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Biol113 week4 evolution
This document discusses human evolution and recent insights from genomics. It summarizes that Neanderthals were the closest evolutionary relatives to modern humans and lived in Europe and Western Asia until disappearing 30,000 years ago. A draft sequence of the Neanderthal genome from three individuals was presented, composed of over 4 billion nucleotides. Comparisons with five modern human genomes identified regions potentially affected by selection in ancestral modern humans, involving genes related to metabolism, cognition, and skeletal development. Analysis suggests Neanderthals shared more genetic variants with non-Africans, indicating gene flow from Neanderthals into their ancestors occurred before Eurasian groups diverged.
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Biol113 week4 evolution
- 2. Human evolution Ancestors, relatives& major transitions What about today? Recent insights from genomics
- 3.
- 4. Relatives and recentancestors FAMILY TREE ofhominoidsencompassesthelesserapes(siamangsand gibbons),greatapes(orangutans,gorillasandchimpanzees),andhumans.Most Mioceneapeswereevolutionarydeadends.Butresearchershaveidentifiedahandful ofthemascandidateancestorsoflivingapesandhumans.Proconsul,aprimitive Mioceneape,isthoughttohavebeenthelastcommonancestorofthelivinghominoids; Sivapithecus,anearlygreatape,iswidelyregardedasanorangutanforebear;andeither DryopithecusorOuranopithecusmayhavegivenrisetoAfricanapesandhumans. CATARRHINI HYLOBATIDS CERCOPITHECOIDS PLATYRRHINI SPIDERMONKEY MACAQUE SIAMANG GIBBON ORANGUTAN GORILLA HUMAN CHIMPANZEE HOMINIDS HOMINOIDS SIVAPITHECUSPROCONSUL OURANOPITHECUS 16MYA 19MYA DRYOPITHECUS 40MILLIONYEARSAGO 25MYA 14MYA 9MYA 6MYA Potential common ancestors (Miocene) New world monkeys Old world monkeys Lesser apes “Higher primates” = Old world monkeys + Apes Apes Great Apes © Scientific American
- 5. Proconsul Some ape-like features Somemonkey-like features 10-18 Mya
- 6.
- 7. Relatives and recentancestors © Scientific American simple chewing surfaces—a feeding ap- paratus well suited to a diet of soft, ripe suspensory locomotion, especially in the elbow joint, which was fully extend- east Asia. Most phylogenetic analyses concur that it is from Sivapithecus that FAMILY TREE ofhominoidsencompassesthelesserapes(siamangsand gibbons),greatapes(orangutans,gorillasandchimpanzees),andhumans.Most Mioceneapeswereevolutionarydeadends.Butresearchershaveidentifiedahandful ofthemascandidateancestorsoflivingapesandhumans.Proconsul,aprimitive Mioceneape,isthoughttohavebeenthelastcommonancestorofthelivinghominoids; Sivapithecus,anearlygreatape,iswidelyregardedasanorangutanforebear;andeither DryopithecusorOuranopithecusmayhavegivenrisetoAfricanapesandhumans. CATARRHINI HYLOBATIDS CERCOPITHECOIDS PLATYRRHINI SPIDERMONKEY MACAQUE SIAMANG GIBBON ORANGUTAN GORILLA HUMAN CHIMPANZEE HOMINIDS HOMINOIDS SIVAPITHECUSPROCONSUL OURANOPITHECUS 16MYA 19MYA DRYOPITHECUS 40MILLIONYEARSAGO 25MYA 14MYA 9MYA 6MYA Potential common ancestors (Miocene)
- 8. Major transitions inhuman evolution • Bipedalism (down from the trees) • Increased brain size In which order?
- 9. • Life intrees. • Occasionally go down • New context required going down more often? Australopithecus africanus/! A. afarensis! Ardipithecus ramidus! Orrorin tugenensis! Homo! P. robustus! Million! years! Climate! Cold! Warm! Glacial cycles! Arctic icecap! Antarctic icecap! WP!
- 10. Why bipedalism? • Energyefficient locomotion (for distant food sources) • Less exposure to sun? • Free the hands? (for gathering/ hunting?) • Seeing farther: Finding food & avoiding predators? • Anti-predator displays? • Cultural - escalated through Sexual selection? • Sexual displays? Habitat! fragmentation! Climate! cooling! Mid Miocene! Late Miocene! Australopithecus africanus/! A. afarensis! Ardipithecus ramidus! Orrorin tugenensis! Homo! P. robustus! Million! years! Climate! Cold! Warm! Glacial cycles! Arctic icecap! Antarctic icecap!
- 11. Running • sweating forthermoregulation. • arched foot + achilles tendon • head stabilization • early Homo? • first: improved scavenging. • then persistence hunting
- 12. Major transitions inhuman evolution • Bipedalism (down from the trees) • Increased brain size • Use of simple stone tools • Fire, spears & other sophisticated tools (stone, bone...) • Language, complex culture • Agriculture... In which order?
