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Ben Braiek M, Szymczak S, André C, Bardou P, Fidelle F, Granado-Tajada I, Plisson-Petit F, Sarry J, Woloszyn F, Moreno-Romieux C, Fabre S. A single base pair duplication in the SLC33A1 gene is associated with fetal losses and neonatal lethality in Manech Tête Rousse dairy sheep. Anim Genet 2024; 55:644-657. [PMID: 38922751 DOI: 10.1111/age.13459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/07/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024]
Abstract
We recently discovered that the Manech Tête Rousse (MTR) deficient homozygous haplotype 2 (MTRDHH2) probably carries a recessive lethal mutation in sheep. In this study, we fine-mapped this region through whole-genome sequencing of five MTRDHH2 heterozygous carriers and 95 non-carriers from various ovine breeds. We identified a single base pair duplication within the SLC33A1 gene, leading to a frameshift mutation and a premature stop codon (p.Arg246Alafs*3). SLC33A1 encodes a transmembrane transporter of acetyl-coenzyme A that is crucial for cellular metabolism. To investigate the lethality of this mutation in homozygous MTR sheep, we performed at-risk matings using artificial insemination (AI) between heterozygous SLC33A1 variant carriers (SLC33A1_dupG). Pregnancy was confirmed 15 days post-AI using a blood test measuring interferon Tau-stimulated MX1 gene expression. Ultrasonography between 45 and 60 days post-AI revealed a 12% reduction in AI success compared with safe matings, indicating embryonic/fetal loss. This was supported by the MX1 differential expression test suggesting fetal losses between 15 and 60 days of gestation. We also observed a 34.7% pre-weaning mortality rate in 49 lambs born from at-risk matings. Homozygous SLC33A1_dupG lambs accounted for 47% of this mortality, with deaths occurring mostly within the first 5 days without visible clinical signs. Therefore, appropriate management of SLC33A1_dupG with an allele frequency of 0.04 in the MTR selection scheme would help increase overall fertility and lamb survival.
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Affiliation(s)
- Maxime Ben Braiek
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan, France
| | - Soline Szymczak
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan, France
| | | | | | | | - Itsasne Granado-Tajada
- Department of Animal Production, NEIKER-BRTA Basque Institute of Agricultural Research and Development, Arkaute, Spain
| | | | - Julien Sarry
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan, France
| | - Florent Woloszyn
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan, France
| | | | - Stéphane Fabre
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan, France
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2
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Cook AL, Sur S, Dobbyn L, Watson E, Cohen JD, Ptak B, Lee BS, Paul S, Hsiue E, Popoli M, Vogelstein B, Papadopoulos N, Bettegowda C, Gabrielson K, Zhou S, Kinzler KW, Wyhs N. Identification of nonsense-mediated decay inhibitors that alter the tumor immune landscape. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573594. [PMID: 38234817 PMCID: PMC10793421 DOI: 10.1101/2023.12.28.573594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Despite exciting developments in cancer immunotherapy, its broad application is limited by the paucity of targetable antigens on the tumor cell surface. As an intrinsic cellular pathway, nonsense-mediated decay (NMD) conceals neoantigens through the destruction of the RNA products from genes harboring truncating mutations. We developed and conducted a high throughput screen, based on the ratiometric analysis of transcripts, to identify critical mediators of NMD. This screen implicated disruption of kinase SMG1's phosphorylation of UPF1 as a potential disruptor of NMD. This led us to design a novel SMG1 inhibitor, KVS0001, that elevates the expression of transcripts and proteins resulting from truncating mutations in vivo and in vitro . Most importantly, KVS0001 concomitantly increased the presentation of immune-targetable HLA class I-associated peptides from NMD-downregulated proteins on the surface of cancer cells. KVS0001 provides new opportunities for studying NMD and the diseases in which NMD plays a role, including cancer and inherited diseases. One Sentence Summary Disruption of the nonsense-mediated decay pathway with a newly developed SMG1 inhibitor with in-vivo activity increases the expression of T-cell targetable cancer neoantigens resulting from truncating mutations.
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3
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Tritto V, Bettinaglio P, Mangano E, Cesaretti C, Marasca F, Castronovo C, Bordoni R, Battaglia C, Saletti V, Ranzani V, Bodega B, Eoli M, Natacci F, Riva P. Genetic/epigenetic effects in NF1 microdeletion syndrome: beyond the haploinsufficiency, looking at the contribution of not deleted genes. Hum Genet 2024; 143:775-795. [PMID: 38874808 PMCID: PMC11186880 DOI: 10.1007/s00439-024-02683-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/03/2024] [Indexed: 06/15/2024]
Abstract
NF1 microdeletion syndrome, accounting for 5-11% of NF1 patients, is caused by a deletion in the NF1 region and it is generally characterized by a severe phenotype. Although 70% of NF1 microdeletion patients presents the same 1.4 Mb type-I deletion, some patients may show additional clinical features. Therefore, the contribution of several pathogenic mechanisms, besides haploinsufficiency of some genes within the deletion interval, is expected and needs to be defined. We investigated an altered expression of deletion flanking genes by qPCR in patients with type-1 NF1 deletion, compared to healthy donors, possibly contributing to the clinical traits of NF1 microdeletion syndrome. In addition, the 1.4-Mb deletion leads to changes in the 3D chromatin structure in the 17q11.2 region. Specifically, this deletion alters DNA-DNA interactions in the regions flanking the breakpoints, as demonstrated by our 4C-seq analysis. This alteration likely causes position effect on the expression of deletion flanking genes.Interestingly, 4C-seq analysis revealed that in microdeletion patients, an interaction was established between the RHOT1 promoter and the SLC6A4 gene, which showed increased expression. We performed NGS on putative modifier genes, and identified two "likely pathogenic" rare variants in RAS pathway, possibly contributing to incidental phenotypic features.This study provides new insights into understanding the pathogenesis of NF1 microdeletion syndrome and suggests a novel pathomechanism that contributes to the expression phenotype in addition to haploinsufficiency of genes located within the deletion.This is a pivotal approach that can be applied to unravel microdeletion syndromes, improving precision medicine, prognosis and patients' follow-up.
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Affiliation(s)
- Viviana Tritto
- Department of Medical Biotechnology and Translational Medicine (BIOMETRA), University of Milan, Segrate, Milan, Italy
| | - Paola Bettinaglio
- Department of Medical Biotechnology and Translational Medicine (BIOMETRA), University of Milan, Segrate, Milan, Italy
| | - Eleonora Mangano
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate (Milan), Italy
| | - Claudia Cesaretti
- Medical Genetics Unit, Woman-Child-Newborn Department, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Federica Marasca
- Genome Biology Unit, Istituto Nazionale di Genetica Molecolare (INGM) "Romeo ed Enrica Invernizzi", Milan, Italy
| | - Chiara Castronovo
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate (Milan), Italy
| | - Roberta Bordoni
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate (Milan), Italy
| | - Cristina Battaglia
- Department of Medical Biotechnology and Translational Medicine (BIOMETRA), University of Milan, Segrate, Milan, Italy
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate (Milan), Italy
| | - Veronica Saletti
- Developmental Neurology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Valeria Ranzani
- Genome Biology Unit, Istituto Nazionale di Genetica Molecolare (INGM) "Romeo ed Enrica Invernizzi", Milan, Italy
| | - Beatrice Bodega
- Genome Biology Unit, Istituto Nazionale di Genetica Molecolare (INGM) "Romeo ed Enrica Invernizzi", Milan, Italy
- Department of Biosciences (DBS), University of Milan, Milan, Italy
| | - Marica Eoli
- Molecular Neuroncology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Federica Natacci
- Medical Genetics Unit, Woman-Child-Newborn Department, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy.
| | - Paola Riva
- Department of Medical Biotechnology and Translational Medicine (BIOMETRA), University of Milan, Segrate, Milan, Italy.
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Kohram M, Sanderson AE, Loui A, Thompson PV, Vashistha H, Shomar A, Oltvai ZN, Salman H. Nonlethal deleterious mutation-induced stress accelerates bacterial aging. Proc Natl Acad Sci U S A 2024; 121:e2316271121. [PMID: 38709929 PMCID: PMC11098108 DOI: 10.1073/pnas.2316271121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/29/2024] [Indexed: 05/08/2024] Open
Abstract
Random mutagenesis, including when it leads to loss of gene function, is a key mechanism enabling microorganisms' long-term adaptation to new environments. However, loss-of-function mutations are often deleterious, triggering, in turn, cellular stress and complex homeostatic stress responses, called "allostasis," to promote cell survival. Here, we characterize the differential impacts of 65 nonlethal, deleterious single-gene deletions on Escherichia coli growth in three different growth environments. Further assessments of select mutants, namely, those bearing single adenosine triphosphate (ATP) synthase subunit deletions, reveal that mutants display reorganized transcriptome profiles that reflect both the environment and the specific gene deletion. We also find that ATP synthase α-subunit deleted (ΔatpA) cells exhibit elevated metabolic rates while having slower growth compared to wild-type (wt) E. coli cells. At the single-cell level, compared to wt cells, individual ΔatpA cells display near normal proliferation profiles but enter a postreplicative state earlier and exhibit a distinct senescence phenotype. These results highlight the complex interplay between genomic diversity, adaptation, and stress response and uncover an "aging cost" to individual bacterial cells for maintaining population-level resilience to environmental and genetic stress; they also suggest potential bacteriostatic antibiotic targets and -as select human genetic diseases display highly similar phenotypes, - a bacterial origin of some human diseases.
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Affiliation(s)
- Maryam Kohram
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA15260
| | - Amy E. Sanderson
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA15260
| | - Alicia Loui
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA15260
| | | | - Harsh Vashistha
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA15260
| | - Aseel Shomar
- Department of Chemical Engineering, Technion–Israel Institute of Technology, Haifa32000, Israel
| | - Zoltán N. Oltvai
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA15260
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA15260
- Department of Pathology and Laboratory Medicine, University of Rochester, Rochester, NY14627
| | - Hanna Salman
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA15260
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5
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Beaumont RN, Hawkes G, Gunning AC, Wright CF. Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance. Genome Med 2024; 16:64. [PMID: 38671509 PMCID: PMC11046769 DOI: 10.1186/s13073-024-01333-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Genetic variants that severely alter protein products (e.g. nonsense, frameshift) are often associated with disease. For some genes, these predicted loss-of-function variants (pLoFs) are observed throughout the gene, whilst in others, they occur only at specific locations. We hypothesised that, for genes linked with monogenic diseases that display incomplete penetrance, pLoF variants present in apparently unaffected individuals may be limited to regions where pLoFs are tolerated. To test this, we investigated whether pLoF location could explain instances of incomplete penetrance of variants expected to be pathogenic for Mendelian conditions. METHODS We used exome sequence data in 454,773 individuals in the UK Biobank (UKB) to investigate the locations of pLoFs in a population cohort. We counted numbers of unique pLoF, missense, and synonymous variants in UKB in each quintile of the coding sequence (CDS) of all protein-coding genes and clustered the variants using Gaussian mixture models. We limited the analyses to genes with ≥ 5 variants of each type (16,473 genes). We compared the locations of pLoFs in UKB with all theoretically possible pLoFs in a transcript, and pathogenic pLoFs from ClinVar, and performed simulations to estimate the false-positive rate of non-uniformly distributed variants. RESULTS For most genes, all variant classes fell into clusters representing broadly uniform variant distributions, but genes in which haploinsufficiency causes developmental disorders were less likely to have uniform pLoF distribution than other genes (P < 2.2 × 10-6). We identified a number of genes, including ARID1B and GATA6, where pLoF variants in the first quarter of the CDS were rescued by the presence of an alternative translation start site and should not be reported as pathogenic. For other genes, such as ODC1, pLoFs were located approximately uniformly across the gene, but pathogenic pLoFs were clustered only at the end, consistent with a gain-of-function disease mechanism. CONCLUSIONS Our results suggest the potential benefits of localised constraint metrics and that the location of pLoF variants should be considered when interpreting variants.
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Affiliation(s)
- Robin N Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, EX1 2LU, UK.
| | - Gareth Hawkes
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, EX1 2LU, UK
| | - Adam C Gunning
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, EX1 2LU, UK
- Exeter Genomics Laboratory, Royal Devon University Healthcare NHS Foundation Trust, Exeter, EX2 5DW, UK
| | - Caroline F Wright
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, EX1 2LU, UK.
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Kolakada D, Fu R, Biziaev N, Shuvalov A, Lore M, Campbell AE, Cortázar MA, Sajek MP, Hesselberth JR, Mukherjee N, Alkalaeva E, Jagannathan S. Systematic analysis of nonsense variants uncovers peptide release rate as a novel modifier of nonsense-mediated mRNA decay efficiency. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575080. [PMID: 38260612 PMCID: PMC10802582 DOI: 10.1101/2024.01.10.575080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Nonsense variants underlie many genetic diseases. The phenotypic impact of nonsense variants is determined by Nonsense-mediated mRNA decay (NMD), which degrades transcripts with premature termination codons (PTCs). NMD activity varies across transcripts and cellular contexts via poorly understood mechanisms. Here, by leveraging human genetic datasets, we uncover that the amino acid preceding the PTC dramatically affects NMD activity in human cells. We find that glycine codons in particular support high levels of NMD and are enriched before PTCs but depleted before normal termination codons (NTCs). Gly-PTC enrichment is most pronounced in human genes that tolerate loss-of-function variants. This suggests a strong biological impact for Gly-PTC in ensuring robust elimination of potentially toxic truncated proteins from non-essential genes. Biochemical assays revealed that the peptide release rate during translation termination is highly dependent on the identity of the amino acid preceding the stop codon. This release rate is the most critical feature determining NMD activity across our massively parallel reporter assays. Together, we conclude that NMD activity is significantly modulated by the "window of opportunity" offered by translation termination kinetics. Integrating the window of opportunity model with the existing framework of NMD would enable more accurate nonsense variant interpretation in the clinic.
