Molecular insights into genome-wide association studies of chronic kidney disease-defining traits

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作者
Xiaoguang Xu
James M. Eales
Artur Akbarov
Hui Guo
Lorenz Becker
David Talavera
Fehzan Ashraf
Jabran Nawaz
Sanjeev Pramanik
John Bowes
Xiao Jiang
John Dormer
Matthew Denniff
Andrzej Antczak
Monika Szulinska
Ingrid Wise
Priscilla R. Prestes
Maciej Glyda
Pawel Bogdanski
Ewa Zukowska-Szczechowska
Carlo Berzuini
Adrian S. Woolf
Nilesh J. Samani
Fadi J. Charchar
Maciej Tomaszewski
机构
[1] University of Manchester,Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health
[2] University of Manchester,Division of Population Health, Health Services Research and Primary Care, Faculty of Medicine, Biology and Health
[3] University of Manchester,Division of Musculoskeletal and Dermatological Sciences, Faculty of Medicine, Biology and Health
[4] University Hospitals of Leicester NHS Trust,Department of Cardiovascular Sciences
[5] University of Leicester,Department of Urology and Uro
[6] Karol Marcinkowski University of Medical Sciences,oncology
[7] Karol Marcinkowski University of Medical Sciences,Department of Internal Medicine, Metabolic Disorders and Hypertension
[8] Federation University Australia,School of Health and Life Sciences
[9] University of Zielona Góra,Department of Transplantology and General Surgery, District Public Hospital
[10] Karol Marcinkowski University of Medical Sciences,Department of Obesity and Metabolic Disorders Treatment and Clinical Dietetics
[11] Silesian Medical College,Department of Health Care
[12] Manchester University NHS Foundation Trust,Department of Paediatric Nephrology, Royal Manchester Children’s Hospital
[13] Glenfield Hospital,NIHR Leicester Biomedical Research Centre
[14] University of Melbourne,Department of Physiology
[15] Manchester Academic Health Science Centre,Division of Medicine, Manchester University NHS Foundation Trust
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摘要
Genome-wide association studies (GWAS) have identified >100 loci of chronic kidney disease-defining traits (CKD-dt). Molecular mechanisms underlying these associations remain elusive. Using 280 kidney transcriptomes and 9958 gene expression profiles from 44 non-renal tissues we uncover gene expression partners (eGenes) for 88.9% of CKD-dt GWAS loci. Through epigenomic chromatin segmentation analysis and variant effect prediction we annotate functional consequences to 74% of these loci. Our colocalisation analysis and Mendelian randomisation in >130,000 subjects demonstrate causal effects of three eGenes (NAT8B, CASP9 and MUC1) on estimated glomerular filtration rate. We identify a common alternative splice variant in MUC1 (a gene responsible for rare Mendelian form of kidney disease) and observe increased renal expression of a specific MUC1 mRNA isoform as a plausible molecular mechanism of the GWAS association signal. These data highlight the variants and genes underpinning the associations uncovered in GWAS of CKD-dt.
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