Prevalence of loss-of-function, gain-of-function and dominant-negative mechanisms across genetic disease phenotypes

Badonyi, Mihaly; Marsh, Joseph A.

doi:10.1038/s41467-025-63234-3

Cited by 19Genomics and Rare DiseasesGenetic Associations and EpidemiologyBioinformatics and Genomic Networks

Prevalence of loss-of-function, gain-of-function and dominant-negative mechanisms across genetic disease phenotypes

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Badonyi, Mihaly, Marsh, Joseph A.·2025·Nature Communications

DOI 10.1038/s41467-025-63234-3PMC PMC12462468

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Abstract

Molecular disease mechanisms caused by mutations in protein-coding regions are diverse, but they can be broadly categorised into loss-of-function, gain-of-function and dominant-negative effects. Accurately predicting these mechanisms is important, since therapeutic strategies can exploit these mechanisms. Computational predictors tend to perform less well at the identification of pathogenic gain-of-function and dominant-negative variants. Here, we develop a protein structure-based missense loss-of-function likelihood score that can separate recessive loss of function and dominant loss of function from alternative disease mechanisms. Using missense loss-of-function scores, we estimate the prevalence of molecular mechanisms across 2,837 phenotypes in 1,979 Mendelian disease genes, finding that dominant-negative and gain-of-function mechanisms account for 48% of phenotypes in dominant genes. Applying missense loss-of-function scores to genes with multiple phenotypes reveals widespread intragenic mechanistic heterogeneity, with 43% of dominant and 49% of mixed-inheritance genes harbouring both loss-of-function and non-loss-of-function mechanisms. Furthermore, we show that combining missense loss-of-function scores with phenotype semantic similarity enables the prioritisation of dominant-negative mechanisms in mixed-inheritance genes. Our structure-based approach, accessible via a Google Colab notebook, offers a scalable tool for predicting disease mechanisms and advancing personalised medicine.

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Waguespack, S. G., Koller, D. L., White, K. E., Fishburn, T., Carn, G., Buckwalter, K. A., Johnson, M. L., Kocisko, M., Evans, W., Foroud, T., et al. (2003) · Journal of Bone and Mineral Research · doi:10.1359/jbmr.2003.18.8.1513 · Cited by 108 · see citing works
A Restricted Repertoire of De Novo Mutations in ITPR1 Cause Gillespie Syndrome with Evidence for Dominant-Negative Effect
McEntagart, M., Williamson, K. A., Rainger, J. K., Wheeler, A. P., Seawright, A., Baere, E. B. W. D., Verdin, H., Bergendahl, L. T., Quigley, A. J., Rainger, J., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.03.018 · Cited by 101 · cited 3× · see citing works
“Another phenomenon that could explain the tendency for dominant and recessive variants in the same gene to be associated with similar phenotypes is the DN effect, as has been described in cases where DN variants phenocopy recessive disorders 34,39 – 42 .”
InfluentialSelf-citation
Spatial Clustering of de Novo Missense Mutations Identifies Candidate Neurodevelopmental Disorder-Associated Genes
Lelieveld, S. H., Wiel, L., Venselaar, H., Pfundt, R., Vriend, G., Veltman, J. A., Brunner, H. G., Vissers, L. E. L. M., & Gilissen, C. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.08.004 · Cited by 101 · cited 1× · see citing works
“By comparing bootstrapped AUROC estimates, we found that the posterior mechanism-speci ﬁ c scores obtained with the GLM-based mLOF score had a consistently worse performance across the binary class pairs.”
The selective tRNA aminoacylation mechanism based on a single G•U pair
Naganuma, M., Sekine, S., Chong, Y. E., Guo, M., Yang, X., Gamper, H., Hou, Y., SCHIMMEL, P. R., & Yokoyama, S. (2014) · Nature · doi:10.1038/nature13440 · Cited by 101 · see citing works
Homozygous NLRP1 gain-of-function mutation in siblings with a syndromic form of recurrent respiratory papillomatosis
Drutman, S., Haerynck, F., Zhong, F., Hum, D., Hernandez, N., Belkaya, S., Rapaport, F., de Jong, S. J., Creytens, D., Tavernier, S., et al. (2019) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1906184116 · Cited by 98 · see citing works
The Proteins API: accessing key integrated protein and genome information
Nightingale, A., Antunes, R., Alpi, E., Bursteinas, B., Gonzales, L. J. D. C., Liu, W., Luo, J., Qi, G., Turner, E., & Martin, M. J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx237 · Cited by 97 · cited 3× · see citing works
“We note that raw FoldX ΔΔ G values are available for non-disordered and well-predicted regions in human AlphaFold models via the ProtVar API 116 .”
Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes
Skerget, S., Penaherrera, D., Chari, A., Jagannath, S., Siegel, D. S., Vij, R., Orloff, G., Jakubowiak, A., Niesvizky, R., Liles, D., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01853-0 · Cited by 95 · cited 1× · see citing works
“…skewed by study bias, in that multi-phenotype genes areoverrepresented indiseasegenes that have historically attracted more attention (e.g., TP53 , KRAS , and BRCA2 ), mLOF score analysisindicatesthat 43% of dominant and 49%of mixed-inheritance multi-phenotype genes exhibit phenotypes driven by…”
p63-related signaling at a glance
Fisher, M. L., Balinth, S., & Mills, A. A. (2020) · Journal of Cell Science · doi:10.1242/jcs.228015 · Cited by 92 · cited 1× · see citing works
“For example, one SHFM4-associated mutation, p.Ala354Glu, is located in a region responsible for interacting with HIPK2 62 , which phosphorylates TP63 in response to DNA damage 63 .”
Causes and effects of haploinsufficiency
Johnson, A. F., Nguyen, H. T., & Veitia, R. A. (2019) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12527 · Cited by 84 · cited 1× · see citing works
“The test set excludes genes used for training the model (used to construct pGOF) and is limited to proteins with <50% pairwise sequence identity.”
Identification and Characterization of HIPK2 Interacting with p73 and Modulating Functions of the p53 Familyin Vivo
Kim, E.-J., Park, J. S., & Um, S. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m200153200 · Cited by 83 · see citing works
Heterozygous Mutation in the SAM Domain of p63 Underlies Rapp-Hodgkin Ectodermal Dysplasia
Kantaputra, P. N., Hamada, T., Kumchai, T., & McGrath, J. A. (2003) · Journal of Dental Research · doi:10.1177/154405910308200606 · Cited by 80 · cited 1× · see citing works
“RHS is characterised by anhidrotic ectodermal dysplasia and cleft lip and/or palate, and it is associated with LOF mutations in the sterile alpha motif domain (SAM) 58,59 .”
Examining the role of common variants in rare neurodevelopmental conditions
Huang, Q. Q., Wigdor, E. M., Malawsky, D. S., Campbell, P. H., Samocha, K. E., Chundru, V. K., Danecek, P., Lindsay, S., Marchant, T., Koko, M., et al. (2024) · Nature · doi:10.1038/s41586-024-08217-y · Cited by 78 · cited 1× · see citing works
“For example, the presence of common variants may modify the penetrance of inherited rare variants, lowering the liability threshold required for being affected by a disease 88 .”
A Heterozygous Mutation of the Insulin-Like Growth Factor-I Receptor Causes Retention of the Nascent Protein in the Endoplasmic Reticulum and Results in Intrauterine and Postnatal Growth Retardation
Wallborn, T., Wüller, S., Klammt, J., Kruis, T., Kratzsch, J., Schmidt, G., Schlicke, M., Müller, E. C., van de Leur, H. S., Kieß, W., et al. (2010) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2009-2404 · Cited by 76 · cited 1× · see citing works
“For PTPN11, we used the ‘ Enrichment (ave) ’ column and de ﬁ ned LOF and GOF variants as those more than two SD below and above the mean of the distribution for missense variants, respectively.”
