Frequency of Carriers for Rare Metabolic Diseases in a Brazilian Cohort of 320 Patients

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Epidemiological information on these disorders is usually limited in developing countries. Our objective is to assess carrier frequencies of rare autosomal recessive metabolic diseases in a cohort of Brazilian patients that underwent molecular investigation with exome sequencing and estimate the overall frequency of these diseases using the Hardy-Weinberg equation. We reviewed the molecular findings of 320 symptomatic patients who had carrier status for recessive diseases actively searched. A total of 205 rare variants were reported in 138 different genes associated with metabolic diseases from 156 patients, which represents that almost half (48.8%) of the patients were carriers of at least one heterozygous pathogenic/likely pathogenic variant for rare metabolic disorders. Most of these variants are harbored by genes associated with multisystemic involvement. We estimated the overall frequency for rare recessive metabolic diseases to be 10.96/10,000 people, while the frequency of metabolic diseases potentially identified by newborn screening was estimated to be 2.93/10,000. This study shows the potential research utility of exome sequencing to determine carrier status for rare metabolic diseases, which may be a possible strategy to evaluate the clinical and social burden of these conditions at the population level and guide the optimization of health policies and newborn screening programs. Molecular Biology Whole exome sequencing next-generation sequencing NGS rare diseases carrier carrier frequency recessive Mendelian diseases metabolic diseases inborn errors of metabolism. Figures Figure 1 Background Inborn metabolic diseases (in this article referred to as “metabolic diseases”) are a complex group of disorders of genetic background that affect biosynthesis or breakdown of substances within specific pathways, recognizable by specific biochemical tests [1]. The majority of these disorders present monogenic etiology, autosomal recessive inheritance and multisystemic involvement. Etiology determination may be challenging and involve long-established biochemical studies of abnormal metabolites or enzymatic assays, novel approaches with next generations sequencing (NGS) or even evolving techniques such as proteomics, lipidomics, and glycomics [1]. A rapid diagnosis is key for favorable long-term prognosis for several of these disorders because 1) some affected individuals can die during an acute metabolic crisis and 2) several of these disorders are treatable by special diets, vitamins, cleansing drugs or other newly-developed treatments [2]. For this reason, the majority of nations have created universal newborn screening for some treatable metabolic disorders that are potentially recognized through this early approach. These screening programs, though, may vary widely. The Newborn Screening ACT Sheets and Algorithms (ACMG ACT Sheets) [3] are a good resource for mapping metabolic diseases that may be identified through newborn screening. Considering the clinical relevance of these conditions, the possibility of early identification of several diseases through newborn screening and the potential to dramatically change the outcomes with early treatment, algorithms that allow standardized recognition of these diseases become crucial. A recent thorough nosology has mapped 1015 metabolic diseases in 130 groups and an additional 111 diseases lacking definite classification or poorly characterized [4]. These 130 groups of metabolic diseases are divided into nine major groups: A. disorders of nitrogen-containing compounds; B. disorders of vitamins, cofactors, metals and minerals; C. disorders of carbohydrates; D. mitochondrial disorders of energy metabolism; E. disorders of lipids; F. Disorders of tetrapyrroles; G. Storage disorders; H. Disorders of peroxisomes and oxalate and I. Congenital disorders of glycosylation. Epidemiological information on metabolic diseases may also be useful to guide health policies and newborn screening programs, though this information is often inadequate, particularly in developing countries. Our objective is to assess carrier frequencies of rare autosomal recessive metabolic diseases in a cohort of Brazilian patients that underwent molecular investigation with exome sequencing and estimate the overall frequency of these diseases using the Hardy-Weinberg equation. Methods Selection of Cases, Molecular Analysis, and Bioinformatics We reviewed the molecular findings of exome sequencing from 320 symptomatic patients who had carrier status for recessive diseases actively searched. These patients were selected from an original cohort of 500 symptomatic patients who had undergone molecular analysis for suspected diseases with genetic etiology from 2016 to 2020 in facilities of the Fleury Group. Full details of the clinical features of patients, molecular analysis, bioinformatics protocols, clinical data, and molecular data for primary and secondary findings were previously published [5]. All patients or their legal guardians provided consent before exome analysis, and this study was granted ethics committee approval from both institutions involved (Plataforma Brasil; CAAE# 02617018.3.0000.5474; Fleury# 3.372.339). Carrier Status Identification, Variant Classification and Gene Categories Carrier status for autosomal recessive diseases was actively searched for all 320 patients according to the following protocol of analysis: at least two parallel analyses were performed to preselect variants considering 1) relevant reports from databases (e.g., ClinVar and HGMD) and the literature and 2) variants that were not reported in ClinVar that had an allele frequency <1%, had relevant molecular impact (e.g., potentially leading to loss of function) or an in silico prediction of a functional impact and was located in a clinically relevant gene; these preselected variants were then discussed by a board comprising three experts. Variants were classified according to ACMG guidelines [6]. We excluded the following from our analysis: 1) variants for common autosomal recessive diseases with high allele frequencies and low penetrance; 2) variants associated with no clinical impact (such as traits); 3) variants associated with conditions in which the diagnosis depends on other tests (e.g., variants in BTD , G6PD , HFE and SERPINA1 ); and 4) genes with pseudogenes with high homology (such as CYP21A2 ). We have used nosologies, reviews and professional guidelines available in the literature to divide all genes into clinical categories. The overall frequency of pathogenic or likely pathogenic variants associated with all clinical categories initially studied (intellectual disability, cancer, muscular diseases, ciliopathies, skeletal disorders, immune disorders, epilepsy, hearing loss, retinitis pigmentosa and others) along with overall estimations of frequencies of autosomal recessive diseases were previously published [7]. In this article, we explore novel data regarding the rare variants associated with metabolic disorders; the group of genes part of metabolic disorders was selected based on the nosology by Ferreira et al (2019) [4]. The complete list of variants and genes for metabolic disorders along with nomenclature and classification information of all variants are available in the Supplementary Material (section “Data”). The nosology divides all metabolic diseases into the following categories: A. Disorders of nitrogen-containing compounds (Groups 1-24); B. Disorders of vitamins, cofactors, metals and minerals (Groups 25-45); C. Disorders of carbohydrates (Groups 26-53); D. Mitochondrial disorders of energy metabolism (Groups 54-81); E. Disorders of lipids (Groups 82-97); F. Disorders of tetrapyrroles (Groups 98-99); G. Storage disorders (Groups 100-108); H. Disorders of peroxisomes and oxalate (Groups 109-114); I. Congenital disorders of glycosylation (Groups 115-130); and finally, Disorders with individual families described or poorly characterized. We used this division to attribute each individual gene to a specific group of metabolic disorders. Recessive disease frequency estimation We used the Hardy–Weinberg equation to estimate disease frequencies (q 2 ) based on the respective carrier frequencies (2pq) observed in this study [8]. For