American journal of human genetics最新文献

{"title":"Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies.","authors":"Zikun Yang, Chen Wang, Yuridia Selene Posadas-Garcia, Valeria Añorve-Garibay, Badri Vardarajan, Andrés Moreno Estrada, Mashaal Sohail, Richard Mayeux, Iuliana Ionita-Laza","doi":"10.1016/j.ajhg.2025.02.020","DOIUrl":"10.1016/j.ajhg.2025.02.020","url":null,"abstract":"<p><p>Genome-wide association studies (GWASs) in ancestrally diverse populations are rapidly expanding, opening up unique opportunities for novel gene discoveries and increased utility of genetic findings in non-European individuals. A popular technique to identify putative causal variants at GWAS loci is via statistical fine-mapping. Despite tremendous efforts, fine-mapping remains a very challenging task, even in the relatively simple scenario of studies with a single, homogeneous population. For studies with admixed individuals, such as within Latin America and the Caribbean, methods for gene discovery are still limited. Here, we propose a Bayesian model for fine-mapping in admixed populations, CARMA-X, that addresses some of the unique challenges of admixed individuals. The proposed method includes an estimation method for the linkage disequilibrium (LD) matrix that accounts for small reference panels for admixed individuals, heterogeneity across populations and cross-ancestry LD, and a Bayesian hypothesis test that leads to robust fine-mapping when relying on external reference panels of modest size for LD estimation. Using simulations, we compare performance with recently proposed fine-mapping methods for multi-ancestry studies and show that the proposed model provides higher power while controlling false discoveries, especially when using an out-of-sample LD matrix. We further illustrate our approach through applications to two Latin American genetic studies, the Estudio Familiar de Influencia Genética en Alzheimer (EFIGA) study in the Dominican Republic and the Mexican Biobank, where we show the benefit of modeling ancestry-specific effects by prioritizing putative causal variants and genes, including several findings driven by ancestry-specific effects in the African and Native American ancestries.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":" ","pages":"1215-1232"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural biology in variant interpretation: Perspectives and practices from two studies.","authors":"Matthew J Varga, Marcy E Richardson, Adam Chamberlin","doi":"10.1016/j.ajhg.2025.03.010","DOIUrl":"10.1016/j.ajhg.2025.03.010","url":null,"abstract":"<p><p>Structural biology offers a powerful lens through which to assess genetic variants by providing insights into their impact on clinically relevant protein structure and function. Due to the availability of new, user-friendly, web-based tools, structural analyses by wider audiences have become more mainstream. These new tools, including AlphaMissense and AlphaFold, have recently been in the limelight due to their initial success and projected future promise; however, the intricacies and limitations of using these tools still need to be disseminated to the more general audience that is likely to use them in variant analysis. Here, we expound on frameworks applying structural biology to variant interpretation by examining two accompanying articles. To this end, we explore the nuances of choosing the correct protein model, compare and contrast various structural approaches, and highlight both the advantages and limitations of employing structural biology in variant interpretation. Using two articles published in this issue of The American Journal of Human Genetics as a baseline, we focus on case studies in TP53 and BRCA1 to illuminate gene-specific differences in the applications of structural information, which illustrate the complexities inherent in this field. Additionally, we discuss the implications of recent advancements, such as AlphaFold, and provide practical guidance for researchers navigating variant interpretation using structural biology.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":"112 5","pages":"984-992"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence.","authors":"Lobna Ramadane-Morchadi, Nitsan Rotenberg, Ada Esteban-Sánchez, Cristina Fortuno, Alicia Gómez-Sanz, Matthew J Varga, Adam Chamberlin, Marcy E Richardson, Kyriaki Michailidou, Pedro Pérez-Segura, Amanda B Spurdle, Miguel de la Hoya","doi":"10.1016/j.ajhg.2024.12.011","DOIUrl":"10.1016/j.ajhg.2024.12.011","url":null,"abstract":"<p><p>Classification of missense variants is challenging. Lacking compelling clinical and/or functional data, ACMG/AMP lines of evidence are restricted to PM2 (rarity code applied at supporting level) and PP3/BP4 (computational evidence based mostly on multiple-sequence-alignment conservation tools). Currently, the