Detection of human adaptation during the past 2000 years
Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele freq...
Saved in:
Published in | Science (American Association for the Advancement of Science) Vol. 354; no. 6313; pp. 760 - 764 |
---|---|
Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Association for the Advancement of Science
11.11.2016
The American Association for the Advancement of Science |
Subjects | |
Online Access | Get full text |
ISSN | 0036-8075 1095-9203 1095-9203 |
DOI | 10.1126/science.aag0776 |
Cover
Loading…
Abstract | Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2OOO to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans. |
---|---|
AbstractList | Evolutionary analyses aim to identify recent genetic changes that are likely to have been subject to selection. Field et al. present a method to identify such changes, the singleton density score, which they applied to over 3000 human genomes. Over the past ∼100 generations (2000 to 3000 years), Europeans are likely to have experienced selection for genetic variants, including those that affect skin and hair pigmentation, as well as height. Science, this issue p. 760 Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans. Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2OOO to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans. Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans. Identifying genes under recent selectionEvolutionary analyses aim to identify recent genetic changes that are likely to have been subject to selection. Field et al. present a method to identify such changes, the singleton density score, which they applied to over 3000 human genomes. Over the past similar to 100 generations (2000 to 3000 years), Europeans are likely to have experienced selection for genetic variants, including those that affect skin and hair pigmentation, as well as height.Science, this issue p. 760 Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans. Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans. Evolutionary analyses aim to identify recent genetic changes that are likely to have been subject to selection. Field et al. present a method to identify such changes, the singleton density score, which they applied to over 3000 human genomes. Over the past ∼100 generations (2000 to 3000 years), Europeans are likely to have experienced selection for genetic variants, including those that affect skin and hair pigmentation, as well as height. Science , this issue p. 760 The singleton density score identifies evidence of recent selection in Europeans. Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans. |
Author | Boyle, Evan A Gao, Ziyue Rocheleau, Ghislain Telis, Natalie Field, Yair Yengo, Loic Pritchard, Jonathan K. Golan, David Gaulton, Kyle J. Froguel, Philippe McCarthy, Mark I. |
AuthorAffiliation | 3 Program in Biomedical Informatics, Stanford University, Stanford, CA 94305, USA 5 Univ. Lille, CNRS, Institut Pasteur de Lille, UMR 8199–EGID, F-59000 Lille, France 4 Wellcome Trust Center for Human Genetics, and Oxford Center for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK 6 Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia 8 Department of Biology, Stanford University, Stanford, CA, USA 7 Imperial College, Department of Genomics of Common Disease, London Hammersmith Hospital, London, UK 1 Department of Genetics, Stanford University, Stanford, CA 94305, USA 2 Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA |
AuthorAffiliation_xml | – name: 7 Imperial College, Department of Genomics of Common Disease, London Hammersmith Hospital, London, UK – name: 5 Univ. Lille, CNRS, Institut Pasteur de Lille, UMR 8199–EGID, F-59000 Lille, France – name: 4 Wellcome Trust Center for Human Genetics, and Oxford Center for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK – name: 8 Department of Biology, Stanford University, Stanford, CA, USA – name: 2 Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA – name: 6 Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia – name: 1 Department of Genetics, Stanford University, Stanford, CA 94305, USA – name: 3 Program in Biomedical Informatics, Stanford University, Stanford, CA 94305, USA |
Author_xml | – sequence: 1 givenname: Yair surname: Field fullname: Field, Yair – sequence: 2 givenname: Evan A surname: Boyle fullname: Boyle, Evan A – sequence: 3 givenname: Natalie surname: Telis fullname: Telis, Natalie – sequence: 4 givenname: Ziyue surname: Gao fullname: Gao, Ziyue – sequence: 5 givenname: Kyle J. surname: Gaulton fullname: Gaulton, Kyle J. – sequence: 6 givenname: David surname: Golan fullname: Golan, David – sequence: 7 givenname: Loic surname: Yengo fullname: Yengo, Loic – sequence: 8 givenname: Ghislain surname: Rocheleau fullname: Rocheleau, Ghislain – sequence: 9 givenname: Philippe surname: Froguel fullname: Froguel, Philippe – sequence: 10 givenname: Mark I. surname: McCarthy fullname: McCarthy, Mark I. – sequence: 11 givenname: Jonathan K. surname: Pritchard fullname: Pritchard, Jonathan K. |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27738015$$D View this record in MEDLINE/PubMed |
BookMark | eNqNkktrGzEUhUVJaRy3665aBrrpZhK9H5tCSV-BQDfJWmhm7tgyY8mVNIX8-8q1E9pAIAsh0P3O4V7dc4ZOQgyA0FuCzwmh8iL3HkIP586tsFLyBVoQbERrKGYnaIExk63GSpyis5w3GNeaYa_QKVWKaUzEAukvUKAvPoYmjs163rrQuMHtivv7NszJh1VT1tDsXC4NrR7NHbiUX6OXo5syvDneS3T77evN5Y_2-uf3q8vP160TRpeWccokSNwBZ84NmJjaBJMdB0ak5kNXz4hdp5SQ_WjoiIForgzX3SjowNgSfTr47uZuC0MPoSQ32V3yW5fubHTe_l8Jfm1X8bcVRFOsSDX4eDRI8dcMuditzz1MkwsQ52yJVlRTyql4Biq54Nxg9QyUCU60qCtYog-P0E2cU6iftqc0lXu0Uu__nfNhwPtVVUAcgD7FnBOMtveHLdWx_WQJtvtI2GMk7DESVXfxSHdv_bTi3UGxySWmB5xzVcOlDfsDMJ_Csw |
CODEN | SCIEAS |
CitedBy_id | crossref_primary_10_1111_mec_16329 crossref_primary_10_1016_j_gde_2018_05_012 crossref_primary_10_1038_s41580_021_00354_w crossref_primary_10_1002_evl3_263 crossref_primary_10_1073_pnas_2200638119 crossref_primary_10_1093_g3journal_jkab139 crossref_primary_10_1016_j_scib_2023_08_027 crossref_primary_10_1038_nrg_2017_101 crossref_primary_10_1111_mec_14264 crossref_primary_10_1093_genetics_iyab161 crossref_primary_10_1038_s41588_018_0100_5 crossref_primary_10_1016_j_ajhg_2017_09_010 crossref_primary_10_1007_s00439_020_02152_4 crossref_primary_10_1016_j_ajhg_2020_05_014 crossref_primary_10_1111_evo_13792 crossref_primary_10_1093_gbe_evad032 crossref_primary_10_1038_s41467_018_03274_0 crossref_primary_10_3389_fgene_2021_703541 crossref_primary_10_1093_genetics_iyac009 crossref_primary_10_1016_j_gde_2020_06_003 crossref_primary_10_7554_eLife_48376 crossref_primary_10_1038_s41588_018_0059_2 crossref_primary_10_1093_gbe_evaa207 crossref_primary_10_1093_molbev_msx278 crossref_primary_10_1111_1755_0998_13415 crossref_primary_10_1093_cercor_bhaa327 crossref_primary_10_3390_genes13040708 crossref_primary_10_3389_fgene_2022_983646 crossref_primary_10_1186_s13059_021_02425_9 crossref_primary_10_1038_nature_2017_22914 crossref_primary_10_1093_molbev_msx154 crossref_primary_10_1016_j_cell_2017_05_038 crossref_primary_10_3389_fimmu_2021_691475 crossref_primary_10_1002_ajpa_23737 crossref_primary_10_1038_s41586_024_07721_5 crossref_primary_10_1093_molbev_msad157 crossref_primary_10_1038_s41588_018_0101_4 crossref_primary_10_1111_mec_16666 crossref_primary_10_1371_journal_pgen_1009562 crossref_primary_10_1038_s41467_022_34456_6 crossref_primary_10_1038_s41467_019_11112_0 crossref_primary_10_1111_1755_0998_13628 crossref_primary_10_1093_molbev_msx240 crossref_primary_10_1093_molbev_msae115 