BIO 101 UOP Population Genetics And Phenotypes Worksheet

BIO 101 UOP Population Genetics And Phenotypes Worksheet

Sample Answer for BIO 101 UOP Population Genetics And Phenotypes Worksheet Included After Question

BIO/101 Population Genetics 1. 2. 3. 4. 5. 6. 7. 8. Visit https://sepuplhs.org/high/sgi/teachers/evolution_act11_sim.html Click “Next” Select the following characteristics of your choice for Bird 1: Plumage, Body Size, Beak. Click “Next Bird” Repeat for Birds 2 and 3. Click “Next”. Read the text and Click “Continue”. Read the text and respond to the question: Of those that you selected, how fit do you think each phenotype is in the current environment? Click “Continue” Read the text and click “Close” Click “Start” The simulation will run and then stop at strategic intervals to tell you new information. Each time it stops, read the text, make any observations, and then click “Resume”. 13. Repeat until you reach the end of the simulation (500,000 years passed). 14. Click “Continue”. 15. Observe the information and read the text. Answer the following questions: 9. 10. 11. 12. Were your ideas about the fitness of each phenotype you selected correct? Explain. 16. If you would like, you may continue the simulation. However, you may now end the simulation if you choose. 17. Consider your readings from this week about Evolution and Population of species. How do your observations relate to the readings from this week? Copyright 2019 by University of Phoenix. All rights reserved. The Young, the Weak and the Sick: Evidence of Natural Selection by Predation Meritxell Genovart1*, Nieves Negre2, Giacomo Tavecchia1, Ana Bistuer3, Luı́s Parpal4, Daniel Oro1 1 Population Ecology Group, Department of Biodiversity and Conservation, IMEDEA (CSIC-UIB), Esporles, Spain, 2 Fundació Natura Parc, Santa Eugènia, Spain, 3 Servei de Gestió de Residus, Consell de Mallorca, Palma de Mallorca, Spain, 4 Consorci per a la Recuperació de la Fauna de les Illes Balears (COFIB), Santa Eugènia, Spain Abstract It is assumed that predators mainly prey on substandard individuals, but even though some studies partially support this idea, evidence with large sample sizes, exhaustive analysis of prey and robust analysis is lacking. We gathered data from a culling program of yellow-legged gulls killed by two methods: by the use of raptors or by shooting at random. We compared both data sets to assess whether birds of prey killed randomly or by relying on specific individual features of the prey. We carried out a meticulous post-mortem examination of individuals, and analysing multiple prey characteristics simultaneously we show that raptors did not hunt randomly, but rather preferentially predate on juveniles, sick gulls, and individuals with poor muscle condition. Strikingly, gulls with an unusually good muscle condition were also predated more than expected, supporting the mass-dependent predation risk theory. This article provides a reliable example of how natural selection may operate in the wild and proves that predators mainly prey on substandard individuals. Citation: Genovart M, Negre N, Tavecchia G, Bistuer A, Parpal L, et al. (2010) The Young, the Weak and the Sick: Evidence of Natural Selection by Predation. PLoS ONE 5(3): e9774. doi:10.1371/journal.pone.0009774 Editor: Adrian L. R. Thomas, University of Oxford, United Kingdom Received August 17, 2009; Accepted February 24, 2010; Published March 19, 2010 Copyright: ß 2010 Genovart et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors acknowledge support mainly from TIRME, S.A. and also from EMAYA (Empresa Municipal de Agua y Alcantarillado) and the Spanish Ministry of Science (grant ref. CGL2006-04325/BOS). M.G. was supported by an I3P-CSIC fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] many conservation agencies still control gulls by culling. The Local Government of the Balearic Islands (Spain) began a gull culling programme on a refuse tip in the island of Mallorca as a part of the population control of yellow-legged gulls -Larus michahellis- in the Balearic archipelago. From 2003 to 2007 birds were culled by two methods: by shooting or by the use of trained birds of prey (peregrine falcon -Falco peregrinus-, saker falcon -F. cherrug- and Harris’s hawk -Parabuteo unicinctus-). We gathered data from this culling program and examined killed birds to determine 1) the sex and age of the individual, 2) individual body condition, assessed from muscle condition, and 3) any sign of parasitism (internal and external), infection, malformation or chronic disease (e.g. aspergillosis). We used these data to investigate multiple prey traits simultaneously and to assess whether birds of prey killed randomly or by relying on specific individual prey features. Introduction Predation is an important selective force in evolution [1–7] and is generally assumed to select against substandard individuals, i.e. the young, senescent, sick, or individuals in poor physical condition [8–9]. Although some studies support this hypothesis and have contributed substantially to the understanding of selection by predation [10–20], (but see [18]), most of them are based on opportunistic observations or rely on some specific traits of the prey [15,18–20] or parasite load [12,13]. Kenward [10] and Temple [11] investigated morphological traits of individuals together with their healthy state, however, sample sizes were rather small (less than 30 prey) and the effects of different traits were not analysed simultaneously, so the contribution of each trait on differential predation it is difficult to evaluate. Additionally, all evidence typically comes from the typical predator-prey system, where traits may have coevolved in parallel, and thus predation upon substandard individuals could be an opportunistic foraging strategy rather than a response to substandard features of the prey. To fully understand the role of predation as a selective force, it is also necessary to collect evidence of predation outside the typical predator-prey system, gather information on large sample sizes, and investigate multiple traits of prey simultaneously. Populations of large gulls have increased substantially over the last century and some species are currently perceived as a pest by wildlife managers [21–24] but see [25]. As a consequence, many conservation agencies have set up culling programs to control gull populations, which typically consist of the systematic removal of large numbers of eggs, chicks or breeding adults. Even if the efficacy of these culling programs is still under debate [25–28], PLoS ONE | www.plosone.org Results We examined 506 gulls that had been shot and 122 gulls removed by raptors. The age structure in the shooting sample was similar to that observed at the dump over five available censuses (Breslow-Day homogeneity test of odds ratio, x 24 ~3.311, P = 0.507; Mantel-Haenszel, odds-ratio -log transformed- 95% Confidence Intervals: [20.066; 0.102], see Table 1). Post-mortem examination of gulls (Table 2) showed a low prevalence of external parasites; however, half of the individuals examined had internal parasites, mainly cestodes. About 7% of the gulls showed infection by Salmonella and 4% by Aspergillus. A few individuals also showed some kind of congenital malformation (bill deformity) and