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.

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.

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.

Diagnostic Utility of Exome Sequencing for Kidney Disease.

BACKGROUND: Exome sequencing is emerging as a first-line diagnostic method in some clinical disciplines, but its usefulness has yet to be examined for most constitutional disorders in adults, including chronic kidney disease, which affects more than 1 in 10 persons globally. METHODS: We conducted exome sequencing and diagnostic analysis in two cohorts totaling 3315 patients with chronic kidney disease. We assessed the diagnostic yield and, among the patients for whom detailed clinical data were available, the clinical implications of diagnostic and other medically relevant findings. RESULTS: In all, 3037 patients (91.6%) were over 21 years of age, and 1179 (35.6%) were of self-identified non-European ancestry. We detected diagnostic variants in 307 of the 3315 patients (9.3%), encompassing 66 different monogenic disorders. Of the disorders detected, 39 (59%) were found in only a single patient. Diagnostic variants were detected across all clinically defined categories, including congenital or cystic renal disease (127 of 531 patients [23.9%]) and nephropathy of unknown origin (48 of 281 patients [17.1%]). Of the 2187 patients assessed, 34 (1.6%) had genetic findings for medically actionable disorders that, although unrelated to their nephropathy, would also lead to subspecialty referral and inform renal management. CONCLUSIONS: Exome sequencing in a combined cohort of more than 3000 patients with chronic kidney disease yielded a genetic diagnosis in just under 10% of cases. (Funded by the National Institutes of Health and others.).

Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes.

Integration of detailed phenotype information with genetic data is well established to facilitate accurate diagnosis of hereditary disorders. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from heterogeneous EHR narratives remains a challenge. Here, we present EHR-Phenolyzer, a high-throughput EHR framework for extracting and analyzing phenotypes. EHR-Phenolyzer extracts and normalizes Human Phenotype Ontology (HPO) concepts from EHR narratives and then prioritizes genes with causal variants on the basis of the HPO-coded phenotype manifestations. We assessed EHR-Phenolyzer on 28 pediatric individuals with confirmed diagnoses of monogenic diseases and found that the genes with causal variants were ranked among the top 100 genes selected by EHR-Phenolyzer for 16/28 individuals (p < 2.2 × 10-16), supporting the value of phenotype-driven gene prioritization in diagnostic sequence interpretation. To assess the generalizability, we replicated this finding on an independent EHR dataset of ten individuals with a positive diagnosis from a different institution. We then assessed the broader utility by examining two additional EHR datasets, including 31 individuals who were suspected of having a Mendelian disease and underwent different types of genetic testing and 20 individuals with positive diagnoses of specific Mendelian etiologies of chronic kidney disease from exome sequencing. Finally, through several retrospective case studies, we demonstrated how combined analyses of genotype data and deep phenotype data from EHRs can expedite genetic diagnoses. In summary, EHR-Phenolyzer leverages EHR narratives to automate phenotype-driven analysis of clinical exomes or genomes, facilitating the broader implementation of genomic medicine.

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.

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

Background: Exome sequencing is increasingly being used for clinical diagnostics, with an impetus to expand reporting of incidental findings across a wide range of disorders. Analysis of population cohorts can help reduce risk for genetic variant misclassification and resultant unnecessary referrals to subspecialists. Objective: To examine the burden of candidate pathogenic variants for kidney and genitourinary disorders emerging from exome sequencing. Design: Secondary analysis of genetic data. Setting: A tertiary care academic medical center. Patients: A convenience sample of exome sequence data from 7974 self-declared healthy adults. Measurements: Assessment of the prevalence of candidate pathogenic variants in 625 genes associated with Mendelian kidney and genitourinary disorders. Results: Of all participants, 23.3% carried a candidate pathogenic variant, most of which were attributable to previously reported variants that had implausibly high allele frequencies. In particular, 25 genes (discovered before the creation of the Exome Aggregation Consortium, a genetic database comprising data from a large control population) accounted for 67.7% of persons with candidate pathogenic variants. After stringent filtering based on allele frequency, 1.4% of persons still had a candidate pathogenic variant, an excessive rate given the prevalence of monogenic kidney and genitourinary disorders. Manual annotation of a subset of variants showed that the majority would be classified as nonbenign under current guidelines for clinical sequence interpretation and could prompt subspecialty referrals if returned. Limitation: Limited access to health record data prevented comprehensive assessment of the phenotypic concordance with genetic diagnoses. Conclusion: Widespread reporting of incidental genetic findings related to kidney and genitourinary disorders will require stringent curation of clinical variant databases and detailed case-level review to avoid genetic misdiagnosis and unnecessary referrals. These findings motivate similar analyses for genes relevant to other medical subspecialties. Primary Funding Source: National Institute of Diabetes and Digestive and Kidney Diseases and National Human Genome Research Institute.

