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• W2225153589 abstract "Human genes governing innate immunity provide a valuable tool for the study of the selective pressure imposed by microorganisms on host genomes. A comprehensive, genome-wide study of how selective constraints and adaptations have driven the evolution of innate immunity genes is missing. Using full-genome sequence variation from the 1000 Genomes Project, we first show that innate immunity genes have globally evolved under stronger purifying selection than the remainder of protein-coding genes. We identify a gene set under the strongest selective constraints, mutations in which are likely to predispose individuals to life-threatening disease, as illustrated by STAT1 and TRAF3. We then evaluate the occurrence of local adaptation and detect 57 high-scoring signals of positive selection at innate immunity genes, variation in which has been associated with susceptibility to common infectious or autoimmune diseases. Furthermore, we show that most adaptations targeting coding variation have occurred in the last 6,000–13,000 years, the period at which populations shifted from hunting and gathering to farming. Finally, we show that innate immunity genes present higher Neandertal introgression than the remainder of the coding genome. Notably, among the genes presenting the highest Neandertal ancestry, we find the TLR6-TLR1-TLR10 cluster, which also contains functional adaptive variation in Europeans. This study identifies highly constrained genes that fulfill essential, non-redundant functions in host survival and reveals others that are more permissive to change—containing variation acquired from archaic hominins or adaptive variants in specific populations—improving our understanding of the relative biological importance of innate immunity pathways in natural conditions. Human genes governing innate immunity provide a valuable tool for the study of the selective pressure imposed by microorganisms on host genomes. A comprehensive, genome-wide study of how selective constraints and adaptations have driven the evolution of innate immunity genes is missing. Using full-genome sequence variation from the 1000 Genomes Project, we first show that innate immunity genes have globally evolved under stronger purifying selection than the remainder of protein-coding genes. We identify a gene set under the strongest selective constraints, mutations in which are likely to predispose individuals to life-threatening disease, as illustrated by STAT1 and TRAF3. We then evaluate the occurrence of local adaptation and detect 57 high-scoring signals of positive selection at innate immunity genes, variation in which has been associated with susceptibility to common infectious or autoimmune diseases. Furthermore, we show that most adaptations targeting coding variation have occurred in the last 6,000–13,000 years, the period at which populations shifted from hunting and gathering to farming. Finally, we show that innate immunity genes present higher Neandertal introgression than the remainder of the coding genome. Notably, among the genes presenting the highest Neandertal ancestry, we find the TLR6-TLR1-TLR10 cluster, which also contains functional adaptive variation in Europeans. This study identifies highly constrained genes that fulfill essential, non-redundant functions in host survival and reveals others that are more permissive to change—containing variation acquired from archaic hominins or adaptive variants in specific populations—improving our understanding of the relative biological importance of innate immunity pathways in natural conditions. The burden of infectious diseases has been massive throughout human history, particularly before the advent of hygiene, vaccines, antiseptics, and antibiotics, when human populations were ravaged by illnesses that resulted in high childhood mortality and short life expectancy.1Casanova J.L. Abel L. Inborn errors of immunity to infection: the rule rather than the exception.J. Exp. Med. 2005; 202: 197-201Crossref PubMed Scopus (147) Google Scholar In light of this, and given that the human genetic makeup strongly influences an individual’s susceptibility to infectious disease and the resulting clinical outcome,2Casanova J.L. Abel L. Quintana-Murci L. Immunology taught by human genetics.Cold Spring Harb. Symp. Quant. Biol. 2013; 78: 157-172Crossref PubMed Scopus (54) Google Scholar, 3Chapman S.J. Hill A.V. Human genetic susceptibility to infectious disease.Nat. Rev. Genet. 