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Effect of zero selection (pressure) on the population health

Effect of zero selection (pressure) on the population health


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Evolution naturally produces better features: stronger muscles, teeth and minds. Killing the weakest, evolution wipes defective genes out of populations.

The mutations are necessary for advance. However, they are random and, thus, mostly negative. Right? How does the nature eliminate them from the population?

I see that one mechanism is polygamy: male have higher mutation rates. They tend to reproduce as much as possible. Female have lower mutation rates, they couple with only with best men and contribute much more to their offsprings. So, men generate as much random solutions as possible, whereas the role of female is to conserve the best of them. This means that the most of the male (bad mutations) die unreproduced. This eliminates the bad mutations and favours the progress of good qualities. The higher animals started to form harems and tournaments over their ownership. This further fosters the elimination of weak men and profileration of strong genes. However, human monogamy has disabled this selection mechanism. Best men are dedicated to only one woman. Other women cannot mix their genes with other good genes from previous generations. They are forced to look at the low quality men, who carry degenerate mutations. Might be it is monogamy that makes us human but it implies that all genes reproduce.

Yet 100 years ago it was not a big deal since we still had another filter: our grandfathers were born in families that had normally 10 children (I am speaking about Russia) and only 2-3 survived until reproduction (the population increased really slowly). I bet that those who survived had really good health: thanks to the bad health and immense selection pressure, the negative mutants are eliminated immediately and population maintains a perfect genome (aka biologic health). Killing more weakest in the current generation improves the health of next generations.

However everything has changed during the 20th century. The advances in economy and medicine have almost eliminated the selection. The selection pressure has relaxed to essentially zero so that everybody survives and leaves the same amount of offspring (and some scientists are even concerned that degradatory individuals leave more offsprings than the prosperous ones). We have even eliminated the infant mortality. We use baby incubators so that the people with the weakest health could survive and reproduce as normal and we are proud of it. It may sound strange but the mutation rate has not changed at all. Does it mean that the human genome is in danger?

I see the situation like you have inherited a perfect mechanism and decided not recovering it from the inevitably adverse action of entropy, that degrades it constantly. There is a constant chance, d, that every good gene is affected by a mutation. It is a fraction of healthy genes that will fail passing to the next generation. This will leave only (1-d) genes healthy in the next generation. The fraction of healthy genes will melt like 1, (1-d), (1-d)², (1-d)³, an so on, with every generation.

You see the fraction that stays healthy over generations. It decays exponentially. Any utility turns into entropy (aka garbage) exponentially, if not protected. Is this model correct? How quickly does this dissipation go? What is d? Let's assume absolute survival and equality in the reproduction rate among all groups people. How many generations is needed to lose 50% of good qualities?


I need to point out one thing, natural selection does not bring species to perfection. The best mutant may not be selected for many reasons.

When you have no selection pressure then you have neutral evolution concurring and what takes over instead of natural selection is genetic drift. Genetic drift is just sample error. Say you have 1,000 individuals in a population and all of those individuals reproduce, in the next generation all the genotypes would be in the next population. Now imagine if you only had 10 individuals in the population and only 5 of those individuals were randomly selected to produce 10 individuals in the next generation (the parents die every year). There is no selection pressure but just by chance some individuals reproduce and some don't. If you do this over and over again what you will see is that some genes become fixed in the population, meaning that there is no other genotype in the population.

This fixation at small population sizes can occur with either beneficial or deleterious mutations. Obviously if a mutation is very deleterious the individual will die but slightly deleterious mutations can become fixed in the population as well.

There is an entire field of biology devoted to this study. It is called population genetics. The probability for fixation for an allele is its proportion in the population. This proportion can be calculated by 1/2N, where N is the population size.


Even if a species lives in abundance, there is still an evolutionary arms race: who reproduces the fastest? Even slight advantages in reproduction rate multiply over the generations.

There are, however, cases where selection pressures on specific traits have vanished. For example, for fish living in caves, there is not selection pressure to be able to see, and they lost their eyes. A more complete discussion can be found in this paper: "Relaxed selection in the wild" -- see also this summary with a funny cover image of a species living in abundance.


Selection Transforms the Landscape of Genetic Variation Interacting with Hsp90

Affiliations Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America, Department of Biology, Stanford University, Stanford, California, United States of America

Roles Investigation, Resources, Writing – review & editing

Affiliations Department of Biology, Stanford University, Stanford, California, United States of America, Department of Genetics, Stanford University, Stanford, California, United States of America

Roles Investigation, Resources, Writing – review & editing

Affiliation Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America

Roles Investigation, Resources, Writing – review & editing

Affiliation Department of Genetics, University of Georgia, Athens, Georgia, United States of America

Roles Conceptualization, Funding acquisition, Writing – original draft, Writing – review & editing

Affiliation Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America


Effects of natural selection and gene conversion on the evolution of human glycophorins coding for MNS blood polymorphisms in malaria-endemic African populations

Malaria has been a very strong selection pressure in recent human evolution, particularly in Africa. Of the one million deaths per year due to malaria, more than 90% are in sub-Saharan Africa, a region with high levels of genetic variation and population substructure. However, there have been few studies of nucleotide variation at genetic loci that are relevant to malaria susceptibility across geographically and genetically diverse ethnic groups in Africa. Invasion of erythrocytes by Plasmodium falciparum parasites is central to the pathology of malaria. Glycophorin A (GYPA) and B (GYPB), which determine MN and Ss blood types, are two major receptors that are expressed on erythrocyte surfaces and interact with parasite ligands. We analyzed nucleotide diversity of the glycophorin gene family in 15 African populations with different levels of malaria exposure. High levels of nucleotide diversity and gene conversion were found at these genes. We observed divergent patterns of genetic variation between these duplicated genes and between different extracellular domains of GYPA. Specifically, we identified fixed adaptive changes at exons 3-4 of GYPA. By contrast, we observed an allele frequency spectrum skewed toward a significant excess of intermediate-frequency alleles at GYPA exon 2 in many populations the degree of spectrum distortion is correlated with malaria exposure, possibly because of the joint effects of gene conversion and balancing selection. We also identified a haplotype causing three amino acid changes in the extracellular domain of glycophorin B. This haplotype might have evolved adaptively in five populations with high exposure to malaria.