Biallelic inheritance of hypomorphic PKD1 variants is highly prevalent in very early onset polycystic kidney disease
Durkie, M., Chong, J., Valluru, M. K., Harris, P. C., & Ong, A. C. M. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-01026-4 · Cited by 70 · cited 1× · see citing works
“None-theless, the case of PKD1 , where recessive hypomorphic variants have recently been implicated in polycystic kidney disease 37 — the same phenotype for which there is suf ﬁ cient evidence of haploinsuf ﬁ ciency caused by dominant LOF mutations (ClinGen 38 Curation ID: 007675) — underscores the…”
Shifting landscapes of human MTHFR missense-variant effects
Weile, J., Kishore, N., Sun, S., Maaieh, R., Verby, M., Li, R., Fotiadou, I., Kitaygorodsky, J., Wu, Y., Holenstein, A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.05.009 · Cited by 67 · see citing works
Functional Analysis of Paired Box Missense Mutations in The PAX6 Gene
Tang, H. K., Li, C., & Saunders, G. F. (1997) · Human Molecular Genetics · doi:10.1093/hmg/6.3.381 · Cited by 64 · cited 1× · see citing works
“For example, the mutation p.Ile87Arg in PAX6 leads to loss of DNA binding 98,99 , causing an aniridia phenotype through haploinsuf ﬁ ciency.”
Utilizing ClinGen gene‐disease validity and dosage sensitivity curations to inform variant classification
Thaxton, C., Good, M. E., DiStefano, M. T., Luo, X., Andersen, E., Thorland, E. C., Berg, J. S., Martin, C. L., Rehm, H. L., Riggs, E. R., et al. (2021) · Human Mutation · doi:10.1002/humu.24291 · Cited by 62 · cited 2× · see citing works
“Errorbarsare95%credibleintervalscalculated from a posterior distribution of fractions derived using the bootstrap estimates of the optimal mLOF threshold.”
Homozygous mutation of the IGF1 receptor gene in a patient with severe pre- and postnatal growth failure and congenital malformations
Gannagé‐Yared, M., Klammt, J., Chouery, É., Corbani, S., Megarbane, H. A. R., Ghoch, J. A., Choucair, N., Pfäffle, R. W., & Mégarbané, A. (2012) · European Journal of Endocrinology · doi:10.1530/eje-12-0701 · Cited by 59 · cited 1× · see citing works
“For PTPN11, we used the ‘ Enrichment (ave) ’ column and de ﬁ ned LOF and GOF variants as those more than two SD below and above the mean of the distribution for missense variants, respectively.”
ProtVar: mapping and contextualizing human missense variation
Stephenson, J. D., Totoo, P., Burke, D. F., Jänes, J., Beltrão, P., & Martin, M. J. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae413 · Cited by 56 · cited 3× · see citing works
“We note that raw FoldX ΔΔ G values are available for non-disordered and well-predicted regions in human AlphaFold models via the ProtVar API 116 .”
Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants
Bayrak, Ç. S., Stein, D. F., Jain, A., Chaudhary, K., Nadkarni, G. N., Vleck, T. T. V., Puel, A., Boisson‐Dupuis, S., Okada, S., Stenson, P. D., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.10.007 · Cited by 55 · see citing works
Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set
Stein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine · doi:10.1186/s13073-023-01261-9 · Cited by 53 · cited 10× · see citing works
“Furthermore, we evaluated the mLOF score against GOF predictions by the LoGoFunc method 12 .”
Influential
SMC1Aexpression and mechanism of pathogenicity in probands with X-Linked Cornelia de Lange syndrome
Liu, J., Feldman, R., Zhang, Z., Deardorff, M. A., Haverfield, E. V., Kaur, M., Li, J. R., Clark, D. M., Kline, A. D., Waggoner, D., et al. (2009) · Human Mutation · doi:10.1002/humu.21095 · Cited by 52 · see citing works
The G215R Mutation in the Cl−/H+-Antiporter ClC-7 Found in ADO II Osteopetrosis Does Not Abolish Function but Causes a Severe Trafficking Defect
Schulz, P., Werner, J., Stauber, T., Henriksen, K., & Fendler, K. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012585 · Cited by 50 · see citing works
A germline MTOR mutation in Aboriginal Australian siblings with intellectual disability, dysmorphism, macrocephaly, and small thoraces
Baynam, G. S., Overkov, A., Davis, M. R., Mina, K., Schofield, L., Allcock, R. J. N., Laing, N. G., Cook, M., Dawkins, H. J. S., & Goldblatt, J. (2015) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.37070 · Cited by 49 · see citing works
Guidance for estimating penetrance of monogenic disease-causing variants in population cohorts
Wright, C. F., Sharp, L. N., Jackson, L. M., Murray, A., Ware, J. S., MacArthur, D. G., Rehm, H. L., Patel, K., & Weedon, M. N. (2024) · Nature Genetics · doi:10.1038/s41588-024-01842-3 · Cited by 47 · cited 1× · see citing works
“Genomiccoordinatesofthesevariantsweremappedusingthe Ensembl VEP 112 pipeline (as described above) and merged with our ClinVar dataset.”