ClinGen ENIGMA BRCA1/2 Variant Curation Expert Panel uses BayesDel to apply PP3/BP4 to missense variants located in the BRCA1 RING/BRCT domains. The ACMG/AMP framework does not refer explicitly to protein structure as a putative source of pathogenic/benign evidence. Here, we tested the value of incorporating structure-based evidence such as relative solvent accessibility (RSA), folding stability (ΔΔG), and/or AlphaMissense pathogenicity to the classification of BRCA1 missense variants. We used MAVE functional scores as proxies for pathogenicity/benignity. We computed RSA and FoldX5.0 ΔΔG predictions using as alternative input templates for either PDB files or AlphaFold2 models, and we retrieved pre-computed AlphaMissense and BayesDel scores. We calculated likelihood ratios toward pathogenicity/benignity provided by the tools (individually or combined). We performed a clinical validation of major findings using the large-scale BRIDGES case-control dataset. AlphaMissense outperforms ΔΔG and BayesDel, providing similar PP3/BP4 evidence strengths with lower rate of variants in the uninformative score range. AlphaMissense combined with ΔΔG increases evidence strength granularity. AlphaFold2 models perform well as input templates for ΔΔG predictions. Regardless of the tool, BP4 (but not PP3) is highly dependent on RSA, with benignity evidence provided only to variants targeting buried or partially buried residues (RSA ≤ 60%). Stratification by functional domain did not reveal major differences. In brief, structure-based analysis improves PP3/BP4 assessment, uncovering a relevant role for RSA.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":"112 5","pages":"993-1002"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-cost generation of clinical-grade, layperson-friendly pharmacogenetic passports using oligonucleotide arrays.","authors":"Pauline Lanting, Robert Warmerdam, Jelle Slager, Harm Brugge, Taichi Ochi, Marloes Benjamins, Esteban Lopera-Maya, Soesma Jankipersadsing, Jody Gelderloos-Arends, Daphne Teuben, Dennis Hendriksen, Bart Charbon, Lennart Johansson, Thijs Oude Munnink, Nienke de Boer-Veger, Bob Wilffert, Morris Swertz, Daan Touw, Patrick Deelen, Nine Knoers, Jackie Dekens, Lude Franke","doi":"10.1016/j.ajhg.2025.03.003","DOIUrl":"10.1016/j.ajhg.2025.03.003","url":null,"abstract":"<p><p>Pharmacogenomic (PGx) information is essential for precision medicine, enabling drug prescriptions to be personalized according to an individual's genetic background. Almost all individuals will carry a genetic marker that affects their drug response, so the ideal drug prescription for these individuals will differ from the population-level guidelines. Currently, PGx information is often not available at first prescription, reducing its effectiveness. In the Netherlands, pharmacogenetic information is most often obtained using dedicated single-gene assays, making it expensive and time consuming to generate complete multi-gene PGx profiles. We therefore hypothesized that we could also use genome-wide oligonucleotide genotyping arrays to generate comprehensive PGx information (PGx passports), thereby decreasing the cost and time required for PGx testing and lowering the barrier to generating PGx information prior to first prescription. Taking advantage of existing genetic data generated in two biobanks, we developed and validated Asterix, a low-cost, clinical-grade PGx passport pipeline for 12 PGx genes. In these biobanks, we performed and clinically validated genetic variant calling and statistical phasing and imputation. In addition, we developed and validated a CYP2D6 copy-number-variant-calling tool, forgoing the need to use separate PCR-based copy-number detection. Ultimately, we returned 1,227 PGx passports to biobank participants via a layperson-friendly app, improving knowledge of PGx among citizens. Our study demonstrates the feasibility of a low-cost, clinical-grade PGx passport pipeline that could be readily implemented in clinical settings to enhance personalized healthcare, ensuring that patients receive the most effective and safe drug therapy based on their unique genetic makeup.