A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease
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The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.
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Whole genome sequencing and Genomizer analysis identified APOE3ch homozygosity as the subject's most likely genetic modifier.
“Whole genome sequencing and a Genomizer8 analysis were used to comprehensibly identify and rank all potentially significant rare and common variants, confirm the PSEN1 E280A mutation as her primary risk factor, and identify APOE3ch homozygosity as her most likely genetic modifier.”
From Resistance to autosomal dominant Alzheimer’s disease in an APOE3 Christchurch homozygote: a case report (2019) · cites this paper as [8]
Whole genome sequencing and Genomizer analysis confirmed the PSEN1 E280A mutation as the subject's primary risk factor.
“Whole genome sequencing and a Genomizer8 analysis were used to comprehensibly identify and rank all potentially significant rare and common variants, confirm the PSEN1 E280A mutation as her primary risk factor, and identify APOE3ch homozygosity as her most likely genetic modifier.”
From Resistance to autosomal dominant Alzheimer’s disease in an APOE3 Christchurch homozygote: a case report (2019) · cites this paper as [8]
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711 · see citing works
Topological domains in mammalian genomes identified by analysis of chromatin interactions
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829 · see citing works
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 · see citing works
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works
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 · see citing works
An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550 · see citing works
Self-citationPredicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · see citing works
The UCSC Table Browser data retrieval tool
Karolchik, D. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh103 · Cited by 2,269 · see citing works
Clinical Whole-Exome Sequencing for the Diagnosis of Mendelian Disorders
Yang, Y., Muzny, D. M., Reid, J. G., Bainbridge, M. N., Willis, A. S., Ward, P. A., Braxton, A. A., Beuten, J., Xia, F., Niu, Z., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1306555 · Cited by 1,954 · see citing works
Ridge Estimators in Logistic Regression
le Cessie, S., & van Houwelingen, H. C. (1992) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2347628 · Cited by 1,689 · see citing works
Diagnostic exome sequencing in persons with severe intellectual disability
de Ligt, J., Willemsen, M. H., van Bon, B. W. M., Kleefstra, T., Yntema, H. G., Kroes, T., Vulto-van Silfhout, A. T., Koolen, D. A., de Vries, P., Gilissen, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1206524 · Cited by 1,546 · see citing works
Molecular Findings Among Patients Referred for Clinical Whole-Exome Sequencing
Yang, Y., Muzny, D. M., Xia, F., Niu, Z., Person, R., Ding, Y., Ward, P., Braxton, A., Wang, M., Buhay, C., et al. (2014) · JAMA · doi:10.1001/jama.2014.14601 · Cited by 1,360 · see citing works
Distribution and intensity of constraint in mammalian genomic sequence
Cooper, G. M., Stone, E. A., Asimenos, G., Green, E. D., Batzoglou, S., & Sidow, A. (2005) · Genome Research · doi:10.1101/gr.3577405 · Cited by 1,359 · see citing works
The Database of Genomic Variants: a curated collection of structural variation in the human genome
MacDonald, J. R., Ziman, R., Yuen, R. K. C., Feuk, L., & Scherer, S. W. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt958 · Cited by 1,346 · see citing works