this purpose, we considered random mating and the approximation p~1. Clinical impact We used the Human Phenotype Ontology (HPO) database to map possible phenotypic abnormalities encountered in genes associated with metabolic diseases [9]. We chose the following HPO terms that correspond to common clinical findings in metabolic diseases: Intellectual disability (HP:0001249), Encephalopathy (HP:0001298), Seizure (HP:0001250), Acidosis (HP:0001941), Hypoglycemia (HP:0001943), Facial dysmorphism (HP:0001999), Growth abnormality (HP:0001507), Liver disease (HP:0001392), Muscle disease (HP:0003011), Kidney disease (HP:0000077) and Eye disease (HP:0000478). Newborn screening diseases/genes We used the Newborn Screening ACT Sheets and Algorithms (ACMG ACT Sheets) [3] for mapping metabolic diseases that may be identified through newborn screening. The main classes of metabolic diseases catalogued by these documents are: amino acidemias, organic acidemias, fatty acid oxidation disorders, galactosemias and lysosomal storage diseases. We used the IEMbase platform [10] and OMIM to assess the list of genes associated with these diseases. The complete list of metabolic diseases potentially identified through newborn screening along with the genes associated with these diseases are available in the Supplementary Material (section “Newborn screening”). Results Variant Characteristics and Frequency of Heterozygotes We observed a total of 205 occurrences of 172 different variants (23 variants recurred more than once, ranging from two to six times) that were harbored by 138 different genes associated with metabolic diseases in 156 patients, which represents that almost half (48.8%) of the patients were carriers of at least one heterozygous pathogenic/likely pathogenic variant for rare metabolic disorders. On the other hand, P/LP variants associated with metabolic disorders were not found in 164 (51.3%) patients. Individual variant data are available in Supplementary material. Regarding the ACMG classification, 79 different variants were classified as pathogenic and 93 as likely pathogenic. Considering these 172 different variants, the majority of them had been previously observed in the literature (n=124; 72.1%) and were present in the ClinVar Database (n=128; 74.4%). On the other hand, 39 variants (22.7%) were not found in either the literature or the ClinVar database. All these 39 non-reported variants presented at a considerably low frequency in population databases (e.g., frequency lower than 0.01%) along with a predicted loss-of-function mechanism (e.g., stop codon, frameshift), and based on a combination of the PVS1 and PM2 criteria from ACMG guidelines, they were classified as likely pathogenic. Table 1 shows the distribution of genes, variants and the estimated frequency of diseases (q 2 ; estimation per 10,000 individuals) per group of metabolic disorders. Genes and their potential clinical impact Regarding the 138 different genes, 33 (23.9%) belong to group A (disorders of nitrogen-containing compounds), 21 (15.2%) to group D (mitochondrial disorders of energy metabolism) and 19 (13.8%) to group B (disorders of vitamins, cofactors, metals and minerals). The combined frequency of heterozygotes for all the different P/LP variants for the most frequent genes are 1) 1.9% for GBA (Gaucher disease, OMIM#230800), PAH (Phenylketonuria, OMIM#261600) and RNASEH2B (Aicardi-Goutieres syndrome, OMIM#225750); 2) 1.6% for SPG7 (Spastic paraplegia 7, OMIM#607259); 3) 1.3% for ALDOB (Hereditary fructose intolerance, OMIM#229600), ATP7B (Wilson disease, OMIM#277900), CPT2 (CPT II deficiency, OMIM#600649), HEXA (Tay-Sachs disease, OMIM#272800), and PMM2 ( PMM2 -CDG [congenital disorder of glycosylation], OMIM#212065). Considering HPO terms used to estimate the clinical impact and relevance of the genes identified in this study, we observed that the majority (n=125; 90.6%) presented two or more clinical manifestations; more than half (n=81; 58.7%) presented five or more manifestations; eleven genes (8%) presented a sole manifestation and two genes ( CUBN , FMO3 ; 1.4%) did not present any of the 11 clinical manifestation in HPO database. The complete list of genes and their corresponding HPOs are available in the Supplementary Material (section HPO). Table 2 shows the frequency of 11 clinical manifestations from the HPO database in the 138 genes identified in our study. Variants Variants harbored by genes that belong to group A (disorders of nitrogen-containing compounds) were most frequent occurrences (n=51; 24.9%); this group is followed by group G (storage disorders; n= 31; 15.1%) and group D (mitochondrial disorders of energy metabolism; n=29; 14.1%). On the other hand, variants of group F (disorders of tetrapyrroles) were the least frequent (n=6; 2,9%) and we did not find variants harbored by genes associated with disorders with individual families described or poorly characterized (n=0). Table 1 (third column) shows the distribution of occurrences of pathogenic or likely pathogenic (P/LP) variants per group of metabolic disorders. Metabolic diseases frequency estimation Using the Hardy–Weinberg equation, we roughly estimated disease frequencies (q 2 ) based on the respective carrier frequencies (2pq) observed in this study and the approximation of p~1. According to this method, we estimated the following top nine frequencies of recessive diseases per 10,000 people: Gaucher disease: 0.88, phenylketonuria: 0.88, Aicardi-Goutieres syndrome: 0.88, spastic paraplegia: 0.61, hereditary fructose intolerance: 0.39, Wilson disease: 0.39, CPT II deficiency: 0.39, Tay-Sachs disease: 0.39, PMM2 -CDG: 0.39. The combined frequency of these nine diseases is estimated to be 5.2 per 10,000 people, while the combined frequency considering the whole cohort is 10.96/10,000 (Supplementary Material). Table 1 (last column) contains the predicted frequency of diseases per group of metabolic disorders. Newborn Screening diseases A total of 40 variant occurrences (19.5% of total occurrences) are harbored by 24 different genes (17.4% of total genes) that are associated with diseases potentially identified by newborn screening. The combined allele frequencies of these diseases is 0.0625, while the predicted frequency of affected individuals (q 2 ) is 2.93 per 10,000 people. Table 3 shows the distribution of genes, variants and the estimated frequency of diseases (q 2 ; estimation per 10,000 individuals) per group of metabolic disorders potentially identified by newborn screening. Discussion In this article, we have studied the frequencies of carriers for rare metabolic recessive diseases in 320 Brazilian patients and found 205 occurrences of 172 different variants harbored by 138 different genes in 156 patients. It represents 0.64 occurrence per patient in this cohort. Based on our data and the Hardy–Weinberg equation, we estimate an overall frequency of recessive metabolic diseases to be 10.96/10,000 people, or approximately 0.11%. The majority of the variants are harbored by genes associated with multisystemic involvement, which widens clinical and social burden of these conditions. Several of these conditions may manifest with acute crises that often require critical care and may involve seizures (64%), acidosis (32%), hypoglycemia (20%) or encephalopathy (14%). The majority of the diseases may also present with long-term complications, such as growth abnormality (66%), intellectual disability (55%) and other complications addressed in Table 2. It is noteworthy that almost one fifth (18%) of these genes may present with facial dysmorphisms, which is a clinical characteristic generally regarded as non-suggestive of metabolic diseases by clinicians. We also found unexpectedly high frequency of muscle (80%) and ophthalmologic involvement (70%) in the genes harboring P/LP variants. These latter involvements may require specific clinical measurements and should not be neglected in the follow-up of these patients. We have used the HPO database to estimate the clinical impact of the genes found in our study and discussed above. This approach, though, presents some limitations: HPO tends to be highly inclusive even for rare manifestations of diseases; this fact may have overestimated the multisystemic impact of the diseases. On the other hand, some diseases may be neglected; for instance, CUBN gene was not associated with any HPO