Mr Peter Stenson

2025

• Kars, M. E. et al., 2025. Deciphering the digenic architecture of congenital heart disease using trio exome sequencing data. American Journal of Human Genetics 112 (3), pp.583-598. (10.1016/j.ajhg.2025.01.024)
• Stein, D. et al., 2025. Expanding the utility of variant effect predictions with phenotype-specific models. Nature Communications (10.1038/s41467-025-66607-w)

2024

• Kars, M. E. et al., 2024. The landscape of rare genetic variation associated with inflammatory bowel disease and Parkinson’s disease comorbidity. Genome Medicine 16 (1) 66. (10.1186/s13073-024-01335-2)

2023

• Fan, C. et al., 2023. Profiling human pathogenic repeat expansion regions by synergistic and multi-level impacts on molecular connections. Human Genetics 142 , pp.245-274. (10.1007/s00439-022-02500-6)
• Shao, Y. et al., 2023. Phylogenomic analyses provide insights into primate evolution. Science 380 (6648), pp.913-924. (10.1126/science.abn6919)
• Stein, D. et al., 2023. Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set. Genome Medicine 15 (1) 103. (10.1186/s13073-023-01261-9)
• Wu, Y. et al., 2023. Identifying shared genetic factors underlying epilepsy and congenital heart disease in Europeans. Human Genetics 142 , pp.275-288. (10.1007/s00439-022-02502-4)
• Wu, Y. et al., 2023. Identifying high-impact variants and genes in exomes of Ashkenazi Jewish inflammatory bowel disease patients. Nature Communications 14 (1) 2256. (10.1038/s41467-023-37849-3)
• Zhang, P. et al., 2023. Genome-wide detection of human intronic AG-gain variants located between splicing branchpoints and canonical splice acceptor sites. Proceedings of the National Academy of Sciences of the United States of America 120 (46) e2314225120. (10.1073/pnas.2314225120)

2022

• Qi, M. et al., 2022. Distinct sequence features underlie microdeletions and gross deletions in the human genome. Human Mutation 43 (3), pp.328-346. (10.1002/humu.24314)
• Quinodoz, M. et al., 2022. Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity. American Journal of Human Genetics 109 (3), pp.457-470. (10.1016/j.ajhg.2022.01.006)
• Rastogi, R. et al., 2022. X-CAP improves pathogenicity prediction of stopgain variants. Genome Medicine 14 (1) 81. (10.1186/s13073-022-01078-y)
• Zhang, P. et al., 2022. Genome-wide detection of human variants that disrupt intronic branchpoints. Proceedings of the National Academy of Sciences 119 (44) e2211194119. (10.1073/pnas.2211194119)

2021

• Kars, M. E. et al., 2021. The genetic structure of the Turkish population reveals high levels of variation and admixture. Proceedings of the National Academy of Sciences 118 (36) e2026076118. (10.1073/pnas.2026076118)
• Serrano, C. et al., 2021. Compensatory epistasis explored by molecular dynamics simulations. Human Genetics 140 (9), pp.1329-1342. (10.1007/s00439-021-02307-x)

2020

• Birgmeier, J. et al., 2020. AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature. Science Translational Medicine 12 (544) eaau9113. (10.1126/scitranslmed.aau9113)
• Rausell, A. et al., 2020. Common homozygosity for predicted loss-of-function variants reveals both redundant and advantageous effects of dispensable human genes. Proceedings of the National Academy of Sciences 117 (24), pp.13626-13636. (10.1073/pnas.1917993117)
• Stenson, P. D. et al. 2020. The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting. Human Genetics 139 , pp.1197-1207. (10.1007/s00439-020-02199-3)

2019

• Fragoza, R. et al., 2019. Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations. Nature Communications 10 (1) 4141. (10.1038/s41467-019-11959-3)
• Jagadeesh, K. A. et al., 2019. S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing. Nature Genetics 51 (4), pp.755-763. (10.1038/s41588-019-0348-4)
• Maffucci, P. et al., 2019. Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis. Proceedings of the National Academy of Sciences 116 , pp.950-959. (10.1073/pnas.1808403116)
• Zhang, P. et al., 2019. SeqTailor: a user-friendly webserver for the extraction of DNA or protein sequences from next-generation sequencing data. Nucleic Acids Research 47 (W1), pp.W623-W631. (10.1093/nar/gkz326)

2018

• Requena, D. et al., 2018. CDG: an online server proposing biologically closest disease-causing genes and pathologies and its application to primary immunodeficiency. Frontiers in Immunology 9 1340. (10.3389/fimmu.2018.01340)

2017

• Liang, S. et al., 2017. iRegNet3D: three-dimensional integrated regulatory network for the genomic analysis of coding and non-coding disease mutations. Genome Biology 18 (1) 10. (10.1186/s13059-016-1138-2)
• Stenson, P. D. et al. 2017. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Human Genetics 136 (6), pp.665-677. (10.1007/s00439-017-1779-6)

