Introduction:
Chronic kidney disease (CKD) affects more than 1 in 10 persons worldwide, and Global Burden of Diseases 2015 places kidney disease as the 8th leading cause of death in India. Advances in genomic technologies have shown the power to transform the concept of Precision Medicine in many specialities. However, the potential clinical impact of kidney diseases in real-world situations faced by practising nephrologists treating adult and adolescent patients is not known in our country. Some studies have shown diagnostic utility in the pediatric population, especially in developed nations with universal health coverage. In recent years, with increasing access to high throughput massively parallel sequencing, monogenic kidney disease (MKD) is estimated to account for 10% of adult CKD and 20% of early-onset CKD. Glomerular and tubulointerstitial diseases contribute to 30% and 20% of MKD, respectively, and are the most common causes of MKD after autosomal dominant polycystic kidney disease, which is almost always clinically diagnosed. A Google search for the term ‘genetic kidney diseases India’ yielded no relevant hits.
Research Question: 1. In what proportion of adolescent and adult patients with kidney diseases are clinically suspected to have an underlying monogenic diagnosis in pragmatic real-world situations; does the integration of genomic testing in the clinical care algorithm facilitate definitive diagnosis? 2. In what proportion of adolescent and adult patients with kidney diseases are clinically suspected to have an underlying monogenic diagnosis; does the integration of genomic testing positively impact their management through multidisciplinary integrated Kidney Genetics Services (KGS)? Primary: 1. To evaluate the proportion of thoroughly phenotyped kidney disease patients with suspected monogenic aetiology who obtain a positive genetic diagnosis after exome sequencing. Secondary: 2. To study the clinical impact of genomic testing in patients who receive a positive genetic diagnosis at 3 months follow-up. 3. To determine the solve rate for clinical subgroups.
Methods:
150 participants satisfying the inclusion and exclusion criteria have been recruited consecutively from outpatient Nephrology clinics and inpatient Nephrology wards till March 2024 after written informed consent and pre-test genetic counselling. Parent(s) and/ or affected sibling(s) also consented to family segregation analyses to ascertain phenotype-genotype correlations and mode of inheritance. The DNA was extracted using a QIASymphony (QIAGEN) kit with standard protocol and stored in -80-degree freezers till sequencing. All samples were subjected to exome sequencing. The data was analysed with an in-house and commercial (VarSeq, Golden Helix) pipeline to identify diagnostic variants for patients with renal disease. Fastq-mcf (ea-utils-1.1.2-806) was utilised for trimming adapter sequences from the raw reads, and Bedtools-2.17 will be used for coverage metrics. A GATK Best Practices bioinformatics analysis using Sentieon (v201808.01) was used to identify germline variants in the samples. The sequences obtained were quality-checked and aligned to the human reference genome (GRCh37/hg19) using the Sentieon aligner. The alignment files were processed using Sentieon to remove duplicates, recalibrate, and re-align indels. Gene annotation of the variants was performed using the Ensembl Variant Effect Predictor program against the human gene model (release 91). We calculated the effect of nonsynonymous variants with multiple algorithms such as PolyPhen-2, SIFT, MutationTaster2, and LRT. CNVs were detected from targeted sequence data using ExomeDepth (v1.1.10). This algorithm detects CNVs by comparing the test data's read depths with the matched aggregate reference dataset. Gene level annotation was performed using Ensembl gene models. Single-nucleotide variants (SNVs) with >10% overall population allele frequency in 1000Genome Phase 3, gnomAD (v2.1), EVS, dbSNP (v151), 1000 Japanese Genome, as well as MedGenome, SAGE & IndiGen databases for Indian population and deep intronic and intergenic variants, were removed from the list. VarTK, a proprietary artificial intelligence-based software, performed genotype-level variant prioritisation. Diagnostic variants were defined as those that were classified as “pathogenic” or “likely pathogenic” according to the American College of Medical Genetics and Genomics guidelines for clinical sequence interpretation and that were explicative of the patient’s nephropathy. Based on the detailed documented clinical information, the multidisciplinary KGS team vetted to ascertain whether it was explicative for the participants’ kidney disease genetic report. Only mutations classified as ‘Pathogenic’ or ‘Likely Pathogenic’ as per ACMG criteria and judged to be explicative for the individual’s kidney disease were deemed diagnostic. Multiplex Ligation-dependent Probe Amplification (MLPA) has been standardised for CNVs CFHR1-5 genes. Sanger sequencing has been performed in the proband to confirm the variants for the following genes: COL4A5, SDHD, NUP205, and PKD1. Family segregation analyses Sanger sequencing has been performed for the following variants in parents and affected family members to find the association between genotype and phenotypes and to ascertain the mode of inheritance: COL4A5, SDHD, NUP205, PKD1.
Results:
1. ‘Diagnostic utility’ is measured by proportion of ‘Positive Genetic Diagnosis’ with genomic testing. Only mutations that are both classified as ‘Pathogenic’ or ‘Likely Pathogenic’ as per ACMG criteria and are judged to be explicative for the individual’s kidney disease by the Multidisciplinary KGS was deemed diagnostic. Out of 150 probands ES, there was a pathogenic/ likely pathogenic genetic variant identified in 45% of cases.
2. ‘Clinical Impact’ - The various parameters that will be scored objectively include the following: 1. Has the ES provided confirmed genomic diagnosis- 45% 2. Was the genomic diagnosis suspected by the treating nephrologist pre- ES testing-20%? 3. Will the genomic results alter the clinical management of the patients-30%? 4. Ways in which can the clinical management be altered a. Reclassification of disease? 30% b. Change in estimated risk of nephropathy progression? 45% c. Change in treatment? 20% d. Initiate screening for extra-renal involvements 30% (i.e., reverse phenotyping)? e. Initiate screening of family members? 35% f. Are there any available targeted therapies? 10% g. Will the genetic diagnosis alter donor selection for transplantation 20%? h. Will the genetic diagnosis alter the risk of recurrence after transplantation 20% ? i. Will the genetic diagnosis require prenatal counselling? 40%
Conclusions:
Pragmatic genetic testing with WES and in-house bio-informatics is both cost effective and alters the diagnosis and management of patients in approx. 45% of clinically suspected adult and adolescent patients with monogenic kdieny disorder. The study establishes an integrated multi-institutional and multidisciplinary KGS to inform on the diagnostic utility of genomic tests for adolescent and adult kidney disease patients with suspected monogenic aetiology. The integrated database of clinically well-characterised kidney diseases with suspected MKD will be the first of its kind in India.
I have no potential conflict of interest to disclose.
I did not use generative AI and AI-assisted technologies in the writing process.