The importance of family-based sampling for biobanks

• 成果类型: Review
• 署名作者: Davies, Neil M.; Hemani, Gibran; Neiderhiser, Jenae M.; Martin, Hilary C.; Mills, Melinda C.; Visscher, Peter M.; Yengo, Loic; Young, Alexander Strudwick; Keller, Matthew C.
• 署名单位: University of London; University College London; University of London; University College London; Norwegian University of Science & Technology (NTNU); University of Bristol; University of Bristol; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Wellcome Trust Sanger Institute; University of Groningen; University of Groningen; University of Oxford; University of Queensland; University of Oxford; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of Colorado System; University of Colorado Boulder; University of Colorado System; University of Colorado Boulder
• 刊物名称: Nature
• ISSN/ISSBN: 0028-5981
• DOI: 10.1038/s41586-024-07721-5
• 发表日期: 2024-10-24
• 页码: 795-803
• 关键词: epidemiology; prediction; pregnancy; smoking; disease; height

摘要

Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges. This Perspective discusses the strengths and limitations of future biobank sampling strategies based on oversampling close relatives as opposed to the current population-based approach.