A cross-disorder dosage sensitivity map of the human genome.

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.

Patterns of mosaicism for sequence and copy-number variants discovered through clinical deep sequencing of disease-related genes in one million individuals.

DNA variants that arise after conception can show mosaicism, varying in presence and extent among tissues. Mosaic variants have been reported in Mendelian diseases, but further investigation is necessary to broadly understand their incidence, transmission, and clinical impact. A mosaic pathogenic variant in a disease-related gene may cause an atypical phenotype in terms of severity, clinical features, or timing of disease onset. Using high-depth sequencing, we studied results from one million unrelated individuals referred for genetic testing for almost 1,900 disease-related genes. We observed 5,939 mosaic sequence or intragenic copy number variants distributed across 509 genes in nearly 5,700 individuals, constituting approximately 2% of molecular diagnoses in the cohort. Cancer-related genes had the most mosaic variants and showed age-specific enrichment, in part reflecting clonal hematopoiesis in older individuals. We also observed many mosaic variants in genes related to early-onset conditions. Additional mosaic variants were observed in genes analyzed for reproductive carrier screening or associated with dominant disorders with low penetrance, posing challenges for interpreting their clinical significance. When we controlled for the potential involvement of clonal hematopoiesis, most mosaic variants were enriched in younger individuals and were present at higher levels than in older individuals. Furthermore, individuals with mosaicism showed later disease onset or milder phenotypes than individuals with non-mosaic variants in the same genes. Collectively, the large compendium of variants, disease correlations, and age-specific results identified in this study expand our understanding of the implications of mosaic DNA variation for diagnosis and genetic counseling.

Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification.

As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.

Spectrum of splicing variants in disease genes and the ability of RNA analysis to reduce uncertainty in clinical interpretation.

The complexities of gene expression pose challenges for the clinical interpretation of splicing variants. To better understand splicing variants and their contribution to hereditary disease, we evaluated their prevalence, clinical classifications, and associations with diseases, inheritance, and functional characteristics in a 689,321-person clinical cohort and two large public datasets. In the clinical cohort, splicing variants represented 13% of all variants classified as pathogenic (P), likely pathogenic (LP), or variants of uncertain significance (VUSs). Most splicing variants were outside essential splice sites and were classified as VUSs. Among all individuals tested, 5.4% had a splicing VUS. If RNA analysis were to contribute supporting evidence to variant interpretation, we estimated that splicing VUSs would be reclassified in 1.7% of individuals in our cohort. This would result in a clinically significant result (i.e., P/LP) in 0.1% of individuals overall because most reclassifications would change VUSs to likely benign. In ClinVar, splicing VUSs were 4.8% of reported variants and could benefit from RNA analysis. In the Genome Aggregation Database (gnomAD), splicing variants comprised 9.4% of variants in protein-coding genes; most were rare, precluding unambiguous classification as benign. Splicing variants were depleted in genes associated with dominant inheritance and haploinsufficiency, although some genes had rare variants at essential splice sites or had common splicing variants that were most likely compatible with normal gene function. Overall, we describe the contribution of splicing variants to hereditary disease, the potential utility of RNA analysis for reclassifying splicing VUSs, and how natural variation may confound clinical interpretation of splicing variants.

Clinical utility of comprehensive gene panel testing for common and rare causes of skeletal dysplasia and other skeletal disorders: Results from the largest cohort to date.

Molecular genetics enables more precise diagnoses of skeletal dysplasia and other skeletal disorders (SDs). We investigated the clinical utility of multigene panel testing for 5011 unrelated individuals with SD in the United States (December 2019-April 2022). Median (range) age was 8 (0-90) years, 70.5% had short stature and/or disproportionate growth, 27.4% had a positive molecular diagnosis (MDx), and 30 individuals received two MDx. Genes most commonly contributing to MDx were FGFR3 (16.9%), ALPL (13.0%), and COL1A1 (10.3%). Most of the 112 genes associated with ≥1 MDx were primarily involved in signal transduction (n = 35), metabolism (n = 23), or extracellular matrix organization (n = 17). There were implications associated with specific care/treatment options for 84.4% (1158/1372) of MDx-positive individuals; >50% were linked to conditions with targeted therapy approved or in clinical development, including osteogenesis imperfecta, achondroplasia, hypophosphatasia, and mucopolysaccharidosis. Forty individuals with initially inconclusive results became MDx-positive following family testing. Follow-up mucopolysaccharidosis enzyme activity testing was positive in 14 individuals (10 of these were not MDx-positive). Our findings showed that inclusion of metabolic genes associated with SD increased the clinical utility of a gene panel and confirmed that integrated use of comprehensive gene panel testing with orthogonal testing reduced the burden of inconclusive results.

