Expanding the genetics and phenotypes of ocular congenital cranial dysinnervation disorders.

Purpose: To identify genetic etiologies and genotype/phenotype associations for unsolved ocular congenital cranial dysinnervation disorders (oCCDDs). Methods: We coupled phenotyping with exome or genome sequencing of 467 pedigrees with genetically unsolved oCCDDs, integrating analyses of pedigrees, human and animal model phenotypes, and de novo variants to identify rare candidate single nucleotide variants, insertion/deletions, and structural variants disrupting protein-coding regions. Prioritized variants were classified for pathogenicity and evaluated for genotype/phenotype correlations. Results: Analyses elucidated phenotypic subgroups, identified pathogenic/likely pathogenic variant(s) in 43/467 probands (9.2%), and prioritized variants of uncertain significance in 70/467 additional probands (15.0%). These included known and novel variants in established oCCDD genes, genes associated with syndromes that sometimes include oCCDDs (e.g., MYH10, KIF21B, TGFBR2, TUBB6), genes that fit the syndromic component of the phenotype but had no prior oCCDD association (e.g., CDK13, TGFB2), genes with no reported association with oCCDDs or the syndromic phenotypes (e.g., TUBA4A, KIF5C, CTNNA1, KLB, FGF21), and genes associated with oCCDD phenocopies that had resulted in misdiagnoses. Conclusion: This study suggests that unsolved oCCDDs are clinically and genetically heterogeneous disorders often overlapping other Mendelian conditions and nominates many candidates for future replication and functional studies.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

ClinGen variant curation expert panel recommendations for classification of variants in GAMT, GATM and SLC6A8 for cerebral creatine deficiency syndromes.

Cerebral creatine deficiency syndromes (CCDS) are inherited metabolic phenotypes of creatine synthesis and transport. There are two enzyme deficiencies, guanidinoacetate methyltransferase (GAMT), encoded by GAMT and arginine-glycine amidinotransferase (AGAT), encoded by GATM, which are involved in the synthesis of creatine. After synthesis, creatine is taken up by a sodium-dependent membrane bound creatine transporter (CRTR), encoded by SLC6A8, into all organs. Creatine uptake is very important especially in high energy demanding organs such as the brain, and muscle. To classify the pathogenicity of variants in GAMT, GATM, and SLC6A8, we developed the CCDS Variant Curation Expert Panel (VCEP) in 2018, supported by The Clinical Genome Resource (ClinGen), a National Institutes of Health (NIH)-funded resource. We developed disease-specific variant classification guidelines for GAMT-, GATM-, and SLC6A8-related CCDS, adapted from the American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant interpretation guidelines. We applied specific variant classification guidelines to 30 pilot variants in each of the three genes that have variants associated with CCDS. Our CCDS VCEP was approved by the ClinGen Sequence Variant Interpretation Working Group (SVI WG) and Clinical Domain Oversight Committee in July 2022. We curated 181 variants including 72 variants in GAMT, 45 variants in GATM, and 64 variants in SLC6A8 and submitted these classifications to ClinVar, a public variant database supported by the National Center for Biotechnology Information. Missense variants were the most common variant type in all three genes. We submitted 32 new variants and reclassified 34 variants with conflicting interpretations. We report specific phenotype (PP4) using a points system based on the urine and plasma guanidinoacetate and creatine levels, brain magnetic resonance spectroscopy (MRS) creatine level, and enzyme activity or creatine uptake in fibroblasts ranging from PP4, PP4_Moderate and PP4_Strong. Our CCDS VCEP is one of the first panels applying disease specific variant classification algorithms for an X-linked disease. The availability of these guidelines and classifications can guide molecular genetics and genomic laboratories and health care providers to assess the molecular diagnosis of individuals with a CCDS phenotype.

Heterozygous loss-of-function SMC3 variants are associated with variable growth and developmental features.

