Regional missense constraint improves variant deleteriousness prediction

A method and server for predicting damaging missense mutations.

Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194

Analysis of protein-coding genetic variation in 60,706 humans

Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587

SIFT: predicting amino acid changes that affect protein function

Ng, P. C. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg509 · Cited by 6,601

Amino acid substitution matrices from protein blocks.

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A general framework for estimating the relative pathogenicity of human genetic variants

Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183

ClinVar: public archive of relationships among sequence variation and human phenotype

Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., & Maglott, D. R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1113 · Cited by 3,516

MutationTaster2: mutation prediction for the deep-sequencing age

Schwarz, J. M., Cooper, D. N., Schuelke, M., & Seelow, D. (2014) · Nature Methods · doi:10.1038/nmeth.2890 · Cited by 3,362

Synaptic, transcriptional and chromatin genes disrupted in autism

Study, T. D., De Rubeis, S., Autism, H. M. C. F., UK10K Consortium, The Autism Sequencing Consortium, He, X., Goldberg, A. P., Poultney, C. S., Samocha, K., Ercument Cicek, A., et al. (2014) · Nature · doi:10.1038/nature13772 · Cited by 2,746

The contribution of de novo coding mutations to autism spectrum disorder

Iossifov, I., O’Roak, B. J., Sanders, S. J., Ronemus, M., Krumm, N., Levy, D., Stessman, H. A., Witherspoon, K. T., Vives, L., Patterson, K. E., et al. (2014) · Nature · doi:10.1038/nature13908 · Cited by 2,585

Amino Acid Difference Formula to Help Explain Protein Evolution

Grantham, R. L. (1974) · Science · doi:10.1126/science.185.4154.862 · Cited by 2,363

Missense and truncating variants in CHD5 in a dominant neurodevelopmental disorder with intellectual disability, behavioral disturbances, and epilepsy

Parenti, I., Lehalle, D., Nava, C., Torti, E. E., Leitão, E., Person, R., Mizuguchi, T., Matsumoto, N., Kato, M., Nakamura, K., et al. (2021) · Human Genetics · doi:10.1007/s00439-021-02283-2 · Cited by 38

Haploinsufficiency in the ANKS1B gene encoding AIDA-1 leads to a neurodevelopmental syndrome

Carbonell, A. U., Cho, C. H., Tindi, J. O., Counts, P. A., Bates, J. C., Erdjument‐Bromage, H., Cvejic, S., Iaboni, A., Kvint, I., Rosensaft, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11437-w · Cited by 38

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

Pandi, M., Koromina, M., Tsafaridis, I., Patsilinakos, S. P., Christoforou, E., van der Spek, P. J., & Patrinos, G. P. (2021) · Human Genomics · doi:10.1186/s40246-021-00352-1 · Cited by 37

Improved Pathogenic Variant Localization via a Hierarchical Model of Sub-regional Intolerance

Hayeck, T. J., Stong, N., Wolock, C. J., Copeland, B. R., Kamalakaran, S., Goldstein, D. B., & Allen, A. S. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.12.020 · Cited by 35

The 3D mutational constraint on amino acid sites in the human proteome

Li, B., Roden, D. M., & Capra, J. A. (2022) · Nature Communications · doi:10.1038/s41467-022-30936-x · Cited by 45

Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression

Tian, R., Ge, T., Kweon, H., Rocha, D., Lam, M., Liu, J. Z., Singh, K., Biogen Biobank Team, Levey, D. F., Gelernter, J. E., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45774-2 · Cited by 35

Critical assessment of missense variant effect predictors on disease-relevant variant data

Rastogi, R., Chung, R., Li, S., Li, C., Lee, K., Woo, J., Kim, D., Keum, C., Babbi, G., Martelli, P. L., et al. (2025) · Human Genetics · doi:10.1007/s00439-025-02732-2 · Cited by 39

Proposition of adjustments to the ACMG‐AMP framework for the interpretation of MEN1 missense variants

Romanet, P., Odou, M., North, M.-O., Saveanu, A., Coppin, L., Pasmant, É., Mohamed, A., Goudet, P., Borson‐Chazot, F., Calender, A., et al. (2019) · Human Mutation · doi:10.1002/humu.23746 · Cited by 35

De novo variants in HK1 associated with neurodevelopmental abnormalities and visual impairment

Okur, V., Cho, M. T., van Wijk, R., van Oirschot, B. A., Picker, J. D., Coury, S. A., Grange, D. K., Manwaring, L. P., Krantz, I. D., Muraresku, C. C., et al. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-019-0366-9 · Cited by 36

