15.8: Genetic Variation - Biology

What helps ensure the survival of a species?

Genetic variation. Without genetic differences among individuals, "survival of the fittest" would not be likely. Either all survive, or all perish.

Genetic Variation

Sexual reproduction results in infinite possibilities of genetic variation. In other words, sexual reproduction results in offspring that are genetically unique. They differ from both parents and also from each other. This occurs for a number of reasons.

  • When homologous chromosomes form pairs during prophase I of meiosis I, crossing-over can occur. Crossing-over is the exchange of genetic material between homologous chromosomes. It results in new combinations of genes on each chromosome.
  • When cells divide during meiosis, homologous chromosomes are randomly distributed to daughter cells, and different chromosomes segregate independently of each other. This called is called independent assortment. It results in gametes that have unique combinations of chromosomes.
  • In sexual reproduction, two gametes unite to produce an offspring. But which two of the millions of possible gametes will it be? This is likely to be a matter of chance. It is obviously another source of genetic variation in offspring. This is known as random fertilization.

All of these mechanisms working together result in an amazing amount of potential variation. Each human couple, for example, has the potential to produce more than 64 trillion genetically unique children. No wonder we are all different!


Crossing-over occurs during prophase I, and it is the exchange of genetic material between non-sister chromatids of homologous chromosomes. Recall during prophase I, homologous chromosomes line up in pairs, gene-for-gene down their entire length, forming a configuration with four chromatids, known as a tetrad. At this point, the chromatids are very close to each other and some material from two chromatids switch chromosomes, that is, the material breaks off and reattaches at the same position on the homologous chromosome (Figure (PageIndex{2})). This exchange of genetic material can happen many times within the same pair of homologous chromosomes, creating unique combinations of genes. This process is also known as recombination.

During prophase I, chromosomes condense and become visible inside the nucleus. As the nuclear envelope begins to break down, homologous chromosomes move closer together. The synaptonemal complex, a lattice of proteins between the homologous chromosomes, forms at specific locations, spreading to cover the entire length of the chromosomes. The tight pairing of the homologous chromosomes is called synapsis. In synapsis, the genes on the chromatids of the homologous chromosomes are aligned with each other. The synaptonemal complex also supports the exchange of chromosomal segments between non-sister homologous chromatids in a process called crossing over. The crossover events are the first source of genetic variation produced by meiosis. A single crossover event between homologous non-sister chromatids leads to an exchange of DNA between chromosomes. Following crossover, the synaptonemal complex breaks down and the cohesin connection between homologous pairs is also removed. At the end of prophase I, the pairs are held together only at the chiasmata; they are called tetrads because the four sister chromatids of each pair of homologous chromosomes are now visible.

Independent Assortment and Random Fertilization

During metaphase I, the tetrads move to the metaphase plate with kinetochores facing opposite poles. The homologous pairs orient themselves randomly at the equator. This event is the second mechanism that introduces variation into the gametes or spores. In each cell that undergoes meiosis, the arrangement of the tetrads is different. The number of variations is dependent on the number of chromosomes making up a set. There are two possibilities for orientation at the metaphase plate. The possible number of alignments, therefore, equals 2n, where n is the number of chromosomes per set. Given these two mechanisms, it is highly unlikely that any two haploid cells resulting from meiosis will have the same genetic composition.

In humans, there are over 8 million configurations in which the chromosomes can line up during metaphase I of meiosis. It is the specific process of meiosis, resulting in four unique haploid cells, that results in these many combinations. This independent assortment, in which the chromosome inherited from either the father or mother can sort into any gamete, produces the potential for tremendous genetic variation. Together with random fertilization, more possibilities for genetic variation exist between any two people than the number of individuals alive today. Sexual reproduction is the random fertilization of a gamete from the female using a gamete from the male. A sperm cell, with over 8 million chromosome combinations, fertilizes an egg cell, which also has over 8 million chromosome combinations. That is over 64 trillion unique combinations, not counting the unique combinations produced by crossing-over.