others had alteration of internal organs (Table 2), but prevalence of most 1 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation Table 1. Frequencies of gulls of each age-class (sub-adults and adults) counted during the censuses and compared with those of gulls shot at the dump. Table 2. Results of the exhaustive post-mortem examination of culled gulls (N = 506 and 122 for shooting and caught by raptors respectively). method Census no. 1 census age sub-adults adults Total 2 age sub-adults adults Total 3 age sub-adults adults Total 4 age sub-adults adults Total 5 age sub-adults Adults Total Veterinarian findings shooting Total 1045 224 821 31.1% 32.5% 496 1705 68.9% 67.5% 720 2526 3246 491 Shooting Raptors External parasites 2201 Lice 0.83 8.20 Ticks 0.14 0 Mites 0.56 0 Internal parasites 224 267 31.1% 28.9% 496 657 68.9% 71.1% Salmonella 7.36 1.64 720 924 1644 Aspergillosis 0.28 22.95 684 Retromandibular abscess 0 0.82 White spots on liver 0.14 0 White spots on intestinal wall 0 0.82 Lesions from mites 0.14 0 Atrophies 0 0.82 Lung granulomes 0 0.82 1153 224 460 31.1% 30.3% 496 1058 68.9% 69.7% 720 1518 2238 1342 224 1118 31.1% 29.0% 496 2737 68.9% 71.0% 720 3855 224 481 31.1% 32.8% 496 987 68.9% 67.2% 720 1468 Nematode 4.58 8.20 Cestode 47.08 43.44 Infections 1554 Internal organ findings 3233 Pericarditis 0.14 0 Pancreas congestion 0.14 0 4575 Hepatomegaly 0.56 0.82 705 Splenomegaly 7.22 5.74 Peritonitis 0.14 0 Airsacculitis 0.42 0.82 1483 Mechanical dysfunctions 2188 Frequencies and percentages of each age-class were shown separately for each census period. doi:10.1371/journal.pone.0009774.t001 Traumatism 1.53 33.61 Bill deformity 0.14 0.82 Fishing hooks 0 0.82 Arthritis 0.14 0.82 doi:10.1371/journal.pone.0009774.t002 veterinarian findings were low. Individuals that had been shot showed different characteristics than those killed by raptors (Table 3). The Multiple Component Analysis (MCA) scores plot also showed differences between groups, with more healthy adults and a higher average muscle condition within the group of individuals shot, and more juveniles, gulls in poor condition or showing some signs of illness in the group of individuals killed by birds of prey (Fig. 1). To test for the significance of these differences we used logistic regressions with predation by raptors as the response variable, being ‘‘killed by raptor’’ = 1 and ‘‘killed by shooting’’ = 0. In this way we retained shooting as the intercept of the regression to check for differential predation. The overdispersion value of the saturated model was 1.09, indicating a good fit of the data. The best ranked model (based on AIC values, Table S1) included a negative effect of age and a positive effect of both sickness and poor muscle condition on the probability of being predated by birds of prey (Table 4); this model did not include a gender effect. Strikingly, individuals in unusually good condition were also predated more frequently than expected by chance (Table 4). A model including an effect of sex explained the data equally well and was statistically equivalent to the previous model (Table S1) but the effect was not significant PLoS ONE | www.plosone.org Prevalence (z = 21.293, P = 0.196). Note that all models with lower AIC values unequivocally showed that age, muscle condition and sickness were clues for differential predation by birds of prey (see also Fig. 2). When these three variables were tested separately, results showed that muscle condition was the main factor affecting predation, this variable alone explaining 71% of the total variance. Discussion Natural selection of certain prey traits (e.g. morphological traits) has repeatedly been shown to be driven by predation [10–20]. Our paper could not address such particular issue, but on the other hand, the exhaustive analysis of a large number of prey, combined with the simultaneous analysis of a variety of traits, give us some general insights into how predation may operate in the wild. Here we show that predators did not kill individuals at random, but rather selected their prey on the basis of several, not always related traits. Our results indicated that age, muscle condition and sickness influence the probability of being predated, with juveniles, sick gulls, and individuals with poor muscle condition being killed 2 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation Our study also shows that not only individuals with severe diseases but also those with mild diseases are predated preferentially, indicating that subtle changes in behaviour or condition may have been sufficient to increase susceptibility to predation. This was also found by Miller et al (2000) who showed that prion infection in deer increased the rate of predation of deer by mountain lions (Puma concolor) nearly fourfold, even if few of the deer killed were recorded as ‘‘noticeably ill’’ by field observers prior to their deaths [31]. Our results clearly support the mass-dependent predation risk (MDPR) theory, which predicts that birds should keep their mass as low as possible to reduce their likelihood of being killed by predators [32,33]. To date, empirical evidence for this theory comes only from small passerines with body masses between 10–150gr [34,35 and references therein] and recently from one mammal [36]. Additionally, results showed preferential predation on those individuals with poor muscle condition, suggesting that stabilizing selection [37] could be operating on traits linked to body mass. This article provides a reliable, robust example of how natural selection by predation operates in the wild and strongly supports the paradigm that predators kill substandard individuals. Since gulls are an occasional prey of falcons and hawks, results probably indicate the ability of predators to detect substandard individuals in the wild rather than showing an optimal foraging strategy or possible coevolution within a natural predator-prey system. Table 3. Individual traits of gulls removed by shooting or by the use of birds of prey. Category Level Type of disposal Shooting (N) Age Sex Muscle condition Health Raptors (N) Total N Juveniles 9.68% (49) 36.88% (45) 94 2-year-olds 11.07% (56) 18.85% (23) 79 3-year-olds 13.83%(70) 4.09% (5) 75 Adults 65.41% (331) 40.16% (49) 380 Males 46.8% (237) 60.7% (74) 311 Females 53.2% (269) 39.3% (48) 317 Normal 89.9% (455) 37.7% (46) 501 Low 5.1% (26) 49.2% (60) 86 High 4.9% (25) 13.1% (16) 41 Good 78.7% (398) 50.0% (61) 459 Mild sickness 6.9% (35) 19.7% (24) 59 Severe sickness 14.4% (73) 30.3% (37) 110 506 122 628 Removed birds were examined to determine the sex and age of the individual, the individual nutritional state, assessed from fat layers and muscle condition, and any sign of infection, malformation or disease. We identified four age classes by plumage features: juveniles (from 0 to 1 year old), 2 years old (from 1 to 2 years old), 3 years old (from 2 to 3 years old), and adults (.3 years old). We determined three levels of body condition: normal, low and high, depending on the layers of muscle mass). Veterinarians determined if the illness detected was either severe or mild. doi:10.1371/journal.pone.0009774.t003 Materials and Methods From 2003 to 2007, we carried out 5 gull censuses at the landfill and estimated the proportion of birds