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<p>Interpreting how noncoding variants act in specific cell types across human development is a major challenge. Here we generated 3 billion predictions from deep learning sequence models of chromatin accessibility across diverse fetal and adult cellular contexts. These prioritized functional variants and revealed a dichotomy: common variants are more cell-type-specific, whereas ultra-rare variants had larger and broader effects across cell types, with the strongest evidence of purifying selection in fetal neurons. Leveraging these insights, we developed FLARE (Functional Lasso Analysis of Regulatory Evolution), which integrates evolutionary constraint to prioritize noncoding variants with extreme regulatory effects. FLARE provided a general framework for studying regulatory variation, from de novo mutations in childhood disorders to rare variants underlying outlier adult brain expression and common variants enriched for schizophrenia heritability. Together, these results demonstrate how integrating single-cell chromatin accessibility, population genetics and deep learning can identify regulatory variants that influence human development and disease.</p>

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Decoding common and rare noncoding variant effects across cellular and developmental contexts

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Andrew R. Marderstein,
  • Soumya Kundu,
  • Evin M. Padhi,
  • Salil Deshpande,
  • Austin Wang,
  • Esther Robb,
  • Ying Sun,
  • Chang M. Yun,
  • Diego Pomales-Matos,
  • Yilin Xie,
  • Serena H. Chang,
  • Iris M. Chin,
  • Aayushi J. Shah,
  • Zachary A. Gardell,
  • M. Ryan Corces,
  • Daniel Nachun,
  • Selin Jessa,
  • Anshul Kundaje,
  • Stephen B. Montgomery

摘要

Interpreting how noncoding variants act in specific cell types across human development is a major challenge. Here we generated 3 billion predictions from deep learning sequence models of chromatin accessibility across diverse fetal and adult cellular contexts. These prioritized functional variants and revealed a dichotomy: common variants are more cell-type-specific, whereas ultra-rare variants had larger and broader effects across cell types, with the strongest evidence of purifying selection in fetal neurons. Leveraging these insights, we developed FLARE (Functional Lasso Analysis of Regulatory Evolution), which integrates evolutionary constraint to prioritize noncoding variants with extreme regulatory effects. FLARE provided a general framework for studying regulatory variation, from de novo mutations in childhood disorders to rare variants underlying outlier adult brain expression and common variants enriched for schizophrenia heritability. Together, these results demonstrate how integrating single-cell chromatin accessibility, population genetics and deep learning can identify regulatory variants that influence human development and disease.