Cooper, T. A., Wan, L., & Dreyfuss, G. (2009) · Cell · doi:10.1016/j.cell.2009.02.011 · Cited by 1,152 · see citing works
Genome sequencing identifies major causes of severe intellectual disability
Gilissen, C., Hehir-Kwa, J. Y., Thung, D. T., van de Vorst, M., van Bon, B. W. M., Willemsen, M. H., Kwint, M., Janssen, I. M., Hoischen, A., Schenck, A., et al. (2014) · Nature · doi:10.1038/nature13394 · Cited by 1,125 · see citing works
The UCSC Genome Browser database: 2015 update
Rosenbloom, K., Armstrong, J., Barber, G. P., Casper, J., Clawson, H., Diekhans, M., Dreszer, T. R., Fujita, P. A., Guruvadoo, L., Haeussler, M., et al. (2014) · Nucleic Acids Res. · doi:10.1093/nar/gku1177 · Cited by 1,011 · see citing works
Clinical Exome Sequencing for Genetic Identification of Rare Mendelian Disorders
Lee, H., Deignan, J. L., Dorrani, N., Strom, S. P., Kantarci, S., Quintero‐Rivera, F., Das, K., Toy, T. L., Harry, B., Yourshaw, M., et al. (2014) · JAMA · doi:10.1001/jama.2014.14604 · Cited by 973 · see citing works
Convergence of genes and cellular pathways dysregulated in autism spectrum disorders
Pinto, D., Delaby, E., Merico, D., Barbosa, M., Merikangas, A., Klei, L., Thiruvahindrapuram, B., Xu, X., Ziman, R., Wang, Z., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.03.018 · Cited by 969 · see citing works
RefSeq: an update on mammalian reference sequences
Pruitt, K. D., Brown, G., Hiatt, S. M., Thibaud‐Nissen, F., Astashyn, A., Ermolaeva, O. D., Farrell, C. M., Hart, J. A., Landrum, M., McGarvey, K. M., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1114 · Cited by 936 · see citing works
Calvo, S. E., Pagliarini, D. J., & Mootha, V. K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810916106 · Cited by 914 · see citing works
The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data
Köhler, S., Doelken, S., Mungall, C. J., Bauer, S., Firth, H., Bailleul-Forestier, I., Black, G., Brown, D. L., Brudno, M., Campbell, J., et al. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1026 · Cited by 840 · see citing works
Self-citationBeyond GWASs: Illuminating the Dark Road from Association to Function
Edwards, S. L., Beesley, J., French, J. D., & Dunning, A. M. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.10.012 · Cited by 784 · see citing works
Carrier testing for severe childhood recessive diseases by next-generation sequencing
Bell, C. J., Dinwiddie, D. L., Miller, N. A., Hateley, S. L., Ganusova, E. E., Mudge, J., Langley, R. J., Zhang, L., Lee, C. C., Schilkey, F. D., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001756 · Cited by 706 · see citing works
Shihab, H. A., Rogers, M. F., Gough, J., Mort, M. E., Cooper, D. N., Day, I. N. M., Gaunt, T. R., & Campbell, C. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv009 · Cited by 703 · see citing works
A spectral approach integrating functional genomic annotations for coding and noncoding variants
Ionita‐Laza, I., McCallum, K., Xu, B., & Buxbaum, J. D. (2016) · Nature Genetics · doi:10.1038/ng.3477 · Cited by 588 · see citing works
Functional annotation of noncoding sequence variants
Ritchie, G. R. S., Dunham, I., Zeggini, E., & Flicek, P. (2014) · Nature Methods · doi:10.1038/nmeth.2832 · Cited by 505 · see citing works
Next-generation diagnostics and disease-gene discovery with the Exomiser
Smedley, D., Jacobsen, J. O. B., Jäger, M., Köhler, S., Holtgrewe, M., Schubach, M., Siragusa, E., Zemojtel, T., Buske, O. J., Washington, N. L., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2015.124 · Cited by 445 · see citing works
Self-citationRole of 5′‐ and 3′‐untranslated regions of mRNAs in human diseases
Chatterjee, S., & Pal, J. K. (2009) · Biology of the Cell · doi:10.1042/bc20080104 · Cited by 439 · see citing works
Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T., & Flicek, P. R. (2015) · Genome Biology · doi:10.1186/s13059-015-0621-5 · Cited by 429 · see citing works
A method to predict the impact of regulatory variants from DNA sequence
Lee, D., Gorkin, D. U., Baker, M., Strober, B. J., Asoni, A. L., McCallion, A. S., & Beer, M. (2015) · Nature Genetics · doi:10.1038/ng.3331 · Cited by 417 · see citing works
Phenolyzer: phenotype-based prioritization of candidate genes for human diseases
Yang, H., Robinson, P. N., & Wang, K. (2015) · Nature Methods · doi:10.1038/nmeth.3484 · Cited by 402 · see citing works
Self-citationImproved exome prioritization of disease genes through cross-species phenotype comparison
Robinson, P. N., Köhler, S., Oellrich, A., Sanger Mouse Genetics Project, Wang, K., Mungall, C. J., Lewis, S. E., Washington, N., Bauer, S., Seelow, D., et al. (2014) · Genome Research · doi:10.1101/gr.160325.113 · Cited by 375 · see citing works
Self-citationEnredo and Pecan: genome-wide mammalian consistency-based multiple alignment with paralogs.Preprint
Paten, B., Herrero, J., Beal, K., Fitzgerald, S., & Birney, E. (2008) · Genome Research · doi:10.1101/gr.076554.108 · Cited by 348 · see citing works
Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios
Zhu, X., Petrovski, S., Xie, P., Ruzzo, E. K., Lu, Y., McSweeney, K. M., Ben‐Zeev, B., Nissenkorn, A., Anikster, Y., Oz-Levi, D., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2014.191 · Cited by 340 · see citing works
The implications of structured 5′ untranslated regions on translation and disease
Pickering, B. M., & Willis, A. E. (2005) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2004.11.006 · Cited by 339 · see citing works
dbVar and DGVa: public archives for genomic structural variation
Lappalainen, I., Lopez, J. R., Skipper, L. M., Hefferon, T. W., Spalding, D., Garner, J., Chen, C., Maguire, M., Corbett, M., Zhou, G., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1213 · Cited by 310 · see citing works
Gulko, B., Hubisz, M. J., Gronau, I., & Siepel, A. C. (2015) · Nature Genetics · doi:10.1038/ng.3196 · Cited by 266 · see citing works
Translation matters: protein synthesis defects in inherited disease
Scheper, G. C., van der Knaap, M. S., & Proud, C. G. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2142 · Cited by 265 · see citing works
Żemojtel, T., Köhler, S., Mackenroth, L., Jäger, M., Hecht, J., Krawitz, P. M., Graul-Neumann, L. M., Doelken, S. C., Ehmke, N., Spielmann, M., et al. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3009262 · Cited by 252 · see citing works
Self-citationNew Methods for Detecting Lineage-Specific Selection
Siepel, A. C., Pollard, K. S., & Haussler, D. H. (2006) · Lecture notes in computer science · doi:10.1007/11732990_17 · Cited by 239 · see citing works
Podlevsky, J. D., Bley, C. J., Omana, R. V., Qi, X., & Chen, J. J. ‐. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm700 · Cited by 235 · see citing works
Hrdličková, B., de Almeida, R. C., Borek, Z., & Withoff, S. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.03.011 · Cited by 233 · see citing works
Translational pathophysiology: a novel molecular mechanism of human disease
Cazzola, M., & Skoda, R. C. (2000) · Blood · doi:10.1182/blood.v95.11.3280 · Cited by 222 · see citing works
HbVar database of human hemoglobin variants and thalassemia mutations: 2007 update
Giardine, B. M., van Baal, S., Kaimakis, P., Riemer, C., Miller, W., Σαμαρά, Μ., Κollia, P., Anagnou, N. P., Chui, D. H. K., Wajcman, H., et al. (2007) · Human Mutation · doi:10.1002/humu.9479 · Cited by 220 · see citing works
Genome-wide nucleotide-level mammalian ancestor reconstructionPreprint