Copyright © 2011 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Figures

Spatial Distribution of SNPs and…

Spatial Distribution of SNPs and Genetic Difference between Glycophorins (A) Gene structures of…

Geographic Distribution of Malarial Endemicity…

Geographic Distribution of Malarial Endemicity and Sampled Populations in Africa The map of…

Phylogeny of Glycophorins in Great…

Phylogeny of Glycophorins in Great Apes A maximum likelihood tree of glycophorin homologous…

Structural Models of Glycophorins A…

Structural Models of Glycophorins A and B (A) Extracellular domain of GYPA. O-…


A POPULATION PERSPECTIVE

For nations to improve the health of their populations, some have cogently argued, they need to move beyond clinical interventions with high-risk groups. This concept was best articulated by Rose (1992), who noted that “medical thinking has been largely concerned with the needs of sick individuals.” Although this reflects an important mission for medicine and health care, it is a limited one that does little to prevent people from becoming sick in the first place, and it typically has disregarded issues related to disparities in access to and quality of preventive and treatment services. Personal health care is only one, and perhaps the least powerful, of several types of determinants of health, among which are also included genetic, behavioral, social, and environmental factors (IOM, 2000 McGinnis et al., 2002). To modify these, the nation and the intersectoral public health system must identify and exploit the full potential of new options and strategies for health policy and action.

Three realities are central to the development of effective population-based prevention strategies. First, disease risk is currently conceived of as a continuum rather than a dichotomy. There is no clear division between risk for disease and no risk for disease with regard to levels of blood pressure, cholesterol, alcohol consumption, tobacco consumption, physical activity, diet and weight, lead exposure, and other risk factors. In fact, recommended cutoff points for management or treatment of many of these risk factors have changed dramatically and in a downward direction over time (e.g., guidelines for control of “hypertension” and cholesterol), in acknowledgment of the increased risk associated with common moderately elevated levels of a given risk factor. This continuum of risk is also apparent for many social and environmental conditions as well (e.g., socioeconomic status, social isolation, work stress, and environmental exposures). Any population model of prevention should be built on the recognition that there are degrees of risk rather than just two extremes of exposure (i.e., risk and no risk).

The second reality is that most often only a small percentage of any population is at the extremes of high or low risk. The majority of people fall in the middle of the distribution of risk. Rose (1981, 1992) observed that exposure of a large number of people to a small risk can yield a more absolute number of cases of a condition than exposure of a small number of people to a high risk. This relationship argues for the development of strategies that focus on the modification of risk for the entire population rather than for specific high-risk individuals. Rose (1981) termed the preventive approach the “prevention paradox” because it brings large benefits to the community but offers little to each participating individual. In other words, such strategies would move the entire distribution of risk to lower levels to achieve maximal population gains.

The third reality, provided by Rose's (1992) population perspective, is that an individual's risk of illness cannot be considered in isolation from the disease risk for the population to which he or she belongs. Thus, someone in the United States is more likely to die prematurely from a heart attack than someone living in Japan, because the population distribution of high cholesterol in the United States as a whole is higher than the distribution in Japan (i.e., on a graph of the distribution of cholesterol levels in a population, the U.S. mean is shifted to the right of the Japanese mean). Applying the population perspective to a health measure means asking why a population has the existing distribution of a particular risk, in addition to asking why a particular individual got sick (Rose, 1992). This is critical, because the greatest improvements in a population's health are likely to derive from interventions based on the first question. Because the majority of cases of illness arise within the bulk of the population outside the extremes of risk, prevention strategies must be applicable to a broad base of the population. American society experienced this approach to disease prevention and health promotion in the early twentieth century, when measures were taken to promote sanitation and food and water safety (CDC, 1999b), and in more recent policies on seat belt use, unleaded gasoline, vaccination, and water fluoridation, some of which are discussed later in this chapter.

The committee recognizes that achieving the goal of improving population health requires balancing of the strategies aimed at shifting the distribution of risk with other approaches. The committee does, however, endorse a much wider examination, and ultimately the development, of new population-based strategies. Three graphs illustrate different models for risk reduction (see Figure 2𠄱).

FIGURE 2𠄱

Models for risk reduction. SOURCE: Data for current distribution from Schwartz and Woloshin, 1999.

These hypothetical models assume etiological links exist among all exposures and disease outcomes. Figure 2� shows the effects of an intervention aimed at reducing the risk of those in the highest-risk category. In this example, people with the highest body mass index (BMI) 1 are at in creased risk for cardiovascular heart disease and a plethora of chronic illnesses. Intervening medically, for example, to decrease risk (by lowering levels of obesity, as measured by BMI) ultimately decreases the proportion of the population with the highest BMIs. Such measures among very high-risk individuals may even be endorsed in cases where the “intervention” itself carries a substantial risk of poor outcome or side effects. However, use of such an intervention would be acceptable only in those whose medical risk was very high. Moreover, interventions in high-risk groups may have a limited effect on population outcomes because the greater proportion of those with moderate risk levels may ultimately translate into more chronic disease or other poor health outcomes.