Diagnostic and clinical utility of comprehensive multigene panel testing for patients with neuropathy.

BACKGROUND AND AIMS: Prior to next-generation sequencing (NGS), the evaluation of a patient with neuropathy typically consisted of screening for acquired causes, followed by clinical genetic testing of PMP22, MFN2, GJB1, and MPZ in patients with a positive family history and symptom onset prior to age 50. In this study, we examined the clinical utility of NGS in a large cohort of patients analyzed in a commercial laboratory. METHODS: A cohort of 6849 adult patients underwent clinician-ordered peripheral neuropathy multigene panel testing ranging from 66 to 111 genes that included NGS and intragenic deletion/duplication analysis. RESULTS: A molecular diagnosis was identified for 8.4% of the cohort (n = 573/6849). Variants in PMP22, MFN2, GJB1, MPZ, and TTR accounted for 73.8% of molecular diagnoses. Results had potential clinical actionability for 398 (69.5%) patients. Our results suggest that 225/573 (39.3%) of molecular diagnoses and 113/398 (28.4%) of clinical interventions would have been missed if the testing approach had been restricted to older guidelines. INTERPRETATION: Our results highlight the need for expanded genetic testing guidelines that account for the increased number of genes associated with hereditary neuropathy, address the overlap of acquired and hereditary neuropathy, and provide broader access to genetic diagnosis for patients.

Applications of artificial intelligence in clinical laboratory genomics.

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

The landscape of reported VUS in multi-gene panel and genomic testing: Time for a change.

PURPOSE: Variants of uncertain significance (VUS) are a common result of diagnostic genetic testing and can be difficult to manage with potential misinterpretation and downstream costs, including time investment by clinicians. We investigated the rate of VUS reported on diagnostic testing via multi-gene panels (MGPs) and exome and genome sequencing (ES/GS) to measure the magnitude of uncertain results and explore ways to reduce their potentially detrimental impact. METHODS: Rates of inconclusive results due to VUS were collected from over 1.5 million sequencing test results from 19 clinical laboratories in North America from 2020 to 2021. RESULTS: We found a lower rate of inconclusive test results due to VUSs from ES/GS (22.5%) compared with MGPs (32.6%; P < .0001). For MGPs, the rate of inconclusive results correlated with panel size. The use of trios reduced inconclusive rates (18.9% vs 27.6%; P < .0001), whereas the use of GS compared with ES had no impact (22.2% vs 22.6%; P = ns). CONCLUSION: The high rate of VUS observed in diagnostic MGP testing warrants examining current variant reporting practices. We propose several approaches to reduce reported VUS rates, while directing clinician resources toward important VUS follow-up.

Carrier screening in the Mexican Jewish community using a pan-ethnic expanded carrier screening NGS panel.

PURPOSE: The Mexican Jewish community (MJC) is a previously uncharacterized, genetically isolated group composed of Ashkenazi and Sephardi-Mizrahi Jews who migrated in the early 1900s. We aimed to determine the heterozygote frequency of disease-causing variants in 302 genes in this population. METHODS: We conducted a cross-sectional study of the MJC involving individuals representing Ashkenazi Jews, Sephardi-Mizrahi Jews, or mixed-ancestry Jews. We offered saliva-based preconception pan-ethnic expanded carrier screening, which examined 302 genes. We analyzed heterozygote frequencies of pathogenic/likely pathogenic variants and compared them with those in the Genome Aggregation Database (gnomAD). RESULTS: We recruited 208 participants. The carrier screening results showed that 72.1% were heterozygous for at least 1 severe disease-causing variant in 1 of the genes analyzed. The most common genes with severe disease-causing variants were CFTR (16.8% of participants), MEFV (11.5%), WNT10A (6.7%), and GBA (6.7%). The allele frequencies were compared with those in the gnomAD; 85% of variant frequencies were statistically different from those found in gnomAD (P <.05). Finally, 6% of couples were at risk of having a child with a severe disorder. CONCLUSION: The heterozygote frequency of at least 1 severe disease-causing variant in the MJC was 72.1%. The use of carrier screening in the MJC and other understudied populations could help parents make more informed decisions.