- 14. Relatives and recentancestors © Scientific American simple chewing surfaces—a feeding ap- paratus well suited to a diet of soft, ripe suspensory locomotion, especially in the elbow joint, which was fully extend- east Asia. Most phylogenetic analyses concur that it is from Sivapithecus that FAMILY TREE ofhominoidsencompassesthelesserapes(siamangsand gibbons),greatapes(orangutans,gorillasandchimpanzees),andhumans.Most Mioceneapeswereevolutionarydeadends.Butresearchershaveidentifiedahandful ofthemascandidateancestorsoflivingapesandhumans.Proconsul,aprimitive Mioceneape,isthoughttohavebeenthelastcommonancestorofthelivinghominoids; Sivapithecus,anearlygreatape,iswidelyregardedasanorangutanforebear;andeither DryopithecusorOuranopithecusmayhavegivenrisetoAfricanapesandhumans. CATARRHINI HYLOBATIDS CERCOPITHECOIDS PLATYRRHINI SPIDERMONKEY MACAQUE SIAMANG GIBBON ORANGUTAN GORILLA HUMAN CHIMPANZEE HOMINIDS HOMINOIDS SIVAPITHECUSPROCONSUL OURANOPITHECUS 16MYA 19MYA DRYOPITHECUS 40MILLIONYEARSAGO 25MYA 14MYA 9MYA 6MYA Potential common ancestors (Miocene)
- 15. Most lineages went extinct Proconsulidae H.erectus H. neanderthalensis H. sapiens Australopithecines H. habilis
- 17.
- 18. Taung child Nature 1925Australopithecus afarensis 2.5 mya Limestone quarry near Taung, South Africa
- 19. Lucy - Australopithecusafarensis 1978 3.2 mya
- 20. Australopithecines 0 3 000(km) 02 000(mi) Projection de Lambert azimutale équivalente 10° 20° 30° 40° 50° 60°0°10°20°30° 10° 20° 30° 40° 50° 60°0°10°20°30° 0° 10° 20° 30° 10° 20° 30° 0° 10° 20° 30° 10° 30° A. Gahri P. Boisei A. Afarensis A. Anamensis A. Bahrelghazali A. Africanus P. Aethiopicus P. Robustus (Crassidens) Wikipedia Brain size: 35% of modern human
- 22. Evidence for bipedalismin Australopithecines
- 23. Evidence for bipedalismin Australopithecines
- 24. • Pelvis short& broad (like humans), not long & narrow (like gorilla) • Hip & walking muscles arranged like in a bipedal organism • Femur angled as in humans, not straight as in chimps • Feet Evidence for bipedalism in Australopithecines Gorilla Human Australophitecus
- 25. Fossilized tracks at Laetoli(Tanzania) Footprints preserved in volcanic ash from: 2 hominids (Australopithecus afarensis) Numerous other mammals 3.6Mya
- 26. Tool use? • generally:only simple tools (similarly to current non-human great apes). • but Australopithecus garhi (2.5 mya) may have made stone tools.
- 27. Summary:Australopithecines • Major groupof early bipedal hominids (4mya to 1 mya) • Small brains • Only in Africa • Many forms/species
- 29. Most lineages went extinct Proconsulidae Australopithecines H.erectus H. neanderthalensis H. sapiens H. habilis
- 30.
- 31.
- 32. Tool use Chimps andother animals may use objects as tools. H. habilis!H. sapiens! Australopithecine! H. habilis made tools ScrapingCutting
- 33. Stages of human evolutionare defined by the style and sophistication of stone tools…. e.g.: •Oldowan (2.5-1.5 mya) •Achuelian (1.5-0.2 mya)
- 34.
- 35.
- 36.
- 38. Out of Africa- H. erectus
- 39.
- 40. Nariokotome/Turkana boy Found 1984 inKenya. From1.5mya H. erectus
- 41. H. erectus lifestyle •Stone tools (Acheulian) • Fire • Sociality • Hunting • …language?
- 43. Homo floresiensis “TheHobbit” H. florensis vs. H. sapiens skull Nature(2004)vol.431,1043-1044
- 44. Nature (2004) vol.431, 1043-1044
- 46. Most lineages went extinct Proconsulidae Australopithecines H.erectus H. neanderthalensis H. sapiens H. habilis
- 47.
- 48.
- 49. H. neanderthalensis died outwhen H sapiens arrived
- 51. Neanderthals - Summary •Neanderthals were morphologically and genetically distinct from early H. sapiens • disappeared after H. sapiens arrived - possibly because they were culturally less advanced.
- 52. Most lineages went extinct Proconsulidae Australopithecines H.erectus H. neanderthalensis H. sapiens H. habilis
- 53. H. sapiens • 50,000years ago: fully “modern” with language, music, advanced social intelligence, strategic planning etc. • 70,000 years ago: began migrating out of Africa • Simultaneous decline of other Homo species (erectus, neanderthalensis...): competition? • Superior cooperation & learning due to language? • Agriculture ~ 10,000 years ago
- 55. Burial ritual in earlyH. sapiens • At Sungir, Russia, around 28,000 years ago • A 60 year old buried with an elaborate collection of beads, necklaces and bracelets
- 56. Examples of earlyH. sapiens tools
- 57. Lascaux - 35000years ago Lion man, Ulm - 40,000 years ago Flute - 36,000 years ago
- 58. More stuff keepsbeing discovered
- 59. Recent insights fromgenomics
- 60. A Draft Sequenceof the Neandertal Genome Richard E. Green,1 *†‡ Johannes Krause,1 †§ Adrian W. Briggs,1 †§ Tomislav Maricic,1 †§ Udo Stenzel,1 †§ Martin Kircher,1 †§ Nick Patterson,2 †§ Heng Li,2 † Weiwei Zhai,3 †|| Markus Hsi-Yang Fritz,4 † Nancy F. Hansen,5 † Eric Y. Durand,3 † Anna-Sapfo Malaspinas,3 † Jeffrey D. Jensen,6 † Tomas Marques-Bonet,7,13 † Can Alkan,7 † Kay Prüfer,1 † Matthias Meyer,1 † Hernán A. Burbano,1 † Jeffrey M. Good,1,8 † Rigo Schultz,1 Ayinuer Aximu-Petri,1 Anne Butthof,1 Barbara Höber,1 Barbara Höffner,1 Madlen Siegemund,1 Antje Weihmann,1 Chad Nusbaum,2 Eric S. Lander,2 Carsten Russ,2 Nathaniel Novod,2 Jason Affourtit,9 Michael Egholm,9 Christine Verna,21 Pavao Rudan,10 Dejana Brajkovic,11 Željko Kucan,10 Ivan Gušic,10 Vladimir B. Doronichev,12 Liubov V. Golovanova,12 Carles Lalueza-Fox,13 Marco de la Rasilla,14 Javier Fortea,14 ¶ Antonio Rosas,15 Ralf W. Schmitz,16,17 Philip L. F. Johnson,18 † Evan E. Eichler,7 † Daniel Falush,19 † Ewan Birney,4 † James C. Mullikin,5 † Montgomery Slatkin,3 † Rasmus Nielsen,3 † Janet Kelso,1 † Michael Lachmann,1 † David Reich,2,20 *† Svante Pääbo1 *† Neandertals, the closest evolutionary relatives of present-day humans, lived in large parts of Europe and western Asia before disappearing 30,000 years ago. We present a draft sequence of the Neandertal genome composed of more than 4 billion nucleotides from three individuals. Comparisons of the Neandertal genome to the genomes of five present-day humans from different parts of the world identify a number of genomic regions that may have been affected by positive selection in ancestral modern