term studied, though it is known that the conditions associated with this gene may present with growth anomaly, intellectual disability, renal involvement, etc (OMIM#261100). We have estimated the frequency of rare recessive metabolic diseases to be 10.96 per 10,000 people. Other studies using different methodologies have estimated variable frequencies of metabolic disorders: 4.0 (40/100,000) [11], 12,76 (1/784) [12] or 15.6 (66/42,257) [13] per 10,000 people. These estimates, though, are usually challenging especially if they are based on biochemical-based newborn screening programs because several factors may influence the results, including the scope of diseases screened, deaths occurring before a diagnosis is made, ease of access for screening programs, diagnosis confirmation methods, specific regional clinical interests [12]. Additionally, the frequency of these diseases may vary widely in different population groups due to founder effects, endogamy, nonrandom mating, and cultural, religious, social and/or geographical isolation [14]. For example, in endogamous societies this rate can be as high as 8.4% in highly consanguineous populations [15]. Considering the relevant clinical impact of several metabolic diseases, the relatively common combined frequencies and the possibility to improve clinical outcome with early diagnoses, several countries have established newborn screening programs aiming rapid diagnosis and treatment for clinically-actionable metabolic diseases. We have used the Newborn Screening ACT Sheets and Algorithms, from ACMG, to map the metabolic diseases potentially identified by newborn screening programs and we estimate the frequency of these diseases to be 2.93 per 10,000 people. Figure 1 helps to understand the magnitude of the estimated frequency of diseases potentially identified by newborn screening in the universe of predicted frequency of all metabolic disorders. If our estimates are correct, we believe that 26.6% (2.92/10.96) of patients with metabolic disorders may potentially be detected by neonatal screening. Amino acid studies would have the potential to identify 33.6% (0.98/2.92) of potentially detectable diseases, while acylcarnitine analysis the potential to identify 31.8% (fatty acid oxidation disorders: 0.49/2.92 + organic acidemias: 0.44/2.92) and a small fraction (0.7%; 0.02/2.92) would be detected by assays for galactosemia. An important amount of detectable diseases rely on specific tests for lysosomal storage disorders (34.2%; 1.0/2.92) that are generally not widely available in neonatal screening programs. Our study presents several limitations. The first refers to variant classification and pathogenicity presumption: several variants were classified as likely pathogenic based solely on ACMG criteria but were not definitively proven to be pathogenic (disease-causing); while this fact that may have overestimated the carrier frequencies by falsely classifying some variants as P/LP, we took precautions to standardize variant classification by strictly using ACMG classification criteria uniformly for all sequence variants. On the other hand, we may have excluded several variants that are not yet recognized to cause disease, variants not detected by NGS and missense hypomorphic alleles that do not result in a clear and recognizable loss of function. Another important issue is the limited number of patients, which is an important bias that compromises the direct estimation of single monogenic disease frequencies. To minimize this limitation, we used the strategy of studying groups of diseases (instead of single diseases) to estimate frequencies, then diluting the small sample of patients. Our study did not consist of asymptomatic patients, which would be an ideal group for carrier screening studies. However, we took precautions to minimize this bias by eliminating P/LP variants associated with autosomal recessive disorders reported in homozygosity or compound heterozygosity (primary findings) from this analysis because our objective was to study carriers for recessive diseases. Therefore, all patients in this study had only monoallelic variants. Nevertheless, we cannot rule out that some of the variants studied in this work may have contributed to the clinical phenotype of the patient, for example, if the disease presents an unknown codominant, digenic or even oligogenic mechanism or if the other allele harbored a pathogenic or likely pathogenic variant not detected by the NGS methodology (such as intronic or regulatory site variants). Another issue regarding heterozygous carriers is that some monoallelic variants may have clinical impacts and health complications. For instance, some genes (including POLG ) may be associated with autosomal dominant forms that have a generally milder clinical impact. Literature regarding carrier frequencies for autosomal recessive metabolic diseases is limited in Latin America. Most of previously published studies are limited to sole or restricted groups of diseases. Additionally, the majority rely on newborn screening findings without molecular confirmation. We hope that our study encourages other studies that aim to better understand the burden of recessive diseases in our developing countries. Conclusions We studied the frequencies of heterozygotes for rare pathogenic/likely pathogenic alleles of recessive metabolic diseases exome sequencing and observed a total of 205 occurrences of 172 different variants in almost half (48.8%) of the 320 patients. Most of these variants are harbored by genes associated with multisystemic involvement, most frequently muscle disease, eye disease and growth anomalies. Using the Hardy-Weinberg equation, we estimated the overall frequency for rare recessive metabolic diseases to be 10.96/10,000 people, while the frequency of metabolic diseases potentially identified by newborn screening was estimated to be 2.93/10,000 people. This study shows the potential utility to evaluate clinical and social burden of metabolic diseases and guide health policies for these conditions. Declarations Funding: No funding to declare. Conflicts of interest/Competing interests: The following authors are employees (received salary and other bonuses) of Fleury Medicina e Saude: Caio Robledo D' Angioli Costa Quaio, Christine Hsiaoyun Chung, Sandro Felix Perazzio, Aurelio Pimenta Dutra, Caroline Monaco Moreira. Ethics approval This study was granted ethics committee approval from both institutions involved (Plataforma Brasil; CAAE# 02617018.3.0000.5474; Fleury# 3.372.339) Consent to participate All patients or their legal guardians provided written consent before exome analysis Consent for publication All authors are aware, consented and approved this publication. Availability of data and material All data are provided as “Supplementary Material”. Authors' contributions Caio Robledo D’Angioli Costa Quaio, Caroline Monaco Moreira, Christine Hsiaoyun Chung, Sandro Felix Perazzio, Aurelio Pimenta Dutra, Chong Ae Kim: provided the conception and design of the study, acquisition of data, analysis and interpretation of data, drafting the article, revised it critically for important intellectual content and final approval of the version to be submitted. References Morava E, Rahman S, Peters V, Baumgartner MR, Patterson M, Zschocke J (2015) Quo vadis: the re-definition of "inborn metabolic diseases". J Inherit Metab Dis 38:1003-1006. https://doi.org/10.1007/s10545-015-9893-x Saudubray JM, Garcia-Cazorla À (2018) Inborn Errors of Metabolism Overview: Pathophysiology, Manifestations, Evaluation, and Management. Pediatr Clin North Am 65:179-208. https://doi.org/10.1016/j.pcl.2017.11.002 ACMG ACT Sheets and Algorithms [Internet]. Bethesda (MD): American College of Medical Genetics and Genomics; 2001-. Newborn Screening ACT Sheets and Algorithms. Available from: https://www.ncbi.nlm.nih.gov/books/NBK55827/ Ferreira CR, van Karnebeek CDM, Vockley J, Blau N (2019) A proposed nosology of inborn errors of metabolism. Genet Med 21:102-106. https://doi.org/10.1038/s41436-018-0022-8 Quaio CRDC, Moreira CM, Novo-Filho GM, et al (2020) Diagnostic power and clinical impact of exome sequencing in a cohort of 500 patients with rare diseases. Am J Med Genet C Semin Med Genet 184:955-964. https://doi.org/10.1002/ajmg.c.31860 Richards S, Aziz N, Bale S, et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17:405-24. https://doi.org/10.1038/gim.2015.30 