Challenges in the diagnosis and discovery of rare genetic disorders using contemporary sequencing technologies

Seaby, E. G., & Ennis, S. (2020) · Briefings in Functional Genomics · doi:10.1093/bfgp/elaa009 · Cited by 36

Genetic variant pathogenicity prediction trained using disease-specific clinical sequencing data setsPreprint

Evans, P., Wu, C., Lindy, A. S., McKnight, D., Lebo, M. S., Sarmady, M., & Tayoun, A. N. A. (2019) · Genome Research · doi:10.1101/gr.240994.118 · Cited by 33

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for diseasePreprint

Huang, Y.-F., & Siepel, A. C. (2019) · Genome Research · doi:10.1101/gr.245522.118 · Cited by 34

Whole genome sequencing analysis identifies sex differences of familial pattern contributing to phenotypic diversity in autism

Kim, S.-W., Lee, H., Song, D. Y., Lee, G., Ji, J., Park, J. W., Han, J. H., Lee, J., Byun, H. J., Son, J. H., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01385-6 · Cited by 33

CDK13-related disorder

Hamilton, M., & Suri, M. (2018) · Advances in genetics · doi:10.1016/bs.adgen.2018.11.001 · Cited by 30

Unified inference of missense variant effects and gene constraints in the human genome

Huang, Y.-F. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008922 · Cited by 32

De novo missense variants disrupting protein–protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

Chen, S., Wang, J., Çiçek, A. E., Roeder, K., Yu, H., & Devlin, B. (2020) · Molecular Autism · doi:10.1186/s13229-020-00386-7 · Cited by 33

Missense variants in TAF1 and developmental phenotypes: Challenges of determining pathogenicity

Cheng, H., Capponi, S., Wakeling, E. L., Marchi, E. J., Li, Q., Zhao, M., Weng, C., Piatek, S., Ahlfors, H., Kleyner, R. S., et al. (2019) · Human Mutation · doi:10.1002/humu.23936 · Cited by 30

Benchmarking computational variant effect predictors by their ability to infer human traits

Tabet, D. R., Kuang, D., Lancaster, M. C., Li, R., Liu, K., Weile, J., Coté, A. G., Wu, Y., Hegele, R. A., Roden, D. M., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03314-7 · Cited by 39

Discovery and Validation of Novel Genes in a Large Chinese Autism Spectrum Disorder Cohort

Wang, J., Yu, J., Wang, M., Zhang, L., Yang, K., Du, X., Wu, J., Wang, X., Li, F. R., & Qiu, Z. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.06.025 · Cited by 30

Genome sequencing of human in vitro fertilisation embryos for pathogenic variation screening

Murphy, N. M., Samarasekera, T., Macaskill, L., Mullen, J. A., & Rombauts, L. J. F. (2020) · Scientific Reports · doi:10.1038/s41598-020-60704-0 · Cited by 29

InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution

Takeda, J., Nanatsue, K., Yamagishi, R., Ito, M., Haga, N., Hirata, H., Ogi, T., & Ohno, K. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa038 · Cited by 29

Bi-allelic missense disease-causing variants in RPL3L associate neonatal dilated cardiomyopathy with muscle-specific ribosome biogenesis

Ganapathi, M., Argyriou, L., Martínez‐Azorín, F., Morlot, S., Yigit, G., Lee, T. M., Auber, B., von Gise, A., Petrey, D. S., Thiele, H., et al. (2020) · Human Genetics · doi:10.1007/s00439-020-02188-6 · Cited by 29

MTR3D: identifying regions within protein tertiary structures under purifying selection

Silk, M. A., Pires, D. E. V., Rodrigues, C. H. M., D’Souza, E. N., Olshansky, M., Thorne, N., & Ascher, D. B. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab428 · Cited by 29

Pleiotropy of autism-associated chromatin regulators

Lasser, M. C., Sun, N., Xu, Y., Wang, S., Drake, S., Law, K., Gonzalez, S., Wang, B., Drury, V., Castillo, O., et al. (2023) · Development · doi:10.1242/dev.201515 · Cited by 31

De novo variants in H3-3A and H3-3B are associated with neurodevelopmental delay, dysmorphic features, and structural brain abnormalities

Okur, V., Chen, Z., Vossaert, L., Peacock, S., Rosenfeld, J. A., Zhao, L., Du, H., Calamaro, E., Gerard, A., Zhao, S., et al. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00268-8 · Cited by 29

