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PreprintCited by 183Genomics and Rare DiseasesGenetic Associations and EpidemiologyGenomics and Phylogenetic Studies

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The landscape of tolerated genetic variation in humans and primates

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Gao, Hong, Hamp, Tobias, Ede, Jeffrey, Schraiber, Joshua G., McRae, Jeremy, Singer-Berk, Moriel, Yang, Yanshen, Dietrich, Anastasia, Fiziev, Petko, Kuderna, Lukas, Sundaram, Laksshman, Wu, Yibing, Adhikari, Aashish, Field, Yair, Chen, Chen, Batzoglou, Serafim, Aguet, Francois, Lemire, Gabrielle, Reimers, Rebecca, Balick, Daniel, Janiak, Mareike C., Kuhlwilm, Martin, Orkin, Joseph D., Manu, Shivakumara, Valenzuela, Alejandro, Bergman, Juraj, Rouselle, Marjolaine, Silva, Felipe Ennes, Agueda, Lidia, Blanc, Julie, Gut, Marta, Vries, Dorien de, Goodhead, Ian, Harris, R. Alan, Raveendran, Muthuswamy, Jensen, Axel, Chuma, Idriss S., Horvath, Julie, Hvilsom, Christina, Juan, David, Frandsen, Peter, Melo, Fabiano R. de, Bertuol, Fabricio, Byrne, Hazel, Sampaio, Iracilda, Farias, Izeni, Valsecchi do Amaral, João, Messias, Mariluce, Silva, Maria N. F. da, Trivedi, Mihir, Rossi, Rogerio, Hrbek, Tomas, Andriaholinirina, Nicole, Rabarivola, Clément J., Zaramody, Alphonse, Jolly, Clifford J., Phillips-Conroy, Jane, Wilkerson, Gregory, Abee, Christian, Simmons, Joe H., Fernandez-Duque, Eduardo, Kanthaswamy, ee, Shiferaw, Fekadu, Wu, Dongdong, Zhou, Long, Shao, Yong, Zhang, Guojie, Keyyu, Julius D., Knauf, Sascha, Le, Minh D., Lizano, Esther, Merker, Stefan, Navarro, Arcadi, Batallion, Thomas, Nadler, Tilo, Khor, Chiea Chuen, Lee, Jessica, Tan, Patrick, Lim, Weng Khong, Kitchener, Andrew C., Zinner, Dietmar, Gut, Ivo, Melin, Amanda, Guschanski, Katerina, Schierup, Mikkel Heide, Beck, Robin M. D., Umapathy, Govindhaswamy, Roos, Christian, Boubli, Jean P., Lek, Monkol, Sunyaev, Shamil, O’Donnell, Anne, Rehm, Heidi, Xu, Jinbo, Rogers, Jeffrey, Marques-Bonet, Tomas, Farh, Kyle Kai-How·2023·Science
DOI 10.1101/2023.05.01.538953PMC PMC10713091

Preprint — published as 10.1126/science.abn8197

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Abstract

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence Summary: Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

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Research works printed but not linked: 166

  1. 10.1038/nature13127 · DOI · Paper page
  2. 2015, ClinGen, ClinGen and Genetic Testing, N Engl J Med, 373, 1379
  3. 10.1056/NEJMsr1406261 · DOI · Paper page
  4. 10.1093/nar/gkv1222 · DOI · Paper page
  5. 10.1002/humu.22932 · DOI
  6. 10.1007/s00439-013-1358-4 · DOI
  7. 10.1038/nrg.2016.162 · DOI
  8. 10.1038/gim.2017.26 · DOI · Paper page
  9. 10.1111/cge.13190 · DOI
  10. 10.1001/jama.2014.14601 · DOI · Paper page
  11. J. A. SoRelle , D. M. Thodeson , S. Arnold , G. Gotway , J. Y. Park , Clinical Utility of Reinterpreting Previously Reported Genomic Epilepsy Test Results for Pediatric Patients. JAMA Pediatr 173, e182302 (2019). · DOI
  12. 10.1016/j.ajhg.2018.02.019 · DOI
  13. O. Campuzano et al., Reanalysis and reclassification of rare genetic variants associated with inherited arrhythmogenic syndromes. EBioMedicine 54, 102732 (2020). · DOI
  14. 10.1038/gim.2015.30 · DOI · Paper page
  15. 10.3343/alm.2019.39.5.421 · DOI
  16. 10.1086/423146 · DOI
  17. 2018, Predicting the clinical impact of human mutation with deep neural networks, Nat Genet, 50, 1161, 10.1038/s41588-018-0167-z · DOI
  18. 10.1038/nature04072 · DOI · Paper page
  19. 10.1038/nature12228 · DOI
  20. 2018, Ancient hybridization and admixture in macaques (genus Macaca) inferred from whole genome sequences, Mol Phylogenet Evol, 127, 376, 10.1016/j.ympev.2018.03.038 · DOI