Figure 2� illustrates Rose's classic model whereby the greatest benefit is achieved by shifting the entire distribution of risk to a lower level of risk. Because most people are in categories of moderately elevated risk as opposed to very high risk, this strategy offers the greatest benefit in terms of population-attributable risk, assuming that the intervention itself carries little or no risk. The hypothetical example shows what might occur if social policies or other population-wide measures were adopted to promote small decreases in weight in the general population. The committee embraces this kind of model of disease prevention in the case of policies such as seat belt regulation and the reduction of lead levels in gasoline.

The final hypothetical model (Figure 2�), although not discussed by Rose explicitly, illustrates a reduction in the distributions of those at highest and lowest risk with no change in the distribution of those with a mean level of risk. This model is appropriate for illustrating phenomena relating to inequality, where redistribution of some good (e.g., income, education, housing, or health care) reduces inequality without necessarily changing the mean of the distribution of that good. One hypothetical example is the association between low income and poor health. In many cases, there is a curvilinear association between these goods and health outcomes, with decreased health gains experienced by those at the upper bounds of the distribution. For example, data on income suggest that there are large differences in the health gains achieved per dollar earned for those at the lower end of the income distribution and fewer differences in the health gains achieved per dollar earned for those at the upper end. Thus, the curvilinear association, if it were a causal one, would suggest that substantial gains in population-level health outcomes may be achieved by a redistribution of some resources without actual changes in the means.

These graphs help to illustrate three different strategies for improving the health of the population. The nation has often endorsed the first strategy without a critical examination of the other two, especially the second one. The American public has grown accustomed to seeing differences in exposures to risk, both environmental and behavioral, and disparities in health outcomes. Acknowledging these gradients fully will help develop true population-based intervention strategies and help the partners who collaborate to assure the public's health move to take effective actions and make effective policies.

Understanding and ultimately improving a population's health rest not only on understanding this population perspective but also on understanding the ecology of health and the interconnectedness of the biological, behavioral, physical, and socioenvironmental domains. In some ways, conventional public health models (e.g., the agent–host𠄾nvironment triad) have long emphasized an ecological understanding of disease prevention. Enormous gains in the control and eradication of infectious diseases rested upon a deep understanding of the ecology of specific agents and the power of environmental interventions rather than individual or behavioral interventions to control disease. For example, in areas where sanitation and water purification are poor, individual behaviors, such as hand washing and boiling of water, are emphasized to reduce the spread of disease. However, when environmental controls become feasible, it is easy to move to a more “upstream” 2 intervention (like municipal water purification) to improve health. The last several decades of research have resulted in a deeper understanding not only of the physical dimensions of the environment that are toxic but also of a broad range of related conditions in the social environment that are factors in creating poor health. These social determinants challenge the discipline of public health to more fully incorporate them.

Over the past decade, several models have been developed to illustrate the determinants of health and the ecological nature of health (e.g., see Dahlgren and Whitehead [1991], Evans and Stoddart [1990], and Appendix A). Many of these models have been developed in the United Kingdom, Canada, and Scandinavia, where population approaches have started to shape governmental and public health policies. The committee has built on the Dahlgren-Whitehead model—which also guided the Independent Inquiry into Inequalities in Health in the United Kingdom—modifying it to reflect special issues of relevance in the United States (see Figure 2𠄲). This figure serves as a useful heuristic to help us think about the multiple determinants of population health. It may, for instance, help to illustrate how the health sector, which includes governmental public health agencies and the health care delivery system, must work with other sectors of government such as education, labor, economic development, and agriculture to create “healthy” public policy. Furthermore, the governmental sector needs to work in partnership with nongovernmental sectors such as academia, the media, business, community-based organizations and communities themselves to create the intersectoral model of the public health system first alluded to in the 1988 Institute of Medicine (IOM) report and established in this report as critical to effective health action.

FIGURE 2𠄲

A guide to thinking about the determinants of population health. NOTES: Adapted from Dahlgren and Whitehead, 1991. The dotted lines between levels of the model denote interaction effects between and among the various levels of health determinants (Worthman, (more. )

Most models of health determinants identify macro-level conditions and policies (social, economic, cultural, and environmental) as potent forces in shaping midlevel (working conditions, housing) and proximate (behavioral, biological) determinants of health. Macro-level or upstream determinants (such as policies and societal norms) and micro-level determinants (such as sex or the virulence of a disease agent) interact along complex and dynamic pathways to produce health at a population level. As mentioned above, exposures at the environmental level may have a greater influence on population health than individual vulnerabilities, although at an individual level, personal characteristics including genetic predispositions interact with the environment to produce disease. For instance, smoking is a complex biobehavioral activity with both significant genetic heritability and nongenetic, environmental influences, and many studies have shown an interaction between smoking and specific genes in determining the risk of developing cardiovascular disease and cancers. It is also important to note that developmental and historical conditions change over time at both a societal level (e.g., demographic changes) and an individual level (e.g., life course issues) and that disease itself evolves as agents change in virulence.

In the pages that follow, the committee provides a concise discussion of the key determinants that constitute the ecology of health, including environmental and social determinants, and elaborates in more detail on the social influences on health. This decision was made in recognition of a longer history in studying the ways in which environment shapes population health.


