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<p>Here we present the Polygenic Score (PGS) distributions for 3124 common diseases and quantitative traits observed in the Spanish population. To achieve so, the genomes and exomes of 2190 unrelated individuals of Spanish ancestry were used. The analysis covered a wide range of diseases and traits, including both complex disorders, such as various types of cancer, and disorders associated with the digestive, cardiovascular, neuronal, and immune systems, as well as quantitative traits like hematological and anthropometric measurements. The resulting PGS distributions provide valuable insights into the genetic architecture of the Spanish population, offering a comprehensive framework for investigating disease susceptibility and potential risk factors in this specific population. The study has also explored potential relationships between diseases and traits based on PGS pairwise correlations, revealing significant correlations that warrant further investigation. These findings have contributed to increase our understanding of the genetic basis of human traits and have implications for personalized medicine and public health interventions in the Spanish population. In addition, for the sake of reproducibility, we provide a data processing pipeline, enabling the computation of PGS for external genomes and exomes. The pipeline, accessible on GitHub, supports parallel tasks on various computing platforms and contributes to the standardization of PGS comparisons globally. Lastly, a user-friendly web interface facilitates the exploration of PGS reference distributions, featuring a detailed table, distribution plots, and filtering options. This interface enhances accessibility for researchers and clinicians, fostering informed decision-making based on population-specific PGS distributions. The PGS reference distributions can be explored at the SpPGS Atlas repository through the web interface: <a href="https://csvs.clinbioinfosspa.es/?tab=pgs">https://csvs.clinbioinfosspa.es/?tab=pgs</a>.</p>

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The Spanish Polygenic Score reference distribution: a resource for personalized medicine

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月24日

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  • Rosario Carmona,
  • Gema Roldán,
  • Jose L. Fernández-Rueda,
  • Arcadi Navarro,
  • María Peña-Chilet,
  • Angel Alonso,
  • Josefa Salgado-Garrido,
  • Sara Pasalodos-Sanchez,
  • Virginia Aquino,
  • Javier Perez-Florido,
  • Gerrit Bostelmann,
  • Carmen Ayuso,
  • Pablo Minguez,
  • Almudena Avila-Fernandez,
  • Marta Corton,
  • Rafael Artuch,
  • Salud Borrego,
  • Guillermo Antiñolo,
  • Angel Carracedo,
  • Jorge Amigo,
  • Luis Antonio Castaño,
  • Isabel Tejada,
  • Aitor Delmiro,
  • Carmina Espinos,
  • Daniel Grinberg,
  • Encarnación Guillén,
  • Pablo Lapunzina,
  • Jose Antonio Lopez-Escámez,
  • Alvaro Gallego-Martinez,
  • Ramón Martí,
  • Eulalia Rovira,
  • José Mª Millán,
  • Miguel Angel Moreno,
  • Matías Morin,
  • Antonio Moreno-Galdó,
  • Mónica Fernández-Cancio,
  • Beatriz Morte,
  • Victoriano Mulero,
  • Diana García,
  • Virginia Nunes,
  • Francesc Palau,
  • Belén Perez,
  • Rosario Perona,
  • Aurora Pujol,
  • Feliciano Ramos,
  • Esther Lopez,
  • Antonia Ribes,
  • Jordi Rosell,
  • Jordi Surrallés,
  • Joaquín Dopazo,
  • Daniel López-López

摘要

Here we present the Polygenic Score (PGS) distributions for 3124 common diseases and quantitative traits observed in the Spanish population. To achieve so, the genomes and exomes of 2190 unrelated individuals of Spanish ancestry were used. The analysis covered a wide range of diseases and traits, including both complex disorders, such as various types of cancer, and disorders associated with the digestive, cardiovascular, neuronal, and immune systems, as well as quantitative traits like hematological and anthropometric measurements. The resulting PGS distributions provide valuable insights into the genetic architecture of the Spanish population, offering a comprehensive framework for investigating disease susceptibility and potential risk factors in this specific population. The study has also explored potential relationships between diseases and traits based on PGS pairwise correlations, revealing significant correlations that warrant further investigation. These findings have contributed to increase our understanding of the genetic basis of human traits and have implications for personalized medicine and public health interventions in the Spanish population. In addition, for the sake of reproducibility, we provide a data processing pipeline, enabling the computation of PGS for external genomes and exomes. The pipeline, accessible on GitHub, supports parallel tasks on various computing platforms and contributes to the standardization of PGS comparisons globally. Lastly, a user-friendly web interface facilitates the exploration of PGS reference distributions, featuring a detailed table, distribution plots, and filtering options. This interface enhances accessibility for researchers and clinicians, fostering informed decision-making based on population-specific PGS distributions. The PGS reference distributions can be explored at the SpPGS Atlas repository through the web interface: https://csvs.clinbioinfosspa.es/?tab=pgs.