crossref_primary_10_7554_eLife_24284 crossref_primary_10_1002_evl3_97 crossref_primary_10_1038_s41576_018_0082_2 crossref_primary_10_1126_sciadv_adi8419 crossref_primary_10_1111_cobi_14202 crossref_primary_10_1073_pnas_1904159116 crossref_primary_10_1146_annurev_genom_083115_022316 crossref_primary_10_1093_bib_bbac354 crossref_primary_10_1016_j_ajhg_2023_12_014 crossref_primary_10_1111_evo_14432 crossref_primary_10_1038_s41598_023_35312_3 crossref_primary_10_7554_eLife_66697 crossref_primary_10_1007_s40806_018_0153_9 crossref_primary_10_1146_annurev_genet_111523_102651 crossref_primary_10_1038_s41586_021_03236_5 crossref_primary_10_1007_s00122_020_03668_z crossref_primary_10_1371_journal_pbio_2002458 crossref_primary_10_1073_pnas_1902766117 crossref_primary_10_1111_jfb_15243 crossref_primary_10_1038_s41431_021_00938_2 crossref_primary_10_1016_j_xhgg_2021_100071 crossref_primary_10_1093_molbev_msx103 crossref_primary_10_7554_eLife_63177 crossref_primary_10_1038_s41588_024_01841_4 crossref_primary_10_1016_j_cub_2023_08_021 crossref_primary_10_1186_s12859_023_05511_w crossref_primary_10_1534_genetics_119_302662 crossref_primary_10_1038_s41562_023_01528_6 crossref_primary_10_1534_g3_120_401285 crossref_primary_10_1371_journal_pgen_1009495 crossref_primary_10_1534_genetics_117_300489 crossref_primary_10_1038_s41586_024_08496_5 crossref_primary_10_1098_rstb_2020_0406 crossref_primary_10_1371_journal_pgen_1008035 crossref_primary_10_1136_annrheumdis_2019_216644 crossref_primary_10_1146_annurev_genet_021821_020805 crossref_primary_10_1038_s41588_019_0492_x crossref_primary_10_1093_molbev_msy201 crossref_primary_10_1007_s00439_019_02040_6 crossref_primary_10_1016_j_asoc_2022_109428 crossref_primary_10_3390_genes9070358 crossref_primary_10_1371_journal_pcbi_1011175 crossref_primary_10_7554_eLife_49898 crossref_primary_10_1093_molbev_msaa005 crossref_primary_10_1016_j_cell_2019_02_033 crossref_primary_10_1038_s41559_017_0167 crossref_primary_10_1007_s00251_017_1017_3 crossref_primary_10_1371_journal_pgen_1011312 crossref_primary_10_1093_nar_gkad1078 crossref_primary_10_1007_s00239_019_09902_7 crossref_primary_10_1038_nrg_2017_65 crossref_primary_10_7554_eLife_39725 crossref_primary_10_1093_molbev_msae053 crossref_primary_10_1093_gbe_evab272 crossref_primary_10_1038_s41431_020_0699_4 crossref_primary_10_1186_s12711_021_00688_1 crossref_primary_10_1093_g3journal_jkac319 crossref_primary_10_1093_molbev_msae034 crossref_primary_10_1038_s41562_022_01438_z crossref_primary_10_1098_rsos_210382 crossref_primary_10_1002_ajmg_c_31696 crossref_primary_10_1038_nmeth_4606 crossref_primary_10_1093_molbev_msaa115 crossref_primary_10_1093_nar_gkx626 crossref_primary_10_1038_s41586_023_06705_1 crossref_primary_10_1038_nature21347 crossref_primary_10_1186_s13073_018_0532_7 crossref_primary_10_1007_s42977_022_00146_z crossref_primary_10_3389_fphys_2021_696516 crossref_primary_10_1093_gbe_evaa073 crossref_primary_10_1093_bib_bby064 crossref_primary_10_1038_s41576_020_0250_z crossref_primary_10_1371_journal_pbio_3002469 crossref_primary_10_1038_s41598_017_05744_9 crossref_primary_10_1111_mec_16843 crossref_primary_10_1016_j_gde_2020_05_024 crossref_primary_10_3389_fgene_2017_00139 crossref_primary_10_1093_genetics_iyad060 crossref_primary_10_1371_journal_pgen_1007672 crossref_primary_10_1007_s10539_020_9741_8 crossref_primary_10_3389_fendo_2020_00107 crossref_primary_10_1038_s41588_018_0177_x crossref_primary_10_7554_eLife_39702 crossref_primary_10_1186_s12864_017_3964_3 crossref_primary_10_1093_g3journal_jkad035 crossref_primary_10_1093_molbev_msae192 crossref_primary_10_1038_s41576_018_0012_3 crossref_primary_10_1007_s40750_019_00111_6 crossref_primary_10_3389_fgene_2018_00462 crossref_primary_10_1084_jem_20161942 