  21. 2020, Genomic Mechanisms of Physiological and Morphological Adaptations of Limestone Langurs to Karst Habitats, Mol Biol Evol, 37, 952, 10.1093/molbev/msz301 · DOI
  22. L. Wang et al., A high-quality genome assembly for the endangered golden snub-nosed monkey (Rhinopithecus roxellana). Gigascience 8, (2019). · DOI
  23. 10.1038/s41586-020-2876-6 · DOI · Paper page
  24. B. J. Evans et al., Speciation over the edge: gene flow among non-human primate species across a formidable biogeographic barrier. R Soc Open Sci. 4, 170351 (2017). · DOI
  25. 10.1038/ng.3615 · DOI
  26. N. Osada , K. Matsudaira , Y. Hamada , S. Malaivijitnond , Testing sex-biased admixture origin of macaque species using autosomal and X-chromosomal genomic sequences. Genome Biol. Evol. 13, (2021). · DOI
  27. 2021, Primate Behavioral Ecology, (Routledge, New York, ed, 6, 407
  28. 10.1038/nature19057 · DOI · Paper page
  29. 10.1038/s41586-020-2308-7 · DOI · Paper page
  30. E. M. Leffler et al., Revisiting an old riddle: what determines genetic diversity levels within species? PLoS Biol 10, e1001388 (2012). · DOI
  31. A. Estrada et al., Impending extinction crisis of the world’s primates: Why primates matter. Sci Adv 3, e1600946 (2017). · DOI · Paper page
  32. 10.1038/246096a0 · DOI · Paper page
  33. 10.1016/S0168-9525(01)02410-6 · DOI · Paper page
  34. 1992, Population genetics of polymorphism and divergence, Genetics, 132, 1161, 10.1093/genetics/132.4.1161 · DOI
  35. 10.1038/nrg2146 · DOI
  36. 10.1038/nature07485 · DOI
  37. 10.1038/ng.2896 · DOI · Paper page
  38. 10.1038/ng.3186 · DOI
  39. P. K. Albers , G. McVean , Dating genomic variants and shared ancestry in population-scale sequencing data. PLoS biology 18, e3000586 (2020). · DOI
  40. I. Mathieson , G. McVean , Demography and the age of rare variants. PLoS Genet 10, e1004528 (2014). · DOI
  41. 10.1038/ejhg.2015.21 · DOI
  42. 2016, Biallelic CACNA1A mutations cause early onset epileptic encephalopathy with progressive cerebral, cerebellar, and optic nerve atrophy, Am J Med Genet A, 170, 2173, 10.1002/ajmg.a.37678 · DOI
  43. 10.1186/1471-2350-7-77 · DOI
  44. 10.1038/sj.ejhg.5201847 · DOI
  45. 10.1073/pnas.232565499 · DOI
  46. 10.1038/nature14497 · DOI · Paper page
  47. 10.1038/ng.3050 · DOI · Paper page
  48. 10.1093/genetics/159.4.1779 · DOI
  49. R. N. Gutenkunst , R. D. Hernandez , S. H. Williamson , C. D. Bustamante , Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet 5, e1000695 (2009). · DOI · Paper page
  50. 10.1534/genetics.117.200493 · DOI
  51. 10.18637/jss.v067.i01 · DOI · Paper page
  52. 10.1016/S0166-4328(01)00297-2 · DOI · Paper page
  53. 10.1046/j.1469-1809.2003.00028.x · DOI
  54. 10.1007/s004390000273 · DOI
  55. H. Shu et al., The role of CD36 in cardiovascular disease. Cardiovascular Research, (2020). · DOI
  56. 10.1002/humu.10041 · DOI · Paper page
  57. 2007, High carrier frequency of the GJB2 mutation (35delG) in the north of Iran, International journal of pediatric otorhinolaryngology, 71, 863, 10.1016/j.ijporl.2007.02.005 · DOI
  58. 2020, Distribution of CD36 deficiency in different Chinese ethnic groups, Human Immunology, 81, 366, 10.1016/j.humimm.2020.05.004 · DOI
  59. 10.1038/35016636 · DOI
  60. 10.1136/jmg.2003.017632 · DOI
  61. 10.1038/ejhg.2008.225 · DOI
  62. 10.1038/nm0795-703 · DOI
  63. 10.1038/30006 · DOI
  64. 10.1038/ng.2566 · DOI
  65. 2017, TERT promoter mutations in telomere biology, Mutation Research/Reviews in Mutation Research, 771, 15, 10.1016/j.mrrev.2016.11.002 · DOI
  66. 10.1038/s41586-021-04043-8 · DOI · Paper page
  67. 10.4174/astr.2014.86.6.325 · DOI
  68. 10.1111/j.0009-9163.2004.00266.x · DOI
  69. 10.1159/000082933 · DOI
  70. 2010, Germline mutation analysis of STK11 gene using direct sequencing and multiplex ligation-dependent probe amplification assay in Korean children with Peutz-Jeghers syndrome, Dig Dis Sci, 55, 3458, 10.1007/s10620-010-1194-5 · DOI