Systematic use of phenotype evidence in clinical genetic testing reduces the frequency of variants of uncertain significance.

Guidelines for variant interpretation include criteria for incorporating phenotype evidence, but this evidence is inconsistently applied. Systematic approaches to using phenotype evidence are needed. We developed a method for curating disease phenotypes as highly or moderately predictive of variant pathogenicity based on the frequency of their association with disease-causing variants. To evaluate this method's accuracy, we retrospectively reviewed variants with clinical classifications that had evolved from uncertain to definitive in genes associated with curated predictive phenotypes. To demonstrate the clinical validity and utility of this approach, we compared variant classifications determined with and without predictive phenotype evidence. The curation method was accurate for 93%-98% of eligible variants. Among variants interpreted using highly predictive phenotype evidence, the percentage classified as pathogenic or likely pathogenic was 80%, compared with 46%-54% had the evidence not been used. Positive results among individuals harboring variants with highly predictive phenotype-guided interpretations would have been missed in 25%-37% of diagnostic tests and 39%-50% of carrier screens had other approaches to phenotype evidence been used. In summary, predictive phenotype evidence associated with specific curated genes can be systematically incorporated into variant interpretation to reduce uncertainty and increase the clinical utility of genetic testing.

Value of genetic testing for pediatric epilepsy: Driving earlier diagnosis of ceroid lipofuscinosis type 2 Batten disease.

This study assessed the effectiveness of genetic testing in shortening the time to diagnosis of late infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease. Individuals who received epilepsy gene panel testing through Behind the Seizure® , a sponsored genetic testing program (Cohort A), were compared to children outside of the sponsored testing program during the same period (Cohort B). Two cohorts were analyzed: children aged ≥24 to ≤60 months with unprovoked seizure onset at ≥24 months between December 2016 and January 2020 (Cohort 1) and children aged 0 to ≤60 months at time of testing with unprovoked seizure onset at any age between February 2019 and January 2020 (Cohort 2). The diagnostic yield in Cohort 1A (n = 1814) was 8.4% (n = 153). The TPP1 diagnostic yield within Cohort 1A was 2.9-fold higher compared to Cohort 1B (1.0%, n = 18/1814 vs. .35%, n = 8/2303; p = .0157). The average time from first symptom to CLN2 disease diagnosis was significantly shorter than previously reported (9.8 vs. 22.7 months, p < .001). These findings indicate that facilitated access to early epilepsy gene panel testing helps to increase diagnostic yield for CLN2 disease and shortens the time to diagnosis, enabling earlier intervention.

Experience analysing over 190,000 embryo trophectoderm biopsies using a novel FAST-SeqS preimplantation genetic testing assay.