in each age class. We identified four age classes by plumage features: juveniles (from 0 to 1 year old), 2-year-olds (from 1 to 2 years old), 3-year- olds (from 2 to 3 years old), and adults (.3 years old); these data were used to assess whether shooting was performed randomly regarding to age, by means of a goodness-of-fit test. We cannot exclude that shooting was biased in relation to veterinarian findings (health state) and muscular condition; however if a bias existed it would rend our comparison more conservative as individuals that were ill or in poorer condition should be shot preferentially [38]. Post-mortem examination of culled individuals was carried out at the Fundació Natura Parc-COFIB wildlife recovery centre in preferentially; thus strongly supporting the hypothesis that predators prey primarily on substandard individuals [8,9]. Natural selection acts on many characters simultaneously [29] and accordingly, our results dealing with natural selection by predation would suggest that two or more traits may affect fitness in an interactive way (i.e. correlational selection) [7,29,30]. Nevertheless, even if our sample sizes were relatively large, they still lack sufficient power to simultaneously test interactive effects between many characters. Figure 1. Multiple Correspondence Analysis between individuals shot and those killed by raptors. Map of the two main factorial axes from a Multiple Correspondence Analysis between individuals shot (noted by Shoot) and those killed by birds of prey (noted by Falco) depending on individual classification (F: Females; M: Males; J: Juveniles; I2: 2 years old; I3: 3 years old; A: Adults; H: Healthy individuals; S: Sick; AM: Abnormal muscle condition (low and high); NM: normal muscle condition). doi:10.1371/journal.pone.0009774.g001 PLoS ONE | www.plosone.org 3 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation number of gulls captured by falcons and those killed by shooting were not equally distributed throughout the year, with comparatively more gulls shot during the breeding period. As a consequence, we randomly balanced the sample size between both periods to assure the comparability of the two data sets. Individuals were shot at random (see above), with the same probability for all birds at the dump to be shot and shooting did not occur in a spatially segregated manner. Hence, comparison between the birds shot and those killed by falcons should reveal any predator preferences. We first used a MCA analysis including age, muscle condition, and sickness to visualize patterns of differentiation among gulls depending on the type of capture and the correlation among traits. We then used logistic regressions to test for differences between the two groups. We assessed the goodness-of-fit of the saturated model by estimating the overdispersion parameter (a value close to 1 indicated a good fit of the data). Model selection was based on Akaike’s Information Criterion (AIC); the model with the lowest AIC value was considered as the best compromise between model deviance and model parameters [39]. We also calculated the AIC weight as a measure of relative plausibility of each model. To avoid model over-parameterization, only additive models were considered and individual age was treated as a continuous covariate. All statistical analyses were done using the software R (www.r-project.org) and SPSS (version 16.0). Table 4. Estimates of the factors affecting predation from the best ranked model, which included age as a continuous covariate, muscle condition as a factor with three levels (normal, low and high), and sickness, as a factor with two levels (healthy and sick). Estimate Std. Error z value Pr(.|z|) Intercept 20.95 0.35 22.76 0.0058 Age 20.59 0.11 25.63 ,0.0001 Low 3.22 0.32 10.17 ,0.0001 High 1.79 0.40 4.46 ,0.0001 1.20 0.27 4.42 ,0.0001 Muscle condition Sickness doi:10.1371/journal.pone.0009774.t004 Mallorca. Within each age-by-sex class, we sorted individuals as healthy, mildly sick or severely sick depending on the veterinarian diagnosis on infections, mechanical or internal dysfunctions; individuals were also classified according to three levels of body condition depending on the pectoral musculature mass: normal, low or high (extremely large muscle mass). In subsequent analyses, sickness was treated in two ways, one by separating mild and severe sickness and a second one by grouping all sick individuals into a single group. Gulls breed in springtime and differences may exist in the number of individuals of different sex or age visiting the dump throughout the year, so we defined two periods: one encompassing the breeding season, from March to July, and a second one including the non-breeding period, from August to February. The Ethics Statement Animals were killed as a part of a culling program that the Local Government began for conservation issues as well as public health concerns. All animal work has been conducted according to relevant national and international guidelines and permits were Figure 2. Determinants of probability of being predated by the raptors. All juveniles and immature classes were grouped in a single, subadult age class and compared with adult gulls. Smoothing regression surfaces are represented using a Lowess method by iteration of weighted least squares on the selected variables. Highest probability of being killed by predators occurred on sub-adult gulls with severe sicknesses and abnormal muscle condition. doi:10.1371/journal.pone.0009774.g002 PLoS ONE | www.plosone.org 4 March 2010 | Volume 5 | Issue 3 | e9774 Natural Selection by Predation provided by Conselleria de Medi Ambient (Govern Balear). Authors were not responsible nor executed the culling programme. Acknowledgments We are grateful to the people involved in falconry and fieldwork, and to Deborah Bonner and Mike Fowler who corrected the English. Three anonymous referees and Erik Svensson provided helpful comments on previous drafts, and Lluı́s Jover helped with statistics. Supporting Information Table S1 Model selection of individual features of gulls predated by birds of prey compared to those shot at the landfill. Found at: doi:10.1371/journal.pone.0009774.s001 (0.04 MB DOC) Author Contributions Conceived and designed the experiments: MG NN GT AB LP DO. Performed the experiments: NN LP. Analyzed the data: MG. Wrote the paper: MG GT DO. Performed the veterinarian inspections: NN LP. References 1. Darwin CR (1859) On the origin of species. London: Murray. 432 p. 2. Ricklefs RE (1969) Natural Selection and the Development of Mortality Rates in Young Birds. Nature 223: 922–925. 3. Dawkins R, Krebs JR (1979) Arms races between and within species. Proc R Soc Lond Ser B Biol Sci 205: 489–511. 4. Endler JA (1986) Natural Selection in the Wild. Princeton: Princeton University Press. 321 p. 5. Reznick DN, Shaw FH, Rodd FH, Shaw RG (1997) Evaluation of the rate of evolution in natural populations of guppies (Poecilia reticulata). Science 275: 1934–1937. 6. Losos JB, Schoener TW, Langerhans RB, Spiller DA (2006) Rapid temporal reversal in predator-driven natural selection. Science 314: 1111. 7. Svensson EI, Friberg M (2007) Selective Predation on Wing Morphology in Sympatric Damselflies. Am Nat 170: 101–112. 8. Errington PL (1946) Predation and vertebrate populations. Q Rev Biol 21: 144–177. 