Missense variants in ANKRD11 cause KBG syndrome by impairment of stability or transcriptional activity of the encoded protein

de Boer, E., Ockeloen, C. W., Kampen, R. A., Hampstead, J. E., Dingemans, A. J. M., Rots, D., Lütje, L., Ashraf, T., Baker, R., Barat-Houari, M., et al. (2022) · Genetics in Medicine · doi:10.1016/j.gim.2022.06.007 · Cited by 28

Mutation of key signaling regulators of cerebrovascular development in vein of Galen malformations

Zhao, S., Mekbib, K. Y., van der Ent, M. A., Allington, G., Prendergast, A. E., Chau, J. E., Smith, H. L., Shohfi, J., Ocken, J., Durán, D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43062-z · Cited by 28

A systematic review and standardized clinical validity assessment of genes involved in female reproductive failure

Voložonoka, L., Miskova, A., Kornejeva, L., Kempa, I., Bargatina, V., & Gailite, L. (2022) · Reproduction · doi:10.1530/rep-21-0486 · Cited by 27

Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder

Chan, A. J. S., Engchuan, W., Reuter, M. S., Wang, Z., Thiruvahindrapuram, B., Trost, B., Nalpathamkalam, T., Negrijn, C., Lamoureux, S., Pellecchia, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34112-z · Cited by 31

Genomic analysis of 116 autism families strengthens known risk genes and highlights promising candidates

Viggiano, M., Ceroni, F., Visconti, P., Posar, A., Scaduto, M. C., Sandoni, L., Baravelli, I., Cameli, C., Rochat, M. J., Maresca, A., et al. (2024) · npj Genomic Medicine · doi:10.1038/s41525-024-00411-1 · Cited by 27

ASD and ADHD have a similar burden of rare protein-truncating variantsPreprint

Satterstrom, F., Walters, R. K., Singh, T., Wigdor, E., Lescai, F., Demontis, D., Kosmicki, J. A., Grove, J., Stevens, C. R., Bybjerg-Grauholm, J., et al. (2018) · bioRxiv · doi:10.1101/277707 · Cited by 30

Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies

Liao, C., Moysés‐Oliveira, M., de Esch, C. E., Bhavsar, R., Nuttle, X., Li, A., Yu, A., Burt, N. D., Erdin, S., Fu, J. M., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100277 · Cited by 25

Differential functional consequences of GRIN2A mutations associated with schizophrenia and neurodevelopmental disorders

Shepard, N., Báez-Nieto, D., Iqbal, S., Kurganov, E., Budnik, N., Campbell, A. J., Pan, J. Q., Sheng, M., & Farsi, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-53102-3 · Cited by 25

Three-dimensional missense tolerance ratio analysisPreprint

Perszyk, R. E., Kristensen, A. S., Lyuboslavsky, P. N., & Traynelis, S. F. (2021) · Genome Research · doi:10.1101/gr.275528.121 · Cited by 22

SIGMA leverages protein structural information to predict the pathogenicity of missense variants

Zhao, H., Du, H., Zhao, S., Chen, Z., Li, Y., Xu, K., Liu, B., Cheng, X., Wen, W., Li, G., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100687 · Cited by 25

Gene constraint and genotype–phenotype correlations in neurodevelopmental disorders

Betancur, C., & Buxbaum, J. D. (2020) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.05.036 · Cited by 24

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 25

Genomic architecture of autism spectrum disorder in Qatar: The BARAKA-Qatar Study

Abdi, M., ALIYEV, E. E., Trost, B., Kohailan, M., Aamer, W., Syed, N., Shaath, R., Gandhi, G. D., Engchuan, W., Howe, J., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01228-w · Cited by 24

De novo variants in TCF7L2 are associated with a syndromic neurodevelopmental disorder

Dias, C., Pfundt, R., Kleefstra, T., Schuurs-Hoeijmakers, J. H. M., Boon, E. M. J., van Hagen, J. M., Zwijnenburg, P. J. G., Weiss, M. M., Keren, B., Mignot, C., et al. (2021) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.62254 · Cited by 24

AlphaMissense is better correlated with functional assays of missense impact than earlier prediction algorithmsPreprint

Ljungdahl, A., Kohani, S., Page, N. F., Wells, E., Wigdor, E. M., Dong, S., & Sanders, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.24.562294 · Cited by 24

A Foundational Atlas of Autism Protein Interactions Reveals Molecular ConvergencePreprint

Wang, B., Vartak, R. S., Zaltsman, Y., Naing, Z. Z. C., Hennick, K. M., Polacco, B. J., Bashir, A., Eckhardt, M., Bouhaddou, M., Xu, J., et al. (2023) · SSRN Electronic Journal · doi:10.1101/2023.12.03.569805 · Cited by 26