MATERIALS AND METHODS

Data preparation

Seventeen datasets (Li et al., 2008 Teo et al., 2009 Behar et al., 2010 Rasmussen et al., 2010 The, 1000 Genomes Project Consortium, 2010 The International HapMap 3 Consortium, 2010 Metspalu et al., 2011 Pagani et al., 2012 Yunusbayev et al., 2012 Di Cristofaro et al., 2013 Fedorova et al., 2013 Xing et al., 2013 Kovacevic et al., 2014 Raghavan et al., 2014 Yunusbayev et al., 2015 Mondal et al., 2016 Pagani et al., 2016) containing genotype data from worldwide human populations were obtained from the listed resources (Table S1). After downloading, all the genotype data were liftovered to genomic coordinates using the Human Reference Genome Hg19. A merged dataset containing 6531 individuals was obtained after removing duplicated and related individuals. After merging, SNPs with call rate less than 0.99 or individuals with call rate less than 0.95 were removed. SNPs in strong linkage disequilibrium were further removed by applying a window of 200 SNPs advanced by 25 SNPs and an r 2 threshold of 0.8 (--indep-pairwise 200 25 0.8) in PLINK 1.7 (Purcell et al., 2007). This LD-pruning was applied to each population separately. The remaining 61,597 SNPs were used for further analysis. In order to mitigate the bias induced by population migration, potential admixed populations, such as the Middle East People and South Asians, were excluded according to previous studies (Li et al., 2008 Teo et al., 2009 Behar et al., 2010 Rasmussen et al., 2010 The 1000 Genomes Project Consortium, 2010 The International HapMap 3 Consortium, 2010 Metspalu et al., 2011 Pagani et al., 2012 Yunusbayev et al., 2012 Di Cristofaro et al., 2013 Fedorova et al., 2013 Xing et al., 2013 Kovacevic et al., 2014 Raghavan et al., 2014 Yunusbayev et al., 2015 Mondal et al., 2016 Pagani et al., 2016), principal component analysis (PCA) using SMARTPCA (version: 13050) from EIGENSOFT (version: 6.0.1) (Patterson et al., 2006 Price et al., 2006), and F3 test using ADMIXTOOLS (version: 3.0) (Patterson et al., 2012). Finally, 2346 individuals were obtained and divided into six groups according to their geographic regions for further analysis. These groups are West Africans, East Africans, Oceanians, Europeans, North Asians and East Asians. The PCA plot (Fig. S3) shows that these 2346 individuals were properly separated into six population groups.

Data imputation

Genotypes of 19 human pigmentation genes with 500-kb flanking sequences on both sides were obtained from the genotype datasets. Haplotype inference and genotype imputation were performed on the selected genotypes using BEAGLE 4.1 (Browning and Browning, 2007, 2016) with 1000 Genomes phase 3 haplotypes as the reference panel. During phasing and imputation, the effective population size was assumed to be 10,000 (Ne=10,000), and the other parameters were set to the default values. Ten SNPs (rs1110400, rs11547464, rs12203592, rs1800407, rs1805005, rs1805006, rs1805007, rs1805008, rs1805009, rs74653330) were removed because of their low derived allele frequencies in our datasets after imputation (Fig. S4). Because rs12203592 (IRF4) was removed, 18 genes with the remaining 42 SNPs were used for further analysis.

Estimating selection differences between populations and selective pressures over epochs

We used Eqn 1 to estimate the selection differences of the remaining 42 SNPs. We then used Eqn 2 and selected 30 SNPs not in strong linkage disequilibrium (r 2 <0.8) as well as known phenotypes to estimate the total selection differences on human pigmentation between populations. These SNPs were rs3829241, rs56203814, rs916977, rs1800414, rs10424065, rs6119471, rs1408799, rs11230664, rs4959270, rs1800401, rs2378249, rs1042602, rs12350739, rs6058017, rs12821256, rs1393350, rs1426654, rs642742, rs6510760, rs1129038, rs2228479, rs35264875, rs12896399, rs26722, rs16891982, rs885479, rs28777, rs1800404, rs10756819, rs2402130. To dissect selective pressures over epochs, we applied Eqn 4 with the total selection differences from the selected 30 SNPs and the divergence times shown in Fig. 1.

Reproducing the observed selection differences from the optimal solution

We used SLiM 2 (version: 2.6) (Haller and Messer, 2017) to simulate a demographic model of human evolution (Fig. S5) to examine whether the optimal solution could reproduce the observed selection differences. We varied the initial frequency of the beneficial allele with 0.001 and 0.01. We divided the optimal solution by 30 to obtain the average selection coefficient for each SNP, because we used 30 SNPs to estimate the total selection differences on human pigmentation. We used the effective population size of each population estimated by previous studies (McEvoy et al., 2011 Mezzavilla and Ghirotto, 2015). We set both the mutation rate and the recombination rate to 1×10 - 8 per generation per site. In each run, we simulated a fragment with 10 6 base pairs, and set the 50,000th site under selection. We repeated each set of parameters more than 10,000 times and analysed those results in which beneficial alleles were not fixed or lost in all the populations. We compared the average selection differences from simulation with the observed selection differences. We noticed that the selection coefficient in SLiM 2 measures differences in fitness between genotypes instead of alleles. We can transform the selection coefficient of genotypes into that of alleles as follows. Let the fitness of the ancestral allele A be 1, and the relative fitness of the derived allele a is e s . When s is close to 0, we can approximate e s as 1+s using the Taylor series. The fitness of genotype aa becomes (1+s) 2 =1+2s+s 2 ≈1+2s, and the fitness of genotype Aa is 1+s=1+0.5s′. If s′ is the selection coefficient in SLiM 2, then s′=2 s and the dominance coefficient becomes 0.5. Simulations were performed in Digital Ocean (https://cloud.digitalocean.com/) Optimized Droplets. The information of these droplets is as follows: CPU, Intel ® Xeon ® Platinum 8168 Processor Random-access memory, 64 GB Operating system, Ubuntu 16.04.4×64.