Quaio CRDC, Chung CH, Perazzio SF, et al (2021) Frequency of carriers for rare recessive Mendelian diseases in a Brazilian cohort of 320 patients. Am J Med Genet C Semin Med Genet 187:364-372. https://doi.org/10.1002/ajmg.c.31932 Edwards AW (2008) G. H. Hardy (1908) and Hardy-Weinberg equilibrium. Genetics 179:1143-1150. https://doi.org/10.1534/genetics.104.92940 Köhler S, Gargano M, Matentzoglu N, et al (2021) The Human Phenotype Ontology in 2021. Nucleic Acids Res 49:D1207-D1217. https://doi.org/10.1093/nar/gkaa1043 Lee JJY, Wasserman WW, Hoffmann GF, van Karnebeek CDM, Blau N (2018) Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism. Genet Med 20:151-158. https://doi.org/10.1038/gim.2017.108 Applegarth DA, Toone JR, Lowry RB (2000) Incidence of inborn errors of metabolism in British Columbia, 1969-1996. Pediatrics 105:e10. https://doi.org/10.1542/peds.105.1.e10 Sanderson S, Green A, Preece MA, Burton H (2006) The incidence of inherited metabolic disorders in the West Midlands, UK. Arch Dis Child 91:896-9. https://doi.org/10.1136/adc.2005.091637 Zhang W, Yang Y, Peng W, et al (2020) A 7-Year Report of Spectrum of Inborn Errors of Metabolism on Full-Term and Premature Infants in a Chinese Neonatal Intensive Care Unit. Front Genet 10:1302. https://doi.org/10.3389/fgene.2019.01302 Antonarakis SE (2019) Carrier screening for recessive disorders. Nat Rev Genet 20:549-561. https://doi.org/10.1038/s41576-019-0134-2 Keyfi F, Nasseri M, Nayerabadi S, Alaei A, Mokhtariye A, Varasteh A (2018) Frequency of Inborn Errors of Metabolism in a Northeastern Iranian Sample with High Consanguinity Rates. Hum Hered 83:71-78. https://doi.org/10.1159/000488876 Tables Table 1. Distribution of genes, occurrences of pathogenic or likely pathogenic (P/LP) variants, combined allele frequency and predicted frequency of affected individuals per group of metabolic disorders. The first column represents the groups of metabolic disorders according to the nosology by Ferreira et al (2019); the second column represents the number of different genes per group of disorders; the third column congregates all occurrences of variants per group per group of diseases; the fourth column represents the combined allele frequencies per group of metabolic disorders in the cohort of 320 patients; the last column represents the predicted frequency of affected individuals (q 2 ; p~1) per 10,000 individuals (a scale of color was used to compare the distribution of frequencies). The last row congregates all groups. Table 2. Clinical manifestations associated with metabolic diseases genes. The table shows the number of genes presenting each manifestation (second column) and the corresponding frequency in percentage (last column) of 11 clinical phenotypes studied (first column). The clinical phenotypes and the complete list of genes associated with them was based on the Human Phenotype Ontology (HPO) database. A scale of color was used to compare the distribution of frequencies. Table 3. Distribution of genes, occurrences of pathogenic or likely pathogenic (P/LP) variants, combined allele frequency and predicted frequency of affected individuals per group of metabolic disorders potentially identified by newborn screening. The first column represents the groups of metabolic disorders potentially identified by newborn screening according to the Newborn Screening ACT Sheets and Algorithms (ACMG ACT Sheets); the second column represents the number of different genes per group of disorders; the third column congregates all occurrences of variants per group; the fourth column represents the combined allele frequencies in the cohort of 320 patients; the last column represents the predicted frequency of affected individuals (q 2 ; p~1) per 10,000 individuals (a scale of color was used to compare the distribution of frequencies). The last row congregates all groups. Supplementary Files 20211003QuaioSupplement.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 15 Nov, 2021 Reviews received at journal 21 Oct, 2021 Reviewers invited by journal 21 Oct, 2021 Editor assigned by journal 20 Oct, 2021 First submitted to journal 19 Oct, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-995337","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":58182627,"identity":"7482f64f-d7b1-4e4a-b9a1-a13ba98134a2","order_by":0,"name":"Caio Robledo Quaio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYLCCBAYbIMnYeIAULWkgLQ0kaGFgOAwmidPC355j+OFBxXm7te2HgbbU2EQT1CJx5o2xRMKZ28nbziQCtRxLy20gpMVAIsdAIrHtdrLZAaAWxobDRGkx/pHYdi7Z7PxD4rWYAW05YGd2g1hbJM48K7NIOJOcYHYDaEsCMX7hb0/efPNHhZ292fn0hw8+1NgQ1gKKRxBIbECwidRiT5ziUTAKRsEoGJEAAAxIR339vNh/AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2873-2820","institution":"Universidade de São Paulo Instituto da Criança: Universidade de Sao Paulo Instituto da Crianca","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Caio","middleName":"Robledo","lastName":"Quaio","suffix":""},{"id":58182628,"identity":"a915aaf7-9dba-4613-a260-e3f12b194998","order_by":1,"name":"Caroline Monaco Moreira","email":"","orcid":"","institution":"Fleury Group: Grupo Fleury","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"Monaco","lastName":"Moreira","suffix":""},{"id":58182629,"identity":"67a97421-0f46-4813-b8d4-4aadbe654856","order_by":2,"name":"Christine Hsiaoyun Chung","email":"","orcid":"","institution":"Fleury SA: Grupo Fleury","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christine","middleName":"Hsiaoyun","lastName":"Chung","suffix":""},{"id":58182630,"identity":"d0553f23-dc34-47c2-b142-e2bd912689b0","order_by":3,"name":"Sandro Felix Perazzio","email":"","orcid":"","institution":"Fleury SA: Grupo Fleury","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sandro","middleName":"Felix","lastName":"Perazzio","suffix":""},{"id":58182631,"identity":"9b4cd30a-44e0-4df4-94e7-9bf025be1105","order_by":4,"name":"Aurelio Pimenta Dutra","email":"","orcid":"","institution":"Fleury SA: Grupo Fleury","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aurelio","middleName":"Pimenta","lastName":"Dutra","suffix":""},{"id":58182632,"identity":"96918345-73ac-437a-9b13-88d7d2437f84","order_by":5,"name":"Chong Ae Kim","email":"","orcid":"","institution":"Fleury SA: Grupo Fleury","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chong","middleName":"Ae","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2021-10-19 13:30:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-995337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-995337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14864258,"identity":"fba94730-f72e-46fa-8566-d21158f2db1b","added_by":"auto","created_at":"2021-10-25 14:27:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55834,"visible":true,"origin":"","legend":"Estimated frequency of rare recessive metabolic diseases potentially identified by newborn screening. The graphic compares the magnitude of estimated frequencies (q2; numbers per 10,000 individuals) of all metabolic recessive disorders (light yellow) with the subgroups of diseases potentially identified by newborn screening (blue) and the consolidated frequency of all diseases potentially identified by newborn screening (dark yellow).","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-995337/v1/aa839a8f134b7ae133729c05.png"},{"id":14864262,"identity":"d0ff8779-6309-4f8a-ac46-a09a4442c9c4","added_by":"auto","created_at":"2021-10-25 14:27:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":312390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-995337/v1/ac78ef8c-4cf9-4a50-9443-598b095b37ce.pdf"},{"id":14864259,"identity":"8495b68a-013a-4755-a06f-a447db73382e","added_by":"auto","created_at":"2021-10-25 14:27:34","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":255134,"visible":true,"origin":"","legend":"","description":"","filename":"20211003QuaioSupplement.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-995337/v1/d336d8c7b013992f73b20e14.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eFrequency of Carriers for Rare Metabolic Diseases in a Brazilian Cohort of 320 Patients\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eInborn metabolic diseases (in this article referred to as \u0026ldquo;metabolic diseases\u0026rdquo;) are a complex group of disorders of genetic background that affect biosynthesis or breakdown of substances within specific pathways, recognizable by specific biochemical tests [1]. The majority of these disorders present monogenic etiology, autosomal recessive inheritance and multisystemic involvement. Etiology determination may be challenging and involve long-established biochemical studies of abnormal metabolites or enzymatic assays, novel approaches with next generations sequencing (NGS) or even evolving techniques such as proteomics, lipidomics, and glycomics [1].