  1. What is crossing-over and when does it occur?
  2. Describe how crossing-over, independent assortment, and random fertilization lead to genetic variation.
  3. How many combinations of chromosomes are possible from sexual reproduction in humans?
  4. Create a diagram to show how crossing-over occurs and how it creates new gene combinations on each chromosome.

Genetic diversity in India and the inference of Eurasian population expansion

Genetic studies of populations from the Indian subcontinent are of great interest because of India's large population size, complex demographic history, and unique social structure. Despite recent large-scale efforts in discovering human genetic variation, India's vast reservoir of genetic diversity remains largely unexplored.


To analyze an unbiased sample of genetic diversity in India and to investigate human migration history in Eurasia, we resequenced one 100-kb ENCODE region in 92 samples collected from three castes and one tribal group from the state of Andhra Pradesh in south India. Analyses of the four Indian populations, along with eight HapMap populations (692 samples), showed that 30% of all SNPs in the south Indian populations are not seen in HapMap populations. Several Indian populations, such as the Yadava, Mala/Madiga, and Irula, have nucleotide diversity levels as high as those of HapMap African populations. Using unbiased allele-frequency spectra, we investigated the expansion of human populations into Eurasia. The divergence time estimates among the major population groups suggest that Eurasian populations in this study diverged from Africans during the same time frame (approximately 90 to 110 thousand years ago). The divergence among different Eurasian populations occurred more than 40,000 years after their divergence with Africans.


Our results show that Indian populations harbor large amounts of genetic variation that have not been surveyed adequately by public SNP discovery efforts. Our data also support a delayed expansion hypothesis in which an ancestral Eurasian founding population remained isolated long after the out-of-Africa diaspora, before expanding throughout Eurasia.

What Are Mutations?

Mutations are random changes in the sequence of bases in DNA or RNA . The word mutation may make you think of the Ninja Turtles, but that’s a misrepresentation of how most mutations work. First of all, everyone has mutations. In fact, most people have dozens (or even hundreds!) of mutations in their DNA. Secondly, from an evolutionary perspective, mutations are essential. They are needed for evolution to occur because they are the ultimate source of all new genetic variation in any species .

Ms. Lima Biology Teacher

You need to know the conditions required for natural selection to occur. These include: overproduction of offspring, inherited variation, and the struggle to survive, which result in differential reproductive success.

Evolution is a change in the characteristics of a population from one generation to the next.

Darwin proposed that evolution happened due to natural selection. Natural selection is the process by which individuals that have favorable variations and are better adapted to their environment survive and reproduce more successfully than less well adapted individuals do.

Over many generations, natural selection can result in the evolution of new species, which is called speciation.

&ldquoSURVIVAL OF THE FITTEST&rdquo organism&rsquos best suited to their environment survive and reproduce

Natural Selection is based on four main principles:

1. Overproduction of offspring: Each species produces more individuals than can survive to maturity most offspring are lost to predators, disease or other factors.

2. Variation: Genetic variation exists within populations. The individuals of a population may differ in traits such as size, color, strength, speed, ability to find food, or resistance to certain diseases. These variations are inheritable traits.

3. Struggle to survive: The amount of space, food and other resource in nature is finite. Organisms must compete for limited resources. Also, some individuals will be harmed by predation, disease, or unfavorable conditions.

4. Adaptation: Individuals whose traits are best suited for their environment are more likely to survive, produce more offspring and pass their traits to the future generation than individuals that lack those traits. Over time, those traits become more frequent in the population

Payseur Laboratory

Parmenter, M. D., Nelson, J. P., Weigel, S. E., Gray, M. M., Payseur, B. A., and Vinyard, C. J. (2020). Masticatory apparatus performance and functional morphology in the extremely large mice from Gough Island. The Anatomical Record, 303 (1), 167-179.

Otto, S. P., and Payseur, B. A. (2019). Crossover Interference: Shedding Light on the Evolution of Recombination. Annual Review of Genetics, 53, 19-44.

Dapper, A. L., and Payseur, B. A. (2019). Molecular evolution of the meiotic recombination pathway in mammals. Evolution, 73 (12), 2368-2389.

Wang, R. J., Dumont, B. L., Jing, P., and Payseur, B. A. (2019). A first genetic portrait of synaptonemal complex variation. PLoS Genetics, 15(8), e1008337.