  71. M. Varadi et al., AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research, (2021). · DOI · Paper page
  72. 10.1093/nar/gki408 · DOI
  73. 10.1038/nprot.2012.085 · DOI
  74. 10.1093/nar/gkw306 · DOI
  75. D. J. Burgess , The TOPMed genomic resource for human health. Nat Rev Genet 22, 200 (2021). · DOI
  76. 10.1038/s41586-021-03205-y · DOI · Paper page
  77. 10.1038/s41586-018-0579-z · DOI · Paper page
  78. C. Sudlow et al., UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12, e1001779 (2015). · DOI · Paper page
  79. 2019, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 1, 4171
  80. Y. You , et al., in International Conference on Learning Representations. (2020).
  81. 2021, MSA Transformer, Proceedings of the 38th International Conference on Machine Learning, 139, 8844
  82. X. Liu , C. Li , C. Mou , Y. Dong , Y. Tu , dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Medicine 12, 103 (2020). · DOI
  83. 10.1038/nature14135 · DOI · Paper page
  84. 10.1038/nature21062 · DOI · Paper page
  85. 10.1038/s41586-020-2832-5 · DOI · Paper page
  86. J. Y. An et al., Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362, (2018). · DOI · Paper page
  87. 10.1038/nature13772 · DOI · Paper page
  88. 10.1038/nature13908 · DOI · Paper page
  89. 10.1016/j.neuron.2012.04.009 · DOI · Paper page
  90. 10.1016/j.neuron.2015.09.016 · DOI · Paper page
  91. 10.1038/nature10945 · DOI · Paper page
  92. 10.1038/nature10989 · DOI · Paper page
  93. 10.1038/ng.3970 · DOI · Paper page
  94. 10.1073/pnas.1209751109 · DOI · Paper page
  95. M. A. Chiasson et al., Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact. eLife 9, (2020). · DOI
  96. 2021, Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk, American journal of human genetics, 108, 163, 10.1016/j.ajhg.2020.12.003 · DOI
  97. 10.1038/s41588-018-0122-z · DOI · Paper page
  98. 10.1016/j.ajhg.2018.03.018 · DOI · Paper page
  99. 2020, Deep mutational scanning reveals the structural basis for α-synuclein activity, Nature Chemical Biology, 16, 653, 10.1038/s41589-020-0480-6 · DOI
  100. M. Seuma , A. J. Faure , M. Badia , B. Lehner , B. Bolognesi , The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer’s disease mutations. Elife 10, e63364 (2021). · DOI
  101. 10.1038/s41588-018-0204-y · DOI · Paper page
  102. 10.1534/genetics.115.175802 · DOI
  103. E. M. Jones et al., Structural and functional characterization of G protein–coupled receptors with deep mutational scanning. eLife 9, e54895 (2020).
  104. C. E. G. Amorim et al., The population genetics of human disease: The case of recessive, lethal mutations. PLoS Genet 13, e1006915 (2017). · DOI