Paten, B., Herrero, J., Fitzgerald, S., Beal, K., Flicek, P., Holmes, I., & Birney, E. (2008) · Genome Research · doi:10.1101/gr.076521.108 · Cited by 195 · see citing works
Upstream open reading frames: Molecular switches in (patho)physiology
Wethmar, K., Smink, J. J., & Leutz, A. (2010) · Bioessays · doi:10.1002/bies.201000037 · Cited by 192 · see citing works
microRNAs and genetic diseases
Meola, N., Gennarino, V. A., & Banfi, S. (2009) · PathoGenetics · doi:10.1186/1755-8417-2-7 · Cited by 154 · see citing works
Chen, J., Férec, C., & Cooper, D. N. (2006) · Human Genetics · doi:10.1007/s00439-006-0180-7 · Cited by 147 · see citing works
Phen-Gen: combining phenotype and genotype to analyze rare disorders
Javed, A., Agrawal, S., & Ng, P. C. (2014) · Nature Methods · doi:10.1038/nmeth.3046 · Cited by 143 · see citing works
Deletions of chromosomal regulatory boundaries are associated with congenital disease
Ibn-Salem, J., Köhler, S., Love, M. I., Chung, H.-R., Huang, N., Hurles, M. E., Haendel, M., Washington, N. L., Smedley, D., Mungall, C. J., et al. (2014) · Genome Biology · doi:10.1186/s13059-014-0423-1 · Cited by 143 · see citing works
Self-citationGenetic variants in microRNA genes: impact on microRNA expression, function, and disease
Cammaerts, S., Stražišar, M., Rijk, P. D., & Del‐Favero, J. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00186 · Cited by 138 · see citing works
Transcriptional enhancers in development and disease
Sakabe, N. J., Savic, D., & Nóbrega, M. A. (2012) · Genome biology · doi:10.1186/gb-2012-13-1-238 · Cited by 138 · see citing works
Cis-regulatory mutations in human disease
Epstein, D. J. (2009) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/elp021 · Cited by 113 · see citing works
Phenotype-driven strategies for exome prioritization of human Mendelian disease genes
Smedley, D. P., & Robinson, P. N. (2015) · Genome Medicine · doi:10.1186/s13073-015-0199-2 · Cited by 112 · see citing works
Self-citationBone, W. P., Washington, N. L., Buske, O. J., Adams, D. R., Davis, J., Draper, D. D., Flynn, E. D., Gîrdea, M., Godfrey, R. A., Golas, G. A., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.137 · Cited by 103 · see citing works
Self-citationJannovar: A Java Library for Exome Annotation
Jäger, M., Wang, K., Bauer, S., Smedley, D. P., Krawitz, P. M., & Robinson, P. N. (2014) · Human Mutation · doi:10.1002/humu.22531 · Cited by 77 · see citing works
Self-citationTATA box polymorphisms in human gene promoters and associated hereditary pathologies
Savinkova, L. K., Ponomarenko, M. P., Ponomarenko, P. M., Drachkova, I. A., Lysova, M. V., Arshinova, T., & Kolchanov, N. A. (2009) · Biochemistry (Moscow) · doi:10.1134/s0006297909020011 · Cited by 64 · see citing works
Ma, M., Ru, Y., Chuang, L.-S. F., Hsu, N.-Y., Shi, L., Hakenberg, J., Cheng, W., Uzilov, A. V., Ding, W., Glicksberg, B. S., et al. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s8-s3 · Cited by 59 · see citing works
Riggs, E. R., Jackson, L., Miller, D. T., & Vooren, S. V. (2012) · Human Mutation · doi:10.1002/humu.22052 · Cited by 53 · see citing works
Human diseases caused by germline and somatic abnormalities in microRNA and microRNA‐related genes
Kawahara, Y. (2013) · Congenital Anomalies · doi:10.1111/cga.12043 · Cited by 44 · see citing works
Computational approaches to interpreting genomic sequence variation
Ritchie, G. R. S., & Flicek, P. (2014) · Genome Medicine · doi:10.1186/s13073-014-0087-1 · Cited by 39 · see citing works
Use of Model Organism and Disease Databases to Support Matchmaking for Human Disease Gene Discovery