The effects of population migration and substructure

In this section, we examine how the estimated selection difference is affected by population migration and substructure in theory. Here, and are the observed derived and ancestral allele frequencies in the population i, respectively and are the observed derived and ancestral allele frequencies in the population j, respectively and t is the divergence time from populations i and j to their most recent common ancestor. We demonstrate that provides a lower bound of selection difference between populations i and j when migration or substructure exists. We first provide two inequalities that will be used later.


Readings

Textbooks and Chapters

Yang, Yang, and Kenneth C. Land. Age-period-cohort analysis: new models, methods, and empirical applications. CRC Press, 2013.

Keyes, Katherine M., and Guohua Li. “Age–Period–Cohort Modeling.” Injury Research. Springer US, 2012. 409-426.

Glenn, Norval D., ed. Cohort analysis. Vol. 5. Sage, 2005

Hobcraft, John, Jane Menken, and Samuel Preston. Age, period, and cohort effects in demography: a review. Springer New York, 1985.

Methodological Articles

Ryder, Norman B. “The cohort as a concept in the study of social change.”American sociological review (1965): 843-861

Mason, Karen Oppenheim, et al. “Some methodological issues in cohort analysis of archival data.” American sociological review (1973): 242-258

Mason, William M., and Stephen E. Fienberg. “Cohort analysis in social research: beyond the identification problem.” (1985)

Yang, Yang, et al. “The Intrinsic Estimator for Age‐Period‐Cohort Analysis: What It Is and How to Use It1.” American Journal of Sociology 113.6 (2008): 1697-1736.

Keyes, Katherine M., et al. “What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971–2006.” Social science & medicine 70.7 (2010): 1100-1108.

Keyes, K. & Li, G., Age-period-cohort modeling. In Li, G. & Baker, S. (eds.),Injury Research: Theories, Methods, and Approaches. Springer, Chapter 22, pages 409-426. New York, 2012

Application Articles

Keyes, Katherine M., et al. “Age, Period, and Cohort Effects in Psychological Distress in the United States and Canada.” American journal of epidemiology(2014): kwu029.


Background

The Columbian Exchange

The historian Alfred Crosby coined the term “Columbian Exchange” to describe the extensive transfer of life between the Afro-Eurasian (Old World) and American (New World) hemispheres following Christopher Columbus’ voyage of 1492 [1]. The Columbian Exchange was a byproduct of subsequent European colonization and trade efforts in the Americas, and it entailed a bidirectional transfer of numerous species of plants, animals and microbes between the Old and New Worlds (Fig. 1a). This transfer also included human population groups, cultures and technologies, and as such it led to major demographic shifts in both hemispheres [2].

Adaptive introgression via the Columbian Exchange. a Examples of plants, animals and microbes transferred between the Old and New Worlds during the Columbian Exchange. Human populations from Europe, Africa and the Americas were also brought together during this era. b The number of generations needed to fix an adaptive allele is modeled for a selection coefficient (s) of 0.01 and a dominance coefficient (h) of 1.0. The level of per-generation adaptive change in allele frequencies varies over four orders of magnitude and reaches its maximum at intermediate allele frequencies. c Ancestry-enrichment analysis for the adaptive introgression events. An example is shown for a single chromosome from a hypothetical admixed population with African (avg = 30 %) and European (avg = 70 %) ancestry. Locus-specific ancestry is assigned for all chromosomes in the admixed population, and regions with anomalously high (or low) ancestral origins are identified for further investigation

The introduction of calorically rich and nutritional New World crop species – potatoes, maize and cassava in particular – facilitated agricultural developments that allowed for sustained population growth in the Old World [3]. The demographic changes in the New World during this era were even more drastic. The Columbian Exchange brought together previously isolated populations from Europe, Africa and the Americas in the New World colonies over a relatively short period of time. More than 50 million Europeans migrated to the Americas through the nineteenth century [4], and the African slave trade resulted in forced migration of 12 million Africans to the New World over a period of

450 years [5]. The indigenous population of the New World, on the other hand, was reduced by up to 95 % within 100–150 years after Columbus maiden voyage, a loss of an estimated 10–100 million lives [6] this was largely a result of the introduction of Old World infectious diseases, such as small pox, measles and malaria, to which native populations had little or no resistance. One can expect that such a profound human demographic transformation would occasion substantial evolutionary change at the genomic level.

From a population genomic perspective, the Columbian Exchange can be considered to have facilitated genetic admixture among three human population groups – African, European and Native American – that had previously evolved separately for many thousands of years [7–10]. During the time that these populations were isolated, they accumulated numerous genetic (allele frequency) differences. Many of these differences were likely to be neutral changes with no appreciable effects on fitness, whereas others were the result of adaptations to local selection pressures [11, 12]. In either case, the accumulation of such population-characteristic genetic differences resulted in the presence of distinct haplotypes (i.e., combinations of linked alleles) that are specific to individual populations the process of admixture during the Columbian Exchange then led to the repeated introgression of these population-specific haplotypes onto distinct genomic backgrounds. In other words, population mixing during the Columbian Exchange generated novel human genome sequences with combinations of haplotypes that had never previously co-existed in the same genome. I am interested in exploring the implications of the rapid creation of such novel, admixed American genome sequences for adaptive human evolution, fitness and health.