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Affiliation(s)
- Divya Kolakada
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Molecular Biology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Rui Fu
- RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nikita Biziaev
- Engelhardt Institute of Molecular Biology, The Russian Academy of Sciences, 119991 Moscow, Russia
| | - Alexey Shuvalov
- Engelhardt Institute of Molecular Biology, The Russian Academy of Sciences, 119991 Moscow, Russia
| | - Mlana Lore
- Molecular Biology Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Amy E. Campbell
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael A. Cortázar
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Marcin P. Sajek
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland
| | - Jay R. Hesselberth
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Neelanjan Mukherjee
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Elena Alkalaeva
- Engelhardt Institute of Molecular Biology, The Russian Academy of Sciences, 119991 Moscow, Russia
| | - Sujatha Jagannathan
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Lead contact
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7
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Žukauskaitė G, Domarkienė I, Rančelis T, Kavaliauskienė I, Baronas K, Kučinskas V, Ambrozaitytė L. Putative protective genomic variation in the Lithuanian population. Genet Mol Biol 2024; 47:e20230030. [PMID: 38626572 PMCID: PMC11021042 DOI: 10.1590/1678-4685-gmb-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 01/01/2024] [Indexed: 04/18/2024] Open
Abstract
Genomic effect variants associated with survival and protection against complex diseases vary between populations due to microevolutionary processes. The aim of this study was to analyse diversity and distribution of effect variants in a context of potential positive selection. In total, 475 individuals of Lithuanian origin were genotyped using high-throughput scanning and/or sequencing technologies. Allele frequency analysis for the pre-selected effect variants was performed using the catalogue of single nucleotide polymorphisms. Comparison of the pre-selected effect variants with variants in primate species was carried out to ascertain which allele was derived and potentially of protective nature. Recent positive selection analysis was performed to verify this protective effect. Four variants having significantly different frequencies compared to European populations were identified while two other variants reached borderline significance. Effect variant in SLC30A8 gene may potentially protect against type 2 diabetes. The existing paradox of high rates of type 2 diabetes in the Lithuanian population and the relatively high frequencies of potentially protective genome variants against it indicate a lack of knowledge about the interactions between environmental factors, regulatory regions, and other genome variation. Identification of effect variants is a step towards better understanding of the microevolutionary processes, etiopathogenetic mechanisms, and personalised medicine.
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Affiliation(s)
- Gabrielė Žukauskaitė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Ingrida Domarkienė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Tautvydas Rančelis
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Ingrida Kavaliauskienė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Karolis Baronas
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Vaidutis Kučinskas
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
| | - Laima Ambrozaitytė
- Vilnius University, Faculty of Medicine, Institute of Biomedical Sciences, Department of Human and Medical Genetics, Vilnius, Lithuania
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8
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MacGowan SA, Madeira F, Britto-Borges T, Barton GJ. A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites. Commun Biol 2024; 7:447. [PMID: 38605212 PMCID: PMC11009406 DOI: 10.1038/s42003-024-06117-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 03/27/2024] [Indexed: 04/13/2024] Open
Abstract
Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.
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Affiliation(s)
- Stuart A MacGowan
- Division of Computational Biology School of Life Sciences University of Dundee, Dow Street Dundee, DD1 5EH, Scotland, UK
| | - Fábio Madeira
- Division of Computational Biology School of Life Sciences University of Dundee, Dow Street Dundee, DD1 5EH, Scotland, UK
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Thiago Britto-Borges
- Division of Computational Biology School of Life Sciences University of Dundee, Dow Street Dundee, DD1 5EH, Scotland, UK
- Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III and Klaus Tschira Institute for Integrative Computational Cardiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Geoffrey J Barton
- Division of Computational Biology School of Life Sciences University of Dundee, Dow Street Dundee, DD1 5EH, Scotland, UK.
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9
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Durward-Akhurst SA, Marlowe JL, Schaefer RJ, Springer K, Grantham B, Carey WK, Bellone RR, Mickelson JR, McCue ME. Predicted genetic burden and frequency of phenotype-associated variants in the horse. Sci Rep 2024; 14:8396. [PMID: 38600096 PMCID: PMC11006912 DOI: 10.1038/s41598-024-57872-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Disease-causing variants have been identified for less than 20% of suspected equine genetic diseases. Whole genome sequencing (WGS) allows rapid identification of rare disease causal variants. However, interpreting the clinical variant consequence is confounded by the number of predicted deleterious variants that healthy individuals carry (predicted genetic burden). Estimation of the predicted genetic burden and baseline frequencies of known deleterious or phenotype associated variants within and across the major horse breeds have not been performed. We used WGS of 605 horses across 48 breeds to identify 32,818,945 variants, demonstrate a high predicted genetic burden (median 730 variants/horse, interquartile range: 613-829), show breed differences in predicted genetic burden across 12 target breeds, and estimate the high frequencies of some previously reported disease variants. This large-scale variant catalog for a major and highly athletic domestic animal species will enhance its ability to serve as a model for human phenotypes and improves our ability to discover the bases for important equine phenotypes.
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Affiliation(s)
- S A Durward-Akhurst
- Department of Veterinary Clinical Sciences, University of Minnesota, C339 VMC, 1353 Boyd Avenue, St. Paul, MN, 55108, USA.
| | - J L Marlowe
- Department of Veterinary Clinical Sciences, University of Minnesota, C339 VMC, 1353 Boyd Avenue, St. Paul, MN, 55108, USA
| | - R J Schaefer
- Department of Veterinary Population Medicine, University of Minnesota, 225 VMC, 1365 Gortner Avenue, St. Paul, MN, 55108, USA
| | - K Springer
- Department of Veterinary Population Medicine, University of Minnesota, 225 VMC, 1365 Gortner Avenue, St. Paul, MN, 55108, USA
| | - B Grantham
- Interval Bio LLC, 408 Stierline Road, Mountain View, CA, 94043, USA
| | - W K Carey
- Interval Bio LLC, 408 Stierline Road, Mountain View, CA, 94043, USA
| | - R R Bellone
- Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California-Davis, Davis, CA, USA
- Population Health and Reproduction and Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California, Davis, CA, USA
| | - J R Mickelson
- Department of Veterinary and Biomedical Sciences, University of Minnesota, 295F Animal Science Veterinary Medicine Building, 1988 Fitch Avenue, St. Paul, MN, 55108, USA
| | - M E McCue
- Department of Veterinary Population Medicine, University of Minnesota, 225 VMC, 1365 Gortner Avenue, St. Paul, MN, 55108, USA
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10
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Alfayyadh MM, Maksemous N, Sutherland HG, Lea RA, Griffiths LR. Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches. Genes (Basel) 2024; 15:443. [PMID: 38674378 PMCID: PMC11049430 DOI: 10.3390/genes15040443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.
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Affiliation(s)
| | | | | | | | - Lyn R. Griffiths
- Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia; (M.M.A.); (N.M.); (H.G.S.); (R.A.L.)
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11
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Prem S, Dev B, Peng C, Mehta M, Alibutud R, Connacher RJ, St Thomas M, Zhou X, Matteson P, Xing J, Millonig JH, DiCicco-Bloom E. Dysregulation of mTOR signaling mediates common neurite and migration defects in both idiopathic and 16p11.2 deletion autism neural precursor cells. eLife 2024; 13:e82809. [PMID: 38525876 PMCID: PMC11003747 DOI: 10.7554/elife.82809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 03/04/2024] [Indexed: 03/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is defined by common behavioral characteristics, raising the possibility of shared pathogenic mechanisms. Yet, vast clinical and etiological heterogeneity suggests personalized phenotypes. Surprisingly, our iPSC studies find that six individuals from two distinct ASD subtypes, idiopathic and 16p11.2 deletion, have common reductions in neural precursor cell (NPC) neurite outgrowth and migration even though whole genome sequencing demonstrates no genetic overlap between the datasets. To identify signaling differences that may contribute to these developmental defects, an unbiased phospho-(p)-proteome screen was performed. Surprisingly despite the genetic heterogeneity, hundreds of shared p-peptides were identified between autism subtypes including the mTOR pathway. mTOR signaling alterations were confirmed in all NPCs across both ASD subtypes, and mTOR modulation rescued ASD phenotypes and reproduced autism NPC-associated phenotypes in control NPCs. Thus, our studies demonstrate that genetically distinct ASD subtypes have common defects in neurite outgrowth and migration which are driven by the shared pathogenic mechanism of mTOR signaling dysregulation.
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Affiliation(s)
- Smrithi Prem
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
- Graduate Program in Neuroscience, Rutgers UniversityPiscatawayUnited States
| | - Bharati Dev
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
| | - Cynthia Peng
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
| | - Monal Mehta
- Graduate Program in Neuroscience, Rutgers UniversityPiscatawayUnited States
- Center for Advanced Biotechnology and Medicine, Rutgers UniversityPiscatawayUnited States
| | - Rohan Alibutud
- Department of Genetics, Rutgers UniversityPiscatawayUnited States
| | - Robert J Connacher
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
- Graduate Program in Neuroscience, Rutgers UniversityPiscatawayUnited States
| | - Madeline St Thomas
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
- Graduate Program in Neuroscience, Rutgers UniversityPiscatawayUnited States
| | - Xiaofeng Zhou
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
| | - Paul Matteson
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
- Center for Advanced Biotechnology and Medicine, Rutgers UniversityPiscatawayUnited States
| | - Jinchuan Xing
- Department of Genetics, Rutgers UniversityPiscatawayUnited States
| | - James H Millonig
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
- Center for Advanced Biotechnology and Medicine, Rutgers UniversityPiscatawayUnited States
| | - Emanuel DiCicco-Bloom
- Department of Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical SchoolPiscatawayUnited States
- Department of Pediatrics, Rutgers Robert Wood Johnson Medical SchoolNew BrunswickUnited States
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12
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Sridhar GR, Gumpeny L. Emerging significance of butyrylcholinesterase. World J Exp Med 2024; 14:87202. [PMID: 38590305 PMCID: PMC10999061 DOI: 10.5493/wjem.v14.i1.87202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/04/2023] [Accepted: 01/05/2024] [Indexed: 03/19/2024] Open
Abstract
Butyrylcholinesterase (BChE; EC 3.1.1.8), an enzyme structurally related to acetylcholinesterase, is widely distributed in the human body. It plays a role in the detoxification of chemicals such as succinylcholine, a muscle relaxant used in anesthetic practice. BChE is well-known due to variant forms of the enzyme with little or no hydrolytic activity which exist in some endogamous communities and result in prolonged apnea following the administration of succinylcholine. Its other functions include the ability to hydrolyze acetylcholine, the cholinergic neurotransmitter in the brain, when its primary hydrolytic enzyme, acetylcholinesterase, is absent. To assess its potential roles, BChE was studied in relation to insulin resistance, type 2 diabetes mellitus, cognition, hepatic disorders, cardiovascular and cerebrovascular diseases, and inflammatory conditions. Individuals who lack the enzyme activity of BChE are otherwise healthy, until they are given drugs hydrolyzed by this enzyme. Therefore, BChE is a candidate for the study of loss-of-function mutations in humans. Studying individuals with variant forms of BChE can provide insights into whether they are protected against metabolic diseases. The potential utility of the enzyme as a biomarker for Alzheimer's disease and the response to its drug treatment can also be assessed.
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Affiliation(s)
- Gumpeny R Sridhar
- Department of Endocrinology and Diabetes, Endocrine and Diabetes Centre, Visakhapatnam 530002, Andhra Pradesh, India
| | - Lakshmi Gumpeny
- Department of Internal Medicine, Gayatri Vidya Parishad Institute of Healthcare and Medical Technology, Visakhapatnam 530048, Andhra Pradesh, India
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13
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Gustafson JA, Gibson SB, Damaraju N, Zalusky MPG, Hoekzema K, Twesigomwe D, Yang L, Snead AA, Richmond PA, De Coster W, Olson ND, Guarracino A, Li Q, Miller AL, Goffena J, Anderson Z, Storz SHR, Ward SA, Sinha M, Gonzaga-Jauregui C, Clarke WE, Basile AO, Corvelo A, Reeves C, Helland A, Musunuri RL, Revsine M, Patterson KE, Paschal CR, Zakarian C, Goodwin S, Jensen TD, Robb E, McCombie WR, Sedlazeck FJ, Zook JM, Montgomery SB, Garrison E, Kolmogorov M, Schatz MC, McLaughlin RN, Dashnow H, Zody MC, Loose M, Jain M, Eichler EE, Miller DE. Nanopore sequencing of 1000 Genomes Project samples to build a comprehensive catalog of human genetic variation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.05.24303792. [PMID: 38496498 PMCID: PMC10942501 DOI: 10.1101/2024.03.05.24303792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Less than half of individuals with a suspected Mendelian condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control datasets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project ONT Sequencing Consortium aims to generate LRS data from at least 800 of the 1000 Genomes Project samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37x and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.
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Affiliation(s)
- Jonas A. Gustafson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Sophia B. Gibson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Nikhita Damaraju
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Institute for Public Health Genetics, University of Washington, Seattle, WA, USA
| | - Miranda PG Zalusky
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David Twesigomwe
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Lei Yang
- Pacific Northwest Research Institute, Seattle, WA, USA
| | | | | | - Wouter De Coster
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Nathan D. Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Human Technopole, Milan, Italy
| | - Qiuhui Li
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Angela L. Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Joy Goffena
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Zachery Anderson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Sophie HR Storz
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Sydney A. Ward
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Maisha Sinha
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Claudia Gonzaga-Jauregui
- International Laboratory for Human Genome Research, Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México
| | - Wayne E. Clarke
- New York Genome Center, New York, NY, USA
- Outlier Informatics Inc., Saskatoon, SK, Canada
| | | | | | | | | | | | - Mahler Revsine
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Cate R. Paschal
- Department of Laboratories, Seattle Children’s Hospital, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Christina Zakarian
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Esther Robb
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | | | | | | | - Fritz J. Sedlazeck
- Human Genome Sequencing Center Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Justin M. Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Mikhail Kolmogorov
- Cancer Data Science Laboratory, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Richard N. McLaughlin
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - Harriet Dashnow
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Matt Loose
- Deep Seq, School of Life Sciences, University of Nottingham, Nottingham, England
| | - Miten Jain
- Department of Bioengineering, Department of Physics, Khoury College of Computer Sciences, Northeastern University, Boston, MA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Danny E. Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
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14
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Walker E, Hayes W, Bockenhauer D. Inherited non-FGF23-mediated phosphaturic disorders: A kidney-centric review. Best Pract Res Clin Endocrinol Metab 2024; 38:101843. [PMID: 38042745 DOI: 10.1016/j.beem.2023.101843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2023]
Abstract
Phosphate is freely filtered by the glomerulus and reabsorbed exclusively in the proximal tubule by two key transporters, NaPiIIA and NaPiIIC, encoded by SLC34A1 and SLC34A3, respectively. Regulation of these transporters occurs primarily through the hormone FGF23 and, to a lesser degree, PTH. Consequently, inherited non-FGF23 mediated phosphaturic disorders are due to generalised proximal tubular dysfunction, loss-of-function variants in SLC34A1 or SLC34A3 or excess PTH signalling. The corresponding disorders are Renal Fanconi Syndrome, Infantile Hypercalcaemia type 2, Hereditary Hypophosphataemic Rickets with Hypercalciuria and Familial Hyperparathyroidism. Several inherited forms of Fanconi renotubular syndrome (FRTS) have also been described with the underlying genes encoding for GATM, EHHADH, HNF4A and NDUFAF6. Here, we will review their pathophysiology, clinical manifestations and the implications for treatment from a kidney-centric perspective, focussing on those disorders caused by dysfunction of renal phosphate transporters. Moreover, we will highlight specific genetic aspects, as the availability of large population genetic databases has raised doubts about some of the originally proposed gene-disease associations concerning phosphate transporters or their associated proteins.
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Affiliation(s)
- Emma Walker
- Nephrology Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Wesley Hayes
- Nephrology Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Detlef Bockenhauer
- Nephrology Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK; Department of Renal Medicine, University College London, London, UK.