\u003c/p\u003e\n\u003cp\u003eA rapid diagnosis is key for favorable long-term prognosis for several of these disorders because 1) some affected individuals can die during an acute metabolic crisis and 2) several of these disorders are treatable by special diets, vitamins, cleansing drugs or other newly-developed treatments [2]. For this reason, the majority of nations have created universal newborn screening for some treatable metabolic disorders that are potentially recognized through this early approach. These screening programs, though, may vary widely. The Newborn Screening ACT Sheets and Algorithms (ACMG ACT Sheets) [3] are a good resource for mapping metabolic diseases that may be identified through newborn screening.\u003c/p\u003e\n\u003cp\u003eConsidering the clinical relevance of these conditions, the possibility of early identification of several diseases through newborn screening and the potential to dramatically change the outcomes with early treatment, algorithms that allow standardized recognition of these diseases become crucial. A recent thorough nosology has mapped 1015 metabolic diseases in 130 groups and an additional 111 diseases lacking definite classification or poorly characterized [4]. These 130 groups of metabolic diseases are divided into nine major groups: A. disorders of nitrogen-containing compounds; B. disorders of vitamins, cofactors, metals and minerals; C. disorders of carbohydrates; D. mitochondrial disorders of energy metabolism; E. disorders of lipids; F. Disorders of tetrapyrroles; G. Storage disorders; H. Disorders of peroxisomes and oxalate and I. Congenital disorders of glycosylation.\u003c/p\u003e\n\u003cp\u003eEpidemiological information on metabolic diseases may also be useful to guide health policies and newborn screening programs, though this information is often inadequate, particularly in developing countries. Our objective is to assess carrier frequencies of rare autosomal recessive metabolic diseases in a cohort of Brazilian patients that underwent molecular investigation with exome sequencing and estimate the overall frequency of these diseases using the Hardy-Weinberg equation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eSelection of Cases, Molecular Analysis, and Bioinformatics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe reviewed the molecular findings of exome sequencing from 320 symptomatic patients who had carrier status for recessive diseases actively searched. These patients were selected from an original cohort of 500 symptomatic patients who had undergone molecular analysis for suspected diseases with genetic etiology from 2016 to 2020 in facilities of the Fleury Group. Full details of the clinical features of patients, molecular analysis, bioinformatics protocols, clinical data, and molecular data for primary and secondary findings were previously published [5].\u003c/p\u003e\n\u003cp\u003eAll patients or their legal guardians provided consent before exome analysis, and this study was granted ethics committee approval from both institutions involved (Plataforma Brasil; CAAE# 02617018.3.0000.5474; Fleury# 3.372.339).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCarrier Status Identification, Variant Classification and Gene Categories\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCarrier status for autosomal recessive diseases was actively searched for all 320 patients according to the following protocol of analysis: at least two parallel analyses were performed to preselect variants considering 1) relevant reports from databases (e.g., ClinVar and HGMD) and the literature and 2) variants that were not reported in ClinVar that had an allele frequency \u0026lt;1%, had relevant molecular impact (e.g., potentially leading to loss of function) or an in silico prediction of a functional impact and was located in a clinically relevant gene; these preselected variants were then discussed by a board comprising three experts. Variants were classified according to ACMG guidelines [6].\u003c/p\u003e\n\u003cp\u003eWe excluded the following from our analysis: 1) variants for common autosomal recessive diseases with high allele frequencies and low penetrance; 2) variants associated with no clinical impact (such as traits); 3) variants associated with conditions in which the diagnosis depends on other tests (e.g., variants in \u003cem\u003eBTD\u003c/em\u003e, \u003cem\u003eG6PD\u003c/em\u003e, \u003cem\u003eHFE\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSERPINA1\u003c/em\u003e); and 4) genes with pseudogenes with high homology (such as \u003cem\u003eCYP21A2\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eWe have used nosologies, reviews and professional guidelines available in the literature to divide all genes into clinical categories. The overall frequency of pathogenic or likely pathogenic variants associated with all clinical categories initially studied (intellectual disability, cancer, muscular diseases, ciliopathies, skeletal disorders, immune disorders, epilepsy, hearing loss, retinitis pigmentosa and others) along with overall estimations of frequencies of autosomal recessive diseases were previously published [7].\u003c/p\u003e\n\u003cp\u003eIn this article, we explore novel data regarding the rare variants associated with metabolic disorders; the group of genes part of metabolic disorders was selected based on the nosology by Ferreira et al (2019) [4]. The complete list of variants and genes for metabolic disorders along with nomenclature and classification information of all variants are available in the Supplementary Material (section \u0026ldquo;Data\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003eThe nosology divides all metabolic diseases into the following categories: A. Disorders of nitrogen-containing compounds (Groups 1-24); B. Disorders of vitamins, cofactors, metals and minerals (Groups 25-45); C. Disorders of carbohydrates (Groups 26-53); D. Mitochondrial disorders of energy metabolism (Groups 54-81); E. Disorders of lipids (Groups 82-97); F. Disorders of tetrapyrroles (Groups 98-99); G. Storage disorders (Groups 100-108); H. Disorders of peroxisomes and oxalate (Groups 109-114); I. Congenital disorders of glycosylation (Groups 115-130); and finally, Disorders with individual families described or poorly characterized. We used this division to attribute each individual gene to a specific group of metabolic disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecessive disease frequency estimation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used the Hardy\u0026ndash;Weinberg equation to estimate disease frequencies (q\u003csup\u003e2\u003c/sup\u003e) based on the respective carrier frequencies (2pq) observed in this study [8]. For this purpose, we considered random mating and the approximation p~1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical impact\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used the Human Phenotype Ontology (HPO) database to map possible phenotypic abnormalities encountered in genes associated with metabolic diseases [9]. We chose the following HPO terms that correspond to common clinical findings in metabolic diseases: Intellectual disability (HP:0001249), Encephalopathy (HP:0001298), Seizure (HP:0001250), Acidosis (HP:0001941), Hypoglycemia (HP:0001943), Facial dysmorphism (HP:0001999), Growth abnormality (HP:0001507), Liver disease (HP:0001392), Muscle disease (HP:0003011), Kidney disease (HP:0000077) and Eye disease (HP:0000478).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNewborn screening diseases/genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used the Newborn Screening ACT Sheets and Algorithms (ACMG ACT Sheets) [3] for mapping metabolic diseases that may be identified through newborn screening. The main classes of metabolic diseases catalogued by these documents are: amino acidemias, organic acidemias, fatty acid oxidation disorders, galactosemias and lysosomal storage diseases. We used the IEMbase platform [10] and OMIM to assess the list of genes associated with these diseases. The complete list of metabolic diseases potentially identified through newborn screening along with the genes associated with these diseases are available in the Supplementary Material (section \u0026ldquo;Newborn screening\u0026rdquo;).