Adaptive introgression and rapid human evolution

As mutation is the ultimate source of novel adaptive alleles, it can be considered to be a critical rate limiting step for adaptive evolution. Human germline mutation rates are low [13], and accordingly adaptive evolution in human populations is generally considered to be a slow process that takes place over many thousands of years [12, 14, 15]. However, introgression can also be an important source of novel alleles for human adaptation [16]. Indeed, a number of recent studies have provided evidence for adaptive evolution of haplotypes that were introgressed from archaic human genomes (Neandertal and/or Denisovan) into modern human genomes [17–20]. Introgression has the potential to speed adaptive evolution by introducing novel alleles at a relatively rapid rate compared to de novo mutation.

If genetic admixture between previously isolated populations is extensive, it can provide introgressed haplotypes at intermediate to high frequencies to the resulting admixed population. Introgression could thereby increase the rate of adaptive evolution by elevating the frequency of potentially beneficial alleles available in the population. In this sense, introgression of adaptive alleles can be considered to provide an opportunity for so-called ‘soft’ selective sweeps, which are defined as causing rapid molecular evolution via the simultaneous increase in frequency of multiple adaptive alleles in the population [21]. Soft selective sweeps can occur under several distinct evolutionary scenarios, including the case where multiple adaptive alleles pre-exist in the population as standing genetic variation [22]. Introgression on the scale seen for the three-way population mixing that characterized the Columbian Exchange [7–10] could have provided multiple adaptive alleles as standing genetic variation at intermediate to high population frequencies.

Presentation of the hypothesis

I hypothesize that genetic admixture and introgression among the human population groups brought together via the Columbian Exchange provided the opportunity for rapid adaptive evolution based on the existence of numerous pre-adapted haplotypes. In other words, introgression during the Columbian Exchange provided extensive standing genetic variation to New World populations, much of it with potential adaptive significance, which could have provided the raw material for numerous (partial) selective sweeps.

The three ancestral human population groups – African, European and Native American – that were brought together over the last 500 years during the course of the Columbian Exchange began to diverge

60–100,000 years ago (ya) as modern humans emerged from Africa and spread around the world [23]. Europe was populated by anatomically modern humans

40–45,000 ya [24, 25], and humans reached the Americas

15,000 ya via several waves of migration across the Bering Strait [26, 27]. As these three population groups were isolated during the course of human evolution, they diverged genetically, accumulating numerous allele frequency differences. A number of these allele frequency differences were likely to be adaptive substitutions with regional-specific utility for health and fitness [11, 12, 15]. These pre-evolved adaptive alleles, and the ancestry-specific haplotypes on which they reside, could have been selected in the admixed American population based on their utility in the New World environment. The process of selection in this case would be based on differential retainment of ancestry-specific haplotypes that provide relatively higher fitness in the admixed population.

The New World environment that served as the selective crucible for the introgressed haplotypes would have consisted of both the external physical environment as well as the internal microbial environment, which was shaped by the mixture of microbes endemic to the ancestral source populations. The novel microbial environment in particular is likely to have exerted strong selective pressure on New World populations, based on the need to respond to the challenge of infectious pathogens, suggesting that immune system genes would be particularly prone to introgression-accelerated adaptive evolution [28–30].

It should be noted that the

500 years that have elapsed during the era of the Columbian Exchange is an extremely short amount of time with respect to human evolution indeed, it would correspond to a mere 20–25 generations assuming a generation time of 20–25 years. Irrespective of the role of introgression in providing standing adaptive genetic variation to an admixed population, this would not likely be enough time to allow for the complete fixation of adaptive alleles. Thus, the kind of introgression-facilitated adaptive evolution proposed here would amount to partial (or ongoing) selective sweeps [31]. Nevertheless, substantial levels of allele frequency change can in fact occur over this time scale. A standard model for the rate of fixation of an adaptive allele illustrates how selective increase in allele frequency proceeds successively through slow-fast-slow regimes of change (Fig. 1b). The amount of change per generation is highest at intermediate allele frequencies, where the frequency of a beneficial allele could increase by more than 50 % over 25 generations.

Analysis of human population genetic change over such a relatively short time period provides an opportunity to consider the possibility of rapid human evolution in the context of recent studies related to the convergence of evolutionary and ecological time. The question of extremely rapid evolution, i.e. evolution over ecological time scales, has received substantial attention over the last few years [32]. Traditionally, evolutionary and ecological timescales were considered to be very much distinct, but there are numerous studies documenting adaptive evolutionary change that have occurred well within the time span of the Columbian Exchange [33]. However, this line of work has not been explicitly extended to human populations as far as I know, and as such the admixture-introgression conceptual framework outlined here may allow for the evolution-ecology synthesis to incorporate a human dimension.

Testing the hypothesis

The collection of uniquely admixed human populations found in the Americas represents an ideal laboratory to study rapid human adaptation and to test the hypothesis of adaptive introgression via the Columbian Exchange. To test this hypothesis, one would need to compare genome sequences between putative ancestral populations with sequences characterized from admixed American populations. Comparison with genome sequences from ancestral source populations can be used to characterize the admixture contributions to New World populations at various levels: genome-wide and sex-specific ancestry proportions, local chromosomal ancestry assignments, and ancestry probabilities for individual nucleotide variants (i.e., SNPs) when possible. Having characterized the overall ancestry contribution proportions for an admixed American population, one can then search for specific genomic regions (loci) or SNPs that significantly deviate from the expected patterns (Fig. 1c). This approach can be used to identify genomic loci that are enriched for a particular ancestry, or ancestry-specific haplotypes, suggesting the possibility that such regions were differentially retained in the admixed population based on their utility in the New World environment [7, 28–30, 34–36]. This approach is analogous to the mapping by admixture linkage disequilibrium (or admixture mapping) technique, whereby local deviations from genome-wide average admixture patterns are used to identify loci implicated in diseases that have different prevalences in ancestral source populations [37]. Having identified ancestry-enriched loci (haplotypes) in this way, they can be further interrogated with respect to their rates of evolution as well as the functions of the genes encoded therein.