9. Curio E (1976) The ethology of predation. Berlin: Springer Verlag. 250 p. 10. Kenward RE (1978) Hawks and Doves: Factors affecting success and selection in Goshawk attacks on Woodpigeons. J Anim Ecol 47: 449–460. 11. Temple SA (1987) Do predators always capture substandard individuals disproportionately from prey populations? Ecology 68: 669–674. 12. Moller AP, Erritzoe J (2000) Predation against birds with low immunocompetence. Oecologia 122: 500–504. 13. Duffy MA, Hall SR, Tessier AJ, Huebner M (2005) Selective predators and their parasitized prey: top-down control of epidemics. Limn Ocean 50: 412–420. 14. Martı́n J, de Neve L, Polo V, Fargallo JA (2006) Health-dependent vulnerability to predation affects escape responses of unguarded chinstrap penguin chicks. Behav Ecol Soc 60: 778–784. 15. Carlson SM, Rich HB, Quinn TP (2009) Does variation in selection imposed by bears drive divergence among populations in the size and shape of sockeye salmon? Evolution 63: 1244–1261. 16. Penteriani V, Delgado M, Bartolommei P, Maggio C, Alonso-Alvarez C, et al. (2008) Owls and rabbits: predation against substandard individuals of an easy prey. J Avian Biol 39: 215–221. 17. Shine R, LeMaster MP, Moore IT, Olsson MM, Mason RT (2001) Bumpus in the snake den: effects of sex, size and body condition on mortality of red-sided garter snakes. Evolution 55: 598–604. 18. Carlson SM, Hilborn R, Hendry AP, Quinn TP (2007) Predation by Bears Drives Senescence in Natural Populations of Salmon. PLoS ONE 2: e1286. 19. Quinn TP, Hendry AE, Buck GB (2001) Balancing natural and sexual selection in sockeye salmon: interactions between body size, reproductive opportunity and vulnerability to predation by bears. Evol Ecol Res 3: 917–937. 20. Quinn TP, Kinnison MT (1999) Size-selective and sex-selective predation by brown bears on sockeye salmon. Oecologia 121: 273–282. 21. Feare CJ (1991) Control of bird pest populations. In: Perrins CM, Lebreton J-D, Hirons GJM, eds. Bird population studies, relevance to conservation and management. Oxford: Oxford University Press. pp 463–478. PLoS ONE | www.plosone.org 22. Vidal E, Medail F, Tatoni T (1998) Is the yellow-legged gull a superabundant bird species in the Mediterranean? Impact on fauna and flora, conservation measures and research priorities. Biod Cons 7: 1013–1026. 23. 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(2003) Reducing the density of breeding gulls influences the pattern of recruitment of immature Atlantic puffins Fratercula arctica to a breeding colony. J Appl Ecol 40: 545–552. 29. Lande R, Arnold SJ (1983) The measurement of selection on correlated characters. Evolution 37: 1210–1226. 30. Sinervo B, Svensson E (2002) Correlational selection and the evolution of genomic architecture. Heredity 89: 329–338. 31. Miller MW, Swanson HM, Wolfe LL, Quartarone FG, Huwer SL, et al. (2008) Lions and prions and Deer Demise. PLoS ONE 3: e4019. 32. Lima SL (1986) Predation risk and unpredictable feeding conditions-determinants of body mass in birds. Ecology 67: 377–385. 33. Houston AI, McNamara J, Hutchison JMC (1993) General results concerning the trade-off between gaining energy and avoiding predation. Philos Trans R Soc Lond B Biol Sci 341: 375–397. 34. Gosler AG, Greenwood JJD, Perrins C (1995) Predation risk and the cost of being fat. Nature 377: 621–623. 35. 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This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. REVIEWS Natural selection and infectious disease in human populations Elinor K. Karlsson1,2, Dominic P. Kwiatkowski3,4 and Pardis C. Sabeti1,2,5 Abstract |

A Sample Answer For the Assignment: BIO 101 UOP Population Genetics And Phenotypes Worksheet

Title:  BIO 101 UOP Population Genetics And Phenotypes Worksheet

The ancient biological ‘arms race’ between microbial pathogens and humans has shaped genetic variation in modern populations, and this has important implications for the growing field of medical genomics. As humans migrated throughout the world, populations encountered distinct pathogens, and natural selection increased the prevalence of alleles that are advantageous in the new ecosystems in both host and pathogens. This ancient history now influences human infectious disease susceptibility and microbiome homeostasis, and contributes to common diseases that show geographical disparities, such as autoimmune and metabolic disorders. Using new high-throughput technologies, analytical methods and expanding public data resources, the investigation of natural selection is leading to new insights into the function and dysfunction of human biology. Pathogens Viruses, bacteria or other microorganisms that can cause disease. Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA. 2 Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, USA. 3 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. 4 Wellcome Trust Sanger Centre for Human Genetics, Oxford OX3 7BN, UK. 5 Department of Immunology and Infectious Disease, Harvard School of Public Health, Boston, Massachusetts 02115, USA. Correspondence to E.K.K. and P.C.S. e-mails: [email protected]; [email protected] doi:10.1038/nrg3734 Published online 29 April 2014 1 Infectious pathogens are arguably among the strongest selective forces that act on human populations1. Migrations and cultural changes during recent human evolutionary history (the past 100,000 years or so) exposed populations to dangerous pathogens as they colonized new environments, increased in population density and had closer contact with animal disease vectors, including both conventionally domesticated animals (for example, dogs, cattle, sheep, pigs and fowl) and those exploiting permanent human settlement (for example, rodents and sparrows)2,3. Consequently, both birth and mortality rates increased markedly 4. Host genetics strongly influences an individual’s susceptibility to infectious disease 5,6. Pathogens that diminish reproductive potential, either through death or poor health, drive selection on genetic variants that affect resistance; selection is likely to be most evident for pathogens with a long-standing relationship with Homo sapiens, including those that cause malaria, smallpox, cholera, tuberculosis and leprosy 7 (FIG. 1). We also contend with new threats, such as AIDS and severe acute respiratory syndrome (SARS). Some pathogens cause acute illnesses such as smallpox and cholera but, once survived, pose little additional threat. Other pathogens — for example, those causing malaria, tuberculosis and leprosy, as well as parasitic worms — can be carried as chronic infections and impair nutrition, growth, cognitive development and fertility. The timing, strength and direction (that is, positive, negative or balancing) of selection shape the patterns of variation that remain in the genome. These signatures of selection will therefore vary with the age, geographical spread and virulence of the pathogen. For those with access, modern medicine radically diminishes exposure to various pathogens. In developed countries, vaccination, better nutrition and improved public health have eliminated diseases that were common in the past 8. Common immune-mediated diseases may be partly caused by evolutionary adaptations for resistance and symbiosis with potentially