Mungall, C., Washington, N. L., Nguyen-Xuan, J., Condit, C., Smedley, D. P., Köhler, S., Groza, T., Shefchek, K., Hochheiser, H., Robinson, P. N., et al. (2015) · Human Mutation · doi:10.1002/humu.22857 · Cited by 37 · see citing works
Self-citationKhan, I., Mort, M. E., Buckland, P. R., et al. (2006) · In Silico Biology · doi:10.3233/isb-00218 · Cited by 28 · see citing works
Clinical interpretation of CNVs with cross-species phenotype data
Köhler, S., Schoeneberg, U., Czeschik, J. C., Doelken, S. C., Hehir‐Kwa, J. Y., Ibn-Salem, J., Mungall, C., Smedley, D. P., Haendel, M. A., & Robinson, P. N. (2014) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2014-102633 · Cited by 25 · see citing works
Self-citationThe impact of cis-acting polymorphisms on the human phenotype
Jones, B. L., & Swallow, D. M. (2011) · The HUGO Journal · doi:10.1007/s11568-011-9155-4 · Cited by 23 · see citing works
Enhancer mutations and phenotype modularity
Gordon, C. T., & Lyonnet, S. (2013) · Nature Genetics · doi:10.1038/ng.2861 · Cited by 21 · see citing works
Jarinova, O., & Ekker, M. (2012) · Human Mutation · doi:10.1002/humu.22083 · Cited by 18 · see citing works
Interpreting noncoding genetic variation in complex traits and human disease
Ward, L. D., & Kellis, M. (2012) · Nature Biotechnology · doi:10.1038/nbt.2422 · Cited by 6 · see citing works
Chuzhanova, N. A., Cooper, D. N., Férec, C., et al. (2007) · Genomic Medicine · doi:10.1007/s11568-006-9000-3 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [53]53. Hall, M. ∙ Frank, E. ∙ Holmes, G. ... The WEKA data mining software ACM SIGKDD Explor. Newsl. 2009; 11:10 Crossref Google Scholar
- [71]71. Washington, N.L., Haendel, M.A., Köhler, S., Lewis, S.E., Robinson, P.N., Smedley, D., and Mungall, C.J. (2013). How good is your phenotyping? Methods for quality assessment. In Phenoday2014.Biol.-Lark.Org, pp. 1–4. Google Scholar
Cited by
Sorted by citations
100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care — Preliminary Report
The 100, 0. G. P. P. I. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2035790 · Cited by 716 · see citing works
Clark, M. M., Stark, Z., Farnaes, L., Tan, T. Y., White, S. M., Dimmock, D., & Kingsmore, S. F. (2018) · npj Genomic Medicine · doi:10.1038/s41525-018-0053-8 · Cited by 619 · see citing works
Arboleda-Velasquez, J. F., Lopera, F., O’Hare, M., Delgado-Tirado, S., Marino, C., Chmielewska, N., Saez-Torres, K. L., Amarnani, D., Schultz, A. P., Sperling, R. A., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0611-3 · Cited by 598 · see citing works
Mungall, C. J., McMurry, J. A., Köhler, S., Balhoff, J. P., Borromeo, C., Brush, M., Carbon, S., Conlin, T., Dunn, N., Engelstad, M., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1128 · Cited by 441 · see citing works
Self-citationInternational Cooperation to Enable the Diagnosis of All Rare Genetic Diseases
Boycott, K. M., Rath, A., Chong, J. X., Hartley, T., Alkuraya, F. S., Baynam, G., Brookes, A. J., Brudno, M., Carracedo, A., den Dunnen, J. T., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.003 · Cited by 447 · see citing works
Self-citationTowards a comprehensive catalogue of validated and target-linked human enhancers
Gasperini, M., Tome, J. M., & Shendure, J. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0209-0 · Cited by 396 · see citing works
De novo mutations in regulatory elements in neurodevelopmental disorders
Short, P. J., McRae, J. F., Gallone, G., Sifrim, A., Won, H., Geschwind, D. H., Wright, C. F., Firth, H. V., FitzPatrick, D. R., Barrett, J. C., et al. (2018) · Nature · doi:10.1038/nature25983 · Cited by 274 · see citing works
Petrikin, J. E., Cakici, J. A., Clark, M. M., Willig, L. K., Sweeney, N. M., Farrow, E. G., Saunders, C. J., Thiffault, I., Miller, N. A., Zellmer, L., et al. (2018) · npj Genomic Medicine · doi:10.1038/s41525-018-0045-8 · Cited by 229 · see citing works