2012; 13: 175-188PubMed Google Scholar natural selection imposed by pathogens is expected to have profoundly affected the patterns of variability of the human genome.4Barreiro L.B. Quintana-Murci L. From evolutionary genetics to human immunology: how selection shapes host defence genes.Nat. Rev. Genet. 2010; 11: 17-30Crossref PubMed Scopus (359) Google Scholar, 5Grossman S.R. Andersen K.G. Shlyakhter I. Tabrizi S. Winnicki S. Yen A. Park D.J. Griesemer D. Karlsson E.K. Wong S.H. et al.1000 Genomes ProjectIdentifying recent adaptations in large-scale genomic data.Cell. 2013; 152: 703-713Abstract Full Text Full Text PDF PubMed Scopus (238) Google Scholar, 6Fumagalli M. Sironi M. Human genome variability, natural selection and infectious diseases.Curr. Opin. Immunol. 2014; 30: 9-16Crossref PubMed Scopus (51) Google Scholar, 7Karlsson E.K. Kwiatkowski D.P. Sabeti P.C. Natural selection and infectious disease in human populations.Nat. Rev. Genet. 2014; 15: 379-393Crossref PubMed Scopus (263) Google Scholar Indeed, interspecies analyses and within-species studies in humans have established that purifying and positive selection have been pervasive among both genes and functions related to immunity and host defense.5Grossman S.R. Andersen K.G. Shlyakhter I. Tabrizi S. Winnicki S. Yen A. Park D.J. Griesemer D. Karlsson E.K. Wong S.H. et al.1000 Genomes ProjectIdentifying recent adaptations in large-scale genomic data.Cell. 2013; 152: 703-713Abstract Full Text Full Text PDF PubMed Scopus (238) Google Scholar, 8Barreiro L.B. Laval G. Quach H. Patin E. Quintana-Murci L. Natural selection has driven population differentiation in modern humans.Nat. Genet. 2008; 40: 340-345Crossref PubMed Scopus (429) Google Scholar, 9Bustamante C.D. Fledel-Alon A. Williamson S. Nielsen R. Hubisz M.T. Glanowski S. Tanenbaum D.M. White T.J. Sninsky J.J. Hernandez R.D. et al.Natural selection on protein-coding genes in the human genome.Nature. 2005; 437: 1153-1157Crossref PubMed Scopus (595) Google Scholar, 10Leffler E.M. Gao Z. Pfeifer S. Ségurel L. Auton A. Venn O. Bowden R. Bontrop R. Wall J.D. Sella G. et al.Multiple instances of ancient balancing selection shared between humans and chimpanzees.Science. 2013; 339: 1578-1582Crossref PubMed Scopus (190) Google Scholar, 11Nielsen R. Hellmann I. Hubisz M. Bustamante C. Clark A.G. Recent and ongoing selection in the human genome.Nat. Rev. Genet. 2007; 8: 857-868Crossref PubMed Scopus (368) Google Scholar, 12Sabeti P.C. Varilly P. Fry B. Lohmueller J. Hostetter E. Cotsapas C. Xie X. Byrne E.H. McCarroll S.A. Gaudet R. et al.International HapMap ConsortiumGenome-wide detection and characterization of positive selection in human populations.Nature. 2007; 449: 913-918Crossref PubMed Scopus (1332) Google Scholar, 13Voight B.F. Kudaravalli S. Wen X. Pritchard J.K. A map of recent positive selection in the human genome.PLoS Biol. 2006; 4: e72Crossref PubMed Scopus (261) Google Scholar, 14Barreiro L.B. Ben-Ali M. Quach H. Laval G. Patin E. Pickrell J.K. Bouchier C. Tichit M. Neyrolles O. Gicquel B. et al.Evolutionary dynamics of human Toll-like receptors and their different contributions to host defense.PLoS Genet. 2009; 5: e1000562Crossref PubMed Scopus (289) Google Scholar Furthermore, pathogen pressure is increasingly recognized as the underlying cause of such selection signatures, with many immunity-related genes presenting patterns of variation that strongly correlate with pathogen diversity.15Fumagalli M. Sironi M. Pozzoli U. Ferrer-Admetlla A. Pattini L. Nielsen R. Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution.PLoS Genet. 2011; 7: e1002355Crossref PubMed Scopus (372) Google Scholar Over recent decades, the dissection of the form and intensity of selection in the human genome has established the value of population genetics as a complement to clinical and epidemiological genetic studies, in delineating the biological relevance of immunity genes in natura and in predicting their involvement in disease.2Casanova J.L. Abel L. Quintana-Murci L. Immunology taught by human genetics.Cold Spring Harb. Symp. Quant. Biol. 2013; 78: 157-172Crossref PubMed Scopus (54) Google Scholar, 4Barreiro L.B. Quintana-Murci L. From evolutionary genetics to human immunology: how selection shapes host defence genes.Nat. Rev. Genet. 2010; 11: 17-30Crossref PubMed Scopus (359) Google Scholar, 7Karlsson E.K. Kwiatkowski D.P. Sabeti P.C. Natural selection and infectious disease in human populations.Nat. Rev. Genet. 2014; 15: 379-393Crossref PubMed Scopus (263) Google Scholar, 16Alcaïs A. Quintana-Murci L. Thaler D.S. Schurr E. Abel L. Casanova J.L. Life-threatening infectious diseases of childhood: single-gene inborn errors of immunity?.Ann. N Y Acad. Sci. 2010; 1214: 18-33Crossref PubMed Scopus (138) Google Scholar Genes evolving under strong purifying selection are predicted to include those involved in essential mechanisms of host defense, variation in which should lead to severe disorders.16Alcaïs A. Quintana-Murci L. Thaler D.S. Schurr E. Abel L. Casanova J.L. Life-threatening infectious diseases of childhood: single-gene inborn errors of immunity?.Ann. N Y Acad. Sci. 2010; 1214: 18-33Crossref PubMed Scopus (138) Google Scholar This prediction is supported by genome-wide data, because Mendelian disease genes are enriched in signals of purifying selection.8Barreiro L.B. Laval G. Quach H. Patin E. Quintana-Murci L. Natural selection has driven population differentiation in modern humans.Nat. Genet. 2008; 40: 340-345Crossref PubMed Scopus (429) Google Scholar, 9Bustamante C.D. Fledel-Alon A. Williamson S. Nielsen R. Hubisz M.T. Glanowski S. Tanenbaum D.M. White T.J. Sninsky J.J. Hernandez R.D. et al.Natural selection on protein-coding genes in the human genome.Nature. 2005; 437: 1153-1157Crossref PubMed Scopus (595) Google Scholar, 17Blekhman R. Man O. Herrmann L. Boyko A.R. Indap A. Kosiol C. Bustamante C.D. Teshima K.M. Przeworski M. Natural selection on genes that underlie human disease susceptibility.Curr. Biol. 2008; 18: 883-889Abstract Full Text Full Text PDF PubMed Scopus (164) Google Scholar Conversely, genes evolving adaptively—through positive or balancing selection (e.g., HBB [MIM: 141900], DARC [MIM: 613665], FUT2 [MIM: 182100], the HLA locus genes, ABO blood group genes, and TRIM5 [MIM: 608487])—are usually more permissive to functional variation, which can exert a protective effect against infections.2Casanova J.L. Abel L. Quintana-Murci L. Immunology taught by human genetics.Cold Spring Harb. Symp. Quant. Biol. 2013; 78: 157-172Crossref PubMed Scopus (54) Google Scholar, 4Barreiro L.B. Quintana-Murci L. From evolutionary genetics to human immunology: how selection shapes host defence genes.Nat. Rev. Genet. 2010; 11: 17-30Crossref PubMed Scopus (359) Google Scholar, 7Karlsson E.K. Kwiatkowski D.P. Sabeti P.C. Natural selection and infectious disease in human populations.Nat. Rev. Genet. 2014; 15: 379-393Crossref PubMed Scopus (263) Google Scholar, 18Key F.M. Teixeira J.C. de Filippo C. Andrés A.M. Advantageous diversity maintained by balancing selection in humans.Curr. Opin. Genet. Dev. 2014; 29: 45-51Crossref PubMed Scopus (64) Google Scholar These signals of adaptive evolution in immune-related genes, tending to be recent and population specific, further emphasize the important role of pathogens in local adaptation. Besides the occurrence of novel mutations, functional variants transmitted through admixture represent another potential source of adaptive variation. Recent data provided evidence that 1%–6% of modern Eurasian genomes were inherited from ancient hominins, such as Neandertals or Denisovans,19Green R.E. Krause J. Briggs A.W. Maricic T. Stenzel U. Kircher M. Patterson N. Li H. Zhai W. Fritz M.H. et al.A draft sequence of the Neandertal genome.Science. 