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":" ","pages":"1015-1028"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus guidelines for assessing eligibility of pathogenic DNA variants for antisense oligonucleotide treatments.","authors":"David Cheerie, Margaret M Meserve, Danique Beijer, Charu Kaiwar, Logan Newton, Ana Lisa Taylor Tavares, Aubrie Soucy Verran, Emma Sherrill, Stefanie Leonard, Stephan J Sanders, Emily Blake, Nour Elkhateeb, Aastha Gandhi, Nicole S Y Liang, Jack T Morgan, Anna Verwillow, Jan Verheijen, Andrew Giles, Sean Williams, Maya Chopra, Laura Croft, Hormos Salimi Dafsari, Alice E Davidson, Jennifer Friedman, Anne Gregor, Bushra Haque, Rosan Lechner, Kylie-Ann Montgomery, Mina Ryten, Emil Schober, Gabriele Siegel, Patricia J Sullivan, Ella F Whittle, Bianca Zardetto, Timothy W Yu, Matthis Synofzik, Annemieke Aartsma-Rus, Gregory Costain, Marlen C Lauffer","doi":"10.1016/j.ajhg.2025.02.017","DOIUrl":"10.1016/j.ajhg.2025.02.017","url":null,"abstract":"<p><p>Of the around 7,000 known rare diseases worldwide, disease-modifying treatments are available for fewer than 5%, leaving millions of individuals without specialized therapeutic strategies. In recent years, antisense oligonucleotides (ASOs) have shown promise as individualized genetic interventions for rare genetic diseases. However, there is currently no consensus on which disease-causing DNA variants are suitable candidates for this type of genetic therapy. The patient identification working group of the N=1 Collaborative (N1C), alongside an international group of volunteer assessors, has developed and piloted consensus guidelines for assessing the eligibility of pathogenic DNA variants for ASO treatments. We herein present the N1C VARIANT (variant assessments toward eligibility for antisense oligonucleotide treatment) guidelines, including the guiding scientific principles and our approach to consensus building. Pathogenic, disease-causing variants can be assessed for the three currently best-established ASO treatment approaches: splice correction, exon skipping, and downregulation of RNA transcripts. A genetic variant is classified as \"eligible,\" \"likely eligible,\" \"unlikely eligible,\" or \"not eligible\" in relation to the different approaches or as \"unable to assess.\" We also review key considerations related to assessing the upregulation of transcripts from the wild-type allele, an emerging ASO therapeutic strategy. We provide additional tools and training materials to enable clinicians and researchers to use these guidelines for their eligibility assessments. With this initial edition of our N1C VARIANT guidelines, we provide the rare genetic disease community with guidance on how to identify suitable candidates for variant-specific ASO-based therapies and the possibility of integrating such assessments into routine clinical practice.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":" ","pages":"975-983"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143727389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of protein stability and AlphaMissense scores improves bioinformatic impact prediction for p53 missense and in-frame amino acid deletion variants.","authors":"Nitsan Rotenberg, Cristina Fortuno, Matthew J Varga, Adam C Chamberlin, Lobna Ramadane-Morchadi, Bing-Jian Feng, Miguel de la Hoya, Marcy E Richardson, Amanda B Spurdle","doi":"10.1016/j.ajhg.2025.01.012","DOIUrl":"10.1016/j.ajhg.2025.01.012","url":null,"abstract":"<p><p>The clinical classification of germline missense variants and single-amino-acid deletions is challenging. The BayesDel and Align-GVGD bioinformatic prediction tools currently used for ClinGen TP53 variant curation expert panel (VCEP) classification do not directly capture changes in protein folding stability, measured using computed destabilization energies (ΔΔG scores). The AlphaMissense tool recently developed by Google DeepMind to predict pathogenicity for all human proteome missense variants is trained in part using AlphaFold2 architecture. Our study investigated whether protein folding stability and/or AlphaMissense scores could improve impact prediction for p53 missense and single-amino-acid deletion variants. ΔΔG scores were calculated for missense variants using FoldX and for single-amino-acid deletions using an AlphaFold2/RosettaRelax protocol. Residue surface exposure was categorized using relative solvent accessibility (RSA) measures. The predictive values of ΔΔG scores, AlphaMissense, BayesDel, and Align-GVGD were examined using Boruta and binary logistic regression based on functionally defined reference sets. The likelihood ratio (LR) toward pathogenicity was estimated and used to refine optimal categories for predicting variant pathogenicity for different RSA values. We showed that current VCEP predictive approaches for missense variants were improved by integrating ΔΔG scores ≥2.5 kcal/mol for partially buried and buried residues, but better performance was achieved using AlphaMissense with ΔΔG and RSA. For deletion variants, ΔΔG scores ≥4.8 Rosetta energy unit (REU) in buried residues outperformed currently used predictive approaches. Future TP53 VCEP specifications for p53 missense impact prediction may consider AlphaMissense, ΔΔG score, and RSA combined for substitution variants and ΔΔG score alone for deletion variants.