humans, including genes involved in metabolism and in cognitive and skeletal development. We show that Neandertals shared more genetic variants with present-day humans in Eurasia than with present-day humans in sub-Saharan Africa, suggesting that gene flow from Neandertals into the ancestors of non-Africans occurred before the divergence of Eurasian groups from each other. T he morphological features typical of Nean- dertals first appear in the European fossil record about 400,000 years ago (1–3). Progressively more distinctive Neandertal forms subsequently evolved until Neandertals disap- peared from the fossil record about 30,000 years ago (4). During the later part of their history, Neandertals lived in Europe and Western Asia as far east as Southern Siberia (5) and as far south as the Middle East. During that time, Nean- sumed ancestors of present-day Europeans. Similarly, analysis of DNA sequence data from present-day humans has been interpreted as evi- dence both for (12, 13) and against (14) a genetic contribution by Neandertals to present-day hu- mans. The only part of the genome that has been examined from multiple Neandertals, the mito- chondrial DNA (mtDNA) genome, consistently falls outside the variation found in present-day humans and thus provides no evidence for inter- changed parts of their genome with the ances- tors of these groups. Several features of DNA extracted from Late Pleistocene remains make its study challenging. The DNA is invariably degraded to a small aver- age size of less than 200 base pairs (bp) (21, 22), it is chemically modified (21, 23–26), and extracts almost always contain only small amounts of en- dogenous DNA but large amounts of DNA from microbial organisms that colonized the specimens after death. Over the past 20 years, methods for ancientDNAretrievalhavebeen developed(21,22), largely based on the polymerase chain reaction (PCR) (27). In the case of the nuclear genome of Neandertals, four short gene sequences have been determined by PCR: fragments of the MC1R gene involved in skin pigmentation (28), a segment of the FOXP2 gene involved in speech and language (29), parts of the ABO blood group locus (30), and a taste receptor gene (31). However, although PCR of ancient DNA can be multiplexed (32), it does not allow the retrieval of a large proportion of the genome of an organism. The development of high-throughput DNA se- quencing technologies (33, 34) allows large-scale, genome-wide sequencing of random pieces of DNA extracted from ancient specimens (35–37) and has recently made it feasible to sequence ge- RESEARCH ARTICLE 1 Department of Evolutionary Genetics, Max-Planck Institute for Evolutionary Anthropology, D-04103 Leipzig, Germany.2 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. 3 Department of Integrative Biology, University of California, Berkeley, CA 94720, USA. 4 European Molecular Biology Laboratory–European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK. 5 Genome Technology Branch, National Human Genome Re- search Institute, National Institutes of Health, Bethesda, MD 20892, USA. 6 Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655, USA. 7 Howard Hughes Medical Institute, Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. 8 Division of Biological Sciences, University of onMarch24,2013www.sciencemag.orgloadedfrom 2-4% of eurasian Homo sapiens DNA comes from Neanderthals
- 61. Strong reproductive isolationbetween humans and Neanderthals inferred from observed patterns of introgression Mathias Currata,1 and Laurent Excoffierb,c,1 a Anthropology, Genetics, and Peopling History Laboratory, Anthropology Unit, Department of Genetics and Evolution, University of G 1227 Geneva, Switzerland; b Computational and Molecular Population Genetics Laboratory, Institute of Ecology and Evolution, Univers 3012 Berne, Switzerland; and c Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland Edited by Svante Pääbo, Max Planck Institute of Evolutionary Anthropology, Leipzig, Germany, and approved August 3, 2011 (receive May 10, 2011) Recent studies have revealed that 2–3% of the genome of non- Africans might come from Neanderthals, suggesting a more complex scenario of modern human evolution than previously anticipated. In this paper, we use a model of admixture during a spatial expansion to study the hybridization of Neanderthals with modern humans during their spread out of Africa. We find that observed low levels of Neanderthal ancestry in Eurasians are compatible with a very low rate of interbreeding (<2%), potentially attributable to a very strong avoidance of interspecific matings, a low fitness of hybrids, or both. These results suggesting the presence of very effective barriers to gene flow between the two species are robust to uncertainties about the exact demography of the Paleolithic populations, and they are also found to be compatible with the observed lack of mtDNA in- trogression. Our model additionally suggests that similarly low levels of introgression in Europe and Asia may result from distinct admix- ture events having occurred beyond the Middle East, after the split of Europeans and Asians. This hypothesis could be tested because it predicts that different components of Neanderthal ancestry should be present in Europeans and in Asians. To examine these issues and clarify the proce between Neanderthals and modern humans, we istic and spatially explicit model of admixture between modern humans and Neanderthals (3 simulations, we have estimated the interbree between humans and Neanderthals as well as t hybridization that is compatible with the obs Neanderthal ancestry in contemporary humans, latter migrated out of Africa into Eurasia 50 ky Results Low Rates of Interbreeding Between Humans Using spatially explicit simulations, we hav expected amount of Neanderthal ancestry in pr from Europe (France) and Asia (China) for admixture with Neanderthals and over variou derthal ranges (Fig. 1). Under our model of range expansion, we find that observed low leve introgression into Eurasians imply the existe Strong reproductive isolation between human and Neanderthals inferred from observed patterns of introgression