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15
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Hukerikar N, Hingorani AD, Asselbergs FW, Finan C, Schmidt AF. Prioritising genetic findings for drug target identification and validation. Atherosclerosis 2024; 390:117462. [PMID: 38325120 DOI: 10.1016/j.atherosclerosis.2024.117462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation.
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Affiliation(s)
- Nikita Hukerikar
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
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16
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Kerdoncuff E, Skov L, Patterson N, Zhao W, Lueng YY, Schellenberg GD, Smith JA, Dey S, Ganna A, Dey AB, Kardia SL, Lee J, Moorjani P. 50,000 years of Evolutionary History of India: Insights from ~2,700 Whole Genome Sequences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580575. [PMID: 38405782 PMCID: PMC10888882 DOI: 10.1101/2024.02.15.580575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
India has been underrepresented in whole genome sequencing studies. We generated 2,762 high coverage genomes from India-including individuals from most geographic regions, speakers of all major languages, and tribal and caste groups-providing a comprehensive survey of genetic variation in India. With these data, we reconstruct the evolutionary history of India through space and time at fine scales. We show that most Indians derive ancestry from three ancestral groups related to ancient Iranian farmers, Eurasian Steppe pastoralists and South Asian hunter-gatherers. We uncover a common source of Iranian-related ancestry from early Neolithic cultures of Central Asia into the ancestors of Ancestral South Indians (ASI), Ancestral North Indians (ANI), Austro-asiatic-related and East Asian-related groups in India. Following these admixtures, India experienced a major demographic shift towards endogamy, resulting in extensive homozygosity and identity-by-descent sharing among individuals. At deep time scales, Indians derive around 1-2% of their ancestry from gene flow from archaic hominins, Neanderthals and Denisovans. By assembling the surviving fragments of archaic ancestry in modern Indians, we recover ~1.5 Gb (or 50%) of the introgressing Neanderthal and ~0.6 Gb (or 20%) of the introgressing Denisovan genomes, more than any other previous archaic ancestry study. Moreover, Indians have the largest variation in Neanderthal ancestry, as well as the highest amount of population-specific Neanderthal segments among worldwide groups. Finally, we demonstrate that most of the genetic variation in Indians stems from a single major migration out of Africa that occurred around 50,000 years ago, with minimal contribution from earlier migration waves. Together, these analyses provide a detailed view of the population history of India and underscore the value of expanding genomic surveys to diverse groups outside Europe.
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Affiliation(s)
- Elise Kerdoncuff
- Department of Molecular and Cell Biology, University of California, Berkeley, United States of America
| | - Laurits Skov
- Department of Molecular and Cell Biology, University of California, Berkeley, United States of America
| | - Nick Patterson
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Wei Zhao
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yuk Yee Lueng
- Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Gerard D. Schellenberg
- Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States of America
| | - Jennifer A. Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sharmistha Dey
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | - AB Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Sharon L.R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jinkook Lee
- Department of Economics, and Center for Economic & Social Research, University of Southern California, Los Angeles, California, United States of America
| | - Priya Moorjani
- Department of Molecular and Cell Biology, University of California, Berkeley, United States of America
- Center for Computational Biology, University of California, Berkeley, United States of America
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17
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Boumajdi N, Bendani H, Kartti S, Alouane T, Belyamani L, Ibrahimi A. A Comprehensive Analysis of 3 Moroccan Genomes Revealed Contributions From Both African and European Ancestries. Evol Bioinform Online 2024; 20:11769343241229278. [PMID: 38327511 PMCID: PMC10848790 DOI: 10.1177/11769343241229278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/12/2024] [Indexed: 02/09/2024] Open
Abstract
Genetic variations in the human genome represent the differences in DNA sequence within individuals. This highlights the important role of whole human genome sequencing which has become the keystone for precision medicine and disease prediction. Morocco is an important hub for studying human population migration and mixing history. This study presents the analysis of 3 Moroccan genomes; the variant analysis revealed 6 379 606 single nucleotide variants (SNVs) and 1 050 577 small InDels. Of those identified SNVs, 219 152 were novel, with 1233 occurring in coding regions, and 5580 non-synonymous single nucleotide variants (nsSNP) variants were predicted to affect protein functions. The InDels produced 1055 coding variants and 454 non-3n length variants, and their size ranged from -49 and 49 bp. We further analysed the gene pathways of 8 novel coding variants found in the 3 genomes and revealed 5 genes involved in various diseases and biological pathways. We found that the Moroccan genomes share 92.78% of African ancestry, and 92.86% of Non-Finnish European ancestry, according to the gnomAD database. Then, population structure inference, by admixture analysis and network-based approach, revealed that the studied genomes form a mixed population structure, highlighting the increased genetic diversity in Morocco.
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Affiliation(s)
- Nasma Boumajdi
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
- Mohammed VI Center for Research & Innovation (CM6), Rabat, Morocco
| | - Houda Bendani
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
- Mohammed VI Center for Research & Innovation (CM6), Rabat, Morocco
| | - Souad Kartti
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
- Mohammed VI Center for Research & Innovation (CM6), Rabat, Morocco
| | - Tarek Alouane
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
| | - Lahcen Belyamani
- Mohammed VI Center for Research & Innovation (CM6), Rabat, Morocco
- Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
- Emergency Department, Military Hospital Mohammed V, Rabat Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
| | - Azeddine Ibrahimi
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
- Mohammed VI Center for Research & Innovation (CM6), Rabat, Morocco
- Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
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18
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Balachandran S, Prada-Medina CA, Mensah MA, Kakar N, Nagel I, Pozojevic J, Audain E, Hitz MP, Kircher M, Sreenivasan VKA, Spielmann M. STIGMA: Single-cell tissue-specific gene prioritization using machine learning. Am J Hum Genet 2024; 111:338-349. [PMID: 38228144 PMCID: PMC10870135 DOI: 10.1016/j.ajhg.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 01/18/2024] Open
Abstract
Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.
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Affiliation(s)
- Saranya Balachandran
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Cesar A Prada-Medina
- Human Molecular Genetics Group, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Martin A Mensah
- Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; BIH Charité Digital Clinician Scientist Program, BIH Biomedical Innovation Academy, Anna-Louisa-Karsch-Strasse 2, 10178 Berlin, Germany; RG Development & Disease, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Naseebullah Kakar
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany; Department of Biotechnology, BUITEMS, Quetta, Pakistan
| | - Inga Nagel
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Jelena Pozojevic
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Enrique Audain
- Institute of Medical Genetics, Carl von Ossietzky University, 26129 Oldenburg, Germany; DZHK e.V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck; Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital of Schleswig-Holstein, 24105 Kiel, Germany
| | - Marc-Phillip Hitz
- Institute of Medical Genetics, Carl von Ossietzky University, 26129 Oldenburg, Germany; DZHK e.V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck; Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital of Schleswig-Holstein, 24105 Kiel, Germany
| | - Martin Kircher
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany
| | - Varun K A Sreenivasan
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany.
| | - Malte Spielmann
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Germany; Human Molecular Genetics Group, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany; DZHK e.V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck.
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19
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Zekavat SM, Jorshery SD, Rauscher FG, Horn K, Sekimitsu S, Koyama S, Nguyen TT, Costanzo MC, Jang D, Burtt NP, Kühnapfel A, Shweikh Y, Ye Y, Raghu V, Zhao H, Ghassemi M, Elze T, Segrè AV, Wiggs JL, Del Priore L, Scholz M, Wang JC, Natarajan P, Zebardast N. Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health. Sci Transl Med 2024; 16:eadg4517. [PMID: 38266105 DOI: 10.1126/scitranslmed.adg4517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
The human retina is a multilayered tissue that offers a unique window into systemic health. Optical coherence tomography (OCT) is widely used in eye care and allows the noninvasive, rapid capture of retinal anatomy in exquisite detail. We conducted genotypic and phenotypic analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed OCT layer cross-phenotype association analyses (OCT-XWAS), associating retinal thicknesses with 1866 incident conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association studies (GWASs), identifying inherited genetic markers that influence retinal layer thicknesses and replicated our associations among the LIFE-Adult Study (N = 6313). Last, we performed a comparative analysis of phenome- and genome-wide associations to identify putative causal links between retinal layer thicknesses and both ocular and systemic conditions. Independent associations with incident mortality were detected for thinner photoreceptor segments (PSs) and, separately, ganglion cell complex layers. Phenotypic associations were detected between thinner retinal layers and ocular, neuropsychiatric, cardiometabolic, and pulmonary conditions. A GWAS of retinal layer thicknesses yielded 259 unique loci. Consistency between epidemiologic and genetic associations suggested links between a thinner retinal nerve fiber layer with glaucoma, thinner PS with age-related macular degeneration, and poor cardiometabolic and pulmonary function with a thinner PS. In conclusion, we identified multiple inherited genetic loci and acquired systemic cardio-metabolic-pulmonary conditions associated with thinner retinal layers and identify retinal layers wherein thinning is predictive of future ocular and systemic conditions.
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Affiliation(s)
- Seyedeh Maryam Zekavat
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Saman Doroodgar Jorshery
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Computer Science/Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Franziska G Rauscher
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
- Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig 04103, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
| | | | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Trang T Nguyen
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Maria C Costanzo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dongkeun Jang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Noël P Burtt
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andreas Kühnapfel
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Yixuan Ye
- Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT 06511, USA
| | - Vineet Raghu
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hongyu Zhao
- Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT 06511, USA
- School of Public Health, Yale University, New Haven, CT 06510, USA
| | - Marzyeh Ghassemi
- Departments of Computer Science/Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tobias Elze
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Ayellet V Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lucian Del Priore
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT 06510, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
- Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig 04103, Germany
| | - Jay C Wang
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT 06510, USA
- Northern California Retina Vitreous Associates, Mountain View, CA 94040, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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20
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Chaumier T, Yang F, Manirakiza E, Ait-Mohamed O, Wu Y, Chandola U, Jesus B, Piganeau G, Groisillier A, Tirichine L. Genome-wide assessment of genetic diversity and transcript variations in 17 accessions of the model diatom Phaeodactylum tricornutum. ISME COMMUNICATIONS 2024; 4:ycad008. [PMID: 38304080 PMCID: PMC10833087 DOI: 10.1093/ismeco/ycad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/01/2023] [Accepted: 12/13/2023] [Indexed: 02/03/2024]
Abstract
Diatoms, a prominent group of phytoplankton, have a significant impact on both the oceanic food chain and carbon sequestration, thereby playing a crucial role in regulating the climate. These highly diverse organisms show a wide geographic distribution across various latitudes. In addition to their ecological significance, diatoms represent a vital source of bioactive compounds that are widely used in biotechnology applications. In the present study, we investigated the genetic and transcriptomic diversity of 17 accessions of the model diatom Phaeodactylum tricornutum including those sampled a century ago as well as more recently collected accessions. The analysis of the data reveals a higher genetic diversity and the emergence of novel clades, indicating an increasing diversity within the P. tricornutum population structure, compared to the previous study and a persistent long-term balancing selection of genes in old and newly sampled accessions. However, the study did not establish a clear link between the year of sampling and genetic diversity, thereby, rejecting the hypothesis of loss of heterozygoty in cultured strains. Transcript analysis identified novel transcript including noncoding RNA and other categories of small RNA such as PiwiRNAs. Additionally, transcripts analysis using differential expression as well as Weighted Gene Correlation Network Analysis has provided evidence that the suppression or downregulation of genes cannot be solely attributed to loss-of-function mutations. This implies that other contributing factors, such as epigenetic modifications, may play a crucial role in regulating gene expression. Our study provides novel genetic resources, which are now accessible through the platform PhaeoEpiview (https://PhaeoEpiView.univ-nantes.fr), that offer both ease of use and advanced tools to further investigate microalgae biology and ecology, consequently enriching our current understanding of these organisms.
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Affiliation(s)
| | - Feng Yang
- Nantes Université, CNRS, US2B, UMR 6286, Nantes F-44000, France
| | - Eric Manirakiza
- Nantes Université, CNRS, US2B, UMR 6286, Nantes F-44000, France
| | - Ouardia Ait-Mohamed
- Immunity and Cancer Department, Institut Curie, PSL Research University, INSERM U932, Paris 75005, France
| | - Yue Wu
- Nantes Université, CNRS, US2B, UMR 6286, Nantes F-44000, France
| | - Udita Chandola
- Nantes Université, CNRS, US2B, UMR 6286, Nantes F-44000, France
| | - Bruno Jesus
- Institut des Substances et Organismes de la Mer, ISOMer, Nantes Université, UR 2160, Nantes F-44000, France
| | - Gwenael Piganeau
- Sorbonne Université, CNRS, Laboratoire de Biodiversité et Biotechnologies Microbiennes, LBBM, F-66650 Banyuls-sur-Mer, France
| | | | - Leila Tirichine
- Nantes Université, CNRS, US2B, UMR 6286, Nantes F-44000, France
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21
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Yoshida H. Dissecting the Immune System through Gene Regulation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1444:219-235. [PMID: 38467983 DOI: 10.1007/978-981-99-9781-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.
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Affiliation(s)
- Hideyuki Yoshida
- YCI Laboratory for Immunological Transcriptomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
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22
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Liu D, Zhao Y, Xue X, Hou X, Xu H, Zhao X, Tian Y, Tang W, Guo J, Xu C. Novel compound heterozygous pathogenic variants in the SLC3A1 gene in a Chinese family with cystinuria. BMC Med Genomics 2023; 16:333. [PMID: 38114997 PMCID: PMC10731833 DOI: 10.1186/s12920-023-01767-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Cystinuria is an autosomal recessive disorder characterized by a cystine transport deficiency in the renal tubules due to mutations in two genes: SLC3A1 and SLC7A9. Cystinuria can be classified into three forms based on the genotype: type A, due to mutations in the SLC3A1 gene; type B, due to mutations in the SLC7A9 gene; and type AB, due to mutations in both genes. METHODS We report a 12-year-old boy from central China with cystine stones. He was from a non-consanguineous family that had no known history of genetic disease. A physical examination showed normal development and neurological behaviors. Whole-exome and Sanger sequencing were used to identify and verify the suspected pathogenic variants. RESULTS The compound heterozygous variants c.898_905del (p.Arg301AlafsTer6) is located in exon5 and c.1898_1899insAT (p.Asp634LeufsTer46) is located in exon10 of SLC3A1 (NM_000341.4) were deemed responsible for type A cystinuria family. The variant c.898_905del was reported in a Japanese patient in 2000, and the variant c.1898_1899insAT is novel. CONCLUSION A novel pathogenic heterozygous variant pair of the SLC3A1 gene was identified in a Chinese boy with type A cystinuria, enriching the mutational spectrum of the SLC3A1 gene. We attempted to find a pattern for the association between the genotype of SLC3A1 variants and the manifestations of cystinuria in patients with different onset ages. Our findings have important implications for genetic counseling and the early clinical diagnosis of cystinuria.