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.

Expanding opportunities and emerging challenges: broadening the scope of genetic testing in nephrology.

Massively parallel sequencing technologies such as exome sequencing are increasingly applied across medicine. Connaughton et al. report a high diagnostic yield of exome sequencing among adults with hereditary nephropathy or nephropathy of unknown cause. Their findings support broader use of genomic sequencing in nephrology and highlight key associated questions, including how to identify those patients for whom testing is indicated, pinpoint pathogenic variants, and balance the resultant health care benefits and clinical follow-up burden.

Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel.

PURPOSE: To provide a validated method to confidently identify exon-containing copy-number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs. METHODS: DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples, when the target log2 ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap). RESULTS: Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation, and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multiexon and 29 single-exon CNVs with high C-scores were assessed by Multiplex Ligation-dependent Probe Amplification (MLPA). CONCLUSION: Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We propose guidelines and criteria to identify high confidence single-exon CNVs.

Whole-Exome Sequencing in Adults With Chronic Kidney Disease: A Pilot Study.

Background: The utility of whole-exome sequencing (WES) for the diagnosis and management of adult-onset constitutional disorders has not been adequately studied. Genetic diagnostics may be advantageous in adults with chronic kidney disease (CKD), in whom the cause of kidney failure often remains unknown. Objective: To study the diagnostic utility of WES in a selected referral population of adults with CKD. Design: Observational cohort. Setting: A major academic medical center. Patients: 92 adults with CKD of unknown cause or familial nephropathy or hypertension. Measurements: The diagnostic yield of WES and its potential effect on clinical management. Results: Whole-exome sequencing provided a diagnosis in 22 of 92 patients (24%), including 9 probands with CKD of unknown cause and encompassing 13 distinct genetic disorders. Among these, loss-of-function mutations were identified in PARN in 2 probands with tubulointerstitial fibrosis. PARN mutations have been implicated in a short telomere syndrome characterized by lung, bone marrow, and liver fibrosis; these findings extend the phenotype of PARN mutations to renal fibrosis. In addition, review of the American College of Medical Genetics actionable genes identified a pathogenic BRCA2 mutation in a proband who was diagnosed with breast cancer on follow-up. The results affected clinical management in most identified cases, including initiation of targeted surveillance, familial screening to guide donor selection for transplantation, and changes in therapy. Limitation: The small sample size and recruitment at a tertiary care academic center limit generalizability of findings among the broader CKD population. Conclusion: Whole-exome sequencing identified diagnostic mutations in a substantial number of adults with CKD of many causes. Further study of the utility of WES in the evaluation and care of patients with CKD in additional settings is warranted. Primary Funding Source: New York State Empire Clinical Research Investigator Program, Renal Research Institute, and National Human Genome Research Institute of the National Institutes of Health.

Cooking increases net energy gain from a lipid-rich food.

Starch, protein, and lipid are three major sources of calories in the human diet. The unique and universal human practice of cooking has been demonstrated to increase the energy gained from foods rich in starch or protein. Yet no studies have tested whether cooking has equivalent effects on the energy gained from lipid-rich foods. Using mice as a model, we addressed this question by examining the impact of cooking on the energy gained from peanuts, a lipid-rich oilseed, and compared this impact against that of nonthermal processing (blending). We found that cooking consistently increased the energy gained per calorie, whereas blending had no detectable energetic benefits. Assessment of fecal fat excretion showed increases in lipid digestibility when peanuts were cooked, and examination of diet microstructure revealed concomitant alterations to the integrity of cell walls and the oleosin layer of proteins that otherwise shield lipids from digestive lipases. Both effects were consistent with the greater energy gain observed with cooking. Our findings highlight the importance of cooking in increasing dietary energy returns for humans, both past and present.

A 15-Month-Old with Faltering Growth.

AbstractMorning Report is a time-honored tradition where physicians-in-training present cases to their colleagues and clinical experts to collaboratively examine an interesting patient presentation. The Morning Report section seeks to carry on this tradition by presenting a patient's chief concern and story, inviting the reader to develop a differential diagnosis and discover the diagnosis alongside the authors of the case. This report examines the story of a 15-month-old with faltering growth and short stature. Using questions, physical examination, and testing, an illness script for the presentation emerges. As the clinical course progresses, the differential is refined until a diagnosis is made.

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.

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.

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.

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.