crossref_primary_10_7554_eLife_45380 crossref_primary_10_1093_molbev_msy092 crossref_primary_10_1111_1755_0998_13371 crossref_primary_10_1126_science_abi8206 crossref_primary_10_18699_VJGB_23_79 crossref_primary_10_1111_mec_15067 crossref_primary_10_1534_genetics_118_300786 crossref_primary_10_7717_peerj_9554 crossref_primary_10_1080_03014460_2021_1936634 crossref_primary_10_1073_pnas_2404393122 crossref_primary_10_1093_genetics_iyae024 crossref_primary_10_1111_age_13355 crossref_primary_10_1093_emph_eoy036 crossref_primary_10_1073_pnas_2117312119 crossref_primary_10_1038_s41467_022_30526_x crossref_primary_10_3389_fpsyg_2018_02343 crossref_primary_10_1266_ggs_18_00008 crossref_primary_10_1093_molbev_msy180 crossref_primary_10_24072_pci_evolbiol_100092 crossref_primary_10_1534_genetics_118_301786 crossref_primary_10_1038_s41467_017_00366_1 crossref_primary_10_1016_j_meegid_2017_08_021 crossref_primary_10_1080_03014460_2019_1644373 crossref_primary_10_1266_ggs_19_00012 crossref_primary_10_1093_gbe_evaa021 crossref_primary_10_3389_fgene_2021_714491 crossref_primary_10_1038_d41586_022_00693_4 crossref_primary_10_1093_molbev_msy053 crossref_primary_10_1002_ajpa_70010 crossref_primary_10_1007_s00439_022_02471_8 crossref_primary_10_1038_s41467_018_04191_y crossref_primary_10_3389_fcvm_2019_00127 crossref_primary_10_1111_mec_15169 crossref_primary_10_4000_bmsap_7460 crossref_primary_10_1093_gbe_evab237 crossref_primary_10_1371_journal_pgen_1010934 crossref_primary_10_1007_s13752_020_00350_x crossref_primary_10_1371_journal_pgen_1010134 crossref_primary_10_1016_j_gde_2020_05_037 crossref_primary_10_1038_ncomms15927 crossref_primary_10_1038_s41598_023_42578_0 crossref_primary_10_1111_age_13454 crossref_primary_10_1038_s41598_020_65322_4 crossref_primary_10_1016_j_tig_2017_04_002 crossref_primary_10_1371_journal_pgen_1010494 crossref_primary_10_1016_j_ajhg_2024_08_023 crossref_primary_10_1016_j_neubiorev_2024_105745 crossref_primary_10_1093_molbev_msad216 crossref_primary_10_1007_s00335_018_9746_7 crossref_primary_10_1016_j_ajhg_2020_12_005 crossref_primary_10_1016_j_isci_2024_110050 crossref_primary_10_1534_genetics_118_301687 crossref_primary_10_1101_gr_276862_122 crossref_primary_10_1098_rstb_2018_0248 crossref_primary_10_1126_science_abo0498 crossref_primary_10_1038_s41467_024_54052_0 crossref_primary_10_1007_s00439_020_02167_x crossref_primary_10_3390_life11080797 crossref_primary_10_1080_19485565_2019_1689351 crossref_primary_10_1016_j_bbr_2020_112521 crossref_primary_10_1038_s41588_019_0484_x crossref_primary_10_7554_eLife_82824 crossref_primary_10_3389_fgene_2021_643883 crossref_primary_10_1371_journal_pbio_2002985 crossref_primary_10_1016_j_tig_2019_12_008 crossref_primary_10_1111_obr_12625 crossref_primary_10_1093_hmg_ddab007 crossref_primary_10_1186_s13100_021_00250_2 crossref_primary_10_1038_s41562_021_01232_3 crossref_primary_10_1016_j_cell_2020_09_017 crossref_primary_10_1186_s12915_017_0434_y crossref_primary_10_1093_genetics_iyad139 crossref_primary_10_1111_mec_16900 crossref_primary_10_1186_s12864_024_10115_6 crossref_primary_10_1016_j_evolhumbehav_2021_08_001 crossref_primary_10_1016_j_tig_2020_03_006 crossref_primary_10_1242_dmm_049601 crossref_primary_10_3389_fpsyg_2019_02218 crossref_primary_10_1007_s11154_019_09505_z crossref_primary_10_1038_s41559_018_0773_2 crossref_primary_10_1073_pnas_2312377121 crossref_primary_10_1186_s12864_017_4262_9 crossref_primary_10_1534_g3_120_401304 crossref_primary_10_1016_j_gde_2020_05_004 crossref_primary_10_1038_s41586_021_03205_y crossref_primary_10_1073_pnas_2009227118 crossref_primary_10_1038_s41588_022_01062_7 crossref_primary_10_1007_s40200_021_00745_y crossref_primary_10_1080_19336934_2017_1337612 crossref_primary_10_1038_s41588_021_00931_x crossref_primary_10_1016_j_ajhg_2017_06_005 crossref_primary_10_1038_s41562_018_0476_3 crossref_primary_10_1007_s11914_019_00527_9 crossref_primary_10_1016_j_cub_2023_02_049 crossref_primary_10_1016_j_cois_2019_08_013 crossref_primary_10_1371_journal_pgen_1008773 crossref_primary_10_1038_s41366_018_0046_9 crossref_primary_10_1038_s41562_021_01231_4 crossref_primary_10_1098_rstb_2021_0243 crossref_primary_10_1371_journal_pbio_3001072 crossref_primary_10_3389_fgene_2022_833190 |
