Using Population Genetics to Interrogate the Monogenic Nephrotic Syndrome Diagnosis in a Case Cohort

Abstract

To maximize clinical benefits of genetic screening of patients with nephrotic syndrome (NS) to diagnose monogenic causes, reliably distinguishing NS-causing variants from the background of rare, noncausal variants prevalent in all genomes is vital. To determine the prevalence of monogenic NS in a North American case cohort while accounting for background prevalence of genetic variation, we sequenced 21 implicated monogenic NS genes in 312 participants from the Nephrotic Syndrome Study Network and 61 putative controls from the 1000 Genomes Project (1000G). These analyses were extended to available sequence data from approximately 2500 subjects from the 1000G. A typical pathogenicity filter identified causal variants for NS in 4.2% of patients and 5.8% of subjects from the 1000G. We devised a more stringent pathogenicity filtering strategy, reducing background prevalence of causal variants to 1.5%. When applying this stringent filter to patients, prevalence of monogenic NS was 2.9%; of these patients, 67% were pediatric, and 44% had FSGS on biopsy. The rate of complete remission did not associate with monogenic classification. Thus, we identified factors contributing to inaccurate monogenic classification of NS and developed a more accurate variant filtering strategy. The prevalence and clinical correlates of monogenic NS in this sporadically affected cohort differ substantially from those reported for patients referred for genetic analysis. Particularly in unselected, population-based cases, considering putative causal variants in known NS genes from a probabilistic rather than a deterministic perspective may be more precise. We also introduce GeneVetter, a web tool for monogenic assessment of rare disease.

Keywords: 1000 Genomes; expressivity; focal segmental glomerulosclerosis; genetic renal disease; penetrance; steroid resistant nephrotic syndrome.

Figure 1.

Substantial discordance between variant level functional prediction methods. From 1220 variants in 21 SRNS genes identified in the 1000G, the top 25% scored as most deleterious (n=255) by each of Polyphen2, SIFT, and MutationTaster were studied for concordance of prediction. Requiring one of three methods to classify variants as damaging implicates 493 variants as causal. Two of three implicate 210 variants as causal. Requiring all three programs to be in concordance reduced the number of causal variants to 62.

Figure 2.

Transcript-specific filtering influences the accuracy of monogenic diagnosis. The horizontal axis represents the base position in the coding sequence of the INF2 gene on the basis of the longest transcript. The vertical axis is the combined annotated–dependent depletion (CADD) score of each potential variant. All possible missense and nonsense variants in this region are plotted in gray. Blue indicates variants observed in the 1000G, and orange indicates variants observed in 311 subjects with NS. Yellow indicates known pathogenic variants reported in ClinVar 138. The green interval represents the exons for the most selected transcript with the highest TIMS score (ENST00000398337). It is apparent that rare variants found in patients with NS are enriched in this transcript, whereas rare variants found in the 1000G are not. CDS, coding DNA sequence.

Figure 3.

Increasing the number of genes sequenced increased the prevalence of incorrectly identified variants. The horizontal axis is the number of candidate genes sequenced assuming median gene length (1271 bp), and the vertical axis is the proportion of individuals classified with a monogenic diagnosis. Under assumptions of a fixed background prevalence and false negative rate, curves represent the total proportions of monogenic diagnosis, those expected to be correct, and those expected to be incorrect.

Figure 4.

Effect of prevalence of true monogenic NS on the relative risk of a variant found in the 21–gene SRNS gene set being in a patient who is monogenic versus population control. The horizontal axis is the prevalence of monogenic NS in a population. As the prevalence of monogenic NS increases, the relative risk of the variant being in a patient versus a subject in the 1000G increases as well.

Figure 5.

Achievement of complete remission does not significantly differ by monogenic status. Unadjusted Kaplan–Meier plot of the proportion of CR in patients with NS stratified by monogenic status. Plot is truncated at day 1000. P value was determined with an unadjusted Cox proportional hazards model of CR and monogenic status. HR, hazard ratio.

Figure 6.

Genome–wide background prevalence of rare and deleterious variants. All coding variants observed in the 1000G were classified as pathogenic or benign on the basis of stringent filtering criteria (maximum ancestry–specific EVS MAF<0.1%, 1000G MAF<5%, and loss of function or nonsynonymous and passing the two of three functional filter). Then, for each gene, the resulting background prevalence of predicted pathogenic mutations is calculated. A background prevalence of 1% for a given gene indicates that 1% of subjects in the 1000G carry a rare and deleterious variant in this gene. Subsets of genes (Concise Methods) and inheritance models considered are represented on the horizontal axis. Bars depict the proportions of genes within a gene set having background prevalence of predicted pathogenic variation within specific ranges. hOMIM, hand curated Online Mendelian Inheritance in Man; MAF, minor allele frequency.