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eVariant Characteristics and Frequency of Heterozygotes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe observed a total of 205 occurrences of 172 different variants (23 variants recurred more than once, ranging from two to six times) that were harbored by 138 different genes associated with metabolic diseases in 156 patients, which represents that almost half (48.8%) of the patients were carriers of at least one heterozygous pathogenic/likely pathogenic variant for rare metabolic disorders. On the other hand, P/LP variants associated with metabolic disorders were not found in 164 (51.3%) patients. Individual variant data are available in Supplementary material.\u003c/p\u003e\n\u003cp\u003eRegarding the ACMG classification, 79 different variants were classified as pathogenic and 93 as likely pathogenic. Considering these 172 different variants, the majority of them had been previously observed in the literature (n=124; 72.1%) and were present in the ClinVar Database (n=128; 74.4%). On the other hand, 39 variants (22.7%) were not found in either the literature or the ClinVar database. All these 39 non-reported variants presented at a considerably low frequency in population databases (e.g., frequency lower than 0.01%) along with a predicted loss-of-function mechanism (e.g., stop codon, frameshift), and based on a combination of the PVS1 and PM2 criteria from ACMG guidelines, they were classified as likely pathogenic.\u003c/p\u003e\n\u003cp\u003eTable 1 shows the distribution of genes, variants and the estimated frequency of diseases (q\u003csup\u003e2\u003c/sup\u003e; estimation per 10,000 individuals) per group of metabolic disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenes and their potential clinical impact\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the 138 different genes, 33 (23.9%) belong to group A (disorders of nitrogen-containing compounds), 21 (15.2%) to group D (mitochondrial disorders of energy metabolism) and 19 (13.8%) to group B (disorders of vitamins, cofactors, metals and minerals).\u003c/p\u003e\n\u003cp\u003eThe combined frequency of heterozygotes for all the different P/LP variants for the most frequent genes are 1) 1.9% for \u003cem\u003eGBA\u0026nbsp;\u003c/em\u003e(Gaucher disease, OMIM#230800), \u003cem\u003ePAH\u0026nbsp;\u003c/em\u003e(Phenylketonuria, OMIM#261600) and \u003cem\u003eRNASEH2B\u0026nbsp;\u003c/em\u003e(Aicardi-Goutieres syndrome, OMIM#225750); 2) 1.6% for \u003cem\u003eSPG7\u0026nbsp;\u003c/em\u003e(Spastic paraplegia 7, OMIM#607259); 3) 1.3% for \u003cem\u003eALDOB\u0026nbsp;\u003c/em\u003e(Hereditary fructose intolerance, OMIM#229600), \u003cem\u003eATP7B\u0026nbsp;\u003c/em\u003e(Wilson disease, OMIM#277900), \u003cem\u003eCPT2\u0026nbsp;\u003c/em\u003e(CPT II deficiency, OMIM#600649), \u003cem\u003eHEXA\u0026nbsp;\u003c/em\u003e(Tay-Sachs disease, OMIM#272800), and \u003cem\u003ePMM2\u0026nbsp;\u003c/em\u003e(\u003cem\u003ePMM2\u003c/em\u003e-CDG [congenital disorder of glycosylation], OMIM#212065).\u003c/p\u003e\n\u003cp\u003eConsidering HPO terms used to estimate the clinical impact and relevance of the genes identified in this study, we observed that the majority (n=125; 90.6%) presented two or more clinical manifestations; more than half (n=81; 58.7%) presented five or more manifestations; eleven genes (8%) presented a sole manifestation and two genes (\u003cem\u003eCUBN\u003c/em\u003e, \u003cem\u003eFMO3\u003c/em\u003e; 1.4%) did not present any of the 11 clinical manifestation in HPO database. The complete list of genes and their corresponding HPOs are available in the Supplementary Material (section HPO). Table 2 shows the frequency of 11 clinical manifestations from the HPO database in the 138 genes identified in our study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVariants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eVariants harbored by genes that belong to group A (disorders of nitrogen-containing compounds) were most frequent occurrences (n=51; 24.9%); this group is followed by group G (storage disorders; n= 31; 15.1%) and group D (mitochondrial disorders of energy metabolism; n=29; 14.1%). On the other hand, variants of group F (disorders of tetrapyrroles) were the least frequent (n=6; 2,9%) and we did not find variants harbored by genes associated with disorders with individual families described or poorly characterized (n=0). Table 1 (third column) shows the distribution of occurrences of pathogenic or likely pathogenic (P/LP) variants per group of metabolic disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMetabolic diseases frequency estimation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Hardy\u0026ndash;Weinberg equation, we roughly estimated disease frequencies (q\u003csup\u003e2\u003c/sup\u003e) based on the respective carrier frequencies (2pq) observed in this study and the approximation of p~1. According to this method, we estimated the following top nine frequencies of recessive diseases per 10,000 people: Gaucher disease: 0.88, phenylketonuria: 0.88, Aicardi-Goutieres syndrome: 0.88, spastic paraplegia: 0.61, hereditary fructose intolerance: 0.39, Wilson disease: 0.39, CPT II deficiency: 0.39, Tay-Sachs disease: 0.39, \u003cem\u003ePMM2\u003c/em\u003e-CDG: 0.39. The combined frequency of these nine diseases is estimated to be 5.2 per 10,000 people, while the combined frequency considering the whole cohort is 10.96/10,000 (Supplementary Material). Table 1 (last column) contains the predicted frequency of diseases per group of metabolic disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNewborn Screening diseases\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 40 variant occurrences (19.5% of total occurrences) are harbored by 24 different genes (17.4% of total genes) that are associated with diseases potentially identified by newborn screening. The combined allele frequencies of these diseases is 0.0625, while the predicted frequency of affected individuals (q\u003csup\u003e2\u003c/sup\u003e) is 2.93 per 10,000 people. Table 3 shows the distribution of genes, variants and the estimated frequency of diseases (q\u003csup\u003e2\u003c/sup\u003e; estimation per 10,000 individuals) per group of metabolic disorders potentially identified by newborn screening.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this article, we have studied the frequencies of carriers for rare metabolic recessive diseases in 320 Brazilian patients and found 205 occurrences of 172 different variants harbored by 138 different genes in 156 patients. It represents 0.64 occurrence per patient in this cohort. Based on our data and the Hardy\u0026ndash;Weinberg equation, we estimate an overall frequency of recessive metabolic diseases to be 10.96/10,000 people, or approximately 0.11%.\u003c/p\u003e\n\u003cp\u003eThe majority of the variants are harbored by genes associated with multisystemic involvement, which widens clinical and social burden of these conditions. Several of these conditions may manifest with acute crises that often require critical care and may involve seizures (64%), acidosis (32%), hypoglycemia (20%) or encephalopathy (14%). The majority of the diseases may also present with long-term complications, such as growth abnormality (66%), intellectual disability (55%) and other complications addressed in Table 2.\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that almost one fifth (18%) of these genes may present with facial dysmorphisms, which is a clinical characteristic generally regarded as non-suggestive of metabolic diseases by clinicians. We also found unexpectedly high frequency of muscle (80%) and ophthalmologic involvement (70%) in the genes harboring P/LP variants. These latter involvements may require specific clinical measurements and should not be neglected in the follow-up of these patients.