dangerous microorganisms9–12. For example, decreased gut microbiome diversity in residents of developed countries13 may alter mucosal immune responses14. Understanding host– pathogen interactions will inform the development of new therapies both to counter ongoing pathogen evolution and to better manage immune-mediated diseases15. Here, we review how the technological revolution in genomics allows us to examine human adaptation to infectious disease in new ways. Natural selection leaves distinctive signatures in the genome, as genetic variants that improve survival and reproduction increase in frequency, and detrimental variants vanish. Hundreds of candidate regions of selection were identified in early genomic data sets, but only few adaptive variants were identified16. High-throughput biotechnology enables large-scale surveys of genome diversity, genome-wide association studies (GWASs), next-generation sequencing, and high-throughput experimental and bioengineering approaches17. Together with expanding computational capacity 18, these tools offer new power to find and functionally elucidate adaptive changes. Pathogen susceptibility and immune traits are particularly amenable NATURE REVIEWS | GENETICS VOLUME 15 | JUNE 2014 | 379 © 2014 Macmillan Publishers Limited. All rights reserved REVIEWS Malaria ~200,000 years ago Modern humans emerge in Africa Tuberculosis Smallpox ~100,000–50,000 years ago Migrations within Africa Signatures of selection An unusual pattern of allele frequencies that marks a selected locus. Migrations out of Africa Leprosy Cholera 12,000 years ago Early agriculture (neolithic demographic transition) AIDS 2,200 years ago Silk Road links Africa, Europe and Asia 500 years ago European colonization of Americas begins Today Globalization Figure 1 | Pathogen emergence during human history. Key events in recent human evolution (boxes outlined in black) are juxtaposed with the estimated ages of infectious disease emergence (boxes outlined in red).Nature The fragmentation of the Reviews | Genetics human lineage into genetically and geographically distinct populations (blue lines) accelerates with migration out of Africa. Later, these populations started mixing more (blue shaded regions between the populations) along trade routes (such as the Silk Road), through colonization and through high rates of global travel nowadays. Frequency Prevalence of an allele in a population. Genome-wide association studies (GWASs). Examination of variants that are distributed across the entire genome for correlation with particular traits. Next-generation sequencing New high-throughput, parallelized, low-cost sequencing technologies that do not use the chain termination Sanger method. Genetic diversity Total amount of genetic variation in a population. Bottlenecks Sharp decreases in the effective sizes of populations. Admixture Interbreeding between two genetically separated populations. Ascertainment bias Nonrandom selection of variants for genotyping. Neutral variation Genetic variation that confers no selective advantage or disadvantage and that varies in frequency by random drift. Linkage disequilibrium (LD). The nonrandom association of alleles at different genomic loci. Fixation The increase in frequency of an allele to 100% in a population. to mapping approaches that combine scans for natural selection and genetic association. We consider how these new genomic analyses provide insights into human evolution and have implications for human health. We focus primarily on examples in which selection is connected to infectious disease susceptibility through additional phenotypic associations or functional investigations. Methods and technologies Signatures of selection. Natural selection is the tendency for traits to increase or decrease in frequency in a population depending on the reproductive success of those exhibiting them. Positive selection increases the frequency of favoured alleles, negative selection eliminates detrimental alleles, and balancing selection favours diversity. This process leaves unusual patterns of genetic diversity that mark selected loci (that is, signatures of selection) when compared with the background distribution of genetic variation in the genome, which is assumed to evolve under neutrality to a large extent 19. Population events that alter genetic diversity — including bottlenecks , expansions, splits and admixture — complicate accurate detection of selected loci. Scans for natural selection have been made possible by statistical tools to detect signatures of selection and by rapidly expanding whole-genome data sets in multiple human populations. Whereas early scans relied on single-nucleotide polymorphism (SNP) genotyping arrays, next-generation sequencing technologies now enable generation of whole-genome sequence data sets for analysis. Such data sets have several advantages. They do not suffer from ascertainment bias, which is the distortion in measures of genetic diversity and neutral variation20 created by the nonrandom sampling of SNPs on arrays. In addition, sequence data make it feasible to dissect loci with complex patterns of selection and short blocks of linkage disequilibrium (LD), such as the haemoglobin beta (HBB) gene that is associated with sickle cell anaemia21. Finally, as sequencing can detect potentially all variation throughout an individual’s genome, the search for the precise causal variant driving selection is facilitated; however, as for SNP genotyping arrays, the comprehensiveness of capturing population-wide variation will depend on the number of individuals sampled. Here, we briefly describe a few commonly used signatures that can help to elucidate human adaptations to pathogens. Various excellent resources provide more detailed background on statistical methods for detecting selection22–26. Signatures of positive selection. Positive selection increases the prevalence of genetic variants that improve survival and fertility. For example, a mutation that protects against malaria by disrupting expression of the Duffy antigen gene DARC (also known as FY), which encodes the receptor used by the Plasmodium vivax malarial parasite to enter red blood cells, has reached fixation in most of sub-Saharan Africa27. Positive selection can act on new variants or on standing variation that becomes favourable owing to environmental changes28,29. The test used to detect positive selection depends on when the selection occurred and on whether the variant is standing or new 28. Very ancient selection may leave an excess of fixed, functional (for example, protein-coding) genetic changes that have been acquired over millions of years and through repeated selective sweeps. A selected variant that increases rapidly in frequency in the past ~250,000 years can be detected as an unusual reduction in genetic diversity. Recent positive selection (within the past 5,000–100,000 years) can be found with three different signals: unusually large allele frequency differences between populations, unusually high frequency of newly derived variants and unusually extended LD caused by the rapid increase in frequency of a single allele. The LD‑based methods30 are particularly useful for detecting incomplete sweeps (that is, variants increasing in prevalence but not to fixation), which are more common in recent human evolution than complete sweeps31–34. 