RESEARCH QUESTION: Is FAST-SeqS an accurate methodology for preimplantation genetic testing for whole-chromosome aneuploidy (PGT-A)? What additional types of chromosomal abnormalities can be assessed? What are the observed aneuploidy rates in a large clinical cohort? DESIGN: FAST-SeqS, a next-generation sequencing (NGS)-based assay amplifying genome-wide LINE1 repetitive sequences, was validated using reference samples. Sensitivity and specificity were calculated. Clinically derived trophectoderm biopsies submitted for PGT-A were assessed, and aneuploidy and mosaicism rates among biopsies were determined. Clinician-provided outcome rates were calculated. RESULTS: Sensitivity and specificity were over 95% for all aneuploidy types tested in the validation. Comparison of FAST-SeqS with VeriSeq showed high concordance (98.5%). Among embryos with actionable results (n = 182,827), 46.2% were aneuploid. Whole-chromosome aneuploidies were most observed (72.9% without or 8.7% with a segmental aneuploidy), with rates increasing with egg age; segmental aneuploidy rates did not. Segmental aneuploidy (n = 20,557) was observed on all chromosomes (most commonly deletions), with frequencies associated with chromosome length. Mosaic-only abnormalities constituted 10.1% (n = 3862/38145) of samples. Abnormal ploidy constituted 1.8% (n = 2370/128,991) of samples, triploidy being the most common (73.6%). Across 3297 frozen embryo transfers, the mean clinical pregnancy rate was 62% (range 38-80%); the mean combined ongoing pregnancy and live birth rate was 57% (range 38-72%). CONCLUSION: FAST-SeqS is a clinically reliable and scalable method for PGT-A, is comparable to whole-genome amplification-based platforms, and detects additional information related to ploidy using SNP analysis. Results suggest ongoing benefit of PGT-A using FAST-SeqS, consistent with other platforms.

The genomic basis of sporadic and recurrent pregnancy loss: a comprehensive in-depth analysis of 24,900 miscarriages.

RESEARCH QUESTION: What is the genetic cause of sporadic and recurrent pregnancy loss and does the frequency and nature of chromosomal abnormalities play a role? Types and frequency of all identifiable chromosomal abnormalities were determined to inform our understanding, medical management and recurrence risk for patients experiencing pregnancy loss. DESIGN: Genome-wide single-nucleotide polymorphism-based chromosomal microarray (SNP-CMA) were used to evaluate 24,900 products of conception samples from various forms of pregnancy losses. RESULTS: Sporadic miscarriage (64.7%) or recurrent pregnancy loss (RPL) (22%) were the most common referrals. Clinically significant abnormalities were observed in 55.8% (13,910) of samples, variants of uncertain significance in 1.8%, and normal results in 42.4%. In addition to autosomal trisomies (in 36% of samples), polyploidy and large segmental imbalances were identified in 7.8% and 2.8% of samples, respectively. Analysis of sequential samples from 1103 patients who had experienced RPL provided important insight into possible predispositions to RPL. CONCLUSIONS: This expansive chromosomal microarray analyses of pregnancy loss samples illuminates our understanding of the full spectrum, relative frequencies and the role of genomic abnormalities in pregnancy loss. The empiric observations described here provide useful insight for clinicians and highlight the importance of high-resolution genomic testing for comprehensive evaluation and risk assessment of individuals experiencing pregnancy loss.

Elucidating clinical phenotypic variability associated with the polyT tract and TG repeats in CFTR.

Biallelic pathogenic variants in CFTR manifest as cystic fibrosis (CF) or other CFTR-related disorders (CFTR-RDs). The 5T allele, causing alternative splicing and reduced protein activity, is modulated by the adjacent TG repeat element, though previous data have been limited to small, selective cohorts. Here, the risk and spectrum of phenotypes associated with the CFTR TG-T5 haplotype variants (TG11T5, TG12T5, and TG13T5) in the absence of the p.Arg117His variant are evaluated. Individuals who received physician-ordered next-generation sequencing of CFTR were included. TG[11-13]T5 variant frequencies (biallelic or with another CF-causing variant [CFvar]) were calculated. Clinical information reported by the ordering provider or the individual was examined. Among 548,300 individuals, the T5 minor allele frequency (MAF) was 4.2% (TG repeat distribution: TG11 = 68.1%, TG12 = 29.5%, TG13 = 2.4%). When present with a CFvar, each TG[11-13]T5 variant was significantly enriched in individuals with a high suspicion of CF or CFTR-RD (personal/family history of CF/CFTR-RD) compared to those with a low suspicion for CF or CFTR-RD (hereditary cancer screening, CFTR not requisitioned). Compared to CFvar/CFvar individuals, those with TG[11-13]T5/CFvar generally had single-organ involvement, milder symptoms, variable expressivity, and reduced penetrance. These data improve our understanding of disease risks associated with TG[11-13]T5 variants and have important implications for reproductive genetic counseling.

Democratizing genomics: Leveraging software to make genetics an integral part of routine care.