Heterozygous missense variants and in-frame indels in SMC3 are a cause of Cornelia de Lange syndrome (CdLS), marked by intellectual disability, growth deficiency, and dysmorphism, via an apparent dominant-negative mechanism. However, the spectrum of manifestations associated with SMC3 loss-of-function variants has not been reported, leading to hypotheses of alternative phenotypes or even developmental lethality. We used matchmaking servers, patient registries, and other resources to identify individuals with heterozygous, predicted loss-of-function (pLoF) variants in SMC3, and analyzed population databases to characterize mutational intolerance in this gene. Here, we show that SMC3 behaves as an archetypal haploinsufficient gene: it is highly constrained against pLoF variants, strongly depleted for missense variants, and pLoF variants are associated with a range of developmental phenotypes. Among 14 individuals with SMC3 pLoF variants, phenotypes were variable but coalesced on low growth parameters, developmental delay/intellectual disability, and dysmorphism, reminiscent of atypical CdLS. Comparisons to individuals with SMC3 missense/in-frame indel variants demonstrated an overall milder presentation in pLoF carriers. Furthermore, several individuals harboring pLoF variants in SMC3 were nonpenetrant for growth, developmental, and/or dysmorphic features, and some had alternative symptomatologies with rational biological links to SMC3. Analyses of tumor and model system transcriptomic data and epigenetic data in a subset of cases suggest that SMC3 pLoF variants reduce SMC3 expression but do not strongly support clustering with functional genomic signatures of typical CdLS. Our finding of substantial population-scale LoF intolerance in concert with variable growth and developmental features in subjects with SMC3 pLoF variants expands the scope of cohesinopathies, informs on their allelic architecture, and suggests the existence of additional clearly LoF-constrained genes whose disease links will be confirmed only by multilayered genomic data paired with careful phenotyping.

Inferring compound heterozygosity from large-scale exome sequencing data.

Recessive diseases arise when both copies of a gene are impacted by a damaging genetic variant. When a patient carries two potentially causal variants in a gene, accurate diagnosis requires determining that these variants occur on different copies of the chromosome (that is, are in trans) rather than on the same copy (that is, in cis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. Here we developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in the Genome Aggregation Database (v2, n = 125,748 exomes). Our approach estimates phase with 96% accuracy, both in trio data and in patients with Mendelian conditions and presumed causal compound heterozygous variants. We provide a public resource of phasing estimates for coding variants and counts per gene of rare variants in trans that can aid interpretation of rare co-occurring variants in the context of recessive disease.

The diagnostic yield of exome sequencing in liver diseases from a curated gene panel.

Exome sequencing (ES) has been used in a variety of clinical settings but there are limited data on its utility for diagnosis and/or prediction of monogenic liver diseases. We developed a curated list of 502 genes for monogenic disorders associated with liver phenotypes and analyzed ES data for these genes in 758 patients with chronic liver diseases (CLD). For comparison, we examined ES data in 7856 self-declared healthy controls (HC), and 2187 patients with chronic kidney disease (CKD). Candidate pathogenic (P) or likely pathogenic (LP) variants were initially identified in 19.9% of participants, most of which were attributable to previously reported pathogenic variants with implausibly high allele frequencies. After variant annotation and filtering based on population minor allele frequency (MAF ≤ 10-4 for dominant disorders and MAF ≤ 10-3 for recessive disorders), we detected a significant enrichment of P/LP variants in the CLD cohort compared to the HC cohort (X2 test OR 5.00, 95% CI 3.06-8.18, p value = 4.5e-12). A second-level manual annotation was necessary to capture true pathogenic variants that were removed by stringent allele frequency and quality filters. After these sequential steps, the diagnostic rate of monogenic disorders was 5.7% in the CLD cohort, attributable to P/LP variants in 25 genes. We also identified concordant liver disease phenotypes for 15/22 kidney disease patients with P/LP variants in liver genes, mostly associated with cystic liver disease phenotypes. Sequencing results had many implications for clinical management, including familial testing for early diagnosis and management, preventative screening for associated comorbidities, and in some cases for therapy. Exome sequencing provided a 5.7% diagnostic rate in CLD patients and required multiple rounds of review to reduce both false positive and false negative findings. The identification of concordant phenotypes in many patients with P/LP variants and no known liver disease also indicates a potential for predictive testing for selected monogenic liver disorders.