  105. 2014, Medical genomics: The intricate path from genetic variant identification to clinical interpretation, Appl Transl Genom, 3, 60
  106. 10.1038/s41588-019-0528-2 · DOI · Paper page
  107. 10.1038/s41467-019-10016-3 · DOI · Paper page
  108. 2017, Estimating the selective effects of heterozygous protein-truncating variants from human exome data, Nat Genet, 49, 806, 10.1038/ng.3831 · DOI
  109. 10.1093/nar/gkw1134 · DOI
  110. 10.1101/gr.229102. Article published online before print in May 2002 · DOI · Paper page
  111. 10.1038/s41586-020-2876-6 · DOI · Paper page
  112. 2015, UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics (Oxford, England, 31, 926
  113. 10.1006/jmbi.1993.1626 · DOI · Paper page
  114. R. K. Pasumarthi et al., TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2970–2978 (2019). · DOI
  115. 10.1093/bioinformatics/btm098 · DOI · Paper page
  116. 10.1093/bioinformatics/btu739 · DOI · Paper page
  117. 2021, Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk, Am J Hum Genet, 108, 163, 10.1016/j.ajhg.2020.12.003 · DOI
  118. M. A. Chiasson et al., Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact. Elife 9, (2020). · DOI
  119. 10.1534/genetics.115.175802 · DOI
  120. 10.1038/s41588-018-0204-y · DOI · Paper page
  121. E. M. Jones et al., Structural and functional characterization of G protein–coupled receptors with deep mutational scanning. Elife 9, e54895 (2020).
  122. 10.1038/s41586-021-03758-y · DOI
  123. D. Vanderpool et al., Primate phylogenomics uncovers multiple rapid radiations and ancient interspecific introgression. PLoS Biol 18, e3000954 (2020). · DOI
  124. 10.1093/bioinformatics/btp163 · DOI · Paper page
  125. 10.1101/gr.210500.116 · DOI
  126. 10.1126/science.aag2602 · DOI
  127. 10.1126/science.1234070 · DOI
  128. K. E. Eilertson , J. G. Booth , C. D. Bustamante , SnIPRE: Selection Inference Using a Poisson Random Effects Model. PLoS Comput Biol 8, e1002806 (2012). · DOI
  129. 10.1006/jmbi.1990.9999 · DOI · Paper page
  130. L. S. Johnson , S. R. Eddy , E. Portugaly , Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinformatics 11, 431 (2010). · DOI
  131. 10.1126/science.abj8754 · DOI · Paper page
  132. D. S. Marks et al., Protein 3D structure computed from evolutionary sequence variation. PLoS One 6, e28766 (2011). · DOI · Paper page
  133. D. Kingma , J. Ba , Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, (2014). · arXiv
  134. 10.1093/nar/gkw1081 · DOI
  135. M. Steinegger et al., HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019). · DOI · Paper page
  136. R. M. Rao et al., M. Meila, T. Zhang, Eds. (PMLR, 2021), vol. 139, pp. 8844–8856.
  137. J. L. Ba , J. R. Kiros , G. E. Hinton , paper presented at the Advances in NIPS 2016 Deep Learning Symposium, 2016 2016.