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":"112 5","pages":"1003-1014"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes.","authors":"Verena Zuber, Toinét Cronjé, Na Cai, Dipender Gill, Leonardo Bottolo","doi":"10.1016/j.ajhg.2025.03.005","DOIUrl":"10.1016/j.ajhg.2025.03.005","url":null,"abstract":"<p><p>Current Mendelian randomization (MR) methods do not reflect complex relationships among multiple exposures and outcomes as is typical for real-life applications. We introduce MrDAG, a Bayesian causal graphical model for summary-level MR analysis to detect dependency relations within the exposures, the outcomes, and between them to improve causal effects estimation. MrDAG combines three causal inference strategies. It uses genetic variation as instrumental variables to account for unobserved confounders. It performs structure learning to detect and orientate the direction of the dependencies within the exposures and the outcomes. Finally, interventional calculus is employed to derive principled causal effect estimates. In MrDAG the directionality of the causal effects between the exposures and the outcomes is assumed known, i.e., the exposures can only be potential causes of the outcomes, and no reverse causation is allowed. In the simulation study, MrDAG outperforms recently proposed one-outcome-at-a-time and multi-response multi-variable Bayesian MR methods as well as causal graphical models under the constraint on edges' orientation from the exposures to the outcomes. MrDAG was motivated to unravel how lifestyle and behavioral exposures impact mental health. It highlights first, education and second, smoking as effective points of intervention given their important downstream effects on mental health. It also enables the identification of a novel path between smoking and the genetic liability to schizophrenia and cognition, demonstrating the complex pathways toward mental health. These insights would have been impossible to delineate without modeling the paths between multiple exposures and outcomes at once.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":" ","pages":"1173-1198"},"PeriodicalIF":8.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143778738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Widespread recessive effects on common diseases in a cohort of 44,000 British Pakistanis and Bangladeshis with high autozygosity.","authors":"Teng Hiang Heng, Klaudia Walter, Qin Qin Huang, Juha Karjalainen, Mark J Daly, Henrike O Heyne, Daniel S Malawsky, Georgios Kalantzis, Sarah Finer, David A van Heel, Hilary C Martin","doi":"10.1016/j.ajhg.2025.03.020","DOIUrl":"https://doi.org/10.1016/j.ajhg.2025.03.020","url":null,"abstract":"<p><p>Genetic association studies have focused on testing additive models in cohorts with European ancestry. Little is known about recessive effects on common diseases, specifically for non-European ancestry. Genes & Health is a cohort of British Pakistani and Bangladeshi individuals with elevated rates of consanguinity and endogamy, making it suitable to study recessive effects. We imputed variants into a genotyped dataset (n = 44,190) by using two reference panels: a set of 4,982 whole-exome sequences from within the cohort and the Trans-Omics for Precision Medicine (TOPMed-r2) panel. We performed association testing with 898 diseases from electronic health records. 185 independent loci reached genome-wide significance (p < 5 × 10^-8) under the recessive model, with p values lower than under the additive model, and >40% of these were novel. 140 loci demonstrated nominally significant (p < 0.05) dominance deviation p values, confirming a recessive association pattern. Sixteen loci in three clusters were significant at a Bonferroni threshold, accounting for multiple phenotypes tested (p < 5.4 × 10^-12). In FinnGen, we replicated 44% of the expected number of Bonferroni-significant loci we were powered to replicate, at least one from each cluster, including an intronic variant in patatin-like phospholipase domain-containing protein 3 (PNPLA3; rs66812091) and non-alcoholic fatty liver disease, a previously reported additive association. We present evidence suggesting that the association is recessive instead (odds ratio [OR] = 1.3, recessive p = 2 × 10^-12, additive p = 2 × 10^-11, dominance deviation p = 3 × 10^-2, and FinnGen recessive OR = 1.3 and p = 6 × 10^-12). We identified a novel protective recessive association between a missense variant in SGLT4 (rs61746559), a sodium-glucose transporter with a possible role in the renin-angiotensin-aldosterone system, and hypertension (OR = 0.2, p = 3 × 10^-8, dominance deviation p = 7 × 10^-6). These results motivate interrogating recessive effects on common diseases more widely.