Cites_doi | 10.1023/A:1008800423698 10.1038/ng.608 10.1098/rstb.2010.0268 10.1016/j.cell.2007.10.055 10.1038/ng.3211 10.1093/bioinformatics/bti584 10.1136/bmjopen-2013-003735 10.1038/nature04072 10.1073/pnas.1316513111 10.1038/ng.386 10.1038/ng.2368 10.1101/058024 10.1186/1477-7827-8-115 10.1554/05-273.1 10.1371/journal.pgen.1000500 10.1210/er.2002-0019 10.1371/journal.pbio.0040072 10.1093/hmg/ddu150 10.1038/nature01140 10.1126/science.1116238 10.1073/pnas.1418652112 10.1093/bioinformatics/18.2.337 10.1101/gr.178756.114 10.1371/journal.pgen.1004412 10.1093/bioinformatics/btq322 10.1371/journal.pcbi.1000491 10.1093/molbev/msu049 10.1093/molbev/mss207 10.1093/genetics/134.4.1289 10.1101/038216 10.1371/journal.pgen.1004528 10.1038/ng.2007.13 10.1016/j.ajhg.2011.07.025 10.1038/ng.3406 10.1038/nature16152 10.1126/science.1217876 10.1146/annurev-genet-111212-133526 10.1159/000346826 10.1038/ng.3401 10.1126/science.1234070 10.1371/journal.pone.0006369 10.1016/j.cub.2009.11.055 10.1093/hmg/dds384 10.1371/journal.pgen.1000471 10.1086/421051 10.1534/genetics.104.036947 10.1371/journal.pgen.0030090 10.1371/journal.pcbi.1004842 10.1038/nature14962 10.1126/science.1190371 10.1093/molbev/msu211 10.1126/science.1219240 10.1111/j.1469-1809.2006.00341.x 10.1214/aos/1176347265 10.1038/ng.3097 10.1038/nature15393 10.1101/059436 10.1093/nar/gkt1229 10.1073/pnas.1310398110 10.1186/1471-2105-10-166 10.1038/nature11632 |
ContentType | Journal Article |
Copyright | Copyright © 2016 American Association for the Advancement of Science Copyright © 2016, American Association for the Advancement of Science. Copyright © 2016, American Association for the Advancement of Science |
Copyright_xml | – notice: Copyright © 2016 American Association for the Advancement of Science – notice: Copyright © 2016, American Association for the Advancement of Science. – notice: Copyright © 2016, American Association for the Advancement of Science |
DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QG 7QL 7QP 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7SS 7T7 7TA 7TB 7TK 7TM 7U5 7U9 8BQ 8FD C1K F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D M7N P64 RC3 7X8 5PM |
DOI | 10.1126/science.aag0776 |
DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Entomology Abstracts (Full archive) Industrial and Applied Microbiology Abstracts (Microbiology A) Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Nucleic Acids Abstracts Solid State and Superconductivity Abstracts Virology and AIDS Abstracts METADEX Technology Research Database Environmental Sciences and Pollution Management ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library AIDS and Cancer Research Abstracts Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Nucleic Acids Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Materials Business File Environmental Sciences and Pollution Management Aerospace Database Copper Technical Reference Library Engineered Materials Abstracts Genetics Abstracts Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) AIDS and Cancer Research Abstracts Chemoreception Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Virology and AIDS Abstracts Electronics & Communications Abstracts Ceramic Abstracts Ecology Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Entomology Abstracts Animal Behavior Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic |
