The Burden of Candidate Pathogenic Variants for Kidney and Genitourinary Disorders Emerging From Exome Sequencing

Key Summary Points Exome or genome sequencing can uncover incidental genetic findings unrelated to the primary indications for sequencing. However, routine reporting of incidental findings without rigorous interpretation can lead to misdiagnosis, patient distress, and unnecessary clinical follow-up. The frequency of a genetic variant in the general population, its predicted effect on protein function, and its prior reports in diseased persons are used to determine the variant's likely pathogenicity. Commonly used databases began collecting information about genetic variants before the creation of large control databases and can contain variants not associated with disease. Candidate pathogenic variants for kidney and genitourinary diseases were detected in 23% of a group of healthy adults who underwent sequencing. Even though filtering using stringent criteria reduced the prevalence of predicted pathogenic variants to less than 1.5%, this rate is still much higher than the prevalence of genetic conditions involving the kidney and genitourinary tract. More robust automated annotation methods are needed before reporting of incidental findings for kidney and genitourinary diseases becomes routine, because such analyses currently carry a high risk for subjecting many patients to evaluation for conditions that they do not actually have. Whole-exome sequencing (WES) (Appendix 1) is increasingly being applied across clinical medicine (13). Many studies have shown that WES findings have diagnostic value; in many cases, they also have clinical care implications that include informing initiation of targeted surveillance, choice of therapy, and referral for disease-specific clinical trials (14). Whole-exome sequencing can also uncover medically relevant genetic findings unrelated to the primary indication for testing. To date, the American College of Medical Genetics and Genomics (ACMG) has recommended returning such incidental findings for 59 genes (the ACMG 59) (5). However, the potential to discover medically actionable information has led physicians to advocate returning all incidental genetic findings, as in other diagnostic procedures (6, 7). Because most persons having genome-wide sequencing prefer to be informed of all findings potentially relevant to their health (8), many laboratories have already expanded the ACMG 59 list to include additional genes deemed actionable (911). However, given the large number of rare, putatively damaging variants in each human genome (12), analysis for diagnostic and incidental findings requires rigorous interpretive approaches to avoid genetic misdiagnosis and unnecessary medical referrals. Current guidelines for clinical variant interpretation recommend evaluating candidate pathogenic variants on the basis of a scoring system that considers rarity (based on minor allele frequency [MAF]), prior reports of disease association, and effect on the function of the encoded protein (13). Accordingly, disease causality is strongly considered for a rare variant previously reported as pathogenic or predicted to cause loss of function of the encoded protein due to premature truncation (13, 14). The Human Gene Mutation Database (HGMD) and ClinVar are 2 leading databases that collectively house classifications for over 325000 variants in more than 4800 genes (15, 16) and represent key resources guiding identification and clinical interpretation of variants previously reported as pathogenic (Glossary). However, both HGMD and ClinVar contain many variants that were reported as pathogenic before the availability of large population databases housing genetic data from many thousands of individuals (10, 12, 17). Subsequent analyses using all of those databases have shown that many variants previously reported as pathogenic have population frequencies incompatible with causality for the associated diseases (1820). Although such variants are increasingly being reclassified as benign, likely benign, or variants of uncertain significance (VUS) (Glossary), a substantial number remain listed as pathogenic (10), increasing the already considerable burden of diagnostic sequence analysis on clinical geneticists. Studies to date have focused on examining the burden of misclassified variants for the ACMG 59 and selected Mendelian disorders (1721). As clinical sequencing is deployed more widely, a systematic, subspecialty-based review is required to assess the potential for variant misclassification for each medical discipline. Here, we focus on kidney and genitourinary disorders, which affect 10% to 15% of adults worldwide (22, 23). Mendelian causes account for an estimated 10% of all such disorders (24), corresponding to a prevalence of approximately 1% to 1.5% in the general population. Because early-stage kidney disease is often clinically silent (23, 25, 26), many affected persons go undiagnosed, which highlights the potential utility of genetic