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Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs

Author

Listed:
  • Seong Kyu Han

    (Division of Pediatric Nephrology, Boston Children’s Hospital
    Harvard Medical School
    Kidney Disease Initiative, Broad Institute)

  • Michelle T. McNulty

    (Division of Pediatric Nephrology, Boston Children’s Hospital
    Kidney Disease Initiative, Broad Institute)

  • Christopher J. Benway

    (Division of Pediatric Nephrology, Boston Children’s Hospital
    Kidney Disease Initiative, Broad Institute)

  • Pei Wen

    (Center for Precision Disease Modeling, University of Maryland, School of Medicine)

  • Anya Greenberg

    (Division of Pediatric Nephrology, Boston Children’s Hospital
    Kidney Disease Initiative, Broad Institute)

  • Ana C. Onuchic-Whitford

    (Division of Pediatric Nephrology, Boston Children’s Hospital
    Kidney Disease Initiative, Broad Institute
    Division of Renal Medicine, Brigham and Women’s Hospital)

  • Dongkeun Jang

    (Programs in Metabolism and Medical and Population Genetics, Broad Institute)

  • Jason Flannick

    (Harvard Medical School
    Programs in Metabolism and Medical and Population Genetics, Broad Institute
    Division of Genetics and Genomics, Boston Children’s Hospital)

  • Noël P. Burtt

    (Programs in Metabolism and Medical and Population Genetics, Broad Institute)

  • Parker C. Wilson

    (Washington University in St. Louis)

  • Benjamin D. Humphreys

    (Washington University in St. Louis
    Washington University in St. Louis)

  • Xiaoquan Wen

    (University of Michigan)

  • Zhe Han

    (Center for Precision Disease Modeling, University of Maryland, School of Medicine)

  • Dongwon Lee

    (Division of Pediatric Nephrology, Boston Children’s Hospital
    Harvard Medical School
    Kidney Disease Initiative, Broad Institute
    Manton Center for Orphan Disease Research, Boston Children’s Hospital)

  • Matthew G. Sampson

    (Division of Pediatric Nephrology, Boston Children’s Hospital
    Harvard Medical School
    Kidney Disease Initiative, Broad Institute
    Division of Renal Medicine, Brigham and Women’s Hospital)

Abstract

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an “integrative prior” for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.

Suggested Citation

  • Seong Kyu Han & Michelle T. McNulty & Christopher J. Benway & Pei Wen & Anya Greenberg & Ana C. Onuchic-Whitford & Dongkeun Jang & Jason Flannick & Noël P. Burtt & Parker C. Wilson & Benjamin D. Humph, 2023. "Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37691-7
    DOI: 10.1038/s41467-023-37691-7
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    References listed on IDEAS

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    Cited by:

    1. Alexandra Barry & Michelle T. McNulty & Xiaoyuan Jia & Yask Gupta & Hanna Debiec & Yang Luo & China Nagano & Tomoko Horinouchi & Seulgi Jung & Manuela Colucci & Dina F. Ahram & Adele Mitrotti & Aditi , 2023. "Multi-population genome-wide association study implicates immune and non-immune factors in pediatric steroid-sensitive nephrotic syndrome," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Xiaoguang Xu & Chachrit Khunsriraksakul & James M. Eales & Sebastien Rubin & David Scannali & Sushant Saluja & David Talavera & Havell Markus & Lida Wang & Maciej Drzal & Akhlaq Maan & Abigail C. Lay , 2024. "Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets," Nature Communications, Nature, vol. 15(1), pages 1-29, December.
    3. Chamlee Cho & Beomsu Kim & Dan Say Kim & Mi Yeong Hwang & Injeong Shim & Minku Song & Yeong Chan Lee & Sang-Hyuk Jung & Sung Kweon Cho & Woong-Yang Park & Woojae Myung & Bong-Jo Kim & Ron Do & Hyon K., 2024. "Large-scale cross-ancestry genome-wide meta-analysis of serum urate," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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