint 12,361 putative causal variant-trait associations, demonstrating enhanced causal signal discovery and improved fine-mapping resolution compared with imputed array-based approaches. Rare-variant aggregate testing reveals 1,527 conditionally independent protein-coding gene-trait pairs, with 32.9% being unique to non-coding regions. We estimate the heritability at a median of <i>h</i><sup><i>2</i></sup> = 0.31, nearly tripling the heritability estimates obtained from array-based approaches. Integrating our findings with disease genetics reveals 245 potential causal associations, such as that between omega-3 fatty acids and cholelithiasis risk. Prioritizing proteins with concordant effects on metabolites and clinical outcomes revealed 410 potential targets, offering opportunities for therapeutic strategies and drug repurposing. Our open-access resource (<a href="https://metabolome-whole-genome-landscape.com/">https://metabolome-whole-genome-landscape.com/</a>) provides a foundation for future research into metabolic pathways, disease mechanisms, and therapeutic development.</p>

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Analysis of whole genome sequencing and plasma metabolomics unveil genetic determinants and clinical implications for human health

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Yi-Xuan Wang,
  • Yi-Xuan Qiang,
  • Yi-Jun Ge,
  • Yue-Ting Deng,
  • Bang-Sheng Wu,
  • Liu Yang,
  • Yi-Lin Chen,
  • Xiao-Yu He,
  • Yu He,
  • Bing-Ran Yao,
  • Chen-Jie Fei,
  • Rui-Ying Yin,
  • Jia You,
  • Jian-Feng Feng,
  • Wei Cheng,
  • Jin-Tai Yu

摘要

Blood metabolites are critical biomarkers linking genetic variation to diverse health outcomes, exhibiting significant variability across individuals. Here, we analyze and integrate whole-genome sequencing with plasma metabolomics to investigate the genetic architecture of 313 metabolic biomarkers in up to 199,138 UK Biobank participants. Through single-variant analyses, we identify 36,105 independent signals, with 22.20% being novel. Furthermore, we pinpoint 12,361 putative causal variant-trait associations, demonstrating enhanced causal signal discovery and improved fine-mapping resolution compared with imputed array-based approaches. Rare-variant aggregate testing reveals 1,527 conditionally independent protein-coding gene-trait pairs, with 32.9% being unique to non-coding regions. We estimate the heritability at a median of h2 = 0.31, nearly tripling the heritability estimates obtained from array-based approaches. Integrating our findings with disease genetics reveals 245 potential causal associations, such as that between omega-3 fatty acids and cholelithiasis risk. Prioritizing proteins with concordant effects on metabolites and clinical outcomes revealed 410 potential targets, offering opportunities for therapeutic strategies and drug repurposing. Our open-access resource (https://metabolome-whole-genome-landscape.com/) provides a foundation for future research into metabolic pathways, disease mechanisms, and therapeutic development.