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":" ","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143956606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitochondrial DNA disease discovery through evaluation of genotype and phenotype data: The Solve-RD experience.","authors":"Thiloka Ratnaike, Ida Paramonov, Catarina Olimpio, Alexander Hoischen, Sergi Beltran, Leslie Matalonga, Rita Horváth","doi":"10.1016/j.ajhg.2025.04.003","DOIUrl":"https://doi.org/10.1016/j.ajhg.2025.04.003","url":null,"abstract":"<p><p>The diagnosis of mitochondrial DNA (mtDNA) diseases remains challenging with next-generation sequencing, where bioinformatic analysis is usually more focused on the nuclear genome. We developed a workflow for the evaluation of mtDNA diseases and applied it in a large European rare disease cohort (Solve-RD). A semi-automated bioinformatic pipeline with MToolBox was used to filter the unsolved Solve-RD cohort for rare mtDNA variants after validating this pipeline on exome datasets of 42 individuals previously diagnosed with mtDNA variants. Variants were filtered based on blood heteroplasmy levels (≥1%) and reported association with disease. Overall, 10,157 exome and genome datasets from 9,923 affected individuals from 9,483 families within Solve-RD met the quality inclusion criteria. 136 mtDNA variants in 135 undiagnosed individuals were prioritized using the filtering approach. A focused MitoPhen-based phenotype similarity scoring method was tested in a separate genetically diagnosed \"phenotype test cohort\" consisting of nuclear gene and mtDNA diseases using a receiving operator characteristic evaluation. We applied the MitoPhen-based phenotype similarity score of >0.3, which was highly sensitive for detecting mtDNA diseases in the phenotype test cohort, to the filtered cohort of 135 undiagnosed individuals. This aided the prioritization of 34 out of 37 (92%) individuals who received confirmed and likely causative mtDNA disease diagnoses. The phenotypic evaluation was limited by the quality of input data in some individuals. The overall pipeline led to an additional diagnostic yield of 0.4% in a cohort where mitochondrial disease was not initially suspected. This highlights the value of our mtDNA analysis pipeline in diverse datasets.</p>","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":" ","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bi-allelic UGGT1 variants cause a congenital disorder of glycosylation.","authors":"Zain Dardas,Laura Harrold,Daniel G Calame,Claire G Salter,Takashi Kikuma,Kevin P Guay,Bobby G Ng,Kanae Sano,Ahmad K Saad,Haowei Du,Riccardo Sangermano,Sohil G Patankar,Shalini N Jhangiani,Semra Gürsoy,Mohamed S Abdel-Hamid,Mahmoud K H Ahmed,Reza Maroofian,Rauan Kaiyrzhanov,Kamran Salayev,Wendy D Jones,Ana Pérez Caballero,Lucy McGavin,Michael Spiller,Miranda Durkie,Nick Wood,Lauren O'Grady,Paula Goldenberg,Ann M Neumeyer,Amber Begtrup,Sherif F Abdel-Ghafar,Maha S Zaki,Hilde Van Esch,Jennifer E Posey,Olivia K Wenger,Ethan M Scott,Kinga M Bujakowska,Richard A Gibbs,Davut Pehlivan,Dana Marafi,Joseph S Leslie,Nishanka Ubeyratna,Jacob Day,Martina Owens,Jessica Settle,Soher Balkhy,Abdullah Tamim,Lama Alabdi,Fowzan S Alkuraya,Yoichi Takeda,Hudson H Freeze,Daniel N Hebert,James R Lupski,Andrew H Crosby,Emma L Baple","doi":"10.1016/j.ajhg.2025.03.018","DOIUrl":"https://doi.org/10.1016/j.ajhg.2025.03.018","url":null,"abstract":"Congenital disorders of glycosylation (CDGs) comprise a large heterogeneous group of metabolic conditions caused by defects in glycoprotein and glycolipid glycan assembly and remodeling, a fundamental molecular process with wide-ranging biological roles. Herein, we describe bi-allelic UGGT1 variants in fifteen individuals from ten unrelated families of various ethnic backgrounds as a cause of a distinctive CDG of variable severity. The cardinal clinical features of UGGT1-CDG involve developmental delay, intellectual disability, seizures, characteristic facial features, and microcephaly in the majority (9/11 affected individuals for whom measurements were available). The more severely affected individuals display congenital heart malformations, variable skeletal abnormalities including scoliosis, and hepatic and renal involvement, including polycystic kidneys mimicking autosomal recessive polycystic kidney disease. Clinical studies defined genotype-phenotype correlations, showing bi-allelic UGGT1 loss-of-function variants associated with increased disease severity, including death in infancy. UGGT1 encodes UDP-glucose:glycoprotein glucosyltransferase 1, an enzyme critical for maintaining quality control of N-linked glycosylation. Molecular studies showed that pathogenic UGGT1 variants impair UGGT1 glucosylation and catalytic activity, disrupt mRNA splicing, or inhibit endoplasmic reticulum (ER) retention. Collectively, our data provide a comprehensive genetic, clinical, and molecular characterization of UGGT1-CDG, broadening the spectrum of N-linked glycosylation disorders.","PeriodicalId":7659,"journal":{"name":"American journal of human genetics","volume":"24 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}