DatabaseTitleList | Materials Research Database MEDLINE - Academic Ecology Abstracts Solid State and Superconductivity Abstracts MEDLINE CrossRef |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Sciences (General) Biology |
EISSN | 1095-9203 |
EndPage | 764 |
ExternalDocumentID | PMC5182071 4246316401 27738015 10_1126_science_aag0776 44710989 |
Genre | Journal Article Feature |
GrantInformation_xml | – fundername: NLM NIH HHS grantid: T15 LM007033 – fundername: NIMH NIH HHS grantid: R01 MH084703 – fundername: NHGRI NIH HHS grantid: R01 HG008140 – fundername: NIEHS NIH HHS grantid: R01 ES025009 – fundername: Howard Hughes Medical Institute – fundername: NHGRI NIH HHS grantid: T32 HG000044 – fundername: NIMH NIH HHS grantid: R01 MH101825 – fundername: NHGRI NIH HHS grantid: K22 HG000044 |
GroupedDBID | --- --Z -DZ -ET -~X .-4 ..I .55 .DC 08G 0R~ 0WA 123 18M 2FS 2KS 2WC 2XV 34G 36B 39C 3R3 53G 5RE 66. 6OB 6TJ 7X2 7~K 85S 8F7 AABCJ AACGO AAIKC AAMNW AANCE AAWTO AAYJJ ABBHK ABDBF ABDEX ABDQB ABEFU ABIVO ABJNI ABOCM ABPLY ABPMR ABPPZ ABQIJ ABTLG ABWJO ABXSQ ABZEH ACBEA ACBEC ACGFO ACGFS ACGOD ACHIC ACIWK ACMJI ACNCT ACPRK ACQOY ACUHS ADDRP ADMHC ADQXQ ADUKH ADULT ADXHL AEGBM AENEX AETEA AEUPB AEXZC AFBNE AFFDN AFFNX AFHKK AFQFN AFRAH AGFXO AGNAY AGSOS AHMBA AIDAL AIDUJ AJGZS ALIPV ALMA_UNASSIGNED_HOLDINGS ALSLI AQVQM ASPBG AVWKF BKF BLC C45 C51 CS3 DB2 DCCCD DU5 EBS EJD EMOBN F5P FA8 FEDTE HZ~ I.T IAO IEA IGS IH2 IHR INH INR IOF IOV IPO IPSME IPY ISE JAAYA JBMMH JCF JENOY JHFFW JKQEH JLS JLXEF JPM JSG JST KCC L7B LSO LU7 M0P MQT MVM N9A NEJ NHB O9- OCB OFXIZ OGEVE OMK OVD P-O P2P PQQKQ PZZ QS- RHI RXW SA0 SC5 SJN TAE TEORI TN5 TWZ UBW UCV UHB UKR UMD UNMZH UQL USG VVN WH7 WI4 X7M XJF XZL Y6R YK4 YKV YNT YOJ YR2 YR5 YRY YSQ YV5 YWH YYP YYQ YZZ ZCA ZE2 ~02 ~G0 ~KM ~ZZ AAYXX ABCQX CITATION K-O CGR CUY CVF ECM EIF NPM 7QF 7QG 7QL 7QP 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7SS 7T7 7TA 7TB 7TK 7TM 7U5 7U9 8BQ 8FD C1K F28 FR3 H8D H8G H94 JG9 JQ2 K9. KR7 L7M L~C L~D M7N P64 RC3 7X8 5PM |
ID | FETCH-LOGICAL-a598t-34236e60be43aad01900936b4e31684db84df0ab7756cf92f0e1847948bf52d33 |
ISSN | 0036-8075 1095-9203 |
IngestDate | Thu Aug 21 14:09:10 EDT 2025 Thu Jul 10 20:18:16 EDT 2025 Fri Jul 11 05:14:15 EDT 2025 Fri Jul 11 01:23:18 EDT 2025 Fri Jul 25 10:12:11 EDT 2025 Mon Jul 21 06:01:38 EDT 2025 Tue Jul 01 00:37:22 EDT 2025 Thu Apr 24 23:04:05 EDT 2025 Thu Jul 03 22:16:58 EDT 2025 |
IsDoiOpenAccess | false |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 6313 |
Language | English |
License | Copyright © 2016, American Association for the Advancement of Science. |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-a598t-34236e60be43aad01900936b4e31684db84df0ab7756cf92f0e1847948bf52d33 |
Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work. |
OpenAccessLink | https://hdl.handle.net/1721.1/124745 |
PMID | 27738015 |
PQID | 1838268354 |
PQPubID | 1256 |
PageCount | 5 |
ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_5182071 proquest_miscellaneous_1872822425 proquest_miscellaneous_1864544907 proquest_miscellaneous_1835418500 proquest_journals_1838268354 pubmed_primary_27738015 crossref_citationtrail_10_1126_science_aag0776 crossref_primary_10_1126_science_aag0776 jstor_primary_44710989 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2016-11-11 |
PublicationDateYYYYMMDD | 2016-11-11 |
PublicationDate_xml | – month: 11 year: 2016 text: 2016-11-11 day: 11 |
PublicationDecade | 2010 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: Washington |
PublicationTitle | Science (American Association for the Advancement of Science) |