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Affiliation(s)
- Danhua Liu
- The Research and Application Center of Precision Medicine, The Second Affiliated Hospital, Zhengzhou University, Zhengzhou, 450000, China
| | - Yongli Zhao
- Department of Urology, the Second Affiliated Hospital of Zhengzhou University, NO. 2 Jingba Road, Zhengzhou, 450014, China
| | - Xia Xue
- Henan Key Laboratory of Helicobacter Pylori & Microbiota and Gastrointestinal Cancer, Marshall Medical Research Center, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, 450002, China
| | - Xinyue Hou
- Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450000, China
| | - Hongen Xu
- Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450000, China
| | - Xinghua Zhao
- Department of Urology, the Second Affiliated Hospital of Zhengzhou University, NO. 2 Jingba Road, Zhengzhou, 450014, China
| | - Yongan Tian
- Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450000, China
| | - Wenxue Tang
- The Research and Application Center of Precision Medicine, The Second Affiliated Hospital, Zhengzhou University, Zhengzhou, 450000, China
- Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450000, China
- Henan Institute of Medical and Pharmaceutical Sciences, BGI College, Zhengzhou University, Zhengzhou, 450052, China
| | - Jiancheng Guo
- The Research and Application Center of Precision Medicine, The Second Affiliated Hospital, Zhengzhou University, Zhengzhou, 450000, China
- Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450000, China
- Henan Institute of Medical and Pharmaceutical Sciences, BGI College, Zhengzhou University, Zhengzhou, 450052, China
| | - Changbao Xu
- Department of Urology, the Second Affiliated Hospital of Zhengzhou University, NO. 2 Jingba Road, Zhengzhou, 450014, China.
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23
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Maksiutenko EM, Barbitoff YA, Nasykhova YA, Pachuliia OV, Lazareva TE, Bespalova ON, Glotov AS. The Landscape of Point Mutations in Human Protein Coding Genes Leading to Pregnancy Loss. Int J Mol Sci 2023; 24:17572. [PMID: 38139401 PMCID: PMC10743817 DOI: 10.3390/ijms242417572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Pregnancy loss is the most frequent complication of a pregnancy which is devastating for affected families and poses a significant challenge for the health care system. Genetic factors are known to play an important role in the etiology of pregnancy loss; however, despite advances in diagnostics, the causes remain unexplained in more than 30% of cases. In this review, we aggregated the results of the decade-long studies into the genetic risk factors of pregnancy loss (including miscarriage, termination for fetal abnormality, and recurrent pregnancy loss) in euploid pregnancies, focusing on the spectrum of point mutations associated with these conditions. We reviewed the evolution of molecular genetics methods used for the genetic research into causes of pregnancy loss, and collected information about 270 individual genetic variants in 196 unique genes reported as genetic cause of pregnancy loss. Among these, variants in 18 genes have been reported by multiple studies, and two or more variants were reported as causing pregnancy loss for 57 genes. Further analysis of the properties of all known pregnancy loss genes showed that they correspond to broadly expressed, highly evolutionary conserved genes involved in crucial cell differentiation and developmental processes and related signaling pathways. Given the features of known genes, we made an effort to construct a list of candidate genes, variants in which may be expected to contribute to pregnancy loss. We believe that our results may be useful for prediction of pregnancy loss risk in couples, as well as for further investigation and revealing genetic etiology of pregnancy loss.
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Affiliation(s)
| | - Yury A. Barbitoff
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, Mendeleevskaya Line 3, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.N.); (O.V.P.); (T.E.L.); (O.N.B.)
| | | | | | | | | | - Andrey S. Glotov
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, Mendeleevskaya Line 3, 199034 St. Petersburg, Russia; (E.M.M.); (Y.A.N.); (O.V.P.); (T.E.L.); (O.N.B.)
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24
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Yu X, Yuan J, Chen ZJ, Li K, Yao Y, Xing S, Xue Z, Zhang Y, Peng H, An G, Yu X, Qu J, Su J. Whole-Exome Sequencing Among School-Aged Children With High Myopia. JAMA Netw Open 2023; 6:e2345821. [PMID: 38039006 PMCID: PMC10692858 DOI: 10.1001/jamanetworkopen.2023.45821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023] Open
Abstract
Importance High myopia (HM) is one of the leading causes of visual impairment worldwide. Genetic factors are known to play an important role in the development of HM. Objective To identify risk variants in a large HM cohort and to examine the implications of genetic testing of schoolchildren with HM. Design, Setting, and Participants This cohort study retrospectively reviewed whole-exome sequencing (WES) results in 6215 schoolchildren with HM who underwent genetic testing between September 2019 and July 2020 in Wenzhou City, China. HM is defined as a spherical equivalent refraction (SER) of -6.00 diopters (D) or less. The study setting was a genetic testing laboratory and a multicenter school census. Data were analyzed from July 2021 to June 2022. Main Outcomes and Measures The frequency and distribution of positive germline variants, the percentage of individuals with HM in both eyes, and subsequent variant yield for common high myopia (CHM; -8.00 D ≤ SER ≤ -6.00 D), ultra myopia (UM; -10.00 D ≤ SER < -8.00 D), and extreme myopia (EM; SER < -10.00 D). Results Of the 6215 schoolchildren with HM, 3278 (52.74%) were male. Their mean (SD) age was 14.87 (2.02) years, including 355 students in primary school, 1970 in junior high school, and 3890 in senior high school. The mean (SD) SER was -7.51 (-1.36) D for the right eye and -7.46 (-1.34) D for the left eye. Among schoolchildren with HM, genetic testing yielded 271 potential pathogenic variants in 75 HM candidate genes in 964 diagnoses (15.52%). A total of 36 known variants were found in 490 HM participants (7.88%) and 235 protein-truncating variants (PTVs) in 506 participants (8.14%). Involved variant yield was significantly positively associated with SER (Cochran-Armitage test for trend Z = 2.5492; P = .01), which ranged from 7.66% in the CHM group, 8.70% in the UM group, to 11.90% in the EM group. We also found that primary school students with EM had the highest variant yield of PTVs (8 of 35 students [22.86%]), which was 1.77 and 4.78 times that of the UM and CHM, respectively. Conclusions and Relevance In this cohort study of WES for HM, several potential pathogenic variants were identified in a substantial number of schoolchildren with HM. The high variation frequency in younger students with EM can provide clues for genetic screening and clinical examinations of HM to promote long-term follow-up assessment.
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Affiliation(s)
- Xiangyi Yu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Jian Yuan
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Zhen Ji Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Laboratory for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang, China
| | - Kai Li
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Yinghao Yao
- Oujiang Laboratory, Zhejiang Laboratory for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang, China
| | - Shilai Xing
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Institute of PSI Genomics, Wenzhou, China
| | - Zhengbo Xue
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Yue Zhang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Hui Peng
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Gang An
- Institute of PSI Genomics, Wenzhou, China
| | | | - Jia Qu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Laboratory for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Jianzhong Su
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Laboratory for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
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25
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Lecluze E, Lettre G. Association Analyses of Predicted Loss-of-Function Variants Prioritized 15 Genes as Blood Pressure Regulators. Can J Cardiol 2023; 39:1888-1897. [PMID: 37451613 DOI: 10.1016/j.cjca.2023.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/26/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Hypertension, clinically defined by elevated blood pressure (BP), is an important cause of mortality and morbidity worldwide. Many risk factors for hypertension are known, including a positive family history, which suggests that genetics contribute to interindividual BP variation. Genome-wide association studies (GWAS) have identified > 1000 loci associated with BP, yet the identity of the genes responsible for these associations remains largely unknown. METHODS To pinpoint genes that causally affect variation of BP in humans, we analyzed predicted loss-of-function (pLoF) variants in the UK Biobank whole-exome sequencing dataset (n = 454,709 participants, 6% non-European ancestry). We analyzed genetic associations between systolic or diastolic BP (SBP/DBP) and single pLoF variants (additive and recessive genetic models) as well as with the burden of very rare pLoF variants (minor allele frequency [MAF] < 0.01%). RESULTS Single pLoF variants in 10 genes were associated with BP (ANKDD1B, ENPEP, PNCK, BTN3A2, C1orf145 [OBSCN-AS1], CASP9, DBH, KIAA1161 [MYORG], OR4X1, and TMC3). We also found a burden of rare pLoF variants in 5 additional genes associated with BP (TTN, NOS3, FES, SMAD6, COL21A1). Except for PNCK, which is located on the X-chromosome, these genes map near variants previously associated with BP by GWAS, validating the study of pLoF variants to prioritize causal genes at GWAS loci. CONCLUSIONS Our study highlights 15 genes that likely modulate BP in humans, including 5 genes that harbour pLoF variants associated with lower BP.
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Affiliation(s)
- Estelle Lecluze
- Montreal Heart Institute, Montréal, Québec, Canada; Faculté de Médecine, Université de Montréal, Montréal, Québec, Canada
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, Québec, Canada; Faculté de Médecine, Université de Montréal, Montréal, Québec, Canada.
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26
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Kawai Y, Watanabe Y, Omae Y, Miyahara R, Khor SS, Noiri E, Kitajima K, Shimanuki H, Gatanaga H, Hata K, Hattori K, Iida A, Ishibashi-Ueda H, Kaname T, Kanto T, Matsumura R, Miyo K, Noguchi M, Ozaki K, Sugiyama M, Takahashi A, Tokuda H, Tomita T, Umezawa A, Watanabe H, Yoshida S, Goto YI, Maruoka Y, Matsubara Y, Niida S, Mizokami M, Tokunaga K. Exploring the genetic diversity of the Japanese population: Insights from a large-scale whole genome sequencing analysis. PLoS Genet 2023; 19:e1010625. [PMID: 38060463 PMCID: PMC10703243 DOI: 10.1371/journal.pgen.1010625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 10/24/2023] [Indexed: 12/18/2023] Open
Abstract
The Japanese archipelago is a terminal location for human migration, and the contemporary Japanese people represent a unique population whose genomic diversity has been shaped by multiple migrations from Eurasia. We analyzed the genomic characteristics that define the genetic makeup of the modern Japanese population from a population genetics perspective from the genomic data of 9,287 samples obtained by high-coverage whole-genome sequencing (WGS) by the National Center Biobank Network. The dataset comprised populations from the Ryukyu Islands and other parts of the Japanese archipelago (Hondo). The Hondo population underwent two episodes of population decline during the Jomon period, corresponding to the Late Neolithic, and the Edo period, corresponding to the Early Modern era, while the Ryukyu population experienced a population decline during the shell midden period of the Late Neolithic in this region. Haplotype analysis suggested increased allele frequencies for genes related to alcohol and fatty acid metabolism, which were reported as loci that had experienced positive natural selection. Two genes related to alcohol metabolism were found to be 12,500 years out of phase with the time when they began to increase in the allele frequency; this finding indicates that the genomic diversity of Japanese people has been shaped by events closely related to agriculture and food production.
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Affiliation(s)
- Yosuke Kawai
- Genome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Yusuke Watanabe
- Genome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Yosuke Omae
- Genome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
- Central Biobank, National Center Biobank Network, Shinjuku-ku, Tokyo, Japan
| | - Reiko Miyahara
- Central Biobank, National Center Biobank Network, Shinjuku-ku, Tokyo, Japan
| | - Seik-Soon Khor
- Genome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Eisei Noiri
- Central Biobank, National Center Biobank Network, Shinjuku-ku, Tokyo, Japan
| | - Koji Kitajima
- Central Biobank, National Center Biobank Network, Shinjuku-ku, Tokyo, Japan
- Department of Data Science Center for Clinical Sciences, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Hideyuki Shimanuki
- Central Biobank, National Center Biobank Network, Shinjuku-ku, Tokyo, Japan
- Department of Data Science Center for Clinical Sciences, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Hiroyuki Gatanaga
- AIDS Clinical Center, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Kenichiro Hata
- Department of Maternal-Fetal Biology, National Center for Child Health and Development, Setagaya-ku, Tokyo, Japan
| | - Kotaro Hattori
- Department of Bioresources, Medical Genome Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Aritoshi Iida
- Department of Clinical Genome Analysis, Medical Genome Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | | | - Tadashi Kaname
- Department of Genome Medicine, National Center for Child Health and Development, Setagaya-ku, Tokyo, Japan
| | - Tatsuya Kanto
- Department of Liver Disease, Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Ichikawa, Chiba, Japan
| | - Ryo Matsumura
- Department of Bioresources, Medical Genome Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Kengo Miyo
- Center for Medical Informatics Intelligence, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Michio Noguchi
- NCVC Biobank, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Kouichi Ozaki
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Masaya Sugiyama
- Department of Viral Pathogenesis and Controls, Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Ayako Takahashi
- NCVC Biobank, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Haruhiko Tokuda
- Core Facility Administration, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
- Department of Metabolic Research, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
- Department of Clinical Laboratory, Hospital, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Tsutomu Tomita
- NCVC Biobank, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Akihiro Umezawa
- Center for Regenerative Medicine, Research Institute, National Center for Child Health and Development, Setagaya-ku, Tokyo, Japan
| | - Hiroshi Watanabe
- Core Facility Administration, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
- Innovation Center for Translational Research, Hospital, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Sumiko Yoshida
- Department of Bioresources, Medical Genome Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Yu-ichi Goto
- Medical Genome Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Yutaka Maruoka
- Department of Oral and Maxillofacial Surgery, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
| | - Yoichi Matsubara
- National Center for Child Health and Development, Setagaya-ku, Tokyo, Japan
| | - Shumpei Niida
- Core Facility Administration, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Masashi Mizokami
- Genome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Ichikawa, Chiba, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan
- Central Biobank, National Center Biobank Network, Shinjuku-ku, Tokyo, Japan
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27
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Shingwenyana B, Rossouw B, Thom J, Louw N, Krause A, Lombard Z. Research participants' perspectives regarding the feedback of secondary findings-A cohort from the DDD-Africa study, South Africa. J Genet Couns 2023:10.1002/jgc4.1830. [PMID: 37965991 PMCID: PMC11093881 DOI: 10.1002/jgc4.1830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 10/08/2023] [Accepted: 10/14/2023] [Indexed: 11/16/2023]
Abstract
Genomic researchers face an ethical dilemma regarding feedback of individual results generated from genomic studies. In the African setting, genomic research is still not widely implemented and, coupled with this, the limited African-specific guidelines on how to feedback on individual research findings. A qualitative study was performed to assess participants' expectations and preferences regarding the feedback of secondary findings from genomic research. Participants were parents of children with a developmental disorder, enrolled in the Deciphering Developmental Disorders in Africa (DDD-Africa) research project, and were purposefully selected. Three deliberative focus group discussions were conducted with 14 participants. Each deliberative focus group consisted of two separate audio-recorded interviews and presented different case scenarios for different types of secondary findings that could be theoretically detected during genomic research. We aimed to explore participants' preferences for the extent, nature, timing, and methods for receiving individual study results, specifically pertaining to secondary findings. Thematic content analysis was done, with a deductive approach to coding. Four themes emerged which included participants' perception of readiness to receive secondary findings, queries raised around who has access to research findings and feedback of findings consent, responsibilities of the researcher, and reasons for not wanting/wanting secondary findings. Overall, participants expressed that they want to receive feedback on secondary findings irrespective of disease severity and treatment availability. Lifestyle changes, early prevention or treatment, impact on future generations, and preparedness were strong motivations for wanting feedback on results. Participants felt that when the research involved minors, it was the parents' right to receive results on behalf of their children. This study provides new insights into participants' preferences around feedback on genomic research results and could serve as an important basis for creating guidelines and recommendations for feedback on genomic results in the African context.
