@article{72424cff2fa146ea9163590ae4aad084,
title = "KidneyNetwork: Using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease",
abstract = "Abstract: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It uses expression patterns and established gene-phenotype associations to predict which genes could be related to what (disease) phenotypes in an unbiased manner. We applied KidneyNetwork to rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis to prioritize candidate genes. KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. The intersection of prioritized genes with genes carrying rare variants in a patient with kidney and liver cysts identified ALG6 as plausible candidate gene. We strengthen this plausibility by identifying ALG6 variants in several cystic kidney and liver disease cases without alternative genetic explanation. We present KidneyNetwork, a publicly available kidney-specific co-expression network with optimized gene-phenotype predictions for kidney disease phenotypes. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research. Translational statement: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient{\textquoteright}s disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.",
author = "{Genomics England Research Consortium} and Floranne Boulogne and Claus, {Laura R.} and Henry Wiersma and Roy Oelen and Floor Schukking and {de Klein}, Niek and Shuang Li and Westra, {Harm Jan} and {van der Zwaag}, Bert and {van Reekum}, Franka and Dana Sierks and Ria Sch{\"o}nauer and Zhigui Li and Bijlsma, {Emilia K.} and Bos, {Willem Jan W.} and Jan Halbritter and Knoers, {Nine V.A.M.} and Whitney Besse and Patrick Deelen and Lude Franke and {van Eerde}, {Albertien M.}",
note = "Funding Information: This work was supported by the Dutch Kidney Foundation (18OKG19). The collaboration project is co-funded by the PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health to stimulate public-private partnerships. The Yale polycystic kidney and liver disease cohort and its analysis is supported by the US National Institutes of Health (NIH) K08DK119642 and PKD Foundation Research Grant and Fellowship to Dr. Besse (217G18a and 190F15a). Exome sequencing for this cohort was made possible by the Yale Center for Genome Analysis, NIH grant 1S10OD018521-01, and the Yale Center for Mendelian Genomics (5U54HG006504). Dr Schoenauer receives funding from Else Kroener-Fresenius Foundation (EKFS) andDeutsche Forschungsgemeinschaft (DFG). Dr Halbritter obtains funding from DFG(HA 6908/3-1, HA 6908/4-1, HA 6908/7-1, HA 6908/8-1). Several authors of this publication are members of the European Reference Network for Rare Kidney Diseases (ERKNet) Project ID No 739532. This research was made possible through access to the data and findings generated by the GeNepher biobank (TCBio 22-076) and the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council (MRC) have also funded research infrastructure. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (commonfund.nih.gov/GTEx). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI\Leidos Biomedical Research, Inc. subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to the The Broad Institute, Inc. Biorepository operations were funded through a Leidos Biomedical Research, Inc. subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research, Inc.(HHSN261200800001E). The Brain Bank was supported supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941 & MH101814), the University of Chicago (MH090951,MH090937, MH101825, & MH101820), the University of North Carolina -Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810), and to the University of Pennsylvania (MH101822). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000424.v8.p2. Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
month = nov,
doi = "10.1038/s41431-023-01296-x",
language = "English",
volume = "31",
pages = "1300–1308",
journal = "European Journal of Human Genetics",
issn = "1018-4813",
publisher = "Nature Publishing Group",
}