Mathias Currata,1 and Laurent Excoffierb,c,1 a Anthropology, Genetics, and Peopling History Laboratory, Anthropology Unit, Department of Genetics and Evolution, University 1227 Geneva, Switzerland; b Computational and Molecular Population Genetics Laboratory, Institute of Ecology and Evolution, Uni 3012 Berne, Switzerland; and c Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland Edited by Svante Pääbo, Max Planck Institute of Evolutionary Anthropology, Leipzig, Germany, and approved August 3, 2011 (rece May 10, 2011) Recent studies have revealed that 2–3% of the genome of non- Africans might come from Neanderthals, suggesting a more complex scenario of modern human evolution than previously anticipated. In this paper, we use a model of admixture during a spatial expansion to study the hybridization of Neanderthals with modern humans during their spread out of Africa. We find that observed low levels of Neanderthal ancestry in Eurasians are compatible with a very low rate of interbreeding (<2%), potentially attributable to a very strong avoidance of interspecific matings, a low fitness of hybrids, or both. These results suggesting the presence of very effective barriers to gene flow between the two species are robust to uncertainties about the exact demography of the Paleolithic populations, and they are also found to be compatible with the observed lack of mtDNA in- trogression. Our model additionally suggests that similarly low levels of introgression in Europe and Asia may result from distinct admix- ture events having occurred beyond the Middle East, after the split of Europeans and Asians. This hypothesis could be tested because it predicts that different components of Neanderthal ancestry should be present in Europeans and in Asians. To examine these issues and clarify the pro between Neanderthals and modern humans, istic and spatially explicit model of admixt between modern humans and Neanderthals simulations, we have estimated the interb between humans and Neanderthals as well a hybridization that is compatible with the o Neanderthal ancestry in contemporary huma latter migrated out of Africa into Eurasia 50 Results Low Rates of Interbreeding Between Huma Using spatially explicit simulations, we expected amount of Neanderthal ancestry in from Europe (France) and Asia (China) fo admixture with Neanderthals and over var derthal ranges (Fig. 1). Under our model range expansion, we find that observed low l
- 62. • Only knownremains(all found since 2010): phalanx (finger bone), three teeth, a toe bone. From 41,000 years ago. • Amazingly well preserved DNA (Siberia; average temperature 0°C). sequenced the genome. • Common ancestor with Neanderthal: 600,000 years ago • Interbreeding with Homo sapiens: 4-6% of Melanesian genomes are from Denisovan. Nature 2010ARTICLE doi:10.1038/nature09710 Genetic history of an archaic hominin group from Denisova Cave in Siberia David Reich1,2 *, Richard E. Green3,4 *, Martin Kircher3 *, Johannes Krause3,5 *, Nick Patterson2 *, Eric Y. Durand6 *, Bence Viola3,7 *, Adrian W. Briggs1,3 , Udo Stenzel3 , Philip L. F. Johnson8 , Tomislav Maricic3 , Jeffrey M. Good9 , Tomas Marques-Bonet10,11 , Can Alkan10 , Qiaomei Fu3,12 , Swapan Mallick1,2 , Heng Li2 , Matthias Meyer3 , Evan E. Eichler10 , Mark Stoneking3 , Michael Richards7,13 , Sahra Talamo7 , Michael V. Shunkov14 , Anatoli P. Derevianko14 , Jean-Jacques Hublin7 , Janet Kelso3 , Montgomery Slatkin6 & Svante Pa¨a¨bo3 Using DNA extracted from a finger bone found in Denisova Cave in southern Siberia, we have sequenced the genome of an archaic hominin to about 1.9-fold coverage. This individual is from a group that shares a common origin with Neanderthals. This population was not involved in the putative gene flow from Neanderthals into Eurasians; however, the data suggestthat it contributed 4–6% ofits geneticmaterial tothe genomes ofpresent-dayMelanesians.We designate this hominin population ‘Denisovans’ and suggest that it may have been widespread in Asia during the Late Pleistocene epoch. Atooth found in Denisova Cave carries a mitochondrialgenome highly similar tothatof the finger bone. This tooth shares no derived morphological features with Neanderthals or modern humans, further indicating that Denisovans have an evolutionary history distinct from Neanderthals and modern humans. Less than 200,000 years ago, anatomically modern humans (that is, humans with skeletons similar to those of present-day humans) appeared in Africa. At that time, as well as later when modern humans appeared in Eurasia, other ‘archaic’ hominins were already present in Eurasia.InEuropeandwestern Asia,hominins definedasNeanderthals on the basis of their skeletal morphology lived from at least 230,000 mitochondrial (mt)DNA sequences have been determined from Neanderthals9–17 . This has shown that all Neanderthals studied so far share a common mtDNA ancestor on the order of 100,000 years ago10 , and in turn, share a common ancestor with the mtDNAs of present-day humans about 500,000 years ago10,18,19 (as expected, this is older than the Neanderthal–modern human population split time of
- 63. Most lineages went extinct Proconsulidae Australopithecines H.erectus H. neanderthalensis H. sapiens Denisovan H. habilis
- 64. African ori of mtDNA lationshave of our spec the deepest sity14,62–65 . G view7–9 , and humans ind within mod from south mately 115 humans fir divergences 35–50 kya13 of a strong our genome close correla in a populat ulation from Stoneking & Krause 2011 Stoneking & Krause 2011 Nature Reviews Genetics ? No additional admixture detected despite probable overlap ! detected admixture (location uncertain)