Systems genetics analysis identifies calcium-signaling defects as novel cause of congenital heart disease

Izarzugaza, J. M. G., Ellesøe, S. G., Doğanlı, C., Ehlers, N. S., Dalgaard, M., Audain, E., Dombrowsky, G., Banasik, K., Sifrim, A., Wilsdon, A., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00772-z · Cited by 23

Rare X-linked variants carry predominantly male risk in autism, Tourette syndrome, and ADHD

Wang, S., Wang, B., Drury, V., Drake, S., Sun, N., Alkhairo, H., Arbelaez, J., Duhn, C., Genetics), T. I. C. G. (., Bromberg, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43776-0 · Cited by 26

MVP: predicting pathogenicity of missense variants by deep learningPreprint

Qi, H., Chen, C., Zhang, H., Long, J. J., Chung, W. K., Guan, Y., & Shen, Y. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/259390 · Cited by 23

Framework for microRNA variant annotation and prioritization using human population and disease datasets

Oak, N., Ghosh, R., Huang, K., Wheeler, D. A., Ding, L., & Plon, S. E. (2018) · Human Mutation · doi:10.1002/humu.23668 · Cited by 22

Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder

Huang, K., Wu, Y., Shin, J., Zheng, Y., Siahpirani, A. F., Lin, Y., Ni, Z., Chen, J., You, J., Keleş, S., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009309 · Cited by 23

Exome sequencing of 20,979 individuals with epilepsy reveals shared and distinct ultra-rare genetic risk across disorder subtypesPreprint

Chen, S.-W., Abou-Khalil, B., Afawi, Z., Ali, Q. Z., Amadori, E., Anderson, A., Anderson, J., Andrade, D. M., Annesi, G., Arslan, M., et al. (2023) · medRxiv · doi:10.1101/2023.02.22.23286310 · Cited by 45

TBX5 variants and cardiac phenotype: A systematic review of the literature and a novel variant

Nielsen, A. K. M., Dehn, A. M., Hjortdal, V. E., & Larsen, L. A. (2024) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2024.104920 · Cited by 21

From chemoproteomic‐detected amino acids to genomic coordinates: insights into precise multi‐omic data integration

Palafox, M. F., Desai, H. S., Arboleda, V. A., & Backus, K. M. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209840 · Cited by 19

The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome

Pei, J., & Grishin, N. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166915 · Cited by 22

MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction

Ge, F., Arif, M., Yan, Z., Alahmadi, H., Worachartcheewan, A., Yu, D., & Shoombuatong, W. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00950 · Cited by 21

A method to delineate de novo missense variants across pathways prioritizes genes linked to autism

Koire, A. M., Buchovecky, C., Katsonis, P., Kim, Y. W., Wilson, S. J., & Lichtarge, O. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abc1739 · Cited by 22

Predicting functional consequences of mutations using molecular interaction network features

Öztürk, K., & Carter, H. K. (2021) · Human Genetics · doi:10.1007/s00439-021-02329-5 · Cited by 20

Missense Variants Reveal Functional Insights Into the Human ARID Family of Gene Regulators

Deák, G., & Cook, A. G. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167529 · Cited by 20

Neurological disorder-associated genetic variants in individuals with psychogenic nonepileptic seizures

Leu, C., Bautista, J. F., Sudarsanam, M., Niestroj, L., Stefanski, A., Ferguson, L., Daly, M. J., Jehi, L. E., Najm, I., Busch, R. M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72101-8 · Cited by 21

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease

Kim, S. S., Dey, K. K., Weissbrod, O., Márquez‐Luna, C., Gazal, S., & Price, A. L. (2020) · Nature Communications · doi:10.1038/s41467-020-20087-2 · Cited by 19

Predicting Functional Effects of Missense Variants in Voltage-Gated Sodium and Calcium ChannelsPreprint

Heyne, H., Báez-Nieto, D., Iqbal, S., Palmer, D. S., Brunklaus, A., Collaborative, T. E., Johannesen, K. M., Lauxmann, S., Lemke, J. R., Møller, R. S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/671453 · Cited by 20

Genome sequencing data analysis for rare disease gene discovery

Umlai, U. I., Bangarusamy, D. K., Estivill, X., & Jithesh, P. V. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab363 · Cited by 20

ITSN1: a novel candidate gene involved in autosomal dominant neurodevelopmental disorder spectrum

Bruel, A., Vitobello, A., Thiffault, I., Manwaring, L. P., Willing, M. C., Agrawal, P. B., Bayat, A., Kitzler, T. M., Brownstein, C. A., Genetti, C. A., et al. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00985-9 · Cited by 22