Heterozygous loss-of-function SMC3 variants are associated with variable and incompletely penetrant growth and developmental features.

Heterozygous missense variants and in-frame indels in SMC3 are a cause of Cornelia de Lange syndrome (CdLS), marked by intellectual disability, growth deficiency, and dysmorphism, via an apparent dominant-negative mechanism. However, the spectrum of manifestations associated with SMC3 loss-of-function variants has not been reported, leading to hypotheses of alternative phenotypes or even developmental lethality. We used matchmaking servers, patient registries, and other resources to identify individuals with heterozygous, predicted loss-of-function (pLoF) variants in SMC3, and analyzed population databases to characterize mutational intolerance in this gene. Here, we show that SMC3 behaves as an archetypal haploinsufficient gene: it is highly constrained against pLoF variants, strongly depleted for missense variants, and pLoF variants are associated with a range of developmental phenotypes. Among 13 individuals with SMC3 pLoF variants, phenotypes were variable but coalesced on low growth parameters, developmental delay/intellectual disability, and dysmorphism reminiscent of atypical CdLS. Comparisons to individuals with SMC3 missense/in-frame indel variants demonstrated a milder presentation in pLoF carriers. Furthermore, several individuals harboring pLoF variants in SMC3 were nonpenetrant for growth, developmental, and/or dysmorphic features, some instead having intriguing symptomatologies with rational biological links to SMC3 including bone marrow failure, acute myeloid leukemia, and Coats retinal vasculopathy. Analyses of transcriptomic and epigenetic data suggest that SMC3 pLoF variants reduce SMC3 expression but do not result in a blood DNA methylation signature clustering with that of CdLS, and that the global transcriptional signature of SMC3 loss is model-dependent. Our finding of substantial population-scale LoF intolerance in concert with variable penetrance in subjects with SMC3 pLoF variants expands the scope of cohesinopathies, informs on their allelic architecture, and suggests the existence of additional clearly LoF-constrained genes whose disease links will be confirmed only by multi-layered genomic data paired with careful phenotyping.

Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and with new innovative methods can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the GREGoR consortium. Each family's CNV data was analyzed using the seqr platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 CNVs as likely pathogenic/pathogenic and 20 CNVs as high interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency. Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.

Beyond the exome: What's next in diagnostic testing for Mendelian conditions.

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.

Inferring compound heterozygosity from large-scale exome sequencing data.

Recessive diseases arise when both the maternal and the paternal copies of a gene are impacted by a damaging genetic variant in the affected individual. When a patient carries two different potentially causal variants in a gene for a given disorder, accurate diagnosis requires determining that these two variants occur on different copies of the chromosome (i.e., are in trans) rather than on the same copy (i.e. in cis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. We developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in exome sequencing data from the Genome Aggregation Database (gnomAD v2, n=125,748). When applied to trio data where phase can be determined by transmission, our approach estimates phase with 95.7% accuracy and remains accurate even for very rare variants (allele frequency < 1×10-4). We also correctly phase 95.9% of variant pairs in a set of 293 patients with Mendelian conditions carrying presumed causal compound heterozygous variants. We provide a public resource of phasing estimates from gnomAD, including phasing estimates for coding variants across the genome and counts per gene of rare variants in trans, that can aid interpretation of rare co-occurring variants in the context of recessive disease.

Beyond the exome: what's next in diagnostic testing for Mendelian conditions.

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order and emerging technologies, such as optical genome mapping and long-read DNA or RNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders.

Unique Capabilities of Genome Sequencing for Rare Disease Diagnosis.