2010; 328: 710-722Crossref PubMed Scopus (2485) Google Scholar, 20Meyer M. Kircher M. Gansauge M.T. Li H. Racimo F. Mallick S. Schraiber J.G. Jay F. Prüfer K. de Filippo C. et al.A high-coverage genome sequence from an archaic Denisovan individual.Science. 2012; 338: 222-226Crossref PubMed Scopus (1139) Google Scholar, 21Reich D. Green R.E. Kircher M. Krause J. Patterson N. Durand E.Y. Viola B. Briggs A.W. Stenzel U. Johnson P.L. et al.Genetic history of an archaic hominin group from Denisova Cave in Siberia.Nature. 2010; 468: 1053-1060Crossref PubMed Scopus (1141) Google Scholar with specific genomic regions presenting up to 64% of Neandertal ancestry.22Sankararaman S. Mallick S. Dannemann M. Prüfer K. Kelso J. Pääbo S. Patterson N. Reich D. The genomic landscape of Neanderthal ancestry in present-day humans.Nature. 2014; 507: 354-357Crossref PubMed Scopus (573) Google Scholar In the context of immunity, there is increasing evidence to suggest that modern humans have acquired advantageous variation through admixture with ancient hominins, as documented by candidate gene approaches for HLA class I genes, STAT2 (MIM: 600556), or the OAS gene cluster (MIM: 164350, 603350).23Abi-Rached L. Jobin M.J. Kulkarni S. McWhinnie A. Dalva K. Gragert L. Babrzadeh F. Gharizadeh B. Luo M. Plummer F.A. et al.The shaping of modern human immune systems by multiregional admixture with archaic humans.Science. 2011; 334: 89-94Crossref PubMed Scopus (337) Google Scholar, 24Mendez F.L. Watkins J.C. Hammer M.F. A haplotype at STAT2 introgressed from neanderthals and serves as a candidate of positive selection in Papua New Guinea.Am. J. Hum. Genet. 2012; 91: 265-274Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar, 25Mendez F.L. Watkins J.C. Hammer M.F. Neandertal origin of genetic variation at the cluster of OAS immunity genes.Mol. Biol. Evol. 2013; 30: 798-801Crossref PubMed Scopus (66) Google Scholar Among the two arms that form the immune system, innate immunity constitutes the front line of host defense and provides a valuable model for the study of the selective pressure imposed by microorganisms—pathogenic and symbiotic—on host genomes.2Casanova J.L. Abel L. Quintana-Murci L. Immunology taught by human genetics.Cold Spring Harb. Symp. Quant. Biol. 2013; 78: 157-172Crossref PubMed Scopus (54) Google Scholar, 26Quintana-Murci L. Clark A.G. Population genetic tools for dissecting innate immunity in humans.Nat. Rev. Immunol. 2013; 13: 280-293Crossref PubMed Scopus (88) Google Scholar Innate immunity relies on receptors that sense conserved microbial patterns or molecules and activate signaling pathways that involve the coordinated action of an array of downstream molecules, including adaptors, regulators, transcription factors, and effector molecules, all of which are required for the eradication of pathogens and to maintain homeostasis.27Takeuchi O. Akira S. Pattern recognition receptors and inflammation.Cell. 2010; 140: 805-820Abstract Full Text Full Text PDF PubMed Scopus (5739) Google Scholar Importantly, unlike adaptive immunity whose parameter variation is mostly somatic and presents limited heritability, variation of innate immunity is germline encoded and thus needs to be best-adapted to ensure host survival.28Brodin P. Jojic V. Gao T. Bhattacharya S. Angel C.J. Furman D. Shen-Orr S. Dekker C.L. Swan G.E. Butte A.J. et al.Variation in the human immune system is largely driven by non-heritable influences.Cell. 2015; 160: 37-47Abstract Full Text Full Text PDF PubMed Scopus (622) Google Scholar, 29Casanova J.L. Abel L. Disentangling inborn and acquired immunity in human twins.Cell. 2015; 160: 13-15Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar Population genetic studies have shown that the impact of selection on some families of innate immune receptors and downstream signaling molecules (e.g., Toll-like receptors, interferons, or antimicrobial peptides) varies considerably,14Barreiro L.B. Ben-Ali M. Quach H. Laval G. Patin E. Pickrell J.K. Bouchier C. Tichit M. Neyrolles O. Gicquel B. et al.Evolutionary dynamics of human Toll-like receptors and their different contributions to host defense.PLoS Genet. 