Kidney360Genetic & Genomic MedicinePeer-reviewed journal article
Clinical utility of genetic and genomic testing in the precision diagnosis and management of pediatric patients with kidney and urinary tract diseases
Nasim BekheirniaiD, Kevin E. GlintoniD, Linda Z. RossettiiD, Joshua ManoriD, Wuyan Chen, Dolores J. LambiD, Michael BrauniD, Mir Reza BekheirniaiD
Abstract
As genetic and genomic testing increasingly integrates into the practice of nephrology, our understanding of the basis of many kidney disorders has exponentially increased. Given this, we recently initiated a Renal Genetics Clinic (RGC) at our large, urban childrens hospital for patients with either a personal or strong family history of renal disorders. Genetic testing was performed in Clinical Laboratory Improvement Amendments (CLIA) certified laboratories using single gene testing, multi-gene panels, chromosomal microarray (CMA), or exome sequencing (ES). A total of 192 patients (185 probands) were evaluated in this clinic, with cystic kidney disease (49/192) being the most common reason for referral followed by Congenital Anomalies of the Kidney and Urinary Tract (CAKUT: 41/192) and hematuria (38/192). Genetic testing was performed for 153 probands with an overall diagnostic yield of 75/153 (49%). In the patients who reached a molecular diagnosis, 13/75 (17.3%), medical or surgical treatment of the patients were impacted, and in 12/75 (16%), previous clinical diagnoses were changed to more accurate molecular diagnoses. Such testing provided an accurate diagnosis for children and in some cases led to further diagnosis in seemingly asymptomatic family members and changes to overall medical management. Molecular testing, as facilitated by such a specialized clinical setting, thus appears to have clear utility in the diagnosis and counseling of patients with a wide range of renal manifestations.
- organism: human
- assay: whole-exome sequencing
- research focus: differential expression
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 17
- FWCI
- 1.17
- Field percentile
- top 23%
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