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Affiliation(s)
- Barry Shingwenyana
- Barry Shingwenyana and Bianca Rossouw should be considered joint first author
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, The University of the Witwatersrand, Johannesburg, South Africa
| | - Bianca Rossouw
- Barry Shingwenyana and Bianca Rossouw should be considered joint first author
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, The University of the Witwatersrand, Johannesburg, South Africa
| | - Jamey Thom
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, The University of the Witwatersrand, Johannesburg, South Africa
- Wessex Clinical Genetics Department, Princess Anne Hospital, Southampton, United Kingdom
| | - Nadja Louw
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, The University of the Witwatersrand, Johannesburg, South Africa
| | - Amanda Krause
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, The University of the Witwatersrand, Johannesburg, South Africa
| | - Zané Lombard
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, The University of the Witwatersrand, Johannesburg, South Africa
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28
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Franco A, Li J, Kelly DP, Hershberger RE, Marian AJ, Lewis RM, Song M, Dang X, Schmidt AD, Mathyer ME, Edwards JR, Strong CDG, Dorn GW. A human mitofusin 2 mutation can cause mitophagic cardiomyopathy. eLife 2023; 12:e84235. [PMID: 37910431 PMCID: PMC10619978 DOI: 10.7554/elife.84235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 09/26/2023] [Indexed: 11/03/2023] Open
Abstract
Cardiac muscle has the highest mitochondrial density of any human tissue, but mitochondrial dysfunction is not a recognized cause of isolated cardiomyopathy. Here, we determined that the rare mitofusin (MFN) 2 R400Q mutation is 15-20× over-represented in clinical cardiomyopathy, whereas this specific mutation is not reported as a cause of MFN2 mutant-induced peripheral neuropathy, Charcot-Marie-Tooth disease type 2A (CMT2A). Accordingly, we interrogated the enzymatic, biophysical, and functional characteristics of MFN2 Q400 versus wild-type and CMT2A-causing MFN2 mutants. All MFN2 mutants had impaired mitochondrial fusion, the canonical MFN2 function. Compared to MFN2 T105M that lacked catalytic GTPase activity and exhibited normal activation-induced changes in conformation, MFN2 R400Q and M376A had normal GTPase activity with impaired conformational shifting. MFN2 R400Q did not suppress mitochondrial motility, provoke mitochondrial depolarization, or dominantly suppress mitochondrial respiration like MFN2 T105M. By contrast to MFN2 T105M and M376A, MFN2 R400Q was uniquely defective in recruiting Parkin to mitochondria. CRISPR editing of the R400Q mutation into the mouse Mfn2 gene induced perinatal cardiomyopathy with no other organ involvement; knock-in of Mfn2 T105M or M376V did not affect the heart. RNA sequencing and metabolomics of cardiomyopathic Mfn2 Q/Q400 hearts revealed signature abnormalities recapitulating experimental mitophagic cardiomyopathy. Indeed, cultured cardiomyoblasts and in vivo cardiomyocytes expressing MFN2 Q400 had mitophagy defects with increased sensitivity to doxorubicin. MFN2 R400Q is the first known natural mitophagy-defective MFN2 mutant. Its unique profile of dysfunction evokes mitophagic cardiomyopathy, suggesting a mechanism for enrichment in clinical cardiomyopathy.
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Affiliation(s)
- Antonietta Franco
- Department of Internal Medicine, Pharmacogenomics, Washington University School of MedicineSt LouisUnited States
| | - Jiajia Li
- Department of Internal Medicine, Pharmacogenomics, Washington University School of MedicineSt LouisUnited States
| | - Daniel P Kelly
- Department of Medicine, Cardiovascular Institute, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Ray E Hershberger
- Department of Internal Medicine, Divisions of Human Genetics and Cardiovascular Medicine, Ohio State UniversityColumbusUnited States
| | - Ali J Marian
- Center for Cardiovascular Genetic Research, University of Texas Health Science Center at HoustonHoustonUnited States
| | - Renate M Lewis
- Department of Neurology, Washington University School of MedicineSt. LouisUnited States
| | - Moshi Song
- Department of Internal Medicine, Pharmacogenomics, Washington University School of MedicineSt LouisUnited States
| | - Xiawei Dang
- Department of Internal Medicine, Pharmacogenomics, Washington University School of MedicineSt LouisUnited States
| | - Alina D Schmidt
- Department of Internal Medicine (Dermatology), Washington University School of MedicineSt. LouisUnited States
| | - Mary E Mathyer
- Department of Internal Medicine (Dermatology), Washington University School of MedicineSt. LouisUnited States
| | - John R Edwards
- Department of Internal Medicine, Pharmacogenomics, Washington University School of MedicineSt LouisUnited States
| | - Cristina de Guzman Strong
- Department of Internal Medicine (Dermatology), Washington University School of MedicineSt. LouisUnited States
| | - Gerald W Dorn
- Department of Internal Medicine, Pharmacogenomics, Washington University School of MedicineSt LouisUnited States
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29
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Clark JSC, Podsiadło K, Sobalska-Kwapis M, Marciniak B, Rydzewska K, Ciechanowicz A, van de Wetering T, Strapagiel D. rs67047829 genotypes of ERV3-1/ZNF117 are associated with lower body mass index in the Polish population. Sci Rep 2023; 13:17118. [PMID: 37816715 PMCID: PMC10564729 DOI: 10.1038/s41598-023-43323-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 09/22/2023] [Indexed: 10/12/2023] Open
Abstract
There is now substantial evidence that zinc-finger proteins are implicated in adiposity. Aims were to datamine for high-frequency (near-neutral selection) pretermination-codon (PTC) single-nucleotide polymorphisms (SNPs; n = 141) from a database with > 550,000 variants and analyze possible association with body mass index in a large Polish sample (n = 5757). BMI was regressed (males/females together or separately) against genetic models. Regression for rs67047829 uncovered an interaction-independent association with BMI with both sexes together: mean ± standard deviation, kg/m2: [G];[G], 25.4 ± 4.59 (n = 3650); [G](;)[A], 25.0 ± 4.28 (n = 731); [A];[A], 23.4 ± 3.60 (n = 44); additive model adjusted for age and sex: p = 4.08 × 10-5; beta: - 0.0458, 95% confidence interval (CI) - 0.0732 : - 0.0183; surviving Bonferroni correction; for males: [G];[G], 24.8 ± 4.94 (n = 1878); [G](;)[A], 24.2 ± 4.31 (n = 386); [A];[A], 22.4 ± 3.69 (n = 23); p = 4.20 × 10-4; beta: - 0.0573, CI - 0.0947 : - 0.0199. For average-height males the difference between [G];[G] and [A];[A] genotypes would correspond to ~ 6 kg, suggesting considerable protection against increased BMI. rs67047829 gives a pretermination codon in ERV3-1 which shares an exonic region and possibly promoter with ZNF117, previously associated with adiposity and type-2 diabetes. As this result occurs in a near-neutral Mendelian setting, a drug targetting ERV3-1/ZNF117 might potentially provide considerable benefits with minimal side-effects. This result needs to be replicated, followed by analyses of splice-variant mRNAs and protein expression.
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Affiliation(s)
- Jeremy S C Clark
- Department of Clinical and Molecular Biochemistry, Pomeranian Medical University, al. Powstańców Wlkp. 72, 70-111, Szczecin, Zachodniopomorskie, Poland.
| | - Konrad Podsiadło
- Department of Clinical and Molecular Biochemistry, Pomeranian Medical University, al. Powstańców Wlkp. 72, 70-111, Szczecin, Zachodniopomorskie, Poland
| | - Marta Sobalska-Kwapis
- Biobank Lab, Department of Oncobiology and Epigenetics, Faculty of Biology and Environmental Protection, University of Łodż, 90-237, Łódż, Poland
| | - Błażej Marciniak
- Biobank Lab, Department of Oncobiology and Epigenetics, Faculty of Biology and Environmental Protection, University of Łodż, 90-237, Łódż, Poland
| | - Kamila Rydzewska
- Department of Clinical and Molecular Biochemistry, Pomeranian Medical University, al. Powstańców Wlkp. 72, 70-111, Szczecin, Zachodniopomorskie, Poland
| | - Andrzej Ciechanowicz
- Department of Clinical and Molecular Biochemistry, Pomeranian Medical University, al. Powstańców Wlkp. 72, 70-111, Szczecin, Zachodniopomorskie, Poland
| | - Thierry van de Wetering
- Department of Clinical and Molecular Biochemistry, Pomeranian Medical University, al. Powstańców Wlkp. 72, 70-111, Szczecin, Zachodniopomorskie, Poland
| | - Dominik Strapagiel
- Biobank Lab, Department of Oncobiology and Epigenetics, Faculty of Biology and Environmental Protection, University of Łodż, 90-237, Łódż, Poland
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30
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Falk I, Zhao M, Nait Saada J, Guo Q. Learning the kernel for rare variant genetic association test. Front Genet 2023; 14:1245238. [PMID: 37886683 PMCID: PMC10598548 DOI: 10.3389/fgene.2023.1245238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/14/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction: Compared to Genome-Wide Association Studies (GWAS) for common variants, single-marker association analysis for rare variants is underpowered. Set-based association analyses for rare variants are powerful tools that capture some of the missing heritability in trait association studies. Methods: We extend the convex-optimized SKAT (cSKAT) test set procedure which learns from data the optimal convex combination of kernels, to the full Generalised Linear Model (GLM) setting with arbitrary non-genetic covariates. We call this extended cSKAT (ecSKAT) and show that the resulting optimization problem is a quadratic programming problem that can be solved with no additional cost compared to cSKAT. Results: We show that a modified objective is related to an upper bound for the p-value through a decreasing exponential term in the objective function, indicating that optimizing this objective function is a principled way of learning the combination of kernels. We evaluate the performance of the proposed method on continuous and binary traits using simulation studies and illustrate its application using UK Biobank Whole Exome Sequencing data on hand grip strength and systemic lupus erythematosus rare variant association analysis. Discussion: Our proposed ecSKAT method enables correcting for important confounders in association studies such as age, sex or population structure for both quantitative and binary traits. Simulation studies showed that ecSKAT can recover sensible weights and achieve higher power across different sample sizes and misspecification settings. Compared to the burden test and SKAT method, ecSKAT gives a lower p-value for the genes tested in both quantitative and binary traits in the UKBiobank cohort.
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Affiliation(s)
- Isak Falk
- Department of Computer Science, University College London, London, United Kingdom
- Computational Statistics and Machine Learning, Italian Institute of Technology, Genoa, Italy
| | | | | | - Qi Guo
- BenevolentAI, London, United Kingdom
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31
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Nigenda-Morales SF, Lin M, Nuñez-Valencia PG, Kyriazis CC, Beichman AC, Robinson JA, Ragsdale AP, Urbán R J, Archer FI, Viloria-Gómora L, Pérez-Álvarez MJ, Poulin E, Lohmueller KE, Moreno-Estrada A, Wayne RK. The genomic footprint of whaling and isolation in fin whale populations. Nat Commun 2023; 14:5465. [PMID: 37699896 PMCID: PMC10497599 DOI: 10.1038/s41467-023-40052-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/10/2023] [Indexed: 09/14/2023] Open
Abstract
Twentieth century industrial whaling pushed several species to the brink of extinction, with fin whales being the most impacted. However, a small, resident population in the Gulf of California was not targeted by whaling. Here, we analyzed 50 whole-genomes from the Eastern North Pacific (ENP) and Gulf of California (GOC) fin whale populations to investigate their demographic history and the genomic effects of natural and human-induced bottlenecks. We show that the two populations diverged ~16,000 years ago, after which the ENP population expanded and then suffered a 99% reduction in effective size during the whaling period. In contrast, the GOC population remained small and isolated, receiving less than one migrant per generation. However, this low level of migration has been crucial for maintaining its viability. Our study exposes the severity of whaling, emphasizes the importance of migration, and demonstrates the use of genome-based analyses and simulations to inform conservation strategies.
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Affiliation(s)
- Sergio F Nigenda-Morales
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, 36824, Mexico.
- Department of Biological Sciences, California State University San Marcos, San Marcos, CA, 92096, USA.
| | - Meixi Lin
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, 94305, USA.
| | - Paulina G Nuñez-Valencia
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, 36824, Mexico
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México (UNAM), Cuernavaca, Morelos, México
| | - Christopher C Kyriazis
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Annabel C Beichman
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Jacqueline A Robinson
- Institute for Human Genetics, University of California, San Francisco (UCSF), San Francisco, CA, 94143, USA
| | - Aaron P Ragsdale
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, 36824, Mexico
- Department of Integrative Biology, University of Wisconsin, Madison, WI, 53706, USA
| | - Jorge Urbán R
- Departamento de Ciencias Marinas y Costeras, Universidad Autónoma de Baja California Sur (UABCS), La Paz, Baja California Sur, Mexico
| | - Frederick I Archer
- Marine Mammal and Turtle Division, Southwest Fisheries Science Center, La Jolla, CA, 92037, USA
| | - Lorena Viloria-Gómora
- Departamento de Ciencias Marinas y Costeras, Universidad Autónoma de Baja California Sur (UABCS), La Paz, Baja California Sur, Mexico
| | - María José Pérez-Álvarez
- Escuela de Medicina Veterinaria, Facultad de Medicina y Ciencias de la Salud, Universidad Mayor, Santiago, Chile
- Millennium Institute Biodiversity of Antarctic and Subantarctic Ecosystems (BASE), Universidad de Chile, Santiago, Chile
| | - Elie Poulin
- Millennium Institute Biodiversity of Antarctic and Subantarctic Ecosystems (BASE), Universidad de Chile, Santiago, Chile
| | - Kirk E Lohmueller
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA, 90095, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Andrés Moreno-Estrada
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, 36824, Mexico.
| | - Robert K Wayne
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
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32
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Singer-Berk M, Gudmundsson S, Baxter S, Seaby EG, England E, Wood JC, Son RG, Watts NA, Karczewski KJ, Harrison SM, MacArthur DG, Rehm HL, O'Donnell-Luria A. Advanced variant classification framework reduces the false positive rate of predicted loss-of-function variants in population sequencing data. Am J Hum Genet 2023; 110:1496-1508. [PMID: 37633279 PMCID: PMC10502856 DOI: 10.1016/j.ajhg.2023.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/28/2023] Open
Abstract
Predicted loss of function (pLoF) variants are often highly deleterious and play an important role in disease biology, but many pLoF variants may not result in loss of function (LoF). Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines' PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 genes associated with autosomal-recessive disease from the Genome Aggregation Database (gnomAD v.2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in a low proportion expressed across transcripts (pext) scored region, or the presence of cryptic in-frame splice rescues. Variants predicted to evade LoF or to be potential artifacts were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of pLoF variants predicted as likely not LoF/not LoF, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.