- 65. PATCHWORK PLANET 2% 98% Most people’sgenomes contain remnants of archaic DNA from ancient interbreeding3–6 . Sub-Saharan Africa Eurasia and Americas Australia and New Guinea Genes* African Unknown archaic African source Neanderthal Denisovan 2.5% 5%2.5% 97.5% 92.5% *Figures are approximate, and for Africa, based on limited data6 . A WINDING PATH After early modern humans left Africa around 60,000 years ago (top right), they spread across the globe and interbred with other descendants of Homo heidelbergensis. Homo sapiens 2.0 1.6 1.2 0.8 Millionyearsago 0.4 0 Homo erectus Homo antecessor Homo floresiensis Denisovans Neanderthals Homo heidelbergensis Homo erectus Wavy branch edges suggest presumed fluctuations in population. H. floresiensis originated in an unknown location and reached remote parts of Indonesia. H. erectus spread to western Asia, then east Asia and Indonesia. Its presence in Europe is uncertain, but it gave rise to H. antecessor, found in Spain. H. heidelbergensis originated from H. erectus in an unknown location and dispersed across Africa, southern Asia and southern Europe. H. sapiens spread from Africa to western Asia and then to Europe and southern Asia, eventually reaching Australasia and the Americas. Stringer 2012 Nature
- 67. What about today? Doesnatural selection (still) act on humans?
- 68. But... Triple (?) misunderstandings: 1.Evolution (ie genetic change) is not only through natural selection •Drift •Sexual selection •... 2. Medicine can reduce effect of deleterious traits. •OK many are ‘‘alive who otherwise would have perished’’ •But many still have limited access to medicine. 3. Even with the best medical care • there are differences in reproductive success
- 69.
- 70. Pathogens/disease • “Spanish” Flupandemic of 1918 killed 1-3% of the world’s population
- 73. Pathogens/disease • “Spanish” Flupandemic of 1918 killed 1-3% of the world’s population • AIDS • Dengue,Typhus, Malaria...
- 74. LETTER doi:10.1038/nature15390 A novellocus of resistance to severe malaria in a region of ancient balancing selection Malaria Genomic Epidemiology Network* The high prevalence of sickle haemoglobin in Africa shows that malaria has been a major force for human evolutionary selection, but surprisingly few other polymorphisms have been proven to confer resistance to malaria in large epidemiological studies1–3 . To address this problem, we conducted a multi-centre genome- wide association study (GWAS) of life-threatening Plasmodium falciparum infection (severe malaria) in over 11,000 African chil- dren, with replication data in a further 14,000 individuals. Here we report a novel malaria resistance locus close to a cluster of genes encoding glycophorins that are receptors for erythrocyte invasion by P. falciparum. We identify a haplotype at this locus that pro- vides 33% protection against severe malaria (odds ratio 5 0.67, 95% confidence interval 5 0.60–0.76, P value 5 9.5 3 10211 ) and is linked to polymorphisms that have previously been shown to have features of ancient balancing selection, on the basis of haplo- type sharing between humans and chimpanzees4 . Taken together with previous observations on the malaria-protective role of blood group O1–3,5 , these data reveal that two of the strongest GWAS signals for severe malaria lie in or close to genes encoding the glycosylated surface coat of the erythrocyte cell membrane, both within regions of the genome where it appears that evolution has maintained diversity for millions of years. These findings provide new insights into the host–parasite interactions that are critical in determining the outcome of malaria infection. In the discovery phase of this study, we analysed GWAS data on 5,633 children with severe malaria and 5,919 population controls from The Gambia, Kenya and Malawi, and in the replication phase we analysed candidate single-nucleotide polymorphisms (SNPs) in a fur- ther 13,946 case–control samples from Burkina Faso, Cameroon, The Gambia, Ghana, Malawi, Mali and Tanzania (Extended Data Fig. 1). The majority of samples used in the discovery phase have been analysed previously by lower-resolution GWAS methods3 . For this analysis we improved resolution by directly typing all samples at approximately 2.5 million SNPs using the Illumina Omni2.5M plat- form, followed by quality control (Extended Data Figs 1 and 2) and imputation of genotypes at over 10 million SNPs using haplotype data from the 1000 Genomes Project6 . Imputation performance varied parasite8 . Specifically, we used a Bayesian approach that combines evidence across multiple models of association by specifying a prior probability on the size and similarity of the genetic effect across popu- lations, as well as the mode of inheritance1 . A single statistical summary of the signal of association was obtained by averaging the evidence across models, weighting each by its prior probability, and comparing the evidence to the null model of no association (model-averaged Bayes factor (BFavg)). Having observed the data, a posterior probability was assigned to each model, conditional on it being a true association and the model assumptions, which are described in Methods and Extended Data Fig. 5. We replicated previously reported GWAS signals2,3,9 at the HBB (BFavg 5 5.83 1024 ), ABO (BFavg 5 6.73 109 ) and ATP2B4 (BFavg 5 4.43 105 ) loci, and a detailed analysis of key vari- ants at these and other previously reported loci is presented elsewhere1 . A previously reported association near the gene MARVELD3 (ref. 2) did not replicate in this data set (Supplementary Note 1). Genome-wide patterns of association with severe malaria at the 34 regions of the genome containing a variant with either a Bayes factor for the most probable model (BFmax) . 2.53 104 or with a BFavg . 