Diverging gain-of-function mechanisms of two novel KRAS mutations associated with Noonan and cardio-facio-cutaneous syndromes
Cirstea, I. C., Gremer, L., Dvorský, R., Zhang, S.-C., Piekorz, R. P., Zenker, M., & Ahmadian, M. R. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds426 · Cited by 45 · cited 1× · see citing works
“Supporting this, functional studies on the CFC2-associated variant p.Lys147Glu revealed weak GTP binding, falling short of the oncogenic threshold 54 .”
Generation of the first autosomal dominant osteopetrosis type II (ADO2) disease models
Alam, I., Gray, A. K., Chu, K., Ichikawa, S., Mohammad, K. S., Capannolo, M., Capulli, M., Maurizi, A., Muraca, M., Teti, A. M., et al. (2013) · Bone · doi:10.1016/j.bone.2013.10.021 · Cited by 42 · see citing works
Correspondence between functional scores from deep mutational scans and predicted effects on protein stability
Gerasimavicius, L., Livesey, B. J., & Marsh, J. A. (2023) · Protein Science · doi:10.1002/pro.4688 · Cited by 41 · cited 1× · see citing works
“…and ΔΔ G estimation, incorporating predicted structures of protein complexes 102 – 107 could substantially improve the accuracy of missense LOF likelihood estimation by providing more biologically representative structural insights through the consideration of inter-molecular interactions 16,108 .”
Self-citation
Novel pathogenic mutations in C1QTNF5 support a dominant negative disease mechanism in late-onset retinal degeneration
Stanton, C. M., Borooah, S., Drake, C., Marsh, J. A., Campbell, S. E., Lennon, A., Soares, D. C., Vallabh, N. A., Sahni, J., Cideciyan, A. V., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-11898-3 · Cited by 40 · cited 1× · see citing works
“The test set excludes genes used for training the model (used to construct pGOF) and is limited to proteins with <50% pairwise sequence identity.”
Self-citation
Decoding disease-causing mechanisms of missense mutations from supramolecular structures
Hijikata, A., Tsuji, T., Shionyu, M., & Shirai, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-08902-1 · Cited by 39 · cited 1× · see citing works
“However, there are also gene-level features that tend to be associated with different mechanisms 2,15 – 17 .”
Structure and Kinetic Stability of the p63 Tetramerization Domain
Natan, E., & Joerger, A. C. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2011.11.007 · Cited by 35 · cited 1× · see citing works
“Because TP63 forms tetramers via its oli-gomerisation domain 60 , and may form extended polymeric structures mediated by its SAM domain 61 , these structural features could suggest an assembly-mediated GOF (dominant-positive 1 ) effect underlying SHFM4.”
Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation
van Loggerenberg, W., Sowlati‐Hashjin, S., Weile, J., Hamilton, R., Chawla, A., Sheykhkarimli, D., Gebbia, M., Kishore, N., Frésard, L., Mustajoki, S., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.08.012 · Cited by 32 · see citing works
Destabilizing NF1 variants act in a dominant negative manner through neurofibromin dimerization
Young, L. C., de Salazar, R. G., Han, S., Huang, Z. Y. S., Merk, A., Drew, M. R., Darling, J. E., Wall, V. E., Grisshammer, R., Cheng, A. S., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2208960120 · Cited by 32 · cited 1× · see citing works
“The test set excludes genes used for training the model (used to construct pGOF) and is limited to proteins with <50% pairwise sequence identity.”