380 | JUNE 2014 | VOLUME 15 www.nature.com/reviews/genetics © 2014 Macmillan Publishers Limited. All rights reserved REVIEWS These methods have detected hundreds of loci with signatures of selection in the human genome16,28,30,35–41. Their sensitivity to recent events depends on the strength of selection, as more advantageous variants increase to detectable frequencies faster. The comparison of many individuals from closely related populations using treebased methods can help to detect smaller selectiondriven changes in allele frequencies 42. Combining several different tests for selection improves sensitivity to a wider range of selection regimes, helps to narrow down candidate regions and pinpoints a small number of top causal candidates28,33,43. Pathogen resistance alleles are prime candidates for discovery, as they are likely to be both recent and common, and have increased in frequency owing to the burden of disease. Standing variation Existing genetic variation within a population. Selective sweeps Reductions in genetic variation caused by positive selection at particular loci. Incomplete sweeps Partial or ongoing selective sweeps of advantageous alleles to 1%) human genetic variation by sequencing >1,000 individuals90, and the Encyclopedia of DNA Elements (ENCODE) Project, which aims to characterize all functional elements in the genome91. Breakthroughs in next-generation sequencing, high-throughput functional screens, single-cell genomics, microfluidics, chromosome conformation capture and genome engineering approaches now make it possible to test many variants in parallel, to investigate non-genic regions and to functionally screen the whole genome17,92–99. Genetics of infectious disease resistance The dynamics of host–pathogen interactions (for example, length of exposure, geographical spread, morbidity and mortality, and co‑occurring environmental events) influences the genetic architecture of resistance variants in modern populations (FIG. 4). Many different modes of selection shape patterns of variation in humans (reviewed in REFS 25,26), and selection scans using current methods and data sets can only detect a subset of selected loci. The most conspicuous signals are perhaps left by positive selection in recent human evolutionary history. This is the timeframe during which many major pathogens first emerged (FIG. 1; TABLE 1), which suggests that this mode of selection is particularly relevant to studies of infectious disease susceptibility. Moreover, finding beneficial variants that are favoured by recent selection could suggest new medical therapies. Transforming genetic discoveries into improved healthcare will take time, but understanding natural resistance is an important first step. We note that some have questioned the extent of recent positive selection in humans in light of a recent paper 31. In that paper, the authors estimated that during the past 250,000 years, ~0.5% of nonsynonymous substitutions have swept to fixation (that is, 100% prevalence), and such an observation has been interpreted to suggest that very few positively selected variants can be found in humans. In actuality, this corresponds to NATURE REVIEWS | GENETICS VOLUME 15 | JUNE 2014 | 383 © 2014 Macmillan Publishers Limited. All rights reserved REVIEWS A Variant found with selection scan B Disease-associated variant Aa Signal of selection Ba Signal of disease association Ser → Gly Ile → Met 16 Leu → Phe 8 –log10P –log10P 20 10 0 0 APOL1 TLR5 1 cM 160 kb Bb Signal of selection Extended haplotype homozygosity Ab Functional hypothesis Leu616 1 Allele G1 Allele G2 Wild type 0 APOL1 160 kb Ac In vitro pathogen response phenotype NF-κB activity (luminescence) Control Leu Phe P < 0.001 20 Bc In vivo pathogen resistance phenotype Wild type Allele G1 Allele G2 P < 0.01 P < 0.05 10 0 PMA (control) 0 1 5 10 100 Bacterial flagellin (ng ml–1) | Genetics Figure 3 | Selected variants implicated in pathogen resistance. A | A genome-wide scan forNature signalsReviews of positive selection in the Yoruban population of Nigeria found a strongly selected nonsynonymous single-nucleotide polymorphism (SNP) that alters the pathogen recognition protein Toll-like receptor 5 (TLR5) (part Aa) and that is predicted to disrupt TLR5 activation in response to flagellated bacteria33 (part Ab). Cell lines that carry the new TLR5 variant (Leu616Phe) had significantly reduced nuclear factor‑κB (NF‑κB) signalling in response to flagellin, which is potentially protective against some bacterial infections (part Ac). Error bars represent the standard error of the mean over at least three independent experiments; P values are indicated above the bar graphs. B | Two common variants of the apolipoprotein L1 (APOL1) gene (Allele G1 and Allele G2) that are strongly associated with kidney disease in African Americans (part Ba) show evidence of recent positive selection in Yorubans86 (part Bb). In vitro, the G1 and G2 variants lyse subspecies of the Trypanosoma spp. pathogen that are resistant to wild-type APOL1 (part Bc). Arrows point to the swelling lysosome. cM, centimorgan; PMA, phorbol myristate acetate. Part A reprinted from Cell, 152, Sharon R. Grossman et al., Identifying recent adaptations in large-scale genomic data, 703–713, © (2013), with permission from Elsevier. Part B from Genovese, G. et al. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329, 841–845 (2010). Reprinted with permission from AAAS. 384 | JUNE 2014 | VOLUME 15 www.nature.com/reviews/genetics © 2014 Macmillan Publishers Limited. All rights reserved REVIEWS Timeframe of selection Geography of selection GWAS population Ancient Widespread Any population Widespread Any population Recent (after human dispersal) Selected population Power of GWAS and selection Highly variable Example pathogen Plasmodium spp. (malaria) Intermediate Variola virus (smallpox) Most power Vibrio cholerae (cholera) Variable Mycobacterium leprae (leprosy) Specific populations Unselected population New (within last thousands of years) No added power HIV (AIDS) Figure 4 | The power offered by combining natural selection with GWASs depends on the age of selection and Nature Reviews | Genetics populations chosen. For pathogens that predate human dispersal from Africa, ancient and complex signals of selection are shared between human populations, and there are variable implications for genome-wide association studies (GWASs). For widespread pathogens that are more recent, the range of new resistance variants will be more limited, but selection is harder to detect when it is shared between populations. For recent pathogens that affect specific populations, GWASs in the selected populations will be particularly powerful, as causal variants will have been driven to high prevalence. Selection signals from one population may help to detect resistance loci in other unselected populations, but only if resistance variants arise in the same genetic loci. For GWASs of very new diseases, supplementing these studies with methods to detect selection will add no power, unless variants that confer resistance also protect against more ancient pathogens. Examples of pathogens matching each scenario are given on the right. ~340 adaptive nonsynonymous mutations