Interestingly, two studies that were published while this manuscript was in revision both found an excess of African ancestry at the MHC locus in admixed Latin American populations [38, 39]. These results were taken to support the idea of adaptive introgression based on genetic resistance to infectious disease agents, and population genetic modeling was used to provide support for the role of selection in enriching for locus-specific African ancestry. Of course, selection is not the only evolutionary force that could change allele frequencies and lead to the appearance of locus-specific ancestry enrichment. Accordingly, it will be important to explicitly consider the role of other forces, including demography and genetic drift, in shaping patterns of locus-specific ancestry in admixed American populations. Indeed, the need to do so provides an opportunity for the development and application of admixture-specific population genetic models that go beyond the more straightforward sequence-based ancestry enrichment analysis outlined here. The potential for demography to shape patterns of ancestry also underscores the importance of choosing the best possible ancestral population for comparative analysis with admixed American populations. Fortunately, the increasing availability of population genomic sequence data from putative ancestral source populations serves as a rich resource for this purpose.

Implications of the hypothesis

The hypothesis that the Columbian Exchange facilitated rapid adaptive evolution via admixture and introgression has implications for both basic research in human evolution and for more clinical investigations into genetic determinants of human health. The potential for rapid human evolution is a topic of great interest [40, 41], and critical interrogation of the hypothesis proposed here could help to elucidate one specific mechanism by which such rapid adaptive evolution can be facilitated. It should be emphasized that adaptive evolution predicated upon differential retainment of ancestry-specific haplotypes could entail fairly subtle changes in allele frequencies along the mid-range of the frequency spectrum (Fig. 1b). Thus, it will be expected to occur far more rapidly than complete fixation of new alleles introduced by de novo mutation.

Analysis of admixed American populations using this conceptual framework has the potential to reveal human evolution in action. Current methods for detecting the signatures of adaptive evolution (i.e., selective sweeps) in human genome sequences are based on complex statistical models of sequence substitution and may lack power to unambiguously distinguish among different models of selection – e.g., hard versus soft selective sweeps, the role of de novo versus standing genetic variation and the prevalence of polygenic selection [22, 31, 42]. Accordingly, there remains substantial controversy as to the relative importance of these different modes of adaptation in human molecular evolution [21, 42]. The alternative introgression-based framework for the detection of potentially adaptive haplotypes that I have outlined here can be considered to be agnostic with respect to these different models of the adaptation process as well as both conceptually straightforward and sensitive to relatively minor changes in allele frequencies [29].

To date, most population genetic studies of New World human populations have focused on ancestry, utilizing sequence variants as neutral markers of evolutionary lineages. Interrogation of the hypothesis proposed here calls for an explicit connection between human genetic ancestry and genetic determinants of health and fitness. The relationship between ancestry and genetic determinants of human health, often manifested as population-specific health disparities [43], is an important topic with serious public health implications. For example, investigation into how admixed populations have been shaped by selection pressures imposed by infectious disease burden can provide insight into the genetic architecture of immune response [44]. Finally, an emphasis on the study of admixed genomes from across the Americas, which testing of the hypothesis articulated here necessitates, could provide for an important extension of current clinical genomic studies, the vast majority of which have focused on populations of European descent [45].


15 Answers 15

The actual challenge in your question isn't just to prevent the trait from spreading via natural selection - it's also to prevent the trait from going extinct via random drift.

Natural selection is an important factor in evolution, but before you have natural selection you have genetic drift - the natural random fluctuation of how much of a gene there is in a population. Every generation, people with a certain allele have more or less children and pass on the gene to more or less of them, meaning the percentage of people in the population with this allele will go up and down semi-randomly over the generations. It so happens that mathematically, if all alleles (alternate versions of a gene) are equally likely to be passed on to the next generation, after enough generations you will get to a point where there is only one allele left. Simply because when your percentage fluctuates randomly over time, you'll eventually hit "0%" or "100%", and once you've hit either of those numbers you'll stay there (obviously if no individual in the population has an allele they can't pass it on to their offspring). This is called "fixation", when an allele reaches 100% of the population.

The odds of an allele reaching fixation, in the completely random scenario, are proportional to its frequency in the population meaning the higher the allele's percentage in the population, the higher its odds of taking over the population. Conversely, the lower its percentage, the higher its odds of going extinct. Natural selection doesn't prevent this, it just nudges the odds on whether an allele will reach fixation or go extinct.

So the problem you have with your genetic magic ability is that you want your population to be a stable minority over the long term. Most of the suggestions you'll get for preventing natural selection from promoting the allele may indeed prevent the allele from becoming fixed in the population - but in doing so they'll instead guarantee the allele will go extinct over the long term.

What you need is active selection pressure to keep the frequency of your allele at a low but nonzero share of the population. There are many ways of achieving this the basic idea is to have some way in which the gene is beneficial (selected upon) if it's rare, but harmful (selected against) if it's common. The best suggestion in the answers so far is the sickle cell anaemia analogy, though they get the mechanism wrong. It's not that the sickle cell alleles protect from malaria at the expense of ill health it's that if you have one sickle cell allele you're protected from malaria, but if you have two alleles you get sickle cell anaemia. This means that as long as the allele is rare it's going to be beneficial, because most people will get only one copy. But if it becomes too common then you're more likely to get children who have both, which is bad, which means having the allele becomes deleterious.