2009; 5: e1000562Crossref PubMed Scopus (289) Google Scholar, 30Mukherjee S. Sarkar-Roy N. Wagener D.K. Majumder P.P. 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Casanova J.L. et al.Evolutionary genetic dissection of human interferons.J. Exp. Med. 2011; 208: 2747-2759Crossref PubMed Scopus (128) Google Scholar, 38Vasseur E. Patin E. Laval G. Pajon S. Fornarino S. Crouau-Roy B. Quintana-Murci L. The selective footprints of viral pressures at the human RIG-I-like receptor family.Hum. Mol. Genet. 2011; 20: 4462-4474Crossref PubMed Scopus (37) Google Scholar, 39Vasseur E. Boniotto M. Patin E. Laval G. Quach H. Manry J. Crouau-Roy B. Quintana-Murci L. The evolutionary landscape of cytosolic microbial sensors in humans.Am. J. Hum. Genet. 2012; 91: 27-37Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar, 40Hollox E.J. Armour J.A. Directional and balancing selection in human beta-defensins.BMC Evol. Biol. 2008; 8: 113Crossref PubMed Scopus (54) Google Scholar, 41Laayouni H. Oosting M. Luisi P. Ioana M. Alonso S. Ricaño-Ponce I. Trynka G. Zhernakova A. Plantinga T.S. Cheng S.C. et al.Convergent evolution in European and Rroma populations reveals pressure exerted by plague on Toll-like receptors.Proc. Natl. Acad. Sci. USA. 2014; 111: 2668-2673Crossref PubMed Scopus (68) Google Scholar, 42Casals F. Sikora M. Laayouni H. Montanucci L. Muntasell A. Lazarus R. Calafell F. Awadalla P. Netea M.G. Bertranpetit J. Genetic adaptation of the antibacterial human innate immunity network.BMC Evol. Biol. 2011; 11: 202Crossref PubMed Scopus (20) Google Scholar, 43Cagliani R. Forni D. Biasin M. Comabella M. Guerini F.R. Riva S. Pozzoli U. Agliardi C. Caputo D. Malhotra S. et al.Ancient and recent selective pressures shaped genetic diversity at AIM2-like nucleic acid sensors.Genome Biol. Evol. 2014; 6: 830-845Crossref PubMed Scopus (24) Google Scholar helping to delineate the relative functional importance of different immune pathways.2Casanova J.L. Abel L. Quintana-Murci L. Immunology taught by human genetics.Cold Spring Harb. Symp. Quant. Biol. 2013; 78: 157-172Crossref PubMed Scopus (54) Google Scholar, 26Quintana-Murci L. Clark A.G. Population genetic tools for dissecting innate immunity in humans.Nat. Rev. Immunol. 2013; 13: 280-293Crossref PubMed Scopus (88) Google Scholar However, these studies have focused on specific candidate genes or gene families. A comprehensive, genome-wide view of how selection has driven the evolution of innate immunity in humans is thus missing. Here, we took advantage of population whole-genome sequence data to increase our understanding of the degree of essentiality and adaptability of the different genes governing innate immunity and thus, to provide novel insights into their respective biological relevance in host survival. To do so, we first created a hand-curated list of more than 1,500 genes belonging to the different modules constituting the innate immune system in humans (Material and Methods). We then analyzed their patterns of population genetic variability, which we compared to the remainder of the genome, using the 1000 Genomes Project dataset,44Abecasis G.R. Auton A. Brooks L.D. DePristo M.A. Durbin R.M. Handsaker R.E. Kang H.M. Marth G.T. McVean G.A. 1000 Genomes Project ConsortiumAn integrated map of genetic variation from 1,092 human genomes.Nature. 