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@ARTICLE{Korshevniuk:303376,
      author       = {M. Korshevniuk and H.-J. Westra and R. Oelen and M. G. P.
                      van der Wijst and L. Franke and M. J. Bonder$^*$},
      collaboration = {s.-e. Consortium},
      othercontributors = {J. Alquicira-Hernández and D. Kaptijn and M. Korshevniuk
                          and J. T. H. Lee and L. Michielsen and D. Neavin and R.
                          Oelen and A. Ripoll-Cladellas and M. Vochterloo and Y. Ando
                          and O. Bayaraa and I. van Blokland and M. M. Dieng and M. G.
                          Gordon and H. E. Groot and P. van der Harst and C.-C. Hon
                          and Y. Idaghdour and V. Manikanda and J. Moody and M. C.
                          Nawijn and Y. Okada and O. Stegle$^*$ and W.-Y. Park and D.
                          Rajagopalan and T. Shahin and J. W. Shin and G. Trynka and
                          H.-J. Westra and S. Yazar and J. Ye and M. Hemberg and A.
                          Mahfouz and M. Melé and J. E. Powell and L. Franke and M.
                          G. P. van der Wijst and M. J. Bonder},
      title        = {{O}ptimized summary-statistic-based single-cell e{QTL}
                      meta-analysis.},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-01625},
      pages        = {28407},
      year         = {2025},
      abstract     = {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.},
      keywords     = {Quantitative Trait Loci / Single-Cell Analysis: methods /
                      Humans / Sequence Analysis, RNA / Weighted meta-analysis
                      (Other) / eQTL (Other) / scRNA-seq (Other)},
      cin          = {B260},
      ddc          = {600},
      cid          = {I:(DE-He78)B260-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40759673},
      doi          = {10.1038/s41598-025-08808-3},
      url          = {https://inrepo02.dkfz.de/record/303376},
}