Dataset Information

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome.

ABSTRACT: BACKGROUND AND OBJECTIVES:Steroid-resistant nephrotic syndrome overwhelmingly progresses to ESRD. More than 30 monogenic genes have been identified to cause steroid-resistant nephrotic syndrome. We previously detected causative mutations using targeted panel sequencing in 30% of patients with steroid-resistant nephrotic syndrome. Panel sequencing has a number of limitations when compared with whole exome sequencing. We employed whole exome sequencing to detect monogenic causes of steroid-resistant nephrotic syndrome in an international cohort of 300 families. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:Three hundred thirty-five individuals with steroid-resistant nephrotic syndrome from 300 families were recruited from April of 1998 to June of 2016. Age of onset was restricted to <25 years of age. Exome data were evaluated for 33 known monogenic steroid-resistant nephrotic syndrome genes. RESULTS:In 74 of 300 families (25%), we identified a causative mutation in one of 20 genes known to cause steroid-resistant nephrotic syndrome. In 11 families (3.7%), we detected a mutation in a gene that causes a phenocopy of steroid-resistant nephrotic syndrome. This is consistent with our previously published identification of mutations using a panel approach. We detected a causative mutation in a known steroid-resistant nephrotic syndrome gene in 38% of consanguineous families and in 13% of nonconsanguineous families, and 48% of children with congenital nephrotic syndrome. A total of 68 different mutations were detected in 20 of 33 steroid-resistant nephrotic syndrome genes. Fifteen of these mutations were novel. NPHS1, PLCE1, NPHS2, and SMARCAL1 were the most common genes in which we detected a mutation. In another 28% of families, we detected mutations in one or more candidate genes for steroid-resistant nephrotic syndrome. CONCLUSIONS:Whole exome sequencing is a sensitive approach toward diagnosis of monogenic causes of steroid-resistant nephrotic syndrome. A molecular genetic diagnosis of steroid-resistant nephrotic syndrome may have important consequences for the management of treatment and kidney transplantation in steroid-resistant nephrotic syndrome.

SUBMITTER: Warejko JK

PROVIDER: S-EPMC5753307 | biostudies-other | 2018 Jan

REPOSITORIES: biostudies-other

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Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome.

Warejko Jillian K JK Tan Weizhen W Daga Ankana A Schapiro David D Lawson Jennifer A JA Shril Shirlee S Lovric Svjetlana S Ashraf Shazia S Rao Jia J Hermle Tobias T Jobst-Schwan Tilman T Widmeier Eugen E Majmundar Amar J AJ Schneider Ronen R Gee Heon Yung HY Schmidt J Magdalena JM Vivante Asaf A van der Ven Amelie T AT Ityel Hadas H Chen Jing J Sadowski Carolin E CE Kohl Stefan S Pabst Werner L WL Nakayama Makiko M Somers Michael J G MJG Rodig Nancy M NM Daouk Ghaleb G Baum Michelle M Stein Deborah R DR Ferguson Michael A MA Traum Avram Z AZ Soliman Neveen A NA Kari Jameela A JA El Desoky Sherif S Fathy Hanan H Zenker Martin M Bakkaloglu Sevcan A SA Müller Dominik D Noyan Aytul A Ozaltin Fatih F Cadnapaphornchai Melissa A MA Hashmi Seema S Hopcian Jeffrey J Kopp Jeffrey B JB Benador Nadine N Bockenhauer Detlef D Bogdanovic Radovan R Stajić Nataša N Chernin Gil G Ettenger Robert R Fehrenbach Henry H Kemper Markus M Munarriz Reyner Loza RL Podracka Ludmila L Büscher Rainer R Serdaroglu Erkin E Tasic Velibor V Mane Shrikant S Lifton Richard P RP Braun Daniela A DA Hildebrandt Friedhelm F

Clinical journal of the American Society of Nephrology : CJASN 20171110 1

<h4>Background and objectives</h4>Steroid-resistant nephrotic syndrome overwhelmingly progresses to ESRD. More than 30 monogenic genes have been identified to cause steroid-resistant nephrotic syndrome. We previously detected causative mutations using targeted panel sequencing in 30% of patients with steroid-resistant nephrotic syndrome. Panel sequencing has a number of limitations when compared with whole exome sequencing. We employed whole exome sequencing to detect monogenic causes of steroid ...[more]

PMID: 29127259

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Project description:BackgroundIn the absence of effective measures to predict steroid responsiveness, patients with nonhereditary steroid-resistant nephrotic syndrome (SRNS) have a significantly increased risk of progression to end-stage renal disease. In view of the poor outcomes of SRNS, it is urgent to identify the steroid responsiveness of idiopathic nephrotic syndrome (INS) early.MethodsTo build a prediction model for SRNS, we collected 91 subjects; 57 of them had steroid-sensitive nephrotic syndrome, and the others had SRNS. For each subject, 87 clinical variables were measured. In general, only a small part of these variables is informative to SRNS. Thus, we proposed a new variable selection framework including a penalized regression approach (named MLR+TLP) to select variables having a linear effect on the SRNS and a nonparametric screening method (MAC) to select variables having a nonlinear marginal (joint) effect on the SRNS. Thereafter, considering the correlation between selected clinical variables, we used a stepwise method to build our final model for predicting SRNS. In addition, a statistical testing procedure is proposed to test the overfitting of the proposed model.ResultsTwenty-six clinical variables were selected to be informative to SRNS, and an SVM model was built to predict SRNS with a leave-one-out cross-validation (LOO-CV) accuracy of 95.2% (overfitting p value<0.005). To make the model more useful, we incorporate prior medical information into the model and consider the correlation between selected variables. Then, a reduced SVM model including only eight clinical variables (erythrocyte sedimentation rate, urine occult blood, percentage of neutrophils, immunoglobulin A, cholesterol, vinculin autoantibody, aspartate aminotransferase, and prolonged prothrombin time) was built to have a LOO-CV accuracy of 92.8% (overfitting p value<0.005). The validation cohort showed that the reduced model obtained an accuracy of 94.0% (overfitting p value<0.005), with a sensitivity of 90.0% and a specificity of 96.7%. Notably, vinculin autoantibody is the only podocyte autoantibody included in this model. It is linearly related to steroid responsiveness. Finally, our model is freely available as a user-friendly web tool at https://datalinkx.shinyapps.io/srns/.ConclusionThe SRNS prediction model constructed in this study comprehensively and objectively evaluates the internal conditions and disease status of INS patients and will provide scientific guidance for selecting treatment methods for children with nonhereditary SRNS.

Dataset Information

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome.

Publications

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome.

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