2.53 103 are summarized in Extended Data Fig. 6 and Supplementary Table 1. Details of the evidence for association in these regions can be viewed online at http://www.malariagen.net/resource/14. These data provide a rich resource of new candidate loci for further investigation. Here we focus on a region of chromosome 4 shown in Fig. 1, where the strongest signal of association (at SNP rs184895969) is located between the gene FREM3 and a cluster of three glycophorin genes (GYPE, GYPB and GYPA). Glycophorins are sialoglycoproteins that are abundantly expressed in the erythrocyte membrane, providing a hydrophilic surface coat that is necessary for erythrocytes to flow freely in the circulation. A complex system of single-nucleotide and structural variants in this region determine the MNS blood group system10 . These genes have a functional role in invasion of erythrocytes by P. falciparum. Glycophorin A is the receptor for the P. falciparum erythrocyte-binding ligand EBA-175 (ref. 11), and glycophorin B is a receptor for the parasite ligand EBL-1 (ref. 12). To follow up this observation, selected SNPs at this locus were genotyped by Sequenom iPLEX MassArray in the discovery and replication The high prevalence of sickle haemoglobin in Africa shows that malaria has been a major force for human evolutionary selection, but surprisingly few other polymorphisms have been proven to confer resistance to malaria in large epidemiological studies1–3 . To address this problem, we conducted a multi-centre genome- wide association study (GWAS) of life-threatening Plasmodium falciparum infection (severe malaria) in over 11,000 African chil- dren, with replication data in a further 14,000 individuals. Here we report a novel malaria resistance locus close to a cluster of genes encoding glycophorins that are receptors for erythrocyte invasion by P. falciparum. We identify a haplotype at this locus that pro- vides 33% protection against severe malaria (odds ratio 5 0.67, 95% confidence interval 5 0.60–0.76, P value 5 9.5 3 10211 ) and is linked to polymorphisms that have previously been shown to have features of ancient balancing selection, on the basis of haplo- type sharing between humans and chimpanzees4 . Taken together with previous observations on the malaria-protective role of blood group O1–3,5 , these data reveal that two of the strongest GWAS signals for severe malaria lie in or close to genes encoding the glycosylated surface coat of the erythrocyte cell membrane, both within regions of the genome where it appears that evolution has maintained diversity for millions of years. These findings provide new insights into the host–parasite interactions that are critical in determining the outcome of malaria infection. In the discovery phase of this study, we analysed GWAS data on 5,633 children with severe malaria and 5,919 population controls from The Gambia, Kenya and Malawi, and in the replication phase we analysed candidate single-nucleotide polymorphisms (SNPs) in a fur- ther 13,946 case–control samples from Burkina Faso, Cameroon, The Gambia, Ghana, Malawi, Mali and Tanzania (Extended Data Fig. 1). The majority of samples used in the discovery phase have been analysed previously by lower-resolution GWAS methods3 . For this analysis we improved resolution by directly typing all samples at approximately 2.5 million SNPs using the Illumina Omni2.5M plat- form, followed by quality control (Extended Data Figs 1 and 2) and imputation of genotypes at over 10 million SNPs using haplotype data from the 1000 Genomes Project6 . Imputation performance varied across populations, with accuracy varying as a function of the similarity between study and reference individuals (Extended Data Fig. 3). When testing for genetic association, principal components analysis was used to correct for population structure (Extended Data Fig. 4a–e), which reflected both geography and self-reported ethnicity. Similar results were obtained using a mixed-model approach (Extended Data Fig. 4f). To assess the evidence for association in the discovery phase we used an approach that allows for heterogeneity in the protective effect of an allele across different study sites. This could be particularly important in our data, as high levels of genetic and ethnic diversity in Africa can result in variable patterns of linkage disequilibrium between study sites that can complicate GWAS analysis7 . Other potential sources of heterogeneity include allelic heterogeneity and multiple independent origins of malaria resistance loci, as has been well documented at the HBB locus1,3 , as well as the high levels of genetic diversity in the parasite8 . Specifically, we used a Bayesian approach that combines evidence across multiple models of association by specifying a prior probability on the size and similarity of the genetic effect across popu- lations, as well as the mode of inheritance1 . A single statistical summary of the signal of association was obtained by averaging the evidence across models, weighting each by its prior probability, and comparing the evidence to the null model of no association (model-averaged Bayes factor (BFavg)). Having observed the data, a posterior probability was assigned to each model, conditional on it being a true association and the model assumptions, which are described in Methods and Extended Data Fig. 5. We replicated previously reported GWAS signals2,3,9 at the HBB (BFavg 5 5.83 1024 ), ABO (BFavg 5 6.73 109 ) and ATP2B4 (BFavg 5 4.43 105 ) loci, and a detailed analysis of key vari- ants at these and other previously reported loci is presented elsewhere1 . A previously reported association near the gene MARVELD3 (ref. 2) did not replicate in this data set (Supplementary Note 1). Genome-wide patterns of association with severe malaria at the 34 regions of the genome containing a variant with either a Bayes factor for the most probable model (BFmax) . 2.53 104 or with a BFavg . 2.53 103 are summarized in Extended Data Fig. 6 and Supplementary Table 1. Details of the evidence for association in these regions can be viewed online at http://www.malariagen.net/resource/14. These data provide a rich resource of new candidate loci for further investigation. Here we focus on a region of chromosome 4 shown in Fig. 1, where the strongest signal of association (at SNP rs184895969) is located between the gene FREM3 and a cluster of three glycophorin genes (GYPE, GYPB and GYPA). Glycophorins are sialoglycoproteins that are abundantly expressed in the erythrocyte membrane, providing a hydrophilic surface coat that is necessary for erythrocytes to flow freely in the circulation. A complex system of single-nucleotide and structural variants in this region determine the MNS blood group system10 . These genes have a functional role in invasion of erythrocytes by P. falciparum. Glycophorin A is the receptor for the P. falciparum erythrocyte-binding ligand EBA-175 (ref. 11), and glycophorin B is a receptor for the parasite ligand EBL-1 (ref. 12). To follow up this observation, selected SNPs at this