PublicationTitleAlternate | Science |
PublicationYear | 2016 |
Publisher | American Association for the Advancement of Science The American Association for the Advancement of Science |
Publisher_xml | – name: American Association for the Advancement of Science – name: The American Association for the Advancement of Science |
References | e_1_3_2_26_2 e_1_3_2_49_2 e_1_3_2_28_2 e_1_3_2_41_2 e_1_3_2_64_2 e_1_3_2_20_2 e_1_3_2_43_2 e_1_3_2_62_2 e_1_3_2_22_2 e_1_3_2_45_2 e_1_3_2_24_2 e_1_3_2_47_2 e_1_3_2_60_2 e_1_3_2_9_2 e_1_3_2_16_2 e_1_3_2_37_2 e_1_3_2_7_2 e_1_3_2_18_2 e_1_3_2_39_2 e_1_3_2_54_2 e_1_3_2_10_2 e_1_3_2_31_2 e_1_3_2_52_2 e_1_3_2_5_2 e_1_3_2_12_2 e_1_3_2_33_2 e_1_3_2_58_2 e_1_3_2_3_2 e_1_3_2_35_2 e_1_3_2_56_2 e_1_3_2_50_2 e_1_3_2_27_2 e_1_3_2_48_2 e_1_3_2_29_2 e_1_3_2_40_2 e_1_3_2_21_2 e_1_3_2_42_2 e_1_3_2_63_2 e_1_3_2_23_2 e_1_3_2_44_2 e_1_3_2_25_2 e_1_3_2_46_2 e_1_3_2_61_2 e_1_3_2_15_2 e_1_3_2_38_2 e_1_3_2_8_2 e_1_3_2_17_2 e_1_3_2_59_2 e_1_3_2_6_2 e_1_3_2_19_2 e_1_3_2_30_2 e_1_3_2_53_2 e_1_3_2_32_2 e_1_3_2_51_2 e_1_3_2_11_2 e_1_3_2_34_2 e_1_3_2_57_2 e_1_3_2_4_2 e_1_3_2_13_2 e_1_3_2_36_2 e_1_3_2_55_2 e_1_3_2_2_2 29094708 - Nature. 2017 Oct 30;551(7678):15-16. doi: 10.1038/nature.2017.22914. |
References_xml | – ident: e_1_3_2_55_2 doi: 10.1023/A:1008800423698 – ident: e_1_3_2_23_2 doi: 10.1038/ng.608 – ident: e_1_3_2_44_2 doi: 10.1098/rstb.2010.0268 – ident: e_1_3_2_52_2 doi: 10.1016/j.cell.2007.10.055 – ident: e_1_3_2_25_2 doi: 10.1038/ng.3211 – ident: e_1_3_2_32_2 doi: 10.1093/bioinformatics/bti584 – ident: e_1_3_2_61_2 doi: 10.1136/bmjopen-2013-003735 – ident: e_1_3_2_48_2 doi: 10.1038/nature04072 – ident: e_1_3_2_12_2 doi: 10.1073/pnas.1316513111 – ident: e_1_3_2_58_2 doi: 10.1038/ng.386 – ident: e_1_3_2_10_2 doi: 10.1038/ng.2368 – ident: e_1_3_2_41_2 doi: 10.1101/058024 – ident: e_1_3_2_60_2 doi: 10.1186/1477-7827-8-115 – ident: e_1_3_2_8_2 doi: 10.1554/05-273.1 – ident: e_1_3_2_51_2 doi: 10.1371/journal.pgen.1000500 – ident: e_1_3_2_59_2 doi: 10.1210/er.2002-0019 – ident: e_1_3_2_6_2 doi: 10.1371/journal.pbio.0040072 – ident: e_1_3_2_57_2 doi: 10.1093/hmg/ddu150 – ident: e_1_3_2_28_2 doi: 10.1038/nature01140 – ident: e_1_3_2_4_2 doi: 10.1126/science.1116238 – ident: e_1_3_2_30_2 doi: 10.1073/pnas.1418652112 – ident: e_1_3_2_31_2 doi: 10.1093/bioinformatics/18.2.337 – ident: e_1_3_2_62_2 doi: 10.1101/gr.178756.114 – ident: e_1_3_2_20_2 doi: 10.1371/journal.pgen.1004412 – ident: e_1_3_2_35_2 doi: 10.1093/bioinformatics/btq322 – ident: e_1_3_2_43_2 doi: 10.1371/journal.pcbi.1000491 – ident: e_1_3_2_46_2 doi: 10.1093/molbev/msu049 – ident: e_1_3_2_53_2 doi: 10.1093/molbev/mss207 – ident: e_1_3_2_64_2 doi: 10.1093/genetics/134.4.1289 – ident: e_1_3_2_24_2 doi: 10.1101/038216 – ident: e_1_3_2_29_2 doi: 10.1371/journal.pgen.1004528 – ident: e_1_3_2_47_2 doi: 10.1038/ng.2007.13 – ident: e_1_3_2_11_2 doi: 10.1016/j.ajhg.2011.07.025 – ident: e_1_3_2_26_2 doi: 10.1038/ng.3406 – ident: e_1_3_2_13_2 doi: 10.1038/nature16152 – ident: e_1_3_2_39_2 doi: 10.1126/science.1217876 – ident: e_1_3_2_2_2 doi: 10.1146/annurev-genet-111212-133526 – ident: e_1_3_2_63_2 doi: 10.1159/000346826 – ident: e_1_3_2_21_2 doi: 10.1038/ng.3401 – ident: e_1_3_2_18_2 doi: 10.1126/science.1234070 – ident: e_1_3_2_45_2 doi: 10.1371/journal.pone.0006369 – ident: e_1_3_2_9_2 doi: 10.1016/j.cub.2009.11.055 – ident: e_1_3_2_17_2 doi: 10.1093/hmg/dds384 – ident: e_1_3_2_56_2 doi: 10.1371/journal.pgen.1000471 – ident: e_1_3_2_3_2 doi: 10.1086/421051 – ident: e_1_3_2_7_2 doi: 10.1534/genetics.104.036947 – ident: e_1_3_2_50_2 doi: 10.1371/journal.pgen.0030090 – ident: e_1_3_2_34_2 doi: 10.1371/journal.pcbi.1004842 – ident: e_1_3_2_16_2 doi: 10.1038/nature14962 – ident: e_1_3_2_5_2 doi: 10.1126/science.1190371 – ident: e_1_3_2_37_2 doi: 