in the 1000 Genomes Project data. Moreover, much (probably most) adaptive evolution occurred in regulatory, noncoding regions100,101, and most recent selective sweeps are far from complete102. Thus, the actual number of positively selected loci could be much larger. Under the framework of recent positive selection, one would anticipate that genetic variants conferring pathogen resistance that are moderately old (dating since the human migrations from Africa but at least thousands of years old), that were geographically limited in history and that exerted strong positive selective pressure will be most readily detected, provided that the studies are carried out in the population with the history of disease exposure. Many of the pathogen studies so far are imperfect fits for these criteria. Here, we review some of the prominent diseases of human history (TABLE 1), and discuss the strengths and limitations of investigating natural selection and association for these traits. Malaria. Malaria is caused by obligate parasitic Plasmodium spp., which infects hundreds of millions of people and kills ~1 million children annually 103. P. falciparum has afflicted humans for ~100,000 years, and a rapid upsurge of malaria ~10,000 years ago increased selective pressure on some human populations27,104. As a result, incidence of sickle cell disease and other inherited red blood cell disorders that are associated with malaria resistance (for example, α-thalassemia, glucose‑ 6‑phosphate dehydrogenase (G6PD) deficiency and ovalocytosis) coincides with the geographical distribution of malaria27. The presence of a disease in a population may indicate that the pathogen exerts selective pressure, but the inverse — absence of disease — can also be meaningful. Although P. falciparum is common in sub-Saharan Africa, P. vivax is noticeably absent. A mutation in the human DARC gene 105 that disrupts expression of the Duffy antigen receptor to prevent infection106 has become 100% prevalent. In a possible example of NATURE REVIEWS | GENETICS VOLUME 15 | JUNE 2014 | 385 © 2014 Macmillan Publishers Limited. All rights reserved REVIEWS Table 1 | Age and geographical origin of major human pathogens Pathogen Disease Pathogen Pathogen Place of type genome origin size (kb) Approximate Human age of mortality rate pathogen Length of illness GWAS? Population used in GWAS and/or replication Plasmodium falciparum Malaria Protozoa GWAS refs 24,000 Africa before human dispersal >100,000 years 2–30% for severe malaria Variable Yes Ghanaian and Gambian 21, 111 Mycobacterium Tuberculosis Gramtuberculosis positive bacteria 4,000 East Africa 40,000 years 10% develop active tuberculosis, of whom ~70% die Years Yes Ghanaian, Gambian, Indonesian, Japanese, Malawian, Thai and Russian 76, 136, 199, 200 Variola virus DNA virus 186 East Asia or Africa 15,000– 70,000 years 1–30% Weeks Yes European, African American and Hispanic 179, 180 Mycobacterium Leprosy leprae Grampositive bacteria 33,000 East Africa or the Middle East >10,000 years Not typically lethal, but chronic infection reduces fertility Years Yes Chinese 52, 126 Vibrio cholerae Cholera Gramnegative bacteria 4,000 Ganges River Delta >5,000 years 5–50% Days No NA NA HIV‑1 AIDS Lentiviral type of retrovirus 9.2 West and 30 million people140. HIV‑1 infects immune cells and causes a progressive and incurable failure of the immune system that allows other opportunistic infections, such as tuberculosis, to take hold. Both HIV‑1 and the distantly related virus HIV‑2 are predicted to be recent (30,000 years and probably much longer 143. In the last century, at least ten primate-to‑human SIV transmissions have been documented142. This suggests that human populations, particularly those in Africa, may have experienced ancient lentivirus epidemics and driven variants that confer resistance to modern HIV strains to prevalence through natural selection. The role of host genetics in HIV infection has been extensively researched, and the NIH Catalog of published GWASs lists at least 15 HIVrelated publications (4 of which report significant associations of P < 1 × 10−8), but any connection to ancient selection remains unclear. Among the first HIV resistance variants to be elucidated is a 32‑base deletion in the cell surface receptor gene CCR5 (known as CCR5Δ32) that prevents the expression of the receptor on T cells and confers complete HIV immunity on homozygous carriers144,145. On the bases of the high prevalence of the variant in northern Europe and the apparently high LD with nearby variants, some researchers hypothesized that CCR5Δ32 was 300 selected regions. The most strongly selected genes were also associated with cholera susceptibility in a targeted analysis158. A gene set enrichment analysis159 found that two types of genes were statistically overrepresented in the selected regions compared with the rest of the genome: genes encoding potassium channels that are involved in cyclic AMP-mediated chloride secretion, and genes encoding components of the innate immune system that are involved in nuclear factor‑κB (NF‑κB) signalling. The success of the enrichment analysis suggests that cholera resistance in Bangladesh, similarly to pigmentation in Europe, provides an exceptionally strong evolutionary advantage and has driven selection at many different genomic loci. The history of selection could make GWASs of cholera susceptibility in Bangladesh particularly powerful. As cholera is still common in Bangladesh, unlike leprosy in Europe, such a study is feasible. The results could help to elucidate the power for mapping positively selected resistance variants that protect against other pathogens with geographical disparity and high mortality. 388 | JUNE 2014 | VOLUME 15 www.nature.com/reviews/genetics © 2014 Macmillan Publishers Limited. All rights reserved REVIEWS Norovirus. The single-stranded RNA viruses of the genus Norovirus are the leading cause of extremely contagious viral gastroenteritis outbreaks worldwide160; they are particularly dangerous to young children and cause up to 200,000 deaths each year in developing countries161. The origin of Norovirus is obscured by the rapid evolution of these viruses162–164, but complex signals of selection in humans suggest that they could be very old. Individuals who are homozygous for null mutations of the fucosyltransferase 2 (FUT2) gene do not secrete ABO antigens and are protected against some strains165–167. Non-secretors are common worldwide (for example, in 20% of Caucasians). The underlying FUT2 mutational spectrum is unexpectedly complex, as it is comprised of multiple independent mutations that vary in frequency between populations and that have diverse evolutionary signatures, from long-term balancing selection to recent positive selection168. Influenza. Great pandemics inflict massive mortality and are of particular interest to evolutionary geneticists. The most striking modern example is the 1918 influenza pandemic, which was caused by an unusually lethal strain of influenza A that killed 50–100 million people, including many previously healthy young adults 169,170. Influenza is almost certainly very old: Hippocrates described a flu-like illness ~2,400 years ago171. Observational data suggest that host genetics influences susceptibility to severe illness172; for