Penetrance and predictive value of genetic screening in acute porphyria
Baumann, K., & Kauppinen, R. (2020) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2020.02.003 · Cited by 31 · cited 1× · see citing works
“Another factor may be incomplete penetrance 82 – 85 , which causes variable phenotype expressivity within ancestries 83 , sexes 86 , and even families 87 .”
CFTR 5T variant has a low penetrance in females that is partially attributable to its haplotype
Sun, W., Anderson, B., Redman, J. B., Milunsky, A., Buller, A. M., McGinniss, M. J., Quan, F., Anguiano, A. L., Huang, S., Hantash, F. M., et al. (2006) · Genetics in Medicine · doi:10.1097/01.gim.0000223549.57443.16 · Cited by 30 · see citing works
Exploring penetrance of clinically relevant variants in over 800,000 humans from the Genome Aggregation Database
Gudmundsson, S., Singer‐Berk, M., Stenton, S. L., Goodrich, J. K., Wilson, M. W., Einson, J. E., Watts, N. A., Abreu, M. T., Abubakar, A. A., Adolfsson, R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61698-x · Cited by 30 · see citing works
Homozygous Calcium-Sensing Receptor Polymorphism R544Q Presents as Hypocalcemic Hypoparathyroidism
Cavaco, B. M., Canaff, L., Nolin-Lapalme, A., Vieira, M., Silva, T., Saramago, A., Domingues, R., Rutter, M. M., Hudon, J., Gleason, J. L., et al. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-02407 · Cited by 27 · cited 1× · see citing works
“In contrast, as we only have gene-level phenotype:inheritance associations available from OMIM, for those genes with mixed-inheritance associated with the same phenotype, we are unable to dis-tinguishbetweendominantandrecessivevariants,sotheyareconsidered together (AD/AR same ).”
Computing the Human InteractomePreprint
Zhang, J., Humphreys, I. R., Pei, J., Kim, J., Choi, C., Yuan, R., Durham, J., Liu, S., Choi, H., Baek, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.01.615885 · Cited by 27 · cited 1× · see citing works
“The test set excludes genes used for training the model (used to construct pGOF) and is limited to proteins with <50% pairwise sequence identity.”
Predicted mechanistic impacts of human protein missense variantsPreprint
Jänes, J., Müller, M., Selvaraj, S., Manoel, D., Stephenson, J. D., Gonçalves, C. I., Lafita, A., Polacco, B. J., Obernier, K., Alasoo, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.29.596373 · Cited by 26 · see citing works
High-resolution deep mutational scanning of the melanocortin-4 receptor enables target characterization for drug discoveryPreprint
Howard, C. J., Abell, N. S., Osuna, B. A., Jones, E. M., Chan, L. Y., Chan, H. W.-S., Artis, D. R., Asfaha, J. B., Bloom, J. S., Cooper, A. R., et al. (2024) · eLife · doi:10.7554/elife.104725.3 · Cited by 26 · cited 3× · see citing works
“Weidenti ﬁ edsixMAVEsin which at least two of the aforementioned molecular disease mechanisms, or functional consequences in the case of HyC variants, have been con ﬁ dently detected in the same gene: TP53 30 , HRAS 27 , MC4R 28 , HMBS 29 , TP53 30 , PTPN11 31 , and MTHFR 32 .”
VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants
Ge, F., Li, C., Iqbal, S., Arif, M., Li, F., Thafar, M. A., Yan, Z., Worachartcheewan, A., Xu, X., Song, J., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac535 · Cited by 24 · cited 1× · see citing works
“The test set excludes genes used for training the model (used to construct pGOF) and is limited to proteins with <50% pairwise sequence identity.”
Buffering of genetic dominance by allele-specific protein complex assembly
Badonyi, M., & Marsh, J. A. (2023) · Science Advances · doi:10.1126/sciadv.adf9845 · Cited by 24 · cited 2× · see citing works
“[AR]-AD/AR mixed : the gene has at least one AD and one AR phenotype with a suf ﬁ cient number of missense variants, and the phenotype is annotated as AR in HPO.”