Background: Causal variants underlying rare disorders may remain elusive even after expansive gene panels or exome sequencing (ES). Clinicians and researchers may then turn to genome sequencing (GS), though the added value of this technique and its optimal use remain poorly defined. We therefore investigated the advantages of GS within a phenotypically diverse cohort. Methods: GS was performed for 744 individuals with rare disease who were genetically undiagnosed. Analysis included review of single nucleotide, indel, structural, and mitochondrial variants. Results: We successfully solved 218/744 (29.3%) cases using GS, with most solves involving established disease genes (157/218, 72.0%). Of all solved cases, 148 (67.9%) had previously had non-diagnostic ES. We systematically evaluated the 218 causal variants for features requiring GS to identify and 61/218 (28.0%) met these criteria, representing 8.2% of the entire cohort. These included small structural variants (13), copy neutral inversions and complex rearrangements (8), tandem repeat expansions (6), deep intronic variants (15), and coding variants that may be more easily found using GS related to uniformity of coverage (19). Conclusion: We describe the diagnostic yield of GS in a large and diverse cohort, illustrating several types of pathogenic variation eluding ES or other techniques. Our results reveal a higher diagnostic yield of GS, supporting the utility of a genome-first approach, with consideration of GS as a secondary or tertiary test when higher-resolution structural variant analysis is needed or there is a strong clinical suspicion for a condition and prior targeted genetic testing has been negative.

DPP9 deficiency: An inflammasomopathy that can be rescued by lowering NLRP1/IL-1 signaling.

Dipeptidyl peptidase 9 (DPP9) is a direct inhibitor of NLRP1, but how it affects inflammasome regulation in vivo is not yet established. Here, we report three families with immune-associated defects, poor growth, pancytopenia, and skin pigmentation abnormalities that segregate with biallelic DPP9 rare variants. Using patient-derived primary cells and biochemical assays, these variants were shown to behave as hypomorphic or knockout alleles that failed to repress NLRP1. The removal of a single copy of Nlrp1a/b/c, Asc, Gsdmd, or Il-1r, but not Il-18, was sufficient to rescue the lethality of Dpp9 mutant neonates in mice. Similarly, dpp9 deficiency was partially rescued by the inactivation of asc, an obligate downstream adapter of the NLRP1 inflammasome, in zebrafish. These experiments suggest that the deleterious consequences of DPP9 deficiency were mostly driven by the aberrant activation of the canonical NLRP1 inflammasome and IL-1ß signaling. Collectively, our results delineate a Mendelian disorder of DPP9 deficiency driven by increased NLRP1 activity as demonstrated in patient cells and in two animal models of the disease.

Genetic testing for kidney disease of unknown etiology.

In many cases of chronic kidney disease, the cause of disease remains unknown despite a thorough nephrologic workup. Genetic testing has revolutionized many areas of medicine and promises to empower diagnosis and targeted management of such cases of kidney disease of unknown etiology. Recent studies using genetic testing have demonstrated that Mendelian etiologies account for approximately 20% of cases of kidney disease of unknown etiology. Although genetic testing has significant benefits, including tailoring of therapy, informing targeted workup, detecting extrarenal disease, counseling patients and families, and redirecting care, it also has important limitations and risks that must be considered.

Rare genetic causes of complex kidney and urological diseases.