locus were genotyped by Sequenom iPLEX MassArray in the discovery and replication sample sets outlined earlier (Fig. 1 and Extended Data Fig. 7a). The combined data set of 25,498 samples provided convincing evidence of association at rs186873296 by standard fixed-effect meta-analysis (P 5 9.5 3 10211 ) as well as by the Bayesian approach described earlier (BFoverall 5 1.3 3 108 ; Fig. 2 and Methods). The derived (non-ances- tral) allele of rs186873296 was at higher frequency in East Africa than in West Africa, and the greatest evidence of association was seen in Kenya, where the allele was most common with a frequency of approximately 10%. Using only replication data to avoid winner’s curse, and assuming an additive model, we estimate that carrying one copy of the derived allele reduces the risk of severe malaria by about 40% in Kenya (odds ratio (OR) 5 0.60, 95% confidence interval (CI) 5 0.46–0.79), with a slightly smaller effect across all populations (OR 5 0.67, 95% CI 0.56–0.80 in frequentist fixed-effect meta-ana- lysis). Further details are given in Supplementary Note 2. * Lists of participants and their affiliations appear at the end of the paper. 8 O C T O B E R 2 0 1 5 | V O L 5 2 6 | N A T U R E | 2 5 3 G2015 Macmillan Publishers Limited. All rights reserved
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- 76. Natural selection ina contemporary human population Sean G. Byarsa , Douglas Ewbankb , Diddahally R. Govindarajuc , and Stephen C. Stearnsa,1 a Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520-8102; b Population Studies Center, U Philadelphia, PA 19104-6299; and c Department of Neurology, Boston University School of Medicine, Boston, MA 02118-2526 Edited by Peter T. Ellison, Harvard University, Cambridge, MA, and approved September 16, 2009 (received for review June 2 Our aims were to demonstrate that natural selection is operating on contemporary humans, predict future evolutionary change for specific traits with medical significance, and show that for some traits we can make short-term predictions about our future evolu- tion. To do so, we measured the strength of selection, estimated genetic variation and covariation, and predicted the response to selection for women in the Framingham Heart Study, a project of the National Heart, Lung, and Blood Institute and Boston Univer- sity that began in 1948. We found that natural selection is acting to cause slow, gradual evolutionary change. The descendants of these women are predicted to be on average slightly shorter and stouter, to have lower total cholesterol levels and systolic blood pressure, to have their first child earlier, and to reach menopause later than they would in the absence of evolution. Selection is tending to lengthen the reproductive period at both ends. To better understand and predict such changes, the design of planned large, long-term, multicohort studies should include input from evolutionary biologists. evolutionary rates | heritability | Homo sapiens | medical traits sity to identify factors that contribute It is the longest running multigener history. The people originally enrolled dominantly European ancestry (20% Ireland, 10% Italy, 10% Quebec). T 5,209) has been examined every 2 ye between 1948 and 2008. The offsprin been examined approximately every 4 y between 1971 and 2008 (4). There is cohort (n = 4,095) that is not included i in it have not yet completed reproduct many physical and blood chemistry tr questionnaire is administered, yielding are deidentified by the FHS and de Institutes of Health dbGaP database loaded them for analysis. In this study individuals who were measured three o Measuring Selection in a Multicohort Natural selection has been measured Natural selection in a contemporary human population Sean G. Byarsa , Douglas Ewbankb , Diddahally R. Govindarajuc , and Stephen C. Stearnsa,1 a Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520-8102; b Population Studies Center, Philadelphia, PA 19104-6299; and c Department of Neurology, Boston University School of Medicine, Boston, MA 02118-2526 Edited by Peter T. Ellison, Harvard University, Cambridge, MA, and approved September 16, 2009 (received for review June 2 Our aims were to demonstrate that natural selection is operating on contemporary humans, predict future evolutionary change for specific traits with medical significance, and show that for some traits we can make short-term predictions about our future evolu- tion. To do so, we measured the strength of selection, estimated genetic variation and covariation, and predicted the response to selection for women in the Framingham Heart Study, a project of the National Heart, Lung, and Blood Institute and Boston Univer- sity that began in 1948. We found that natural selection is acting to cause slow, gradual evolutionary change. The descendants of these women are predicted to be on average slightly shorter and stouter, to have lower total cholesterol levels and systolic blood pressure, to have their first child earlier, and to reach menopause later than they would in the absence of evolution. Selection is tending to lengthen the reproductive period at both ends. To better understand and predict such changes, the design of planned large, long-term, multicohort studies should include input from evolutionary biologists. evolutionary rates | heritability | Homo sapiens | medical traits sity to identify factors that contribute It is the longest running multigener history. The people originally enrolled dominantly European ancestry (20% Ireland, 10% Italy, 10% Quebec). T 5,209) has been examined every 2 y between 1948 and 2008. The offsprin been examined approximately every 4 y between 1971 and 2008 (4). There i cohort (n = 4,095) that is not included i in it have not yet completed reproduct many physical and blood chemistry tr questionnaire is administered, yielding are deidentified by the FHS and d Institutes of Health dbGaP database loaded them for analysis. In this study individuals who were measured three o Measuring Selection in a Multicohort Natural selection has been measured 5000 people & their kids; 70 traits measured every 2-4 years since 1948.