Self-citation
A multi-systemic mitochondrial disorder due to a dominant p.Y955H disease variant in DNA polymerase gamma
Siibak, T., Clemente, P., Bratić, A. M., Bruhn, H., Kauppila, T. E. S., Macao, B., Rosenberger, F. A., Lesko, N., Wibom, R., Naess, K., et al. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx146 · Cited by 20 · cited 1× · see citing works
“SHFM4, attributed to GOF mutations 58 , presents with clefts in the hands and feet, webbed ﬁ ngers and toes, underdeveloped bones, and sometimes involves cognitive impairment.”
Specific targeting of the KRAS mutational landscape in myeloma as a tool to unveil the elicited antitumor activity
Sacco, A., Federico, C., Todoerti, K., Ziccheddu, B., Palermo, V., Giacomini, A., Ravelli, C., Maccarinelli, F., Bianchi, G., Belotti, A., et al. (2021) · Blood · doi:10.1182/blood.2020010572 · Cited by 20 · cited 1× · see citing works
“Although this proportion may be skewed by study bias, in that multi-phenotype genes areoverrepresented indiseasegenes that have historically attracted more attention (e.g., TP53 , KRAS , and BRCA2 ), mLOF score analysisindicatesthat 43% of dominant and 49%of mixed-inheritance multi-phenotype genes…”
A ubiquitin-like domain is required for stabilizing the N-terminal ATPase module of human SMCHD1
Pedersen, L. C., Inoue, K., Kim, H., Perera, L., & Shaw, N. D. (2019) · Communications Biology · doi:10.1038/s42003-019-0499-y · Cited by 19 · cited 1× · see citing works
“Structural modelling revealed that BAMS-speci ﬁ c mutations cluster on the protein surface, pinpointing a cryptic interface 51 , a ﬁ nding later con ﬁ rmed by the crystal structure of the ATPase domain 52 .”
The chloride antiporter CLCN7 is a modifier of lysosome dysfunction in FIG4 and VAC14 mutants
Cao, X., Lenk, G. M., Mikušević, V., Mindell, J. A., & Meisler, M. H. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010800 · Cited by 19 · see citing works
A novel de novo MTOR gain-of-function variant in a patient with Smith-Kingsmore syndrome and Antiphospholipid syndrome
Rodríguez‐García, M. E., Cotrina‐Vinagre, F. J., Bellusci, M., de Aragón, A. M., Hernández-Sánchez, L. C., Carnicero‐Rodríguez, P., Martín‐Hernández, E., & Martínez‐Azorín, F. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-019-0418-1 · Cited by 19 · cited 1× · see citing works
“GOF variants in MTOR have been previously linked to conditions such as Smith-Kingsmore syndrome 67 and there is a growing body of evidence further implicating MTOR in developmental disorders 68 – 71 , with a recent de novo enrichment analysis detecting a signi ﬁ cant missense burden in a cohort of…”
Pliable DNA Conformation of Response Elements Bound to Transcription Factor p63
Chen, C., Gorlatova, N. V., & Herzberg, O. (2012) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.315820 · Cited by 18 · see citing works
Proteome-scale characterisation of motif-based interactome rewiring by disease mutations
Kliche, J., Simonetti, L., Krystkowiak, I., Kuss, H., Diallo, M., Rask, E., Nilsson, J., Davey, N. E., & Ivarsson, Y. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00055-4 · Cited by 18 · cited 1× · see citing works
“Although pathogenic missense mutations are highly enriched in structured regions 96 , this limitation excludes certainvariants,e.g.,thoseinshortlinearmotifs 97 .”
Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteins
Fawzy, M., & Marsh, J. A. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013400 · Cited by 18 · cited 1× · see citing works
“For example, low-con ﬁ dence AlphaFold predictions often coincide with intrinsically disordered regions, where missense mutations are less likely to destabilise the folded structure and, by extension, less likely to act via a canonical LOF mechanism 101 .”
Self-citation
Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants
McDonnell, A. F., Plech, M., Livesey, B. J., Gerasimavicius, L., Owen, L. J., Hall, H. N., FitzPatrick, D. R., Marsh, J. A., & Kudla, G. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00043-8 · Cited by 16 · cited 1× · see citing works
“The test set excludes genes used for training the model (used to construct pGOF) and is limited to proteins with <50% pairwise sequence identity.”