2016

• Itan, Y. et al., 2016. The mutation significance cutoff: gene-level thresholds for variant predictions [Letter]. Nature Methods 13 (2), pp.109-110. (10.1038/nmeth.3739)
• Lek, M. et al., 2016. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536 , pp.285-291. (10.1038/nature19057)
• Meyer, M. J. et al., 2016. mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome. Human Mutation 37 (5), pp.447-456. (10.1002/humu.22963)

2015

• Douville, C. et al., 2015. Assessing the pathogenicity of insertion and deletion variants with the Variant Effect Scoring Tool (VEST-Indel). Human Mutation 37 (1), pp.28-35. (10.1002/humu.22911)
• Grimm, D. G. et al., 2015. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Human Mutation 36 (5), pp.513-523. (10.1002/humu.22768)
• Johnston, J. et al., 2015. Individualized iterative phenotyping for genome-wide analysis of loss-of-function mutations. American Journal of Human Genetics 96 (6), pp.913-925. (10.1016/j.ajhg.2015.04.013)
• Karageorgos, I. et al., 2015. Identification of cancer predisposition variants in apparently healthy individuals using a next-generation sequencing-based family genomics approach. Human Genomics 9 12. (10.1186/s40246-015-0034-2)
• Rivas, M. A. et al., 2015. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science 348 (6235), pp.666-669. (10.1126/science.1261877)
• The 1000 Genomes Project Consortium, et al., 2015. A global reference for human genetic variation. Nature 526 , pp.68-74. (10.1038/nature15393)
• Turner, T. N. et al., 2015. Proteins linked to autosomal dominant and autosomal recessive disorders harbor characteristic rare missense mutation distribution patterns. Human Molecular Genetics 24 (21), pp.5995-6002. (10.1093/hmg/ddv309)

2014

• Chen, Y. et al., 2014. A probabilistic model to predict clinical phenotypic traits from genome sequencing. PLoS Computational Biology 10 (9) e1003825. (10.1371/journal.pcbi.1003825)
• Das, J. et al., 2014. Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks. Human Mutation 35 (5), pp.585-593. (10.1002/humu.22534)
• Stenson, P. et al. 2014. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Human Genetics 133 (1), pp.1-9. (10.1007/s00439-013-1358-4)

2013

• Douville, C. et al., 2013. CRAVAT: cancer-related analysis of variants toolkit. Bioinformatics 29 (5), pp.647-648. (10.1093/bioinformatics/btt017)
• Gonsalves, S. et al., 2013. Using exome data to identify malignant hyperthermia susceptibility mutations. Anesthesiology 119 (5), pp.1043-1053. (10.1097/ALN.0b013e3182a8a8e7)
• Niknafs, N. et al., 2013. MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures. Human Genetics 132 (11), pp.1235-1243. (10.1007/s00439-013-1325-0)
• Shihab, H. A. et al., 2013. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Human Mutation 34 (1), pp.57-65. (10.1002/humu.22225)

2012

• McVean, G. A. et al., 2012. An integrated map of genetic variation from 1,092 human genomes. Nature 491 (7422), pp.56-65. (10.1038/nature11632)
• Scally, A. et al., 2012. Insights into hominid evolution from the gorilla genome sequence. Nature 483 (7388), pp.169-175. (10.1038/nature10842)
• Stenson, P. D. et al. 2012. The human gene mutation database (HGMD) and its exploitation in the fields of personalized genomics and molecular evolution. Current Protocols in Bioinformatics 39 , pp.1.13.1-1.13.20. (10.1002/0471250953.bi0113s39)
• Xue, Y. et al., 2012. Deleterious- and disease-allele prevalence in healthy individuals: insights from current predictions, mutation databases, and population-scale resequencing. American Journal of Human Genetics 91 (6), pp.1022-1032. (10.1016/j.ajhg.2012.10.015)

2011

• Fechtel, K. et al., 2011. Delineating the Hemostaseome as an aid to individualize the analysis of the hereditary basis of thrombotic and bleeding disorders. Human Genetics 130 (1), pp.149-166. (10.1007/s00439-011-0984-y)
• Ivanov, D. et al. 2011. Comparative analysis of germline and somatic microlesion mutational spectra in 17 human tumor suppressor genes. Human Mutation 32 (6), pp.620-632. (10.1002/humu.21483)
• Necsulea, A. et al., 2011. Meiotic recombination favors the spreading of deleterious mutations in human populations. Human Mutation 32 (2), pp.198-206. (10.1002/humu.21407)
• Wolf, A. et al., 2011. Single base-pair substitutions at the translation initiation sites of human genes as a cause of inherited disease. Human Mutation 32 (10), pp.1137-1143. (10.1002/humu.21547)
• Yan, G. et al., 2011. Genome sequencing and comparison of two nonhuman primate animal models, the cynomolgus and Chinese rhesus macaques [Letter]. Nature Biotechnology 29 (11), pp.1019-1023. (10.1038/nbt.1992)