Li, Q., Zhao, K., Bustamante, C. D., Ma, X., & Wong, W. H. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0439-8 · Cited by 106 · see citing works
Schubach, M., Re, M., Robinson, P. N., & Valentini, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-03011-5 · Cited by 97 · see citing works
Self-citationDeepPVP: phenotype-based prioritization of causative variants using deep learning
Boudellioua, I., Kulmanov, M., Schofield, P. N., Gkoutos, G. V., & Hoehndorf, R. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2633-8 · Cited by 79 · see citing works
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Mao, D., Liu, C., Wang, L., AI-Ouran, R., Deisseroth, C., Pasupuleti, S., Kim, S. Y., Li, L., Rosenfeld, J. A., Meng, L., et al. (2024) · NEJM AI · doi:10.1056/aioa2300009 · Cited by 51 · see citing works
Wang, J., Dayem Ullah, A. Z., & Chelala, C. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky057 · Cited by 35 · see citing works
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Hu, Z., Yu, C., Furutsuki, M., Andreoletti, G., Ly, M., Hoskins, R., Adhikari, A. N., & Brenner, S. E. (2019) · Human Mutation · doi:10.1002/humu.23858 · Cited by 37 · see citing works
GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction
Benegas, G., Albors, C., Aw, A. J., Ye, C., & Song, Y. S. (2023) · doi:10.1101/2023.10.10.561776 · Cited by 118 · see citing works
“Moving on to regulatory variants, we evaluate on the classification of a curated set of variants implicated in Mendelian disorders (OMIM, Online Mendelian Inheritance in Man [28]) vs. gnomAD common variants.”
- Authors
- Smedley, D., Schubach, M., Jacobsen, J. O. B., Köhler, S., Zemojtel, T., Spielmann, M., Jäger, M., Hochheiser, H., Washington, N. L., McMurry, J. A., Haendel, M. A., Mungall, C. J., Lewis, S. E., Groza, T., Valentini, G., & Robinson, P. N.
- Affiliations
- Queen Mary University of London (GB)
- Charité – Universitätsmedizin Berlin (DE)
- Wellcome Sanger Institute (GB)
- Max Planck Institute for Molecular Genetics (DE)
- Berlin-Brandenburger Centrum für Regenerative Therapien (DE)
- University of Pittsburgh (US)
- Lawrence Berkeley National Laboratory (US)
- Oregon Health & Science University (US)
- Garvan Institute of Medical Research (AU)
- University of Milan (IT)
- Corresponding author
- Robinson, Peter N.
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 99
- Issue
- 3
- Pages
- 595-606
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC5011059
- OpenAlex
- W2510298658
- Cited by
- 283
- References
- 74
- Citations by year
- 2026: 17 · 2025: 24 · 2024: 41 · 2023: 17 · 2022: 40 · 2021: 29 · 2020: 20 · 2019: 32 · 2018: 34 · 2017: 21 · 2016: 7
- FWCI
- 25.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (01EC1402B, 0313911)
- Office of Science
- Seventh Framework Programme (602300)
- Basic Energy Sciences
- National Institutes of Health (1 U54 HG006370-01)
- Deutscher Akademischer Austauschdienst (57210259)
- Deutsche Forschungsgemeinschaft (DFG SP1532/2-1)
- NIH Office of the Director (5R24OD011883)
- U.S. Department of Energy (DE-AC02-05CH11231)
- 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 › Genomics and Phylogenetic Studies
- Keywords
- regulatory variants, monogenic disorders, non-coding variants, variant interpretation, CADD, allele frequency, causal variant identification
- MeSH terms
- Machine Learning, Algorithms, Gene Frequency, Humans, Mutation, Phenotype, Genome, Human, Open Reading Frames, Point Mutation, Genetic Diseases, Inborn, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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