Self-citation
Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases
(2024) · doi:10.1371/journal.pone.0307312 · Cited by 15 · cited 6× · see citing works
“Finally, to estimate a posterior mechanism likelihood score, we use pDN, pGOF, and pLOF from our proteome-scalemodelasinformedpriors,whichre ﬂ ectthelikelihoodof observing the given mechanism when missense variants are identi ﬁ ed in a gene 18 .”
InfluentialSelf-citation
Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes
Pino, D. C., Badonyi, M., Semple, C. A. M., & Marsh, J. A. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114905 · Cited by 15 · cited 2× · see citing works
“We achieved this by leveraging fundamental protein structural properties of missense variants, their energetic impact ( ΔΔ G ) and spatial clustering (EDC), both of which have an established and robust relationship to molecular disease mechanisms 16,18,21,24,78 .”
Self-citation
HMMvar-func: a new method for predicting the functional outcome of genetic variants
Liu, M., Watson, L. T., & Zhang, L. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0781-z · Cited by 14 · cited 1× · see citing works
“Current computational methods usually focus on predicting LOF and function-altering mechanisms at the level of individual genetic variants 12 – 14 .”
Phenotypic diversity and genetic complexity of PAX3 ‐related Waardenburg syndrome
Somashekar, P. H., Upadhyai, P., Narayanan, D. L., Kamath, N., Bajaj, S., Girisha, K. M., & Shukla, A. (2020) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.61893 · Cited by 14 · see citing works
Optimized allele-specific silencing of the dominant-negative COL6A1 G293R substitution causing collagen VI-related dystrophy
Brull, A., Sarathy, A., Bolduc, V., Chen, G. S., McCarty, R. M., & Bönnemann, C. G. (2024) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2024.102178 · Cited by 12 · see citing works
Structural insight into the function of human peptidyl arginine deiminase 6
Williams, J. P. C., Mouilleron, S., Trapero, R. H., Bertran, M. T., Marsh, J. A., & Walport, L. J. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.08.019 · Cited by 12 · see citing works
Self-citation
A humanized yeast model reveals dominant-negative properties of neuropathy-associated alanyl-tRNA synthetase mutations
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“62,63 These assumptions do not seem to reflect the very specific GOF/LOF mechanisms observed in the electrophysiological characterisation of ion channels and transporters.”
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“These variants involve protein-coding regions, non-coding regions, larger chromosomal segments, and the epigenome [38–40].”
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Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases
Badonyi, M., & Marsh, J. A. (2023) · doi:10.1101/2023.09.08.556798 · Cited by 6 · see citing works
Self-citation
Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure
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Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect predictionPreprint
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When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants
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Authors: Badonyi, M., & Marsh, J. A.
Affiliations: Institute of Genetics and Cancer (GB)
Corresponding author: Badonyi, Mihaly
Marsh, Joseph A.
ORCID: Badonyi, Mihaly
Marsh, Joseph A.
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2025-09-25
Volume: 16
Issue: 1
Pages: 8392-8392
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-025-63234-3
PMC: PMC12462468
OpenAlex: W4414512364
Cited by: 19
References: 125
Citations by year: 2026: 16 · 2025: 3
FWCI: 11.17
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Nature Communications (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (publishedVersion, cc-by)
Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
OSF Preprints (OSF Preprints) (submittedVersion)
Funders: European Commission (HORIZON2020)
Lister Institute of Preventive Medicine
Medical Research Council (MC_UU_00035/9)
H2020 European Research Council (101001169)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: loss of function, gain-of-function, dominant-negative mechanism, missense mutations, protein structure-based prediction, Mendelian disease genes, variant prioritization, recessive inheritance
MeSH terms: Loss of Function Mutation, Gain of Function Mutation, Genes, Dominant, Humans, Phenotype, Prevalence, Mutation, Missense, Genetic Diseases, Inborn

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Prevalence of loss-of-function, gain-of-function and dominant-negative mechanisms across genetic disease phenotypes

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