Although often considered a single-entity, chronic kidney disease (CKD) comprises many pathophysiologically distinct disorders that result in persistently abnormal kidney structure and/or function, and encompass both monogenic and polygenic aetiologies. Rare inherited forms of CKD frequently span diverse phenotypes, reflecting genetic phenomena including pleiotropy, incomplete penetrance and variable expressivity. Use of chromosomal microarray and massively parallel sequencing technologies has revealed that genomic disorders and monogenic aetiologies contribute meaningfully to seemingly complex forms of CKD across different clinically defined subgroups and are characterized by high genetic and phenotypic heterogeneity. Investigations of prevalent genomic disorders in CKD have integrated genetic, bioinformatic and functional studies to pinpoint the genetic drivers underlying their renal and extra-renal manifestations, revealing both monogenic and polygenic mechanisms. Similarly, massively parallel sequencing-based analyses have identified gene- and allele-level variation that contribute to the clinically diverse phenotypes observed for many monogenic forms of nephropathy. Genome-wide sequencing studies suggest that dual genetic diagnoses are found in at least 5% of patients in whom a genetic cause of disease is identified, highlighting the fact that complex phenotypes can also arise from multilocus variation. A multifaceted approach that incorporates genetic and phenotypic data from large, diverse cohorts will help to elucidate the complex relationships between genotype and phenotype for different forms of CKD, supporting personalized medicine for individuals with kidney disease.

Pilot Study of Return of Genetic Results to Patients in Adult Nephrology.

BACKGROUND AND OBJECTIVES: Actionable genetic findings have implications for care of patients with kidney disease, and genetic testing is an emerging tool in nephrology practice. However, there are scarce data regarding best practices for return of results and clinical application of actionable genetic findings for kidney patients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We developed a return of results workflow in collaborations with clinicians for the retrospective recontact of adult nephrology patients who had been recruited into a biobank research study for exome sequencing and were identified to have medically actionable genetic findings. RESULTS: Using this workflow, we attempted to recontact a diverse pilot cohort of 104 nephrology research participants with actionable genetic findings, encompassing 34 different monogenic etiologies of nephropathy and five single-gene disorders recommended by the American College of Medical Genetics and Genomics for return as medically actionable secondary findings. We successfully recontacted 64 (62%) participants and returned results to 41 (39%) individuals. In each case, the genetic diagnosis had meaningful implications for the patients' nephrology care. Through implementation efforts and qualitative interviews with providers, we identified over 20 key challenges associated with returning results to study participants, and found that physician knowledge gaps in genomics was a recurrent theme. We iteratively addressed these challenges to yield an optimized workflow, which included standardized consultation notes with tailored management recommendations, monthly educational conferences on core topics in genomics, and a curated list of expert clinicians for patients requiring extranephrologic referrals. CONCLUSIONS: Developing the infrastructure to support return of genetic results in nephrology was resource-intensive, but presented potential opportunities for improving patient care. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2020_04_16_12481019.mp3.

Exome-Based Rare-Variant Analyses in CKD.

BACKGROUND: Studies have identified many common genetic associations that influence renal function and all-cause CKD, but these explain only a small fraction of variance in these traits. The contribution of rare variants has not been systematically examined. METHODS: We performed exome sequencing of 3150 individuals, who collectively encompassed diverse CKD subtypes, and 9563 controls. To detect causal genes and evaluate the contribution of rare variants we used collapsing analysis, in which we compared the proportion of cases and controls carrying rare variants per gene. RESULTS: The analyses captured five established monogenic causes of CKD: variants in PKD1, PKD2, and COL4A5 achieved study-wide significance, and we observed suggestive case enrichment for COL4A4 and COL4A3. Beyond known disease-associated genes, collapsing analyses incorporating regional variant intolerance identified suggestive dominant signals in CPT2 and several other candidate genes. Biallelic mutations in CPT2 cause carnitine palmitoyltransferase II deficiency, sometimes associated with rhabdomyolysis and acute renal injury. Genetic modifier analysis among cases with APOL1 risk genotypes identified a suggestive signal in AHDC1, implicated in Xia-Gibbs syndrome, which involves intellectual disability and other features. On the basis of the observed distribution of rare variants, we estimate that a two- to three-fold larger cohort would provide 80% power to implicate new genes for all-cause CKD. CONCLUSIONS: This study demonstrates that rare-variant collapsing analyses can validate known genes and identify candidate genes and modifiers for kidney disease. In so doing, these findings provide a motivation for larger-scale investigation of rare-variant risk contributions across major clinical CKD categories.