  138. D. Hendrycks , K. Gimpel , Gaussian Error Linear Units (GELUs). arXiv preprint arXiv:1606.08415, (2020). · arXiv
  139. 10.5555/2627435.2670313 · DOI
  140. P. Micikevicius , et al., Mixed Precision Training. International Conference on Learning Representations, (2018).
  141. S. Rajbhandari , J. Rasley , O. Ruwase , Y. He , ZeRO: Memory Optimizations Toward Training Trillion Parameter Models. Proceedings of the International Conference for High Performance Computing, Networking , Storage and Analysis, 1–16 (2020). · DOI
  142. P. Bandaru et al., Deconstruction of the Ras switching cycle through saturation mutagenesis. Elife 6, (2017). · DOI · Paper page
  143. J. Weile et al., A framework for exhaustively mapping functional missense variants. Mol Syst Biol 13, 957 (2017). · DOI · Paper page
  144. 10.1016/j.celrep.2016.09.061 · DOI
  145. 10.1056/NEJMoa2105281 · DOI · Paper page
  146. L. Zhang et al., SLCO1B1: Application and Limitations of Deep Mutational Scanning for Genomic Missense Variant Function. Drug Metab Dispos, DMD-AR-2020-000264 (2021). · DOI
  147. 10.1534/g3.119.400535 · DOI
  148. 2010, A method and server for predicting damaging missense mutations, Nature Methods, 7, 248, 10.1038/nmeth0410-248 · DOI · Paper page
  149. 10.1002/humu.23158 · DOI · Paper page
  150. P. Rentzsch , M. Schubach , J. Shendure , M. Kircher , CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Medicine 13, 31 (2021). · DOI · Paper page
  151. 10.1093/bioinformatics/btu703 · DOI
  152. 2017, DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins, Nucleic Acids Research, 45, W201, 10.1093/nar/gkx390 · DOI
  153. 10.1093/nar/gkaa288 · DOI
  154. 10.1038/ng.3703 · DOI
  155. 10.1093/nar/gkab266 · DOI
  156. Y. Choi , G. E. Sims , S. Murphy , J. R. Miller , A. P. Chan , Predicting the functional effect of amino acid substitutions and indels. PLoS One 7, e46688 (2012). · DOI · Paper page
  157. 10.1016/j.ajhg.2016.08.016 · DOI · Paper page
  158. 10.1093/nar/gks539 · DOI
  159. H. Carter , C. Douville , P. D. Stenson , D. N. Cooper , R. Karchin , Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 14 Suppl 3 , S3 (2013). · DOI
  160. 10.1093/bioinformatics/btv009 · DOI
  161. J. Meier et al., Language models enable zero-shot prediction of the effects of mutations on protein function. bioRxiv, (2021). · DOI · Paper page
  162. 2018, Deep generative models of genetic variation capture the effects of mutations, Nat Methods, 15, 816, 10.1038/s41592-018-0138-4 · DOI · Paper page
  163. 10.1136/jmg.2007.055210 · DOI
  164. 10.1007/s00439-020-02199-3 · DOI
  165. 10.1002/prot.20264 · DOI · Paper page
  166. M. Ekeberg , C. Lövkvist , Y. Lan , M. Weigt , E. Aurell , Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models. Physical Review E 87, 012707 (2013). · DOI · Paper page

Metadata

Authors