testing for early diagnosis and targeted interventions (24, 27, 28). We used a large cohort of 7974 adults who self-reported being healthy to evaluate the prevalence of candidate pathogenic variants in genes associated with kidney and genitourinary disorders. We also examined the resultant burden for diagnostic sequence interpretation and clinical follow-up. Methods Identification of Genes Associated With Mendelian Kidney and Genitourinary Disorders We used the Online Mendelian Inheritance in Man database (OMIM.org) and the Orphanet database (Orphadata.org) to identify genes associated with Mendelian kidney and genitourinary disorders (29, 30) and manually curate a set of genes. The genes were annotated with relevant features, including inheritance mode, functional effect of variants previously reported as pathogenic, clinical category of the associated kidney and genitourinary disorders, and genic constraint scores (12). Genes associated with dominant kidney and genitourinary disorders for which ClinVar contained at least 1 pathogenic protein-truncating variant were deemed loss-of-functionassociated (13). Appendix 2 and Appendix Figure 1 give further details about gene identification and curation; the list can be accessed at the laboratory Web site (31). Appendix Figure 1. Identification of genes associated with Mendelian kidney and genitourinary disorders. Genes associated with Mendelian kidney and genitourinary disorders were identified using 2 leading genedisease databases: OMIM (OMIM.org) and Orphanet (Orphadata.org) (Appendixes 1 and 2). We identified 588 genes in the OMIM search and 366 in the Orphanet search. After removal of duplicate entries, 762 genes were identified. A multidisciplinary team of clinicians and geneticists specializing in hereditary kidney and genitourinary disorders manually reviewed the list and removed 188 genes that had insufficient evidence for association with the relevant phenotype or non-Mendelian inheritance and added 51 genes not detected via the automated database search. The final list of 625 genes was then annotated for inheritance mode, phenotypic category, and the functional types of variants previously reported as pathogenic (the latter assessed via review of the clinical variant database ClinVar, www.ncbi.nlm.nih.gov/clinvar). OMIM = Online Mendelian Inheritance in Man. Cohort We analyzed exome data from self-identified healthy adults who consented to have anonymized sequence data available for secondary genetic analysis (Appendix Tables 1 and 2). In this cohort, 47.2% of persons were enrolled as healthy control participants for genetic studies, 50.2% participated as healthy family members of probands with suspected genetic diseases other than kidney and genitourinary disorders, and 2.6% were HIV-infected persons who were otherwise healthy. At least 41% of participants were older than 65 years; 50.6% self-identified as of European ancestry, and 56.4% were female. Samples underwent whole-genome sequencing or WES, with WES using the Roche capture kit accounting for the majority (78%) (Appendix 2). We used KING (Kinship-based INference for Gwas) (32) to remove related individuals, yielding a final cohort of 7974 unrelated adults. Appendix Table 1. Origin of the Control Samples Used* Appendix Table 2. Cohort Characteristics Glossary Sequence Analysis and Variant Annotation Sequence data were annotated using the Analysis Tool for Annotated Variants (33) and ANNOVAR (Annotate Variation) (34). To increase analytic specificity, we used stringent quality filters (Appendix 2) and removed potential technical false-positive insertions and deletions (indels) using a validated method (35). Variants previously reported as pathogenic were identified using HGMD (36) and ClinVar (37); only those annotated as pathogenic by ClinVar or HGMD were included. In addition, we identified protein-truncating variants not previously reported in either database because these are strongly prioritized under the ACMG guidelines (13). In Silico Variant Interpretation For all analyses, we assessed only persons with genotypes consistent with the reported inheritance mode of the associated disorder. Standard Filtering Pipeline Current guidelines (13, 38) recommend considering all variants in disease-associated genes with an MAF less than 1% (13, 39). Thus, we defined rare variants as those with an MAF less than 1% in any of the ethnic subpopulations of the Exome Aggregation Consortium (ExAC) as well as in the aggregate (global) population. Variants had to meet this cutoff with respect to both their MAF in any ExAC population (popmax MAF) and that in our cohort (internal MAF) (13, 40). To reproduce a workflow for diagnostic sequence interpretation, we defined candidate pathogenic variant as any rare variant previously reported as pathogenic and any rare, protein-truncating variant not previously reported in HGMD or ClinVar (Appendix 2). Stringent Filtering Pipeline Because the standard pipeline yielded an implausibly high number of candidate pathogenic vari