2010

• Cooper, D. N. et al. 2010. Genes, mutations, and human inherited disease at the dawn of the age of personalized genomics. Human Mutation 31 (6), pp.631-655. (10.1002/humu.21260)
• Cooper, D. N. et al. 2010. Methylation-mediated deamination of 5-methylcytosine appears to give rise to mutations causing human inherited disease in CpNpG trinucleotides, as well as in CpG dinucleotides. Human Genomics 4 (6), pp.406-410.
• Durbin, R. M. et al., 2010. A map of human genome variation from population-scale sequencing. Nature 467 (7319), pp.1061-1073. (10.1038/nature09534)
• Pagon, R. A. et al., 2010. Databases in human and medical genetics. In: Speicher, M. R. , Antonarakis, S. E. and Motulsky, A. G. eds. Vogel and Motulsky's Human Genetics: Problems and Approaches. 4th ed.. London: Springer. , pp.941-961.
• Quemener, S. et al., 2010. Complete ascertainment of intragenic copy number mutations (CNMs) in theCFTRgene and its implications for CNM formation at other autosomal loci. Human Mutation 31 (4), pp.421-428. (10.1002/humu.21196)
• Stenson, P. D. and Cooper, D. N. 2010. Prospects for the automated extraction of mutation data from the scientific literature [Editorial]. Human Genomics 5 (1), pp.1-4.

2009

• Stenson, P. D. et al. 2009. The Human Gene Mutation Database: providing a comprehensive central mutation database for molecular diagnostics and personalized genomics [Editorial]. Human Genomics 4 (2), pp.69-72.
• Stenson, P. D. et al. 2009. The Human Gene Mutation Database: 2008 update. Genome Medicine 1 (1) 13. (10.1186/gm13)

2008

• Bacolla, A. et al., 2008. Abundance and length of simple repeats in vertebrate genomes are determined by their structural properties. Genome Research 18 (10), pp.1545-1553. (10.1101/gr.078303.108)

2007

• Gibbs, R. A. et al., 2007. Evolutionary and biomedical insights from the rhesus macaque genome. Science 316 (5822), pp.222-34. (10.1126/science.1139247)
• Stenson, P. D. et al. 2007. Human Gene Mutation Database: towards a comprehensive central mutation database [Letter]. Journal of Medical Genetics 45 (2), pp.124-126. (10.1136/jmg.2007.055210)

2006

• Cooper, D. N. , Stenson, P. D. and Chuzhanova, N. A. 2006. The Human Gene Mutation Database (HGMD) and its exploitation in the study of mutational mechanisms. Current Protocols in Bioinformatics 1 (1.13)(10.1002/0471250953.bi0113s12)

2005

• Ball, E. V. et al. 2005. Microdeletions and microinsertions causing human genetic disease: common mechanisms of mutagenesis and the role of local DNA sequence complexity. Human Mutation 26 (3), pp.205-213. (10.1002/humu.20212)
• Chen, J. M. et al., 2005. Complex gene rearrangements caused by serial replication slippage. Human Mutation 26 (2), pp.125-134. (10.1002/humu.20202)
• Chen, J. M. et al., 2005. Intrachromosomal serial replication slippage in trans gives rise to diverse genomic rearrangements involving inversions. Human Mutation 26 (4), pp.363-373. (10.1002/humu.20230)
• Chen, J. M. et al., 2005. A systematic analysis of LINE-1 endonuclease-dependent retrotranspositional events causing human genetic disease [review]. Human Genetics -Berlin- 117 (5), pp.411-427. (10.1007/s00439-005-1321-0)

2004

• Abeysinghe, S. S. et al., 2004. Gross Rearrangement Breakpoint Database (GRaBD)[review]. Human Mutation 23 (3), pp.219-221. (10.1002/humu.20006)
• Gibbs, R. A. et al., 2004. Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428 (6982), pp.493-521. (10.1038/nature02426)
• Huang, H. et al., 2004. Evolutionary conservation and selection of human disease gene orthologs in the rat and mouse genomes. Genome Biology (5), pp.R47-R47. (10.1186/gb-2004-5-7-r47)

2003

• Stenson, P. D. et al. 2003. Human Gene Mutation Database (HGMD): 2003 update. Human Mutation 21 (6), pp.577-581. (10.1002/humu.10212)

2000

• Krawczak, M. et al., 2000. Human gene mutation database-a biomedical information and research resource. Human Mutation 15 (1), pp.45-51. (10.1002/(sici)1098-1004(200001)15:1%3C45::aid-humu10%3E3.0.co;2-t)
• Krawczak, M. et al., 2000. Changes in primary DNA sequence complexity influence the phenotypic consequences of mutations in human gene regulatory regions. Human Genetics -Berlin- 107 (4), pp.362-365. (10.1007/s004390000393)

1999

• Krawczak, M. et al., 1999. HGMD: the human gene mutation database. In: Letovsky., S. I. ed. Bioinformatics; databases and systems. Boston: Kluwer Academic Publishers. , pp.99-104.