Gao, H., Hamp, T., Ede, J., Schraiber, J. G., McRae, J., Singer-Berk, M., Yang, Y., Dietrich, A., Fiziev, P., Kuderna, L., Sundaram, L., Wu, Y., Adhikari, A., Field, Y., Chen, C., Batzoglou, S., Aguet, F., Lemire, G., Reimers, R., Balick, D., Janiak, M. C., Kuhlwilm, M., Orkin, J. D., Manu, S., Valenzuela, A., Bergman, J., Rouselle, M., Silva, F. E., Agueda, L., Blanc, J., Gut, M., Vries, D. D., Goodhead, I., Harris, R. A., Raveendran, M., Jensen, A., Chuma, I. S., Horvath, J., Hvilsom, C., Juan, D., Frandsen, P., Melo, F. R. D., Bertuol, F., Byrne, H., Sampaio, I., Farias, I., Valsecchi do Amaral, J., Messias, M., Silva, M. N. F. D., Trivedi, M., Rossi, R., Hrbek, T., Andriaholinirina, N., Rabarivola, C. J., Zaramody, A., Jolly, C. J., Phillips-Conroy, J., Wilkerson, G., Abee, C., Simmons, J. H., Fernandez-Duque, E., Kanthaswamy, E., Shiferaw, F., Wu, D., Zhou, L., Shao, Y., Zhang, G., Keyyu, J. D., Knauf, S., Le, M. D., Lizano, E., Merker, S., Navarro, A., Batallion, T., Nadler, T., Khor, C. C., Lee, J., Tan, P., Lim, W. K., Kitchener, A. C., Zinner, D., Gut, I., Melin, A., Guschanski, K., Schierup, M. H., Beck, R. M. D., Umapathy, G., Roos, C., Boubli, J. P., Lek, M., Sunyaev, S., O’Donnell, A., Rehm, H., Xu, J., Rogers, J., Marques-Bonet, T., & Farh, K. K.-H.
Affiliations
  • Illumina (United States) (US)
  • Broad Institute (US)
  • Boston Children's Hospital (US)
  • Brigham and Women's Hospital (US)
  • University of Salford (GB)
  • University of Vienna (AT)
  • Barcelona Biomedical Research Park (ES)
  • Centre for Cellular and Molecular Biology (IN)
  • Aarhus University (DK)
  • Université Libre de Bruxelles (BE)
  • Barcelona Institute of Science and Technology (ES)
  • Baylor College of Medicine (US)
  • Uppsala University (SE)
  • Tanzania National Parks (TZ)
  • University of North Carolina at Chapel Hill (US)
  • Copenhagen Zoo (DK)
  • University of Utah (US)
  • Universidade Federal do Pará (BR)
  • Universidade Federal do Amazonas (BR)
  • Universidade Federal de Rondônia (BR)
  • Trinity University (US)
  • Université de Mahajanga (MG)
  • New York University (US)
  • The University of Texas MD Anderson Cancer Center (US)
  • Hawassa University (ET)
  • Zhejiang University (CN)
  • Kunming Institute of Zoology (CN)
  • University of Copenhagen (DK)
  • Tanzania Wildlife Research Institute (TZ)
  • Friedrich-Loeffler-Institut (DE)
  • Institució Catalana de Recerca i Estudis Avançats (ES)
  • Staatliches Museum für Naturkunde Stuttgart (DE)
  • Agency for Science, Technology and Research (SG)
  • SingHealth (SG)
  • National Museums Scotland (GB)
  • German Primate Center (DE)
  • Universitat Pompeu Fabra (ES)
  • University of Calgary (CA)
  • Toyota Technological Institute at Chicago (US)
Corresponding author
  • Rogers, Jeffrey
  • Farh, Kyle Kai-How
ORCID
Venue
Science
Publisher
openRxiv
Published
2023-05-02
Volume
380
Issue
6648
Pages
eabn8153-eabn8153
ISSN
0036-8075 (ISSN-L)
DOI
10.1101/2023.05.01.538953
PMC
PMC10713091
OpenAlex
W4378976943
Cited by
183
References
1
Citations by year
2026: 45 · 2025: 66 · 2024: 51 · 2023: 21
FWCI
30.34
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
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  • Science (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, other-oa)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (acceptedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion, cc-by-nc-nd)
  • Digital Commons-TMC (Texas Medical Center) (submittedVersion)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
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  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