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Affiliation(s)
- Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Sanna Gudmundsson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eleanor G Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Genomic Informatics Group, University Hospital Southampton, Southampton, UK
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan C Wood
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel G Son
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Ambry Genetics, Aliso Viejo, CA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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33
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Arslan A. Pathogenic variants of human GABRA1 gene associated with epilepsy: A computational approach. Heliyon 2023; 9:e20218. [PMID: 37809401 PMCID: PMC10559982 DOI: 10.1016/j.heliyon.2023.e20218] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/17/2023] [Accepted: 09/13/2023] [Indexed: 10/10/2023] Open
Abstract
Critical for brain development, neurodevelopmental and network disorders, the GABRA1 gene encodes for the α1 subunit, an abundantly and developmentally expressed subunit of heteropentameric gamma-aminobutyric acid A receptors (GABAARs) mediating primary inhibition in the brain. Mutations of the GABAAR subunit genes including GABRA1 gene are associated with epilepsy, a group of syndromes, characterized by unprovoked seizures and diagnosed by integrative approach, that involves genetic testing. Despite the diagnostic use of genetic testing, a large fraction of the GABAAR subunit gene variants including the variants of GABRA1 gene is not known in terms of their molecular consequence, a challenge for precision and personalized medicine. Addressing this, one hundred thirty-seven GABRA1 gene variants of unknown clinical significance have been extracted from the ClinVar database and computationally analyzed for pathogenicity. Eight variants (L49H, P59L, W97R, D99G, G152S, V270G, T294R, P305L) are predicted as pathogenic and mapped to the α1 subunit's extracellular domain (ECD), transmembrane domains (TMDs) and extracellular linker. This is followed by the integration with relevant data for cellular pathology and severity of the epilepsy syndromes retrieved from the literature. Our results suggest that the pathogenic variants in the ECD of GABRA1 (L49H, P59L, W97R, D99G, G152S) will probably manifest decreased surface expression and reduced current with mild epilepsy phenotypes while V270G, T294R in the TMDs and P305L in the linker between the second and the third TMDs will likely cause reduced cell current with severe epilepsy phenotypes. The results presented in this study provides insights for clinical genetics and wet lab experimentation.
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Affiliation(s)
- Ayla Arslan
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
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Renaux A, Terwagne C, Cochez M, Tiddi I, Nowé A, Lenaerts T. A knowledge graph approach to predict and interpret disease-causing gene interactions. BMC Bioinformatics 2023; 24:324. [PMID: 37644440 PMCID: PMC10463539 DOI: 10.1186/s12859-023-05451-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results. RESULTS We introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction. CONCLUSION Our method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.
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Affiliation(s)
- Alexandre Renaux
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
- Artificial Intelligence lab, Vrije Universiteit Brussel, Brussels, Belgium
| | - Chloé Terwagne
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
| | - Michael Cochez
- Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Discovery Lab, Elsevier, Amsterdam, The Netherlands
| | - Ilaria Tiddi
- Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ann Nowé
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium
- Artificial Intelligence lab, Vrije Universiteit Brussel, Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
- Artificial Intelligence lab, Vrije Universiteit Brussel, Brussels, Belgium
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Brovkina MV, Chapman MA, Holding ML, Clowney EJ. Emergence and influence of sequence bias in evolutionarily malleable, mammalian tandem arrays. BMC Biol 2023; 21:179. [PMID: 37612705 PMCID: PMC10463633 DOI: 10.1186/s12915-023-01673-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/01/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND The radiation of mammals at the extinction of the dinosaurs produced a plethora of new forms-as diverse as bats, dolphins, and elephants-in only 10-20 million years. Behind the scenes, adaptation to new niches is accompanied by extensive innovation in large families of genes that allow animals to contact the environment, including chemosensors, xenobiotic enzymes, and immune and barrier proteins. Genes in these "outward-looking" families are allelically diverse among humans and exhibit tissue-specific and sometimes stochastic expression. RESULTS Here, we show that these tandem arrays of outward-looking genes occupy AT-biased isochores and comprise the "tissue-specific" gene class that lack CpG islands in their promoters. Models of mammalian genome evolution have not incorporated the sharply different functions and transcriptional patterns of genes in AT- versus GC-biased regions. To examine the relationship between gene family expansion, sequence content, and allelic diversity, we use population genetic data and comparative analysis. First, we find that AT bias can emerge during evolutionary expansion of gene families in cis. Second, human genes in AT-biased isochores or with GC-poor promoters experience relatively low rates of de novo point mutation today but are enriched for non-synonymous variants. Finally, we find that isochores containing gene clusters exhibit low rates of recombination. CONCLUSIONS Our analyses suggest that tolerance of non-synonymous variation and low recombination are two forces that have produced the depletion of GC bases in outward-facing gene arrays. In turn, high AT content exerts a profound effect on their chromatin organization and transcriptional regulation.
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Affiliation(s)
- Margarita V Brovkina
- Graduate Program in Cellular and Molecular Biology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Margaret A Chapman
- Neurosciences Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA.
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.
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Meadows JRS, Kidd JM, Wang GD, Parker HG, Schall PZ, Bianchi M, Christmas MJ, Bougiouri K, Buckley RM, Hitte C, Nguyen AK, Wang C, Jagannathan V, Niskanen JE, Frantz LAF, Arumilli M, Hundi S, Lindblad-Toh K, Ginja C, Agustina KK, André C, Boyko AR, Davis BW, Drögemüller M, Feng XY, Gkagkavouzis K, Iliopoulos G, Harris AC, Hytönen MK, Kalthoff DC, Liu YH, Lymberakis P, Poulakakis N, Pires AE, Racimo F, Ramos-Almodovar F, Savolainen P, Venetsani S, Tammen I, Triantafyllidis A, vonHoldt B, Wayne RK, Larson G, Nicholas FW, Lohi H, Leeb T, Zhang YP, Ostrander EA. Genome sequencing of 2000 canids by the Dog10K consortium advances the understanding of demography, genome function and architecture. Genome Biol 2023; 24:187. [PMID: 37582787 PMCID: PMC10426128 DOI: 10.1186/s13059-023-03023-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 07/25/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20 × data from 1987 individuals, including 1611 dogs (321 breeds), 309 village dogs, 63 wolves, and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility, and genome architecture and function. RESULTS We report the analysis of > 48 M single-nucleotide, indel, and structural variants spanning the autosomes, X chromosome, and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50 bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single-nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high-confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection. CONCLUSIONS We have developed a dense dataset of 1987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available.
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Affiliation(s)
- Jennifer R S Meadows
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 75132, Uppsala, Sweden.
| | - Jeffrey M Kidd
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48107, USA.
| | - Guo-Dong Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Heidi G Parker
- National Human Genome Research Institute, National Institutes of Health, 50 South Drive, Building 50 Room 5351, Bethesda, MD, 20892, USA
| | - Peter Z Schall
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48107, USA
| | - Matteo Bianchi
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 75132, Uppsala, Sweden
| | - Matthew J Christmas
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 75132, Uppsala, Sweden
| | - Katia Bougiouri
- Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Øster Voldgade 5-7, 1350, Copenhagen, Denmark
| | - Reuben M Buckley
- National Human Genome Research Institute, National Institutes of Health, 50 South Drive, Building 50 Room 5351, Bethesda, MD, 20892, USA
| | - Christophe Hitte
- University of Rennes, CNRS, Institute Genetics and Development Rennes - UMR6290, 35000, Rennes, France
| | - Anthony K Nguyen
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48107, USA
| | - Chao Wang
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 75132, Uppsala, Sweden
| | - Vidhya Jagannathan
- Institute of Genetics, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Julia E Niskanen
- Department of Medical and Clinical Genetics, Department of Veterinary Biosciences, University of Helsinki and Folkhälsan Research Center, 02900, Helsinki, Finland
| | - Laurent A F Frantz
- School of Biological and Behavioural Sciences, Queen Mary University of London, London E14NS, UK and Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, D-80539, Munich, Germany
| | - Meharji Arumilli
- Department of Medical and Clinical Genetics, Department of Veterinary Biosciences, University of Helsinki and Folkhälsan Research Center, 02900, Helsinki, Finland
| | - Sruthi Hundi
- Department of Medical and Clinical Genetics, Department of Veterinary Biosciences, University of Helsinki and Folkhälsan Research Center, 02900, Helsinki, Finland
| | - Kerstin Lindblad-Toh
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 75132, Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Catarina Ginja
- BIOPOLIS-CIBIO-InBIO-Centro de Investigação Em Biodiversidade E Recursos Genéticos - ArchGen Group, Universidade Do Porto, 4485-661, Vairão, Portugal
| | | | - Catherine André
- University of Rennes, CNRS, Institute Genetics and Development Rennes - UMR6290, 35000, Rennes, France
| | - Adam R Boyko
- Department of Biomedical Sciences, Cornell University, 930 Campus Road, Ithaca, NY, 14853, USA
| | - Brian W Davis
- Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Michaela Drögemüller
- Institute of Genetics, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Xin-Yao Feng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Konstantinos Gkagkavouzis
- Department of Genetics, School of Biology, ), Aristotle University of Thessaloniki, Thessaloniki, Macedonia 54124, Greece and Genomics and Epigenomics Translational Research (GENeTres), Center for Interdisciplinary Research and Innovation (CIRI-AUTH, Balkan Center, Thessaloniki, Greece
| | - Giorgos Iliopoulos
- NGO "Callisto", Wildlife and Nature Conservation Society, 54621, Thessaloniki, Greece
| | - Alexander C Harris
- National Human Genome Research Institute, National Institutes of Health, 50 South Drive, Building 50 Room 5351, Bethesda, MD, 20892, USA
| | - Marjo K Hytönen
- Department of Medical and Clinical Genetics, Department of Veterinary Biosciences, University of Helsinki and Folkhälsan Research Center, 02900, Helsinki, Finland
| | - Daniela C Kalthoff
- NGO "Callisto", Wildlife and Nature Conservation Society, 54621, Thessaloniki, Greece
| | - Yan-Hu Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Petros Lymberakis
- Natural History Museum of Crete & Department of Biology, University of Crete, 71202, Irakleio, Greece
- Biology Department, School of Sciences and Engineering, University of Crete, Heraklion, Greece
- Palaeogenomics and Evolutionary Genetics Lab, Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Nikolaos Poulakakis
- Natural History Museum of Crete & Department of Biology, University of Crete, 71202, Irakleio, Greece
- Biology Department, School of Sciences and Engineering, University of Crete, Heraklion, Greece
- Palaeogenomics and Evolutionary Genetics Lab, Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Ana Elisabete Pires
- BIOPOLIS-CIBIO-InBIO-Centro de Investigação Em Biodiversidade E Recursos Genéticos - ArchGen Group, Universidade Do Porto, 4485-661, Vairão, Portugal
| | - Fernando Racimo
- Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Øster Voldgade 5-7, 1350, Copenhagen, Denmark
| | | | - Peter Savolainen
- Department of Gene Technology, Science for Life Laboratory, KTH - Royal Institute of Technology, 17121, Solna, Sweden
| | - Semina Venetsani
- Department of Genetics, School of Biology, Aristotle University of Thessaloniki, 54124, Thessaloniki, Macedonia, Greece
| | - Imke Tammen
- Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW, 2570, Australia
| | - Alexandros Triantafyllidis
- Department of Genetics, School of Biology, ), Aristotle University of Thessaloniki, Thessaloniki, Macedonia 54124, Greece and Genomics and Epigenomics Translational Research (GENeTres), Center for Interdisciplinary Research and Innovation (CIRI-AUTH, Balkan Center, Thessaloniki, Greece
| | - Bridgett vonHoldt
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Robert K Wayne
- Department of Ecology and Evolutionary Biology, Ecology and Evolutionary Biology, University of California, Los Angeles, CA, 90095-7246, USA
| | - Greger Larson
- Palaeogenomics and Bio-Archaeology Research Network, School of Archaeology, University of Oxford, Oxford, OX1 3TG, UK
| | - Frank W Nicholas
- Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW, 2570, Australia
| | - Hannes Lohi
- Department of Medical and Clinical Genetics, Department of Veterinary Biosciences, University of Helsinki and Folkhälsan Research Center, 02900, Helsinki, Finland
| | - Tosso Leeb
- Institute of Genetics, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, 50 South Drive, Building 50 Room 5351, Bethesda, MD, 20892, USA.
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An J, Oh JH, Oh B, Oh YJ, Ju JS, Kim W, Kang HJ, Sung CO, Shim JH. Clinicogenomic characteristics and synthetic lethal implications of germline homologous recombination-deficient hepatocellular carcinoma. Hepatology 2023; 78:452-467. [PMID: 36177702 DOI: 10.1002/hep.32812] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/20/2022] [Accepted: 09/24/2022] [Indexed: 12/08/2022]
Abstract
BACKGROUNDS AND AIMS We performed an in-depth examination of pathogenic germline variants (PGVs) and somatic variants in DNA damage response (DDR) genes in hepatocellular carcinoma (HCC) to explore their clinical and genomic impacts. APPROACH AND RESULTS We used a merged whole-exome or RNA sequencing data set derived from in-house ( n = 230) and The Cancer Genome Atlas ( n = 362) databases of multiethnic HCC samples. We also evaluated synthetic lethal approaches targeting mutations in homologous recombination (HR) genes using HCC cells selected from five genomic databases of cancer cell lines. A total of 110 PGVs in DDR pathways in 96 patients were selected. Of the PGV carriers, 44 were HR-altered and found to be independently associated with poorer disease-free survival after hepatectomy. The most frequently altered HR gene in both germline and somatic tissues was POLQ , and this variant was detected in 22.7% (10/44) and 23.8% (5/21) of all the corresponding carriers, respectively. PGVs in HR were significantly associated with upregulation of proliferation and replication-related genes and familial risk of HCC. Samples harboring PGVs in HR with loss of heterozygosity were most strongly correlated with the genomic footprints of deficient HR, such as mutation burden and denovoSig2 (analogous to Catalogue of Somatic Mutations in Cancer [COSMIC] 3), and poor outcome. Pharmacologic experiments with HCC cells defective in BRCA2 or POLQ suggested that tumors with this phenotype are synthetic lethal with poly(ADP-ribose) polymerase inhibitors. CONCLUSIONS Our findings suggest that germline HR defects in HCC tend to confer a poor prognosis and result in distinctive genomic scarring. Tests of the clinical benefits of HR-directed treatments in the affected patients are needed.