\u003c/p\u003e\n\u003cp\u003eWe have used the HPO database to estimate the clinical impact of the genes found in our study and discussed above. This approach, though, presents some limitations: HPO tends to be highly inclusive even for rare manifestations of diseases; this fact may have overestimated the multisystemic impact of the diseases. On the other hand, some diseases may be neglected; for instance, \u003cem\u003eCUBN\u0026nbsp;\u003c/em\u003egene was not associated with any HPO term studied, though it is known that the conditions associated with this gene may present with growth anomaly, intellectual disability, renal involvement, etc (OMIM#261100).\u003c/p\u003e\n\u003cp\u003eWe have estimated the frequency of rare recessive metabolic diseases to be 10.96 per 10,000 people. Other studies using different methodologies have estimated variable frequencies of metabolic disorders: 4.0 (40/100,000) [11], 12,76 (1/784) [12] or 15.6 (66/42,257) [13] per 10,000 people. These estimates, though, are usually challenging especially if they are based on biochemical-based newborn screening programs because several factors may influence the results, including the scope of diseases screened, deaths occurring before a diagnosis is made, ease of access for screening programs, diagnosis confirmation methods, specific regional clinical interests [12]. Additionally, the frequency of these diseases may vary widely in different population groups due to founder effects, endogamy, nonrandom mating, and cultural, religious, social and/or geographical isolation [14]. For example, in endogamous societies this rate can be as high as 8.4% in highly consanguineous populations [15].\u003c/p\u003e\n\u003cp\u003eConsidering the relevant clinical impact of several metabolic diseases, the relatively common combined frequencies and the possibility to improve clinical outcome with early diagnoses, several countries have established newborn screening programs aiming rapid diagnosis and treatment for clinically-actionable metabolic diseases. We have used the Newborn Screening ACT Sheets and Algorithms, from ACMG, to map the metabolic diseases potentially identified by newborn screening programs and we estimate the frequency of these diseases to be 2.93 per 10,000 people. Figure 1 helps to understand the magnitude of the estimated frequency of diseases potentially identified by newborn screening in the universe of predicted frequency of all metabolic disorders. If our estimates are correct, we believe that 26.6% (2.92/10.96) of patients with metabolic disorders may potentially be detected by neonatal screening. Amino acid studies would have the potential to identify 33.6% (0.98/2.92) of potentially detectable diseases, while acylcarnitine analysis the potential to identify 31.8% (fatty acid oxidation disorders: 0.49/2.92 + organic acidemias: 0.44/2.92) and a small fraction (0.7%; 0.02/2.92) would be detected by assays for galactosemia. An important amount of detectable diseases rely on specific tests for lysosomal storage disorders (34.2%; 1.0/2.92) that are generally not widely available in neonatal screening programs.\u003c/p\u003e\n\u003cp\u003eOur study presents several limitations. The first refers to variant classification and pathogenicity presumption: several variants were classified as likely pathogenic based solely on ACMG criteria but were not definitively proven to be pathogenic (disease-causing); while this fact that may have overestimated the carrier frequencies by falsely classifying some variants as P/LP, we took precautions to standardize variant classification by strictly using ACMG classification criteria uniformly for all sequence variants. On the other hand, we may have excluded several variants that are not yet recognized to cause disease, variants not detected by NGS and missense hypomorphic alleles that do not result in a clear and recognizable loss of function.\u003c/p\u003e\n\u003cp\u003eAnother important issue is the limited number of patients, which is an important bias that compromises the direct estimation of single monogenic disease frequencies. To minimize this limitation, we used the strategy of studying groups of diseases (instead of single diseases) to estimate frequencies, then diluting the small sample of patients.\u003c/p\u003e\n\u003cp\u003eOur study did not consist of asymptomatic patients, which would be an ideal group for carrier screening studies. However, we took precautions to minimize this bias by eliminating P/LP variants associated with autosomal recessive disorders reported in homozygosity or compound heterozygosity (primary findings) from this analysis because our objective was to study carriers for recessive diseases. Therefore, all patients in this study had only monoallelic variants. Nevertheless, we cannot rule out that some of the variants studied in this work may have contributed to the clinical phenotype of the patient, for example, if the disease presents an unknown codominant, digenic or even oligogenic mechanism or if the other allele harbored a pathogenic or likely pathogenic variant not detected by the NGS methodology (such as intronic or regulatory site variants). Another issue regarding heterozygous carriers is that some monoallelic variants may have clinical impacts and health complications. For instance, some genes (including \u003cem\u003ePOLG\u003c/em\u003e) may be associated with autosomal dominant forms that have a generally milder clinical impact.\u003c/p\u003e\n\u003cp\u003eLiterature regarding carrier frequencies for autosomal recessive metabolic diseases is limited in Latin America. Most of previously published studies are limited to sole or restricted groups of diseases. Additionally, the majority rely on newborn screening findings without molecular confirmation. We hope that our study encourages other studies that aim to better understand the burden of recessive diseases in our developing countries.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe studied the frequencies of heterozygotes for rare pathogenic/likely pathogenic alleles of recessive metabolic diseases exome sequencing and observed a total of 205 occurrences of 172 different variants in almost half (48.8%) of the 320 patients. Most of these variants are harbored by genes associated with multisystemic involvement, most frequently muscle disease, eye disease and growth anomalies. Using the Hardy-Weinberg equation, we estimated the overall frequency for rare recessive metabolic diseases to be 10.96/10,000 people, while the frequency of metabolic diseases potentially identified by newborn screening was estimated to be 2.93/10,000 people. This study shows the potential utility to evaluate clinical and social burden of metabolic diseases and guide health policies for these conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following authors are employees (received salary and other bonuses) of Fleury Medicina e Saude: Caio Robledo D\u0026apos; Angioli Costa Quaio, Christine Hsiaoyun Chung, Sandro Felix Perazzio, Aurelio Pimenta Dutra, Caroline Monaco Moreira.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was granted ethics committee approval from both institutions involved (Plataforma Brasil; CAAE# 02617018.3.0000.5474; Fleury# 3.372.339)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients or their legal guardians provided written consent before exome analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are aware, consented and approved this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are provided as \u0026ldquo;Supplementary Material\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCaio Robledo D\u0026rsquo;Angioli Costa Quaio, Caroline Monaco Moreira, Christine Hsiaoyun Chung, Sandro Felix Perazzio, Aurelio Pimenta Dutra, Chong Ae Kim: provided the conception and design of the study, acquisition of data, analysis and interpretation of data, drafting the article, revised it critically for important intellectual content and final approval of the version to be submitted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorava E, Rahman S, Peters V, Baumgartner MR, Patterson M, Zschocke J (2015) Quo vadis: the re-definition of \u0026quot;inborn metabolic diseases\u0026quot;. J Inherit Metab Dis 38:1003-1006. https://doi.org/10.1007/s10545-015-9893-x\u003c/li\u003e\n\u003cli\u003eSaudubray JM, Garcia-Cazorla \u0026Agrave; (2018) Inborn Errors of Metabolism Overview: Pathophysiology, Manifestations, Evaluation, and Management. Pediatr Clin North Am 65:179-208. https://doi.org/10.1016/j.pcl.2017.11.002\u003c/li\u003e\n\u003cli\u003eACMG