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<p>Elucidating the genetic regulation of protein expression in specific disease states is important for understanding how genetic variation impact disease pathology. To this end, we conduct a large-scale genome-proteome-wide pQTL analysis on 2901 plasma proteins among 7626 healthy individuals and 28,064 patients across 42 disease statuses. We find 25,987 independent pQTL associations across 2224 regions, and investigate similarities and differences in their regulatory effects across various diseases and health states. We find that pQTL identified in specific diseases are more likely to be disease risk variants. We then integrate the association findings with Mendelian randomisation to identify 110 high-confidence causal proteins associated with 21 diseases, including Apolipoprotein(a) for cardiovascular diseases and angiotensin-converting enzyme for type 2 diabetes. Finally, we develop risk prediction models by integrating pQTL-derived polygenic risk scores and causal-protein-derived protein risk scores, which demonstrate good performance in discriminating populations at high risk for 21 disease types. These results indicate that disease state partly determines the impact of genetic variation on protein expression, implicating disease-related and disease-discordant pQTL associations as regulators of disease progression.</p>

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Genetic profiling of the circulating proteome in common diseases suggests causal proteins and improves risk prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月12日

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  • Qianying Ma,
  • Yimin Cai,
  • Chunyi He,
  • Yanmin Li,
  • Caibo Ning,
  • Ming Zhang,
  • Zequn Lu,
  • Can Chen,
  • Yue Zong,
  • Feitong Wu,
  • Meng Jin,
  • Yongchang Wei,
  • Xiangpan Li,
  • Xiaojun Yang,
  • Chaoqun Huang,
  • Bin Li,
  • Zhaocheng Ma,
  • Ying Zhu,
  • Xiaoping Miao,
  • Jianbo Tian

摘要

Elucidating the genetic regulation of protein expression in specific disease states is important for understanding how genetic variation impact disease pathology. To this end, we conduct a large-scale genome-proteome-wide pQTL analysis on 2901 plasma proteins among 7626 healthy individuals and 28,064 patients across 42 disease statuses. We find 25,987 independent pQTL associations across 2224 regions, and investigate similarities and differences in their regulatory effects across various diseases and health states. We find that pQTL identified in specific diseases are more likely to be disease risk variants. We then integrate the association findings with Mendelian randomisation to identify 110 high-confidence causal proteins associated with 21 diseases, including Apolipoprotein(a) for cardiovascular diseases and angiotensin-converting enzyme for type 2 diabetes. Finally, we develop risk prediction models by integrating pQTL-derived polygenic risk scores and causal-protein-derived protein risk scores, which demonstrate good performance in discriminating populations at high risk for 21 disease types. These results indicate that disease state partly determines the impact of genetic variation on protein expression, implicating disease-related and disease-discordant pQTL associations as regulators of disease progression.