  • OpenAgrar (submittedVersion)
Funders
  • National Science Foundation (1219368, 8303506, 1540255, REU‐0837921, 1848954, 100010434, 1952072, 1026991, 0621020, 1029451, 1232349, 0837921, 0924352, 1503753)
  • Gordon and Betty Moore Foundation
  • Primate Conservation
  • Leakey Foundation (RAPID-1219368, 1026991, 1232349, BCS-0621020, 1503753, 0924352, 1848954, DDIG-1540255, NSF-BCS-0621020)
  • National Geographic Society (RAPID-1219368, NSF-BCS-0621020, DDIG-1540255, 1026991, 1232349, 0924352, 1848954, 1503753)
  • Margot Marsh Biodiversity Foundation
  • Ministry of Science and Technology
  • Rufford Foundation (23117-2, 14861-1, 38786-B)
  • Indian Institute of Science
  • Broad Institute
  • UK Research and Innovation
  • Centres de Recerca de Catalunya (501100011033)
  • European Commission (100010434, 864203, 847648, 2014‐2020, 13039/501100011033, 801505, 501100011033, HORIZON2020, ID 100010434)
  • National Research Foundation
  • National Research Foundation Singapore (MOH-000588)
  • Department of Biotechnology, Ministry of Science and Technology, India
  • Council of Scientific and Industrial Research, India
  • Deutsche Forschungsgemeinschaft (KN 1097/3-1)
  • Canada Research Chairs
  • Vienna Science and Technology Fund (VRG20-001, 10.47379, 10.47379/VRG20001)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (2014-5, 3261/2013)
  • Generalitat de Catalunya (501100011033, BFU2017-86471-P, CEX2018-000792-M, 864203)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (302140/2020-4, 563348/2010, 563348/2010-0, 2014-5, 47379)
  • Vetenskapsrådet (BFU2017-86471-P, 864203, CEX2018-000792-M, 2020-03398)
  • Ministerio de Ciencia e Innovación (2014-2020, MCIN/AEI/10, .13039/501100011033, ID100010434, / AEI/10, 847648, 100010434, CEX2018-000792-M, 501100011033)
  • Fundação de Amparo à Pesquisa do Estado do Amazonas (563348/2010-0)
  • Instituto de Desenvolvimento Sustentável Mamirauá
  • Novo Nordisk Fonden (NNF21OC0069105, NNF18OC0031004)
  • Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona (ID 100010434, 100010434)
  • National Institutes of Health (5p30ag012836-30, 2014-2020, 100010434, P40OD024628)
  • Medical Research Council
  • Natural Environment Research Council (NE/T000341/1)
  • National Medical Research Council (MOH- 000588)
  • Instituto de Salud Carlos III
  • European Regional Development Fund (CEX2018–000792-M, MCIN/AEI/10, BFU2017-86471-P, 2014-2020’, BIO2015-71792-P, 13039/501100011033, 100010434, 501100011033, 864203)
  • Agencia Estatal de Investigación (501100011033, 13039, .13039/501100011033, 864203, AEI/10, #CEX2018-000792-M, BFU2017-86471-P)
  • National Institute on Aging (R24 HD-044964, NICHD R24 HD-044964-11, AG012836, P30-AG-012836, P30 AG012836‐19, R24 HD-044964-11, 100010434)
  • National Human Genome Research Institute (R01HG010898)
  • National Institute of General Medical Sciences (R01GM089753, T32GM007748)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24HD044964)
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
protein-altering variants, allele frequency, variant pathogenicity prediction, deep learning, genetic disorders, comparative genomics

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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