10.1093/molbev/msu211 – ident: e_1_3_2_15_2 doi: 10.1126/science.1219240 – ident: e_1_3_2_27_2 – ident: e_1_3_2_49_2 doi: 10.1111/j.1469-1809.2006.00341.x – ident: e_1_3_2_54_2 doi: 10.1214/aos/1176347265 – ident: e_1_3_2_22_2 doi: 10.1038/ng.3097 – ident: e_1_3_2_38_2 doi: 10.1038/nature15393 – ident: e_1_3_2_42_2 doi: 10.1101/059436 – ident: e_1_3_2_19_2 doi: 10.1093/nar/gkt1229 – ident: e_1_3_2_40_2 doi: 10.1073/pnas.1310398110 – ident: e_1_3_2_33_2 doi: 10.1186/1471-2105-10-166 – ident: e_1_3_2_36_2 doi: 10.1038/nature11632 – reference: 29094708 - Nature. 2017 Oct 30;551(7678):15-16. doi: 10.1038/nature.2017.22914. |
SSID | ssj0009593 |
Score | 2.6275594 |
Snippet | Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer... Evolutionary analyses aim to identify recent genetic changes that are likely to have been subject to selection. Field et al. present a method to identify such... Evolutionary analyses aim to identify recent genetic changes that are likely to have been subject to selection. Field et al. present a method to identify such... Identifying genes under recent selectionEvolutionary analyses aim to identify recent genetic changes that are likely to have been subject to selection. Field... |
SourceID | pubmedcentral proquest pubmed crossref jstor |
SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 760 |
SubjectTerms | Adaptation Adaptation, Physiological - genetics Density Evolutionary biology Eye Color - genetics Frequency shift Gene Frequency Genes Genetic Loci Genetic variance Genetics Genome, Human Genome-Wide Association Study Genomes Hair Hair Color - genetics Haplotypes Humans - genetics Lactase Lactase - genetics Major Histocompatibility Complex - genetics Pedigree Phenotypic variations Pigmentation Population genetics Selection, Genetic United Kingdom |
Title | Detection of human adaptation during the past 2000 years |
URI | https://www.jstor.org/stable/44710989 https://www.ncbi.nlm.nih.gov/pubmed/27738015 https://www.proquest.com/docview/1838268354 https://www.proquest.com/docview/1835418500 https://www.proquest.com/docview/1864544907 https://www.proquest.com/docview/1872822425 https://pubmed.ncbi.nlm.nih.gov/PMC5182071 |
Volume | 354 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fb9MwELZgExIviA0GgYGMxMMQSpXGiZ08tmxlQqO8tFLhJbITe1RC6UTTh_LXc_4RJ4VtGjw0qmKnqXyfz-fz3XcIveWKE5UKEUoQQZjkVRRyAcqQpnlE84pxZRz6n6f0fJ58WqSLLvfEZJc0YlD-ujav5H-kCvdArjpL9h8k638UbsB3kC9cQcJwvZOMT2UjS2_yGXc8r_iVCyB0GYgmF4qvG00eGL3fArLXfYu0ndxgafrTm57MfBjiyAYLtLED7rGeI2HSFrv-ypc-4ne82tp4ZW2xd37TmXRl2Kfae7T04PrIjef223K7kX1_xJDqxDynL52OdRTHfR1LLFO0AxMlQ9JTmsxWFHDrL7Os5n-r9l4xSjng_FITEXWrWHtyP_1STOYXF8XsbDG7j_Zj2D2Avt4fjU_HkxvZmB3nUy-bqn3BjrliI1av24v8GVLbs1Fmj9Ejt7nAI4uUA3RP1ofogS03uj1EB05oa3zi2MbfPUGZBxFeKWxAhDsQYQsiDAjAGkRYgwgbED1F88nZ7MN56MpphDzNsybUXI9U0kjIhHBeaRKBKCdUJFIXL0sqAR8VccFYSkuVxyqSsP0HfZ0JlcYVIUdor17V8jnCJad5qohkTIikpGVGKypZxakaErBgywAN2lErSsc1r0ue_CjMnjOmhRvmwg1zgE78A1eWZuXmrkdGDL5fkuh44iwP0HErl8JN0nUBKxZsoLV3M0BvfDOoUH0uxmu52pg-qeZwiqLb-mjquySP2G19mAnKjtMAPbNw8H8ScEjAGIQWtgMU30HTvO-21Mvvhu491TUW2PDFHd77Ej3spuQx2mt-buQrMJob8dpNgd-Qb8P4 |
linkProvider | EBSCOhost |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detection+of+human+adaptation+during+the+past+2000+years&rft.jtitle=Science+%28American+Association+for+the+Advancement+of+Science%29&rft.au=Field%2C+Yair&rft.au=Boyle%2C+Evan+A&rft.au=Telis%2C+Natalie&rft.au=Gao%2C+Ziyue&rft.date=2016-11-11&rft.issn=0036-8075&rft.volume=354&rft.issue=6313&rft.spage=760&rft.epage=764&rft_id=info:doi/10.1126%2Fscience.aag0776&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0036-8075&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0036-8075&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0036-8075&client=summon |