2012; 491: 56-65Crossref PubMed Scopus (5691) Google Scholar allowing us to evaluate the occurrence and intensity of constraint and adaptation to geographic and environmental pressures with an unprecedented level of resolution. Finally, we estimated the time range at which the bulk of genetic adaptation involving innate immunity has occurred and evaluated the extent to which human populations have acquired innate immunity genetic variation through admixture with Neandertals. We created a hand-curated list of innate immunity genes (IIGs) by combining two public databases, Gene Ontology (GO)45Ashburner M. Ball C.A. Blake J.A. Botstein D. Butler H. Cherry J.M. Davis A.P. Dolinski K. Dwight S.S. Eppig J.T. et al.The Gene Ontology ConsortiumGene ontology: tool for the unification of biology.Nat. Genet. 2000; 25: 25-29Crossref PubMed Scopus (27171) Google Scholar and InnateDB,46Breuer K. Foroushani A.K. Laird M.R. Chen C. Sribnaia A. Lo R. Winsor G.L. Hancock R.E. Brinkman F.S. Lynn D.J. InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation.Nucleic Acids Res. 2013; 41: D1228-D1233Crossref PubMed Scopus (706) Google Scholar as well as by incorporating missing entries. Specifically, we used the GO term “innate immune response” (GO: 0045087) to extract 1,309 entries corresponding to 884 unique annotations (last access January 2015). We removed all non-human taxon entries, non-SwissProt reviewed proteins, entries without gene symbol or not approved by the HUGO Gene Nomenclature Committee, as well as those encoding for HLA proteins and immunoglobulins. This yielded a final set of 806 GO genes. For InnateDB, we retrieved 2,158 entries, corresponding to 989 unique annotations (last access January 2015). Similarly to GO, we removed entries without approved HUGO names, HLA genes, and miRNAs, and obtained a final set of 905 InnateDB genes. When manually reviewing these two gene lists, we noticed the presence of proteins belonging, based on structural homology, to gene families that are commonly accepted to play a role in innate immune processes (e.g., Nod-like receptors), even if the involvement of some of their individual members in innate immunity remains unclear. Because GO and InnateDB did not systematically use this “family-based criteria,” we manually did so for gene families in which some of their individual members were missing (e.g., we added 28 TRIM proteins and 24 C-type lectins). In addition, we noticed the absence of several well-described or recently identified molecules, including some nucleic acid sensors such as ABCF1 (MIM: 603429), DHX15 (MIM: 603403), DHX33 (MIM: 614405), and PYHIN1 (MIM: 612677). By applying these inclusion criteria, we also retrieved some of the filtered GO and InnateDB entries that were initially removed because they were present under a non-approved HUGO symbol. This was the case for the interferons IFNL1 (MIM: 607403), IFNL2 (MIM: 607401), and IFNL3 (MIM: 607402), which were annotated in InnateDB as IL29, IL28A, and IL28B, respectively. We acknowledge that some molecules that we manually added (which were absent from the lists that were downloaded from the databases at the time of the study) have now been included in the corresponding websites. Our manual inclusion of additional genes, based on current knowledge of gene families and functions related to innate immunity (e.g., missing chemokines, defensins, and caspases; see Table S1), was an attempt to update existing databases. Overall, we manually added a set of 187 genes, making a final dataset of 1,553 genes that constituted the basis of all subsequent analyses (Table S1). We classified the 1,553 genes according to their main known (or inferred) function into nine different categories. These include sensors (n = 274), adaptors (n = 46), signal transducers (n = 245), transcription factors (n = 93), effector molecules (n = 284), and secondary receptors (n = 70). We also included regulators of the signaling pathways (n = 310) and accessory molecules (n = 68) necessary for an efficient immune response. This classification was based on the functional information available for each of these genes in InnateDB, UniProt, and/or the corresponding publications. Out of the 1,553 genes, 163 remained