example, cases of the highly pathogenic H5N1 strain show strong familial aggregation173. Recently, interferoninduced transmembrane proteins (IFITMs) have been implicated in resistance to influenza A. IFITMs inhibit in vitro replication of some pathogenic viruses174, and IFITM3 expression protects against infection by multiple strains of influenza A in vitro and in vivo175. Hospitalized patients with severe influenza were significantly more likely to carry a splice acceptor site variant in IFITM3 that reduces its ability to restrict influenza virus replication in vitro175. Versions of IFITM3 that protect against influenza seem likely to confer a selective advantage, and selection scans show signals of recent positive selection in the IFITM3 region175. Pleiotropic effects Effects on multiple unrelated phenotypes. Smallpox. Only a century ago, smallpox — caused by the Variola virus — ravaged human societies with mortality rates of up to 30%. It was an ancient and widespread scourge, and was described in historical records thousands of years old from China, India and Egypt. It is now gone and represents the only infectious disease in humans that has been eradicated by modern medicine. Variola virus has a highly conserved (>99.6% across 45 isolates) 186‑kb double-stranded DNA genome176. Its extremely low mutation rate, simple genetic makeup and reliance on humans as its only host limited its ability to adapt and facilitated its eradication. The age and phylogeography of smallpox is unresolved despite efforts to integrate historical records with sequence data from 45 viral isolates176,177. We noted that, for 32 viral isolates with documented mortality, death rates are lower in Africa (0.4−12%) than elsewhere (4−38%), even though all isolates were from a single phylogenetic clade178. This is consistent with selection for resistance in Africa, where the smallpox virus is predicted to have evolved from a rodent-borne ancestor tens of thousands of years ago and where outbreaks of other poxviruses continue nowadays. Human evolutionary history may help to clarify the origins of smallpox. With smallpox eradicated, vaccine response is used as a crude phenotypic proxy for studying host resistance. Two GWASs that included European, African American and Hispanic populations identified 37 SNPs associated with cytokine response to vaccination179,180 (P < 1 × 10−8). Most of the significant associations (65%) were found in African Americans, even though their sample size was half of that of the European cohorts, which is consistent with a larger effect due to selection in Africa. These results are preliminary: the studies had relatively small sample sizes (~200 African Americans), no overlap in their results and no replication. Incorporating tests for natural selection could add power for detecting true associations. Infectious disease selection and common disease The hygiene hypothesis proposes that autoimmune disorders are partly caused by differences between the pathogen-rich environment in which our immune system evolved and the more sterile modern world. In the absence of diverse pathogens from which to defend ourselves, our immune responses may turn on us12. Loci associated with common inflammatory disorders are enriched for signals of positive selection1,181–183, and GWASs have proved particularly powerful for this class of diseases184. Elucidating the effect of ancient selection for pathogen resistance should help to decipher the aetiology of autoimmune diseases89 but will require more data on immune responses to common pathogens. In cases in which selected variants have pleiotropic effects, pathogen-driven selection may even underlie diseases with no apparent immune component. Inflammatory bowel disease. Inflammatory bowel disease (IBD) is a group of disorders, including Crohn’s disease and ulcerative colitis, that are caused by autoimmune attacks on the gastrointestinal system. One hundred and sixty-three distinct loci have been significantly associated with IBD risk using meta-analyses of up to 75,000 European cases and controls, and these loci are strongly enriched for signatures of selection11,117. Moreover, seven of the eight leprosy susceptibility loci52,126 are also associated with increased IBD risk54,125. Risk allele frequencies at some IBD loci correlate with local pathogen diversity, which is consistent with pathogen-driven selection185. These observations broadly support the hygiene hypothesis and connect autoimmunity to ancient evolution for pathogen resistance. However, the relationship is not straightforward. Of the four leprosy risk loci that precisely overlap IBD association peaks11, the IBD risk variant is associated with decreased leprosy risk at only two loci; the other two associate with increased risk. Further complicating the story, one of those two — the NATURE REVIEWS | GENETICS VOLUME 15 | JUNE 2014 | 389 © 2014 Macmillan Publishers Limited. All rights reserved REVIEWS variant in NOD2 — is associated with both an increased risk of Crohn’s disease and protection against ulcerative colitis186. One potential source of the seemingly discrepant GWAS results is population differences. Whereas the 163 IBD loci were identified in cohorts of European ancestry, the leprosy GWASs were carried out in East Asian populations. The NOD2 pathway association with leprosy resistance in East Asians has not been replicated in other populations127. In addition, East Asians rarely carry the functional knockout mutation in TLR1 that is common in Europe. Experimental data suggest that TLR1 and NOD2 activate distinct pathways in response to leprosy infection187; thus, correlating East Asian pathogen resistance variants with autoimmune disease risk in Europeans may not be informative. A second factor is simply the lack of data on many pathogens. The remarkable overlap between GWAS loci of leprosy and IBD may reflect a bias in the available data, as leprosy is only one of the few pathogen susceptibility GWASs completed. Not a single GWAS has been carried out, for example, on susceptibility to parasitic worms, which are potentially of great relevance to gastrointestinal disorders such as IBD. Coeliac disease. Coeliac disease is a strongly heritable188 (~80%) inflammatory intestinal disorder triggered by gluten consumption. Despite severely affecting nutritional intake, coeliac disease occurs at 1–2% in Europe189 and up to 6% in North African Sahrawi 190. Loci associated with coeliac disease have signatures of positive selection181. A functional analysis of one selected locus in the SH2B adaptor protein 3 (SH2B3) gene found that individuals who are homozygous for the coeliac risk allele (~22% of the European population) have stronger activation of the NOD2 pathway and a 3–5‑fold higher pro-inflammatory cytokine response to lipopolysaccharide191. Better protection against bacterial infection may have conferred a selective advantage that outweighed the increased risk of coeliac disease risk. Inferring selection pressure is problematic, as gluten consumption and thus the selection against coeliac disease probably changed with agriculture. A crude estimate of the age of the SH2B3 variant based simply on haplotype length suggested that it was very recent 191 (

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