ACT Sheets and Algorithms [Internet]. Bethesda (MD): American College of Medical Genetics and Genomics; 2001-. Newborn Screening ACT Sheets and Algorithms. Available from: https://www.ncbi.nlm.nih.gov/books/NBK55827/\u003c/li\u003e\n\u003cli\u003eFerreira CR, van Karnebeek CDM, Vockley J, Blau N (2019) A proposed nosology of inborn errors of metabolism. Genet Med 21:102-106. https://doi.org/10.1038/s41436-018-0022-8\u003c/li\u003e\n\u003cli\u003eQuaio CRDC, Moreira CM, Novo-Filho GM, et al (2020) Diagnostic power and clinical impact of exome sequencing in a cohort of 500 patients with rare diseases. Am J Med Genet C Semin Med Genet 184:955-964. https://doi.org/10.1002/ajmg.c.31860\u003c/li\u003e\n\u003cli\u003eRichards S, Aziz N, Bale S, et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17:405-24. https://doi.org/10.1038/gim.2015.30\u003c/li\u003e\n\u003cli\u003eQuaio CRDC, Chung CH, Perazzio SF, et al (2021) Frequency of carriers for rare recessive Mendelian diseases in a Brazilian cohort of 320 patients. Am J Med Genet C Semin Med Genet 187:364-372. https://doi.org/10.1002/ajmg.c.31932\u003c/li\u003e\n\u003cli\u003eEdwards AW (2008) G. H. Hardy (1908) and Hardy-Weinberg equilibrium. Genetics 179:1143-1150. https://doi.org/10.1534/genetics.104.92940\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;hler S, Gargano M, Matentzoglu N, et al (2021) The Human Phenotype Ontology in 2021. Nucleic Acids Res 49:D1207-D1217. https://doi.org/10.1093/nar/gkaa1043\u003c/li\u003e\n\u003cli\u003eLee JJY, Wasserman WW, Hoffmann GF, van Karnebeek CDM, Blau N (2018) Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism. Genet Med 20:151-158. https://doi.org/10.1038/gim.2017.108\u003c/li\u003e\n\u003cli\u003eApplegarth DA, Toone JR, Lowry RB (2000) Incidence of inborn errors of metabolism in British Columbia, 1969-1996. Pediatrics 105:e10. https://doi.org/10.1542/peds.105.1.e10\u003c/li\u003e\n\u003cli\u003eSanderson S, Green A, Preece MA, Burton H (2006) The incidence of inherited metabolic disorders in the West Midlands, UK. Arch Dis Child 91:896-9. https://doi.org/10.1136/adc.2005.091637\u003c/li\u003e\n\u003cli\u003eZhang W, Yang Y, Peng W, et al (2020) A 7-Year Report of Spectrum of Inborn Errors of Metabolism on Full-Term and Premature Infants in a Chinese Neonatal Intensive Care Unit. Front Genet 10:1302. https://doi.org/10.3389/fgene.2019.01302\u003c/li\u003e\n\u003cli\u003eAntonarakis SE (2019) Carrier screening for recessive disorders. Nat Rev Genet 20:549-561. https://doi.org/10.1038/s41576-019-0134-2\u003c/li\u003e\n\u003cli\u003eKeyfi F, Nasseri M, Nayerabadi S, Alaei A, Mokhtariye A, Varasteh A (2018) Frequency of Inborn Errors of Metabolism in a Northeastern Iranian Sample with High Consanguinity Rates. Hum Hered 83:71-78. https://doi.org/10.1159/000488876\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Distribution of genes, occurrences of pathogenic or likely pathogenic (P/LP) variants, combined allele frequency and predicted frequency of affected individuals per group of metabolic disorders. The first column represents the groups of metabolic disorders according to the nosology by Ferreira et al (2019); the second column represents the number of different genes per group of disorders; the third column congregates all occurrences of variants per group per group of diseases; the fourth column represents the combined allele frequencies per group of metabolic disorders in the cohort of 320 patients; the last column represents the predicted frequency of affected individuals (q\u003csup\u003e2\u003c/sup\u003e; p~1) per 10,000 individuals (a scale of color was used to compare the distribution of frequencies).\u0026nbsp;The last row congregates all groups.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"567\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Clinical manifestations associated with metabolic diseases genes. The table shows the number of genes presenting each manifestation (second column) and the corresponding frequency in percentage (last column) of 11 clinical phenotypes studied (first column). The clinical phenotypes and the complete list of genes associated with them was based on the Human Phenotype Ontology (HPO) database. A scale of color was used to compare the distribution of frequencies.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"447\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Distribution of genes, occurrences of pathogenic or likely pathogenic (P/LP) variants, combined allele frequency and predicted frequency of affected individuals per group of metabolic disorders potentially identified by newborn screening. The first column represents the groups of metabolic disorders potentially identified by newborn screening according to the Newborn Screening ACT Sheets and Algorithms (ACMG ACT Sheets); the second column represents the number of different genes per group of disorders; the third column congregates all occurrences of variants per group; the fourth column represents the combined allele frequencies in the cohort of 320 patients; the last column represents the predicted frequency of affected individuals (q\u003csup\u003e2\u003c/sup\u003e; p~1) per 10,000 individuals (a scale of color was used to compare the distribution of frequencies).\u0026nbsp;The last row congregates all groups.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"539\" 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\" 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Whole exome sequencing, next-generation sequencing, NGS, rare diseases, carrier, carrier frequency, recessive Mendelian diseases, metabolic diseases, inborn errors of metabolism.","lastPublishedDoi":"10.21203/rs.3.rs-995337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-995337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeveral metabolic disorders follow an autosomal recessive inheritance pattern. Epidemiological information on these disorders is usually limited in developing countries. Our objective is to assess carrier frequencies of rare autosomal recessive metabolic diseases in a cohort of Brazilian patients that underwent molecular investigation with exome sequencing and estimate the overall frequency of these diseases using the Hardy-Weinberg equation. We reviewed the molecular findings of 320 symptomatic patients who had carrier status for recessive diseases actively searched. A total of 205 rare variants were reported in 138 different genes associated with metabolic diseases from 156 patients, which represents that almost half (48.8%) of the patients were carriers of at least one heterozygous pathogenic/likely pathogenic variant for rare metabolic disorders. Most of these variants are harbored by genes associated with multisystemic involvement. We estimated the overall frequency for rare recessive metabolic diseases to be 10.96/10,000 people, while the frequency of metabolic diseases potentially identified by newborn screening was estimated to be 2.93/10,000. This study shows the potential research utility of exome sequencing to determine carrier status for rare metabolic diseases, which may be a possible strategy to evaluate the clinical and social burden of these conditions at the population level and guide the optimization of health policies and newborn screening programs.\u003c/p\u003e","manuscriptTitle":"Frequency of Carriers for Rare Metabolic Diseases in a Brazilian Cohort of 320 Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-25 14:27:32","doi":"10.21203/rs.3.rs-995337/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2021-11-15T05:34:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-10-21T13:34:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-10-21T13:04:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-10-20T14:55:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2021-10-19T09:29:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"138711ee-f17f-46e3-9bcb-e790e69320eb","owner":[],"postedDate":"October 25th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":8072098,"name":"Molecular Biology"}],"tags":[],"updatedAt":"2022-02-08T17:15:10+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-25 14:27:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-995337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-995337","identity":"rs-995337","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}