- 77. Deep Human GenealogiesReveal a Selective Advantage to Be on an Expanding Wave Front Claudia Moreau,1 Claude Bhérer,1 Hélène Vézina,2 Michèle Jomphe,2 Damian Labuda,1,3 * Laurent Excoffier1,4,5 * Since their origin, human populations have colonized the whole planet, but the demographic processes governing range expansions are mostly unknown. We analyzed the genealogy of more than one million individuals resulting from a range expansion in Quebec between 1686 and 1960 and reconstructed the spatial dynamics of the expansion. We find that a majority of the present Saguenay Lac-Saint-Jean population can be traced back to ancestors having lived directly on or close to the wave front. Ancestors located on the front contributed significantly more to the current gene pool than those from the range core, likely due to a 20% larger effective fertility of women on the wave front. This fitness component is heritable on the wave front and not in the core, implying that this life-history trait evolves during range expansions. M ost species go through environmental- ly induced range expansions or range shifts (1), promoting the evolution of traits associated with dispersal and reproduction (2). Humans likely colonized the world by a series of range expansions from Africa (3), pos- sibly with episodes of interbreeding with now extinct hominins (4, 5), leading to allele frequen- Quebec parish registers that document the recent temporal and spatial expansion of the settle- ment of the Charlevoix Saguenay Lac-Saint- Jean (ChSLSJ) region, northeast of Quebec City, Canada: a prime example of a recent, fast, and well-documented range expansion (17) (Fig. 1). The European colonization of Quebec was ini- tiated in 1608 with the foundation of Quebec tracing back the founding e As shown in Fig. 1, the inf cess is a mixture of long creating an irregular wave ther, more progressive, sh which then filled gaps an ular wave front. On the basis of the c front index (WFI) (21), we of the Saguenay and the lived more often on or c than expected by chance (W regions) (fig. S1). Indeed, 0.75 observed in Lac-Sai a situation in which half ancestors had lived directly the other half just one ge In contrast, WFI is sign Charlevoix region (P = 0 results are consistent with dynamics of SLSJ and C front was always widespr localities were continuously much smaller in Charlevoi remained in the range core (Fig. 1). New immigrants constituted an important m getting married, with a gre migrants settling on the w range core, especially befo REPORTS s - ll n d at e s n s r Table 2. Age of reproduction and number of children of women from SLSJ in the period 1840 to 1900. Note that this table only includes women with known birth dates, such that age at marriage can be computed. No. of women Mean no. of children (FS) Mean no. of married children (EFS) Mean age at marriage FS ratio WF/RC EFS ratio WF/RC Marriage age ratio WF/RC Wave front (WF) 2663 9.1 4.9 20.5 1.15*** 1.20*** 0.95*** Range core (RC) 1783 7.9 4.1 21.6 ***, t test of difference between means; P < 0.001 185.2 16833 106 158.8 49.5 1.17* 112.9 25990 373 69.7 34.4 1.62*** 59.6 35613 1069 33.3 25.4 1.79*** 22.1 27061 1815 14.9 43.2 1.48*** 8.6 10175 2438 4.2 72.9 2.07*** 4.6 25619 8784 2.9 49.9 1.58*** 2.3 44408 26255 1.7 27.7 1.38*** 40846 40.2 SLSJ 7.4 39 15 2.6 99.6 2.8*** 4.6 15444 4420 3.5 62.3 1.3*** 2.4 35777 19726 1.8 30.9 1.3*** 24161 45.1
- 79. Accounts of humanevolution frequently assume that adult lactose tolerance12,13,15,16 . Estimates for the number How culture shaped the human genome: bringing genetics and the human sciences together Kevin N. Laland*, John Odling-Smee‡ and Sean Myles§ || Abstract | Researchers from diverse backgrounds are converging on the view that human evolution has been shaped by gene–culture interactions. Theoretical biologists have used population genetic models to demonstrate that cultural processes can have a profound effect on human evolution, and anthropologists are investigating cultural practices that modify current selection. These findings are supported by recent analyses of human genetic variation, which reveal that hundreds of genes have been subject to recent positive selection, often in response to human activities. Here, we collate these data, highlighting the considerable potential for cross-disciplinary exchange to provide novel insights into how culture has shaped the human genome. NATURE REVIEWS | GENETICS VOLUME 11 | FEBRUARY 2010 | 137
- 80. Genetics of ourbehavior?
- 81. Evolutionary Psychology • Generosityhigher if affects reputation • Pheromones help identify our mates based on MHC
- 83. Summary • Human evolutionis complicated but fascinating! • (just like any other species!!)
- 84. For more info •http://humanorigins.si.edu/ (Smithonian Institution) • PBS Nova Becoming Human (on youtube) • Stoneking & Krause. Learning about human population history from ancient and modern genomes. Nature Reviews Genetics 2011.