Genetic testing can provide definitive molecular diagnoses and guide clinical management decisions from preconception through adulthood. Innovative solutions for scaling clinical genomics services are necessary if they are to transition from a niche specialty to a routine part of patient care. The expertise of specialists, like genetic counselors and medical geneticists, has traditionally been relied upon to facilitate testing and follow-up, and while ideal, this approach is limited in its ability to integrate genetics into primary care. As individuals, payors, and providers increasingly realize the value of genetics in mainstream medicine, several implementation challenges need to be overcome. These include electronic health record integration, patient and provider education, tools to stay abreast of guidelines, and simplification of the test ordering process. Currently, no single platform offers a holistic view of genetic testing that streamlines the entire process across specialties that begins with identifying at-risk patients in mainstream care settings, providing pretest education, facilitating consent and test ordering, and following up as a "genetic companion" for ongoing management. We describe our vision for using software that includes clinical-grade chatbots and decision support tools, with direct access to genetic counselors and pharmacists within a modular, integrated, end-to-end testing journey.

Physician-directed genetic screening to evaluate personal risk for medically actionable disorders: a large multi-center cohort study.

BACKGROUND: The use of proactive genetic screening for disease prevention and early detection is not yet widespread. Professional practice guidelines from the American College of Medical Genetics and Genomics (ACMG) have encouraged reporting pathogenic variants that confer personal risk for actionable monogenic hereditary disorders, but only as secondary findings from exome or genome sequencing. The Centers for Disease Control and Prevention (CDC) recognizes the potential public health impact of three Tier 1 actionable disorders. Here, we report results of a large multi-center cohort study to determine the yield and potential value of screening healthy individuals for variants associated with a broad range of actionable monogenic disorders, outside the context of secondary findings. METHODS: Eligible adults were offered a proactive genetic screening test by health care providers in a variety of clinical settings. The screening panel based on next-generation sequencing contained up to 147 genes associated with monogenic disorders within cancer, cardiovascular, and other important clinical areas. Sequence and intragenic copy number variants classified as pathogenic, likely pathogenic, pathogenic (low penetrance), or increased risk allele were considered clinically significant and reported. Results were analyzed by clinical area and severity/burden of disease using chi-square tests without Yates' correction. RESULTS: Among 10,478 unrelated adults screened, 1619 (15.5%) had results indicating personal risk for an actionable monogenic disorder. In contrast, only 3.1 to 5.2% had clinically reportable variants in genes suggested by the ACMG version 2 secondary findings list to be examined during exome or genome sequencing, and 2% had reportable variants related to CDC Tier 1 conditions. Among patients, 649 (6.2%) were positive for a genotype associated with a disease of high severity/burden, including hereditary cancer syndromes, cardiovascular disorders, or malignant hyperthermia susceptibility. CONCLUSIONS: This is one of the first real-world examples of specialists and primary care providers using genetic screening with a multi-gene panel to identify health risks in their patients. Nearly one in six individuals screened for variants associated with actionable monogenic disorders had clinically significant results. These findings provide a foundation for further studies to assess the role of genetic screening as part of regular medical care.

One in seven pathogenic variants can be challenging to detect by NGS: an analysis of 450,000 patients with implications for clinical sensitivity and genetic test implementation.

PURPOSE: To evaluate the impact of technically challenging variants on the implementation, validation, and diagnostic yield of commonly used clinical genetic tests. Such variants include large indels, small copy-number variants (CNVs), complex alterations, and variants in low-complexity or segmentally duplicated regions. METHODS: An interlaboratory pilot study used synthetic specimens to assess detection of challenging variant types by various next-generation sequencing (NGS)-based workflows. One well-performing workflow was further validated and used in clinician-ordered testing of more than 450,000 patients. RESULTS: In the interlaboratory study, only 2 of 13 challenging variants were detected by all 10 workflows, and just 3 workflows detected all 13. Limitations were also observed among 11 less-challenging indels. In clinical testing, 21.6% of patients carried one or more pathogenic variants, of which 13.8% (17,561) were classified as technically challenging. These variants were of diverse types, affecting 556 of 1,217 genes across hereditary cancer, cardiovascular, neurological, pediatric, reproductive carrier screening, and other indicated tests. CONCLUSION: The analytic and clinical sensitivity of NGS workflows can vary considerably, particularly for prevalent, technically challenging variants. This can have important implications for the design and validation of tests (by laboratories) and the selection of tests (by clinicians) for a wide range of clinical indications.

Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen).

PURPOSE: Copy-number analysis to detect disease-causing losses and gains across the genome is recommended for the evaluation of individuals with neurodevelopmental disorders and/or multiple congenital anomalies, as well as for fetuses with ultrasound abnormalities. In the decade that this analysis has been in widespread clinical use, tremendous strides have been made in understanding the effects of copy-number variants (CNVs) in both affected individuals and the general population. However, continued broad implementation of array and next-generation sequencing-based technologies will expand the types of CNVs encountered in the clinical setting, as well as our understanding of their impact on human health. METHODS: To assist clinical laboratories in the classification and reporting of CNVs, irrespective of the technology used to identify them, the American College of Medical Genetics and Genomics has developed the following professional standards in collaboration with the National Institutes of Health (NIH)-funded Clinical Genome Resource (ClinGen) project. RESULTS: This update introduces a quantitative, evidence-based scoring framework; encourages the implementation of the five-tier classification system widely used in sequence variant classification; and recommends "uncoupling" the evidence-based classification of a variant from its potential implications for a particular individual. CONCLUSION: These professional standards will guide the evaluation of constitutional CNVs and encourage consistency and transparency across clinical laboratories.

Severe SOPH syndrome due to a novel NBAS mutation in a 27-year-old woman-Review of this pleiotropic, autosomal recessive disorder: Mystery solved after two decades.

Autosomal recessive SOPH syndrome was first described in the Yakuts population of Asia by Maksimova et al. in 2010. It arises from biallelic pathogenic variants in the NBAS gene and is characterized by severe postnatal growth retardation, senile facial appearance, small hands and feet, optic atrophy with loss of visual acuity and color vision, and normal intelligence (OMIM #614800). The presence of Pelger-Hüet anomaly in this disorder led to its name as an acronym for Short stature, Optic nerve atrophy, and Pelger-Hüet anomaly. Recent publications have further contributed to the characterization of this syndrome through additional phenotype-genotype correlations. We review the clinical features described in these publications and report on a 27-year-old woman with dwarfism with osteolysis and multiple skeletal problems, minor anomalies, immunodeficiency, diabetes mellitus, and multiple secondary medical problems. Her condition was considered an unknown autosomal recessive disorder for many years until exome sequencing provided the diagnosis by revealing a founder disease-causing variant that was compound heterozygous with a novel pathogenic variant in NBAS. Based on the major clinical features of this individual and others reported earlier, a revision of the acronym is warranted to facilitate clinical recognition.

Prevalence and properties of intragenic copy-number variation in Mendelian disease genes.

PURPOSE: We investigated the frequencies and characteristics of intragenic copy-number variants (CNVs) in a deep sampling of disease genes associated with monogenic disorders. METHODS: Subsets of 1507 genes were tested using next-generation sequencing to simultaneously detect sequence variants and CNVs in >143,000 individuals referred for genetic testing. We analyzed CNVs in gene panels for hereditary cancer syndromes and cardiovascular, neurological, or pediatric disorders. RESULTS: Our analysis identified 2844 intragenic CNVs in 384 clinically tested genes. CNVs were observed in 1.9% of the entire cohort but in a disproportionately high fraction (9.8%) of individuals with a clinically significant result. CNVs accounted for 4.7-35% of pathogenic variants, depending on clinical specialty. Distinct patterns existed among CNVs in terms of copy number, location, exons affected, clinical classification, and genes affected. Separately, analysis of de-identified data for 599 genes unrelated to the clinical phenotype yielded 4054 CNVs. Most of these CNVs were novel rare events, present as duplications, and enriched in genes associated with recessive disorders or lacking loss-of-function mutational mechanisms. CONCLUSION: Universal intragenic CNV analysis adds substantial clinical sensitivity to genetic testing. Clinically relevant CNVs have distinct properties that distinguish them from CNVs contributing to normal variation in human disease genes.