You could get some interesting side effects if you went with this exact mechanism. If you have a gene with one allele that gives magical powers, and people who are heterozygous for it are magic users, people who are homozygous for the magic allele are reproductive dead-ends (they're sick, they're insane, they're infertile, they die in utero. ), and people with any other version of the gene are nonmagical, then that means every magical family will include nonmagical members (because every magical person necessarily has a non-magical allele to pass on). You would also have negative consequences for magical people who have children with each other, which would discourage "inbreeding" within magical communities.

Of course you don't need to resort to complex genetic effects like this, you could have any mechanism that depends on how many magic users there are. For example, social forces could make having magic beneficial as long as they were rare enough to pass under the radar, but if there were too many magic users the witch hunts would start (though I don't think it's that realistic to assume social forces will be constant on evolutionary timescales). Or there could be properties of magic itself - The Dark Ones seek out magic users to eat them, but only wake if there are a lot of people doing magic in the area. Or you have a tradeoff in how sensitive you are to the magical fields: if you're a little sensitive you can do cool stuff, if you're very sensitive you get incapacitating headaches, and the more people are around doing magic the stronger the magical fields become this would mean when few people have magical powers the threshold for sensitivity is high, and any magic gene is beneficial if many people have powers the strong magical fields mean it takes very little sensitivity to get debilitating headaches, so the magic genes become harmful, and you end up with a stable situation with a small proportion of people who have the genes, and those come in a range of sensitivities, most being average and some unlucky souls getting the headaches (and maybe those need to move away from the other magic users, living as hermits or in places magical people are rare).

Note that this is all assuming alleles don't appear, which in the real world they do of course via mutation. Whether this is worth taking into account or not depends on how likely and common a given mutation is. It is perfectly reasonable to assume the magic genes are a rare mutation that happened only once or a few times, so you only get a magic gene if you inherited it. But you could tweak things a lot if your magic genes are likely to appear via random mutation you could even go to the point, as another answer suggested, that magic users are all de novo mutations and they don't spread beyond that baseline mutation probability because they're all sterile. But at that point why have it be a genetic trait in the first place. Point being, if you allow a baseline rate of new magic mutations you have a guaranteed minimum percentage of magic people without having to worry about them becoming extinct you also have different social dynamics with respect to whether magic is a family thing and how much of a family thing it is.

ETA: I want to note one thing on the mechanism though - you don't have to worry about this if you want to be fuzzy on the whole thing, but if you want natural selection to function in a realistic way you need to keep in mind it acts on individuals. So when talking about whether the magic allele is beneficial or harmful, it isn't enough that it's beneficial or harmful to society, or to the magical community - it would need to be directly beneficial or harmful to the individual that has the allele (even more specifically, it would need to affect how many offspring and grand-offspring that individual can be expected to have).


Acknowledgements

Part of the calculations were performed at sciCORE (http://scicore.unibas.ch/) scientific computing core facility at University of Basel. We thank Xinran Dong and Yaguang Dou for their support in the initial bioinformatics analysis of the sequencing data.

Funding

Funding for this study was provided by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, NIH though the International Centers of Excellence in Research program to Henan Provincial Chest Hospital and Sino-US International Research Center of Tuberculosis by the Ministry of Science and Technology of China (2014DFA30340) and by the Natural Science Foundation of China (91631301). This work was also supported by the Swiss National Science Foundation (grant 310030 166687), the European Research Council (309540-EVODRTB), and SystemsX.ch.

Availability of data and materials

Sequencing reads have been submitted to the EMBL-EBI European Nucleotide Archive (ENA) Sequence Read Archive (SRA) under the study accession numbers PRJEB13325 and PRJEB17864. All the data are available online at doi:10.5281/zenodo.322377. The analysis scripts are available online at doi:10.5281/zenodo.345135 and GitHub (https://github.com/swisstph/TBRU_serialTB).

Author’s contributions

AT, QL, LEV, KE, GZ, SG, CEB, and QG designed and implemented the study. XL, XR, LL, HS, YC, ZW, RL, WZ, GZ, and WW recruited and enrolled subjects. XL, XR, LL, HS, YC, ZW, RL, and WZ collected the sputum samples. LL analyzed the CT scans. JG performed the smear microscopic, sputum culture, and drug susceptibility test. QL and GS set up the variant-calling workflow. AT and QL analyzed the sequencing reads and performed the population genetics analysis. AT, QL, DB, LEV, CEB, SG, and QG drafted the manuscript. All authors critically reviewed and approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Ethics approval and consent to participate

The study to investigate the range of tuberculosis presentation and treatment (NCT01071603, clincaltrials.gov) conducted in Henan Provincial Chest Hospital (HPCH) was approved by the HPCH and National Institute of Allergy and Infectious Diseases institutional review boards. The methods of this study were carried out in accordance with the approved guidelines and written informed consent was obtained from the subjects prior to the study. The experimental methods in this study complied with the Helsinki Declaration.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Sample Statistics

In order to illustrate the computation of sample statistics, we selected a small subset (n=10) of participants in the Framingham Heart Study. The data values for these ten individuals are shown in the table below. The rightmost column contains the body mass index (BMI) computed using the height and weight measurements. We will come back to this example in the module on Summarizing Data, but it provides a useful illustration of some of the terms that have been introduced and will also serve to illustrate the computation of some sample statistics.


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