Peterson, A. L., Miller, N. D., and Payseur, B. A. (2019). Conservation of the Genome-Wide Recombination Rate in White-Footed Mice. Heredity, 123, 442–457.

Jensen, J. D., Payseur, B. A., Stephan, W., Aquadro, C. F., Lynch, M., Charlesworth, D., and Charlesworth, B. (2019). The importance of the Neutral Theory in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution, 73(1), 111-114.

Schwahn, D. J., Wang, R. J., White, M. A., and Payseur, B. A. (2018). Genetic dissection of hybrid male sterility across stages of spermatogenesis. Genetics, 210(4), 1453-1465.

Payseur, B. A., Presgraves, D. C., and Filatov, D. A. (2018). Sex Chromosomes and Speciation. Molecular Ecology, 27(19), 3745-3748.

Hvala, J. A., Frayer, M. E., and Payseur, B. A. (2018). Signatures of hybridization and speciation in genomic patterns of ancestry. Evolution, 72(8), 1540-1552.

Dapper, A. L., and Payseur, B. A. (2018). Effects of Demographic History on the Detection of Recombination Hotspots from Linkage Disequilibrium. Molecular Biology and Evolution, 35(2), 335-353.

Dapper, A. L., and Payseur, B. A. (2017). Connecting Theory and Data in Recombination Rate Evolution. Philosophical Transactions of the Royal Society B, 372, 20160469.

Wang, R. J., and Payseur, B. A. (2017). Genetics of genome-wide recombination rate evolution in mice from an isolated island. Genetics, 206(4), 1841-1852.

Wang, R. J., Gray, M. M., Parmenter, M. D., Broman, K. W., and Payseur, B. A. (2017). Recombination rate variation in mice from an isolated island. Molecular Ecology, 26(2), 457-470.

Parmenter, M. D., Gray, M. M., Hogan, C. A., Ford, I.N., Broman, K.W., Vinyard, C. J., and Payseur, B. A. (2016). Genetics of skeletal evolution in unusually large mice from Gough Island. Genetics, 204(4), 1559-1572.

Payseur, B. A. (2016). Genetic links between recombination and speciation. PLoS Genetics, 12(6), e1006066.

Payseur, B. A., and Rieseberg, L. H. (2016). A genomic perspective on hybridization and speciation. Molecular Ecology, 25(11), 2337-2360.

Gasch, A. P., Payseur, B. A., and Pool, J. E. (2016). The power of natural variation for model organism biology. Trends in Genetics, 32(3), 147-154.

Haasl, R. J., and Payseur, B. A. (2016). Fifteen years of genomewide scans for selection: trends, lessons, and unaddressed genetic sources of complication. Molecular Ecology, 25(1), 5-23.

Gray, M. M., Parmenter, M. D., Hogan, C. A., Ford, I., Cuthbert, R. J., Ryan, P. G., Broman, K. W., and Payseur, B. A. (2015). Genetics of Rapid and Extreme Size Evolution in Island Mice. Genetics, 201(1), 213-228.

Wang, R. J., White, M. A., & Payseur, B. A. (2015). The Pace of Hybrid Incompatibility Evolution in House Mice. Genetics, 201(1), 229-242.

Payseur, B. A. Disproportionate Roles for the X Chromosome and Proteins in Adaptive Evolution. Genetics 196.4 (2014): 931-935.

Haasl, R. J., and B. A. Payseur. "Remarkable selective constraints on exonic dinucleotide repeats." Evolution 68.9 (2014): 2737-2744.

Haasl, R. J., R. C. Johnson, and B. A. Payseur (2014). The effects of microsatellite selection on linked sequence diversity. Genome Biology and Evolution 6:1843-1861.

Gray, M. M., D. Wegmann, R. J. Haasl, M. A. White, S. I. Gabriel, J. B. Searle, R. J. Cuthbert, P. G. Ryan, and B. A. Payseur (2014). Demographic history of a recent invasion of house mice on the isolated island of Gough. Molecular Ecology 23:1923-1939.

