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<p>The identification of expression quantitative trait loci (eQTLs) holds great potential to improve the interpretation of disease-associated genetic variation. As many such disease-associated variants act in a context-, tissue- or even cell-type-specific manner, single-cell RNA-sequencing (scRNA-seq) data is uniquely suitable for identifying the specific cell type or context in which these genetic variants act. However, due to the limited sample sizes in single-cell studies, discovery of cell-type-specific eQTLs is now limited. To improve power to detect such eQTLs, large-scale joint analyses are needed. These are however, complicated by privacy constraints due to sharing of genotype data and the measurement and technical variety across different scRNA-seq datasets as a result of differences in mRNA capture efficiency, experimental protocols, and sequencing strategies. A solution to these issues is a federated weighted meta-analysis (WMA) approach in which summary statistics are integrated using dataset-specific weights. Here, we compare different strategies and provide best practice recommendations for eQTL WMA across scRNA-seq datasets.</p>

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Optimized summary-statistic-based single-cell eQTL meta-analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月4日

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  • Maryna Korshevniuk,
  • Harm-Jan Westra,
  • Roy Oelen,
  • Monique G. P. van der Wijst,
  • Lude Franke,
  • Marc Jan Bonder,
  • José Alquicira-Hernández,
  • Daniel Kaptijn,
  • Maryna Korshevniuk,
  • Jimmy Tsz Hang Lee,
  • Lieke Michielsen,
  • Drew Neavin,
  • Roy Oelen,
  • Aida Ripoll-Cladellas,
  • Martijn Vochterloo,
  • Yoshinari Ando,
  • Odmaa Bayaraa,
  • Irene van Blokland,
  • Mame M. Dieng,
  • M. Grace Gordon,
  • Hilde E. Groot,
  • Pim van der Harst,
  • Chung-Chau Hon,
  • Youssef Idaghdour,
  • Vinu Manikanda,
  • Jonathan Moody,
  • Martijn C. Nawijn,
  • Yukinori Okada,
  • Oliver Stegle,
  • Woong-Yang Park,
  • Deepa Rajagopalan,
  • Tala Shahin,
  • Jay W. Shin,
  • Gosia Trynka,
  • Harm-Jan Westra,
  • Seyhan Yazar,
  • Jimmie Ye,
  • Martin Hemberg,
  • Ahmed Mahfouz,
  • Marta Melé,
  • Joseph E. Powell,
  • Lude Franke,
  • Monique G. P. van der Wijst,
  • Marc Jan Bonder

摘要

The identification of expression quantitative trait loci (eQTLs) holds great potential to improve the interpretation of disease-associated genetic variation. As many such disease-associated variants act in a context-, tissue- or even cell-type-specific manner, single-cell RNA-sequencing (scRNA-seq) data is uniquely suitable for identifying the specific cell type or context in which these genetic variants act. However, due to the limited sample sizes in single-cell studies, discovery of cell-type-specific eQTLs is now limited. To improve power to detect such eQTLs, large-scale joint analyses are needed. These are however, complicated by privacy constraints due to sharing of genotype data and the measurement and technical variety across different scRNA-seq datasets as a result of differences in mRNA capture efficiency, experimental protocols, and sequencing strategies. A solution to these issues is a federated weighted meta-analysis (WMA) approach in which summary statistics are integrated using dataset-specific weights. Here, we compare different strategies and provide best practice recommendations for eQTL WMA across scRNA-seq datasets.