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Affiliation(s)
- Jihyun An
- Gastroenterology and Hepatology , Hanyang University College of Medicine , Guri , Republic of Korea
| | - Ji-Hye Oh
- Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine , Seoul , Republic of Korea
| | - Bora Oh
- Asan Institute for Life Science, Asan Medical Center , Seoul , Republic of Korea
| | - Yoo-Jin Oh
- Asan Institute for Life Science, Asan Medical Center , Seoul , Republic of Korea
| | - Jin-Sung Ju
- Asan Institute for Life Science, Asan Medical Center , Seoul , Republic of Korea
| | - Wonkyung Kim
- Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine , Seoul , Republic of Korea
| | - Hyo Jung Kang
- Pathology, Asan Medical Center , University of Ulsan College of Medicine , Seoul , Republic of Korea
- Asan Liver Center, Asan Medical Center , University of Ulsan College of Medicine , Seoul , South Korea
| | - Chang Ohk Sung
- Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine , Seoul , Republic of Korea
- Pathology, Asan Medical Center , University of Ulsan College of Medicine , Seoul , Republic of Korea
- Center for Cancer Genome Discovery , Asan Institute for Life Science, University of Ulsan College of Medicine, Asan Medical Center , Seoul , Republic of Korea
| | - Ju Hyun Shim
- Asan Liver Center, Asan Medical Center , University of Ulsan College of Medicine , Seoul , South Korea
- Gastroenterology, Asan Medical Center , University of Ulsan College of Medicine , Seoul , Republic of Korea
- Digestive Diseases Research Center , University of Ulsan College of Medicine , Seoul , Republic of Korea
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Kalyta K, Stelmaszczyk W, Szczęśniak D, Kotuła L, Dobosz P, Mroczek M. The Spectrum of the Heterozygous Effect in Biallelic Mendelian Diseases-The Symptomatic Heterozygote Issue. Genes (Basel) 2023; 14:1562. [PMID: 37628614 PMCID: PMC10454578 DOI: 10.3390/genes14081562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Heterozygous carriers of pathogenic/likely pathogenic variants in autosomal recessive disorders seem to be asymptomatic. However, in recent years, an increasing number of case reports have suggested that mild and unspecific symptoms can occur in some heterozygotes, as symptomatic heterozygotes have been identified across different disease types, including neurological, neuromuscular, hematological, and pulmonary diseases. The symptoms are usually milder in heterozygotes than in biallelic variants and occur "later in life". The status of symptomatic heterozygotes as separate entities is often disputed, and alternative diagnoses are considered. Indeed, often only a thin line exists between dual, dominant, and recessive modes of inheritance and symptomatic heterozygosity. Interestingly, recent population studies have found global disease effects in heterozygous carriers of some genetic variants. What makes the few heterozygotes symptomatic, while the majority show no symptoms? The molecular basis of this phenomenon is still unknown. Possible explanations include undiscovered deep-splicing variants, genetic and environmental modifiers, digenic/oligogenic inheritance, skewed methylation patterns, and mutational burden. Symptomatic heterozygotes are rarely reported in the literature, mainly because most did not undergo the complete diagnostic procedure, so alternative diagnoses could not be conclusively excluded. However, despite the increasing accessibility to high-throughput technologies, there still seems to be a small group of patients with mild symptoms and just one variant of autosomes in biallelic diseases. Here, we present some examples, the current state of knowledge, and possible explanations for this phenomenon, and thus argue against the existing dominant/recessive classification.
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Affiliation(s)
- Kateryna Kalyta
- School of Life Sciences, FHNW—University of Applied Sciences, 4132 Muttenz, Switzerland;
| | - Weronika Stelmaszczyk
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK;
| | - Dominika Szczęśniak
- Institute of Psychiatry and Neurology in Warsaw, Genetics Department, 02-957 Warsaw, Poland;
| | - Lidia Kotuła
- Department of Genetics, Medical University, 20-080 Lublin, Poland;
| | - Paula Dobosz
- Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Pawinskiego 5A, 02-106 Warsaw, Poland;
| | - Magdalena Mroczek
- University Hospital Basel, University of Basel, 4031 Basel, Switzerland
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Zhao Y, Wang Y, Shi L, McDonald-McGinn DM, Crowley TB, McGinn DE, Tran OT, Miller D, Lin JR, Zackai E, Johnston HR, Chow EWC, Vorstman JAS, Vingerhoets C, van Amelsvoort T, Gothelf D, Swillen A, Breckpot J, Vermeesch JR, Eliez S, Schneider M, van den Bree MBM, Owen MJ, Kates WR, Repetto GM, Shashi V, Schoch K, Bearden CE, Digilio MC, Unolt M, Putotto C, Marino B, Pontillo M, Armando M, Vicari S, Angkustsiri K, Campbell L, Busa T, Heine-Suñer D, Murphy KC, Murphy D, García-Miñaúr S, Fernández L, Zhang ZD, Goldmuntz E, Gur RE, Emanuel BS, Zheng D, Marshall CR, Bassett AS, Wang T, Morrow BE. Chromatin regulators in the TBX1 network confer risk for conotruncal heart defects in 22q11.2DS. NPJ Genom Med 2023; 8:17. [PMID: 37463940 PMCID: PMC10354062 DOI: 10.1038/s41525-023-00363-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/29/2023] [Indexed: 07/20/2023] Open
Abstract
Congenital heart disease (CHD) affecting the conotruncal region of the heart, occurs in 40-50% of patients with 22q11.2 deletion syndrome (22q11.2DS). This syndrome is a rare disorder with relative genetic homogeneity that can facilitate identification of genetic modifiers. Haploinsufficiency of TBX1, encoding a T-box transcription factor, is one of the main genes responsible for the etiology of the syndrome. We suggest that genetic modifiers of conotruncal defects in patients with 22q11.2DS may be in the TBX1 gene network. To identify genetic modifiers, we analyzed rare, predicted damaging variants in whole genome sequence of 456 cases with conotruncal defects and 537 controls, with 22q11.2DS. We then performed gene set approaches and identified chromatin regulatory genes as modifiers. Chromatin genes with recurrent damaging variants include EP400, KAT6A, KMT2C, KMT2D, NSD1, CHD7 and PHF21A. In total, we identified 37 chromatin regulatory genes, that may increase risk for conotruncal heart defects in 8.5% of 22q11.2DS cases. Many of these genes were identified as risk factors for sporadic CHD in the general population. These genes are co-expressed in cardiac progenitor cells with TBX1, suggesting that they may be in the same genetic network. The genes KAT6A, KMT2C, CHD7 and EZH2, have been previously shown to genetically interact with TBX1 in mouse models. Our findings indicate that disturbance of chromatin regulatory genes impact the TBX1 gene network serving as genetic modifiers of 22q11.2DS and sporadic CHD, suggesting that there are some shared mechanisms involving the TBX1 gene network in the etiology of CHD.
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Affiliation(s)
- Yingjie Zhao
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Yujue Wang
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Lijie Shi
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Donna M McDonald-McGinn
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA
| | - T Blaine Crowley
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA
| | - Daniel E McGinn
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA
| | - Oanh T Tran
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA
| | - Daniella Miller
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Jhih-Rong Lin
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Elaine Zackai
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA
| | - H Richard Johnston
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Eva W C Chow
- Department of Psychiatry, University of Toronto, Ontario, M5G 0A4, Canada
| | - Jacob A S Vorstman
- Program in Genetics and Genome Biology, Research Institute and Autism Research Unit, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Claudia Vingerhoets
- Department of Psychiatry and Psychology, Maastricht University, Maastricht, 6200, MD, the Netherlands
| | - Therese van Amelsvoort
- Department of Psychiatry and Psychology, Maastricht University, Maastricht, 6200, MD, the Netherlands
| | - Doron Gothelf
- The Division of Child & Adolescent Psychiatry, Edmond and Lily Sapfra Children's Hospital, Sheba Medical Center and Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Ramat Gan, 5262000, Israel
| | - Ann Swillen
- Center for Human Genetics, University Hospital Leuven, Department of Human Genetics, University of Leuven (KU Leuven), Leuven, 3000, Belgium
| | - Jeroen Breckpot
- Center for Human Genetics, University Hospital Leuven, Department of Human Genetics, University of Leuven (KU Leuven), Leuven, 3000, Belgium
| | - Joris R Vermeesch
- Center for Human Genetics, University Hospital Leuven, Department of Human Genetics, University of Leuven (KU Leuven), Leuven, 3000, Belgium
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, 1211, Switzerland
| | - Maude Schneider
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, 1211, Switzerland
| | - Marianne B M van den Bree
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Wales, CF24 4HQ, UK
| | - Michael J Owen
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Wales, CF24 4HQ, UK
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, 13202, USA
- Program in Neuroscience, SUNY Upstate Medical University, Syracuse, NY, 13202, USA
| | - Gabriela M Repetto
- Center for Genetics and Genomics, Facultad de Medicina Clinica Alemana-Universidad del Desarrollo, Santiago, 7710162, Chile
| | - Vandana Shashi
- Department of Pediatrics, Duke University, Durham, NC, 27710, USA
| | - Kelly Schoch
- Department of Pediatrics, Duke University, Durham, NC, 27710, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA, 90095, USA
| | - M Cristina Digilio
- Department of Medical Genetics, Bambino Gesù Hospital, Rome, 00165, Italy
| | - Marta Unolt
- Department of Medical Genetics, Bambino Gesù Hospital, Rome, 00165, Italy
- Department of Pediatrics, Gynecology, and Obstetrics, La Sapienza University of Rome, Rome, 00185, Italy
| | - Carolina Putotto
- Department of Pediatrics, Gynecology, and Obstetrics, La Sapienza University of Rome, Rome, 00185, Italy
| | - Bruno Marino
- Department of Pediatrics, Gynecology, and Obstetrics, La Sapienza University of Rome, Rome, 00185, Italy
| | - Maria Pontillo
- Department of Neuroscience, Bambino Gesù Hospital, Rome, 00165, Italy
| | - Marco Armando
- Department of Neuroscience, Bambino Gesù Hospital, Rome, 00165, Italy
- Developmental Imaging and Psychopathology Lab, University of Geneva, Geneva, 1211, Switzerland
| | - Stefano Vicari
- Department of Life Sciences and Public Health, Catholic University and Child & Adolescent Psychiatry Unit at Bambino Gesù Hospital, Rome, 00165, Italy
| | - Kathleen Angkustsiri
- Developmental Behavioral Pediatrics, MIND Institute, University of California, Davis, CA, 95817, USA
| | - Linda Campbell
- School of Psychology, University of Newcastle, Newcastle, 2258, Australia
| | - Tiffany Busa
- Department of Medical Genetics, Aix-Marseille University, Marseille, 13284, France
| | - Damian Heine-Suñer
- Genomics of Health and Unit of Molecular Diagnosis and Clinical Genetics, Son Espases University Hospital, Balearic Islands Health Research Institute, Palma de Mallorca, 07120, Spain
| | - Kieran C Murphy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, 505095, Ireland
| | - Declan Murphy
- Department of Forensic and Neurodevelopmental Sciences, King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, SE5 8AF, UK
- Behavioral and Developmental Psychiatry Clinical Academic Group, Behavioral Genetics Clinic, National Adult Autism and ADHD Service, South London and Maudsley Foundation National Health Service Trust, London, SE5 8AZ, UK
| | - Sixto García-Miñaúr
- Institute of Medical and Molecular Genetics, University Hospital La Paz, Madrid, 28046, Spain
| | - Luis Fernández
- Institute of Medical and Molecular Genetics, University Hospital La Paz, Madrid, 28046, Spain
| | - Zhengdong D Zhang
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Elizabeth Goldmuntz
- Division of Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine of the University of Pennsylvania Philadelphia, Philadelphia, PA, 19104, USA
- Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Beverly S Emanuel
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, USA
| | - Deyou Zheng
- Department of Genetics, Department of Neurology, Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Christian R Marshall
- Division of Genome Diagnostics, The Hospital for Sick Children and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5T 1R8, Canada
| | - Anne S Bassett
- Clinical Genetics Research Program and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalglish Family 22q Clinic, Toronto General Hospital, and Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, M5T 1R8, Canada
| | - Tao Wang
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Bernice E Morrow
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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Kumaran M, Devarajan B. eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease. Genet Med 2023; 25:100862. [PMID: 37092535 DOI: 10.1016/j.gim.2023.100862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/25/2023] Open
Abstract
PURPOSE Disease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model. METHODS We developed an eye disease-specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure. RESULTS eyeVarP outperformed pan disease-specific tools in identifying eye disease-specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders. CONCLUSION eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.
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Affiliation(s)
- Manojkumar Kumaran
- Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, India; School of Chemical and Biotechnology, SASTRA (Deemed to be a university), Thanjavur, Tamil Nadu, India
| | - Bharanidharan Devarajan
- Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, India.
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Khadzhieva MB, Gracheva AS, Belopolskaya OB, Kolobkov DS, Kashatnikova DA, Redkin IV, Kuzovlev AN, Grechko AV, Salnikova LE. COVID-19 severity: does the genetic landscape of rare variants matter? Front Genet 2023; 14:1152768. [PMID: 37456666 PMCID: PMC10339319 DOI: 10.3389/fgene.2023.1152768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.
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Affiliation(s)
- Maryam B. Khadzhieva
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
- The Laboratory of Molecular Immunology, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Alesya S. Gracheva
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
- The Department of Population Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Olesya B. Belopolskaya
- The Resource Center “Bio-bank Center”, Research Park of St. Petersburg State University, St. Petersburg, Russia
- The Laboratory of Genogeography, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Dmitry S. Kolobkov
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Darya A. Kashatnikova
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Ivan V. Redkin
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
| | - Artem N. Kuzovlev
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
| | - Andrey V. Grechko
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
| | - Lyubov E. Salnikova
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
- The Laboratory of Ecological Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
- The Laboratory of Molecular Immunology, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
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Badonyi M, Marsh JA. Buffering of genetic dominance by allele-specific protein complex assembly. SCIENCE ADVANCES 2023; 9:eadf9845. [PMID: 37256959 PMCID: PMC10413657 DOI: 10.1126/sciadv.adf9845] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 04/24/2023] [Indexed: 06/02/2023]
Abstract
Protein complex assembly often occurs while subunits are being translated, resulting in complexes whose subunits were translated from the same mRNA in an allele-specific manner. It has thus been hypothesized that such cotranslational assembly may counter the assembly-mediated dominant-negative effect, whereby co-assembly of mutant and wild-type subunits "poisons" complex activity. Here, we show that cotranslationally assembling subunits are much less likely to be associated with autosomal dominant relative to recessive disorders, and that subunits with dominant-negative disease mutations are significantly depleted in cotranslational assembly compared to those associated with loss-of-function mutations. We also find that complexes with known dominant-negative effects tend to expose their interfaces late during translation, lessening the likelihood of cotranslational assembly. Finally, by combining complex properties with other features, we trained a computational model for predicting proteins likely to be associated with non-loss-of-function disease mechanisms, which we believe will be of considerable utility for protein variant interpretation.