Phenotype and genetic analysis of data collected within the first year of NeuroDev

Kipkemoi, P., Kim, H. A., Christ, B. U., O’Heir, E., Allen, J., Austin-Tse, C. A., Baxter, S. M., Brand, H., Bryant, S., Buser, N., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.06.010 · Cited by 22

Haploinsufficiency underlies the neurodevelopmental consequences of SLC6A1 variants

Silva, D. B., Trinidad, M. I., Ljungdahl, A., Revalde, J. L., Berguig, G. Y., Wallace, W., Patrick, C. S., Bomba, L., Arkin, M. R., Dong, S., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.04.021 · Cited by 23

Variant pathogenicity evaluation in the community-driven Inherited Neuropathy Variant Browser

Saghira, C., Bis‐Brewer, D. M., Staněk, D., Strickland, A. V., Herrmann, D. N., Reilly, M. M., Scherer, S. S., Shy, M. E., & Zuchner, S. L. (2018) · Human Mutation · doi:10.1002/humu.23412 · Cited by 18

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

Detailed Analysis of ITPR1 Missense Variants Guides Diagnostics and Therapeutic Design

Tolonen, J., Schnekenberg, R. P., McGowan, S. J., Sims, D., McEntagart, M., Elmslie, F., Shears, D., Stewart, H. S., Tofaris, G. K., Dabir, T. A., et al. (2023) · Movement Disorders · doi:10.1002/mds.29651 · Cited by 21

Distinct gene-set burden patterns underlie common generalized and focal epilepsies

Koko, M., Krause, R., Sander, T., Bobbili, D. R., Nothnagel, M., May, P., Lerche, H., Feng, Y. A., Howrigan, D. P., Abbott, L. E., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103588 · Cited by 18

Genetic advances in neurodevelopmental disorders

Gao, S., Shan, C., Zhang, R., & Wang, T. (2024) · Medical Review · doi:10.1515/mr-2024-0040 · Cited by 22

Rare genetic variants confer a high risk of ADHD and implicate neuronal biology

Demontis, D., Duan, J., Hsu, Y.-H. H., Pintacuda, G., Grove, J., Nielsen, T. T., Thirstrup, J., Martorana, M., Botts, T., Satterstrom, F. K., et al. (2026) · Nature · doi:10.1038/s41586-025-09702-8 · Cited by 17

Utility of Gene Panels for the Diagnosis of Inborn Errors of Metabolism in a Metabolic Reference Center

Gouveia, S., Vázquez-Mosquera, M. E., González‐Vioque, E., Álvarez, J. V., Chans-Gerpe, R., Laranjeira, F., Martins, E., Ferreira, A. C., Ávila-Álvarez, A., & Couce, M. L. (2021) · Genes · doi:10.3390/genes12081262 · Cited by 18

SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants

Yang, Y., Wang, X., Zhou, D., Wei, D., & Peng, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac014 · Cited by 17

An expanded phenotype centric benchmark of variant prioritisation tools

Anderson, D. R., & Lassmann, T. (2022) · Human Mutation · doi:10.1002/humu.24362 · Cited by 18

Experimental and Computational Analysis of Newly Identified Pathogenic Mutations in the Creatine Transporter SLC6A8

Ferrada, E., Wiedmer, T., Wang, W., Frommelt, F., Steurer, B., Klimek, C., Lindinger, S., Osthushenrich, T., Garofoli, A., Brocchetti, S., et al. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168383 · Cited by 18

A DNA repair disorder caused by de novo monoallelic DDB1 variants is associated with a neurodevelopmental syndrome

White, S. M., Bhoj, E. J. K., Nellåker, C., Lachmeijer, A. M. A., Marshall, A. E., Boycott, K. M., Li, D., Smith, W. E., Hartley, T., McBride, A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.007 · Cited by 16

Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery

Zhang, X., Theotokis, P. I., Li, N., the SHaRe Investigators, Ashley, E. A., Colan, S. D., Day, S. M., Helms, A. S., Ho, C. Y., Ingles, J., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01358-9 · Cited by 26

Genetic correlates of phenotypic heterogeneity in autism

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De novo variants in GABRA4 are associated with a neurological phenotype including developmental delay, behavioral abnormalities and epilepsy

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Whole-genome sequencing identifies novel genes for autism in Chinese trios

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Modelling human genetic disorders in Xenopus tropicalis

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Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

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The landscape of regional missense mutational intolerance quantified from 730,947 exomesPreprint

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Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery

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