unclassified, because their reported molecular description did not allow us to include them in any of the categories above and were thus grouped into a final category termed as “uncharacterized.” Depending on the nature of the analyses performed, we used the high-coverage (∼57×) exome sequencing data and/or the low-coverage (2–6×) sequencing data of the 1000 Genomes Project, which are available for 1,092 individuals from 14 populations from Europe, East Asia, sub-Saharan Africa, and the Americas.44Abecasis G.R. Auton A. Brooks L.D. DePristo M.A. Durbin R.M. Handsaker R.E. Kang H.M. Marth G.T. McVean G.A. 1000 Genomes Project ConsortiumAn integrated map of genetic variation from 1,092 human genomes.Nature. 2012; 491: 56-65Crossref PubMed Scopus (5691) Google Scholar To estimate the strength of purifying selection, we used SnIPRE,47Eilertson K.E. Booth J.G. Bustamante C.D. SnIPRE: selection inference using a Poisson random effects model.PLoS Comput. Biol. 2012; 8: e1002806Crossref PubMed Scopus (58) Google Scholar which relies on the comparison of polymorphism and divergence at synonymous and non-synonymous sites (i.e., McDonald-Kreitman contingency table). This method uses a generalized linear mixed model to model the genome-wide variability among categories of mutations and estimates two population genetics parameters for each gene: γ, the population selection coefficient, and f, the proportion of non-synonymous mutations that are not deleterious. We focused our analyses on f, to quantify the strength of purifying selection: a low f value indicates that a large proportion of non-synonymous alleles were deleterious and have been removed from the population. We retrieved the alignment of the human genome (hg19 release) and the chimpanzee genome (PanTro3 release), used as an outgroup, provided by the UCSC Genome Browser, corresponding to ∼2.5 Gb of aligned sequences. All regions of the human genome that are deleted or have no homology with the chimpanzee were excluded from the analysis. We identified 33.5 million single bases that were different between the two species, which were then functionally annotated with SnpEff,48Cingolani P. Platts A. Wang L. Coon M. Nguyen T. Wang L. Land S.J. Lu X. Ruden D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.Fly (Austin). 2012; 6: 80-92Crossref PubMed Scopus (5677) Google Scholar using the GRCh37.65 build. We obtained 200,676 non-synonymous or synonymous divergent differences between humans and chimpanzees. We next retrieved all human variants that have been identified by the 1000 Genomes Project high-coverage exome dataset. We kept 445,401 variants that were annotated as non-synonymous or synonymous, were outside of gaps in the human-chimpanzee alignment, and were polymorphic in at least one human population. Variants with a fixed alternate allele in the 1000 Genomes Project dataset (i.e., reference allele is absent from the sample) were added to fixed differences between human and chimpanzee. We excluded from human-chimpanzee fixed differences 16,345 positions that were actually polymorphic in humans or chimpanzees, using the dbSNP136 chimpanzee database. We retrieved all human CDS with length >68 bp and considered the longest transcript available for each gene. We deduced from the genetic code the number of synonymous and non-synonymous sites in the 22,571 transcripts obtained, accounting for gaps in the human-chimpanzee alignments. Finally, we excluded all transcripts that had a length <50 bp after accounting for these gaps, had no divergent nor polymorphic mutations, had no HUGO-approved gene symbol, or was not a SwissProt “reviewed” protein. HGNC-approved gene symbols were retrieved with the R BioConductor b" @default.
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• W2225153589 title "Genomic Signatures of Selective Pressures and Introgression from Archaic Hominins at Human Innate Immunity Genes" @default.
• W2225153589 cites W1572265772 @default.
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