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Affiliation(s)
- Mihaly Badonyi
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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43
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Flack N, Drown M, Walls C, Pratte J, McLain A, Faulk C. Chromosome-level, nanopore-only genome and allele-specific DNA methylation of Pallas's cat, Otocolobus manul. NAR Genom Bioinform 2023; 5:lqad033. [PMID: 37025970 PMCID: PMC10071556 DOI: 10.1093/nargab/lqad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 02/10/2023] [Accepted: 03/17/2023] [Indexed: 04/07/2023] Open
Abstract
Pallas's cat, or the manul cat (Otocolobus manul), is a small felid native to the grasslands and steppes of central Asia. Population strongholds in Mongolia and China face growing challenges from climate change, habitat fragmentation, poaching, and other sources. These threats, combined with O. manul's zoo collection popularity and value in evolutionary biology, necessitate improvement of species genomic resources. We used standalone nanopore sequencing to assemble a 2.5 Gb, 61-contig nuclear assembly and 17097 bp mitogenome for O. manul. The primary nuclear assembly had 56× sequencing coverage, a contig N50 of 118 Mb, and a 94.7% BUSCO completeness score for Carnivora-specific genes. High genome collinearity within Felidae permitted alignment-based scaffolding onto the fishing cat (Prionailurus viverrinus) reference genome. Manul contigs spanned all 19 felid chromosomes with an inferred total gap length of less than 400 kilobases. Modified basecalling and variant phasing produced an alternate pseudohaplotype assembly and allele-specific DNA methylation calls; 61 differentially methylated regions were identified between haplotypes. Nearest features included classical imprinted genes, non-coding RNAs, and putative novel imprinted loci. The assembled mitogenome successfully resolved existing discordance between Felinae nuclear and mtDNA phylogenies. All assembly drafts were generated from 158 Gb of sequence using seven minION flow cells.
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Affiliation(s)
- Nicole Flack
- Department of Veterinary and Biomedical Sciences, University of Minnesota, Saint Paul, MN 55108, USA
| | - Melissa Drown
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, MN 55108, USA
| | - Carrie Walls
- Department of Animal Science, University of Minnesota, Saint Paul, MN 55108, USA
| | - Jay Pratte
- Bloomington Parks and Recreation, Miller Park Zoo, Bloomington, IL 61701, USA
| | - Adam McLain
- Department of Biology and Chemistry, SUNY Polytechnic Institute, Utica, NY 13502, USA
| | - Christopher Faulk
- Department of Animal Science, University of Minnesota, Saint Paul, MN 55108, USA
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44
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Zekavat SM, Jorshery SD, Shweikh Y, Horn K, Rauscher FG, Sekimitsu S, Kayoma S, Ye Y, Raghu V, Zhao H, Ghassemi M, Elze T, Segrè AV, Wiggs JL, Scholz M, Priore LD, Wang JC, Natarajan P, Zebardast N. Insights into human health from phenome- and genome-wide analyses of UK Biobank retinal optical coherence tomography phenotypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.16.23290063. [PMID: 37292770 PMCID: PMC10246137 DOI: 10.1101/2023.05.16.23290063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The human retina is a complex multi-layered tissue which offers a unique window into systemic health and disease. Optical coherence tomography (OCT) is widely used in eye care and allows the non-invasive, rapid capture of retinal measurements in exquisite detail. We conducted genome- and phenome-wide analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed phenome-wide association analyses, associating retinal thicknesses with 1,866 incident ICD-based conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association analyses, identifying inherited genetic markers which influence the retina, and replicated our associations among 6,313 individuals from the LIFE-Adult Study. And lastly, we performed comparative association of phenome- and genome- wide associations to identify putative causal links between systemic conditions, retinal layer thicknesses, and ocular disease. Independent associations with incident mortality were detected for photoreceptor thinning and ganglion cell complex thinning. Significant phenotypic associations were detected between retinal layer thinning and ocular, neuropsychiatric, cardiometabolic and pulmonary conditions. Genome-wide association of retinal layer thicknesses yielded 259 loci. Consistency between epidemiologic and genetic associations suggested putative causal links between thinning of the retinal nerve fiber layer with glaucoma, photoreceptor segment with AMD, as well as poor cardiometabolic and pulmonary function with PS thinning, among other findings. In conclusion, retinal layer thinning predicts risk of future ocular and systemic disease. Furthermore, systemic cardio-metabolic-pulmonary conditions promote retinal thinning. Retinal imaging biomarkers, integrated into electronic health records, may inform risk prediction and potential therapeutic strategies.
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Affiliation(s)
- Seyedeh Maryam Zekavat
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saman Doroodgar Jorshery
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departments of Computer Science/Medicine, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology University of Leipzig, Germany and Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig, Germany
| | - Franziska G. Rauscher
- Institute for Medical Informatics, Statistics and Epidemiology University of Leipzig, Germany and Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig, Germany
| | | | - Satoshi Kayoma
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yixuan Ye
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Vineet Raghu
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hongyu Zhao
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- School of Public Health, Yale University, New Haven, CT, USA
| | - Marzyeh Ghassemi
- Departments of Computer Science/Medicine, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tobias Elze
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ayellet V. Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Janey L. Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology University of Leipzig, Germany and Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig, Germany
| | - Lucian Del Priore
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA
| | - Jay C. Wang
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA
- Northern California Retina Vitreous Associates, Mountain View, CA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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45
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Banerjee A, Bahar I. Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions. Int J Mol Sci 2023; 24:8450. [PMID: 37176156 PMCID: PMC10179678 DOI: 10.3390/ijms24098450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/01/2023] [Accepted: 05/06/2023] [Indexed: 05/15/2023] Open
Abstract
The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.
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Affiliation(s)
- Anupam Banerjee
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ivet Bahar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY 11794, USA
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46
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Liao WW, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, Lu S, Lucas JK, Monlong J, Abel HJ, Buonaiuto S, Chang XH, Cheng H, Chu J, Colonna V, Eizenga JM, Feng X, Fischer C, Fulton RS, Garg S, Groza C, Guarracino A, Harvey WT, Heumos S, Howe K, Jain M, Lu TY, Markello C, Martin FJ, Mitchell MW, Munson KM, Mwaniki MN, Novak AM, Olsen HE, Pesout T, Porubsky D, Prins P, Sibbesen JA, Sirén J, Tomlinson C, Villani F, Vollger MR, Antonacci-Fulton LL, Baid G, Baker CA, Belyaeva A, Billis K, Carroll A, Chang PC, Cody S, Cook DE, Cook-Deegan RM, Cornejo OE, Diekhans M, Ebert P, Fairley S, Fedrigo O, Felsenfeld AL, Formenti G, Frankish A, Gao Y, Garrison NA, Giron CG, Green RE, Haggerty L, Hoekzema K, Hourlier T, Ji HP, Kenny EE, Koenig BA, Kolesnikov A, Korbel JO, Kordosky J, Koren S, Lee H, Lewis AP, Magalhães H, Marco-Sola S, Marijon P, McCartney A, McDaniel J, Mountcastle J, Nattestad M, Nurk S, Olson ND, Popejoy AB, Puiu D, Rautiainen M, Regier AA, Rhie A, Sacco S, Sanders AD, Schneider VA, Schultz BI, Shafin K, Smith MW, Sofia HJ, Abou Tayoun AN, Thibaud-Nissen F, Tricomi FF, Wagner J, Walenz B, Wood JMD, Zimin AV, Bourque G, Chaisson MJP, Flicek P, Phillippy AM, Zook JM, Eichler EE, Haussler D, Wang T, Jarvis ED, Miga KH, Garrison E, Marschall T, Hall IM, Li H, Paten B. A draft human pangenome reference. Nature 2023; 617:312-324. [PMID: 37165242 PMCID: PMC10172123 DOI: 10.1038/s41586-023-05896-x] [Citation(s) in RCA: 194] [Impact Index Per Article: 194.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 02/28/2023] [Indexed: 05/12/2023]
Abstract
Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.
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Affiliation(s)
- Wen-Wei Liao
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Center for Genomic Health, Yale University School of Medicine, New Haven, CT, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Mobin Asri
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Daniel Doerr
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Marina Haukness
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Glenn Hickey
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Shuangjia Lu
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Center for Genomic Health, Yale University School of Medicine, New Haven, CT, USA
| | - Julian K Lucas
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Jean Monlong
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Haley J Abel
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Silvia Buonaiuto
- Institute of Genetics and Biophysics, National Research Council, Naples, Italy
| | - Xian H Chang
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Haoyu Cheng
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Justin Chu
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Vincenza Colonna
- Institute of Genetics and Biophysics, National Research Council, Naples, Italy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jordan M Eizenga
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Xiaowen Feng
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Christian Fischer
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Robert S Fulton
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Shilpa Garg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
| | - Cristian Groza
- Quantitative Life Sciences, McGill University, Montréal, Québec, Canada
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Simon Heumos
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Kerstin Howe
- Tree of Life, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Miten Jain
- Northeastern University, Boston, MA, USA
| | - Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Charles Markello
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Fergal J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Adam M Novak
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Hugh E Olsen
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Trevor Pesout
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jonas A Sibbesen
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Jouni Sirén
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Chad Tomlinson
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Flavia Villani
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Division of Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA
| | | | | | - Carl A Baker
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Konstantinos Billis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | | | - Sarah Cody
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Robert M Cook-Deegan
- Barrett and O'Connor Washington Center, Arizona State University, Washington, DC, USA
| | - Omar E Cornejo
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA
| | - Mark Diekhans
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
- Core Unit Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Susan Fairley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Olivier Fedrigo
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA
| | - Adam L Felsenfeld
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | - Giulio Formenti
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Yan Gao
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nanibaa' A Garrison
- Institute for Society and Genetics, College of Letters and Science, University of California, Los Angeles, CA, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Carlos Garcia Giron
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Richard E Green
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
- Dovetail Genomics, Scotts Valley, CA, USA
| | - Leanne Haggerty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Barbara A Koenig
- Program in Bioethics and Institute for Human Genetics, University of California, San Francisco, CA, USA
| | | | - Jan O Korbel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Jennifer Kordosky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - HoJoon Lee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Hugo Magalhães
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Santiago Marco-Sola
- Computer Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
- Departament d'Arquitectura de Computadors i Sistemes Operatius, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Pierre Marijon
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Ann McCartney
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer McDaniel
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | | | - Sergey Nurk
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathan D Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Alice B Popejoy
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Daniela Puiu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mikko Rautiainen
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Allison A Regier
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samuel Sacco
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA
| | - Ashley D Sanders
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Valerie A Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Baergen I Schultz
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | | | - Michael W Smith
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | - Heidi J Sofia
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | - Ahmad N Abou Tayoun
- Al Jalila Genomics Center of Excellence, Al Jalila Children's Specialty Hospital, Dubai, UAE
- Center for Genomic Discovery, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Francesca Floriana Tricomi
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Brian Walenz
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Aleksey V Zimin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Canadian Center for Computational Genomics, McGill University, Montréal, Québec, Canada
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - David Haussler
- Genomics Institute, University of California, Santa Cruz, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Ting Wang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Erich D Jarvis
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Karen H Miga
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany.
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany.
| | - Ira M Hall
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
- Center for Genomic Health, Yale University School of Medicine, New Haven, CT, USA.
| | - Heng Li
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Benedict Paten
- Genomics Institute, University of California, Santa Cruz, CA, USA.
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47
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Abstract
Immunity to infection has been extensively studied in humans and mice bearing naturally occurring or experimentally introduced germline mutations. Mouse studies are sometimes neglected by human immunologists, on the basis that mice are not humans and the infections studied are experimental and not natural. Conversely, human studies are sometimes neglected by mouse immunologists, on the basis of the uncontrolled conditions of study and small numbers of patients. However, both sides would agree that the infectious phenotypes of patients with inborn errors of immunity often differ from those of the corresponding mutant mice. Why is that? We argue that this important question is best addressed by revisiting and reinterpreting the findings of both mouse and human studies from a genetic perspective. Greater caution is required for reverse-genetics studies than for forward-genetics studies, but genetic analysis is sufficiently strong to define the studies likely to stand the test of time. Genetically robust mouse and human studies can provide invaluable complementary insights into the mechanisms of immunity to infection common and specific to these two species.
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Affiliation(s)
- Philippe Gros
- McGill University Research Center on Complex Traits, Department of Biochemistry, and Department of Human Genetics, McGill University, Montréal, Québec, Canada;
| | - Jean-Laurent Casanova
- Howard Hughes Medical Institute and St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA;
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM, and University of Paris Cité, Imagine Institute and Necker Hospital for Sick Children, Paris, France
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48
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Singer-Berk M, Gudmundsson S, Baxter S, Seaby EG, England E, Wood JC, Son RG, Watts NA, Karczewski KJ, Harrison SM, MacArthur DG, Rehm HL, O'Donnell-Luria A. Advanced variant classification framework reduces the false positive rate of predicted loss of function (pLoF) variants in population sequencing data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.08.23286955. [PMID: 36945502 PMCID: PMC10029069 DOI: 10.1101/2023.03.08.23286955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Predicted loss of function (pLoF) variants are highly deleterious and play an important role in disease biology, but many of these variants may not actually result in loss-of-function. Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines's PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 autosomal recessive disease-genes from the Genome Aggregation Database (gnomAD, v2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in low per-base expression (pext) score regions, or the presence of cryptic splice rescues. Variants predicted to be potential artifacts or to evade LoF were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of LoF evading variants assessed, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines, and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.
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Affiliation(s)
- Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Sanna Gudmundsson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eleanor G Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Genomic Informatics Group, University Hospital Southampton, Southampton, United Kingdom
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan C Wood
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel G Son
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ambry Genetics, Aliso Viejo, CA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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49
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Nag A, Dhindsa RS, Middleton L, Jiang X, Vitsios D, Wigmore E, Allman EL, Reznichenko A, Carss K, Smith KR, Wang Q, Challis B, Paul DS, Harper AR, Petrovski S. Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank. Am J Hum Genet 2023; 110:487-498. [PMID: 36809768 PMCID: PMC10027475 DOI: 10.1016/j.ajhg.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/30/2023] [Indexed: 02/22/2023] Open
Abstract
Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.
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Affiliation(s)
- Abhishek Nag
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ryan S Dhindsa
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Lawrence Middleton
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Xiao Jiang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Eleanor Wigmore
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Erik L Allman
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Anna Reznichenko
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA
| | - Benjamin Challis
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Andrew R Harper
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK; Early Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK; Department of Medicine, University of Melbourne, Austin Health, Melbourne, VIC, Australia.
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50
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Ma G, Zhao X, Shentu X, Zhang L. Point mutations of homologs as an adaptive solution to the gene loss. J Genet Genomics 2023:S1673-8527(23)00051-6. [PMID: 36870416 DOI: 10.1016/j.jgg.2023.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/06/2023]
Abstract
Gene loss is common and influences genome evolution trajectories. Multiple adaptive strategies to compensate for gene loss have been observed, including copy number gain of paralogous genes and mutations in genes of the same pathway. By using the Ubl-specific protease 2 (ULP2) eviction model, we identify compensatory mutations in the homologous gene ULP1 by laboratory evolution and find that these mutations are capable of rescuing defects caused by the loss of ULP2. Furthermore, bioinformatics analysis of genomes of yeast gene knockout library and natural yeast isolate datasets suggests that point mutations of a homologous gene might be an additional mechanism to compensate gene loss.
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Affiliation(s)
- Guosheng Ma
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, 201210, Shanghai, China
| | - Xiaojing Zhao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xinyi Shentu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Liye Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, 201210, Shanghai, China.
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