Turner, L. M., M. A. White, D. Tautz, and B. A. Payseur (2014). Genomic networks of hybrid sterility. PLoS Genetics 10:e1004162.

Payseur, B. A. (2013). Dissection of complex trait evolution (Section 5.13). Princeton Guide to Evolution. J. Losos, editor. Princeton University Press.

Wang, R. J., C. Ané, and B. A. Payseur (2013). The evolution of hybrid incompatibilities along a phylogeny. Evolution 67:2905-2922.

Haasl, R. J., McCarty, C. A., and Payseur, B. A. (2013). Genetic ancestry inference using support vector machines, and the active emergence of a unique American population. European Journal of Human Genetics, 21(5), 554-562.

Cutter, A. D., and Payseur, B. A. (2013). Genomic signatures of selection at linked sites: unifying the disparity among species. Nature Reviews Genetics.

Haasl, R. J., and Payseur, B. A. (2013). Microsatellites as targets of natural selection. Molecular biology and evolution, 30(2), 285-298.

Broman, K. W., Kim, S., Sen, S., Ané, C., and Payseur, B. A. (2012). Mapping quantitative trait loci onto a phylogenetic tree. Genetics, 192(1), 267-279.

White, M. A., A. Ikeda, and B. A. Payseur (2012). A pronounced evolutionary shift of the pseudoautosomal region boundary in house mice. Mamm Genome 2012 July 5 [Epub ahead of print]. [pdf]

Broman, K. W., S. Kim, S. Sen, C, Ane, and B. A. Payseur (2012). Mapping quantitative trait loci onto a phylogenetic tree. Genetics Jun 28 [Epub ahead of print]. [pdf]

White, M. A., M. Stubbings, B. L. Dumont, and B. A. Payseur (2012). Genetics and evolution of hybrid male sterility in house mice. Genetics May 2 [Epub ahead of print]. [pdf]

Keane, T. M., L. Goodstadt, P. Danecek, M. A. White, K. Wong, B. Yalcin, A. Heger, A. Agam, G. Slater, M. Goodson, N. A. Furotte, E. Eskin, C. Nellaker, H. Whitley, J. Cleak, D. Janowitz, P. Hernandez-Pliego, A. Edwards, T. G. Belgard, P. Oliver, R. E. McIntyre, A. Bhomra, J. Nicod, X. Gan, W. Yuan, L. v. d. Weyden, C. A. Steward, S. Balasubramaniam, J. Stalker, R. Mott, R. Durbin, I. Jackson, A. Czechanski, J. A. G. Assuno, L. R. Donahue, L. Reinholdt, B. A. Payseur, C. P. Ponting, E. Birney, J. Flint, and D. J. Adams (2011). Mouse genomic variation and its effect on phenotypes and gene regulation. Nature, 477: 289 - 294 . [pdf]

Nachman, M. W., and B. A. Payseur (2011). Recombination rate variation and speciation: theoretical predictions and empirical results from rabbits and mice. Philosophical Transactions of the Royal Society B, 367: 409 - 421. [pdf]

White, M. A., B. Steffy, T. Wiltshire, and B. A. Payseur (2011). Genetic dissection of a key reproductive barrier between nascent subspecies of house mice, Mus musculus domesticus and Mus musculus musculus. Genetics 169:289-304. [pdf]

Dumont, B. L., and B. A. Payseur (2011). Genetic analysis of genome-scale recombination rate evolution in house mice. PLoS Genetics 7(6):e1002116. [pdf]

Dumont, B. L. and B. A. Payseur (2011). Evolution of the Genomic Recombination Rate in Murid Rodents. Genetics 187: 643-657. [pdf]

Dumont, B. L., M. A. White, B. Steffy, T. Wiltshire, and B. A. Payseur (2011). Extensive recombination rate variation in the house mouse species complex inferred from genetic linkage maps. Genome Research 21(1): 114-25 [pdf]

Haasl, R. J., and B. A. Payseur (2011). Multi-locus inference of population structure: A comparison between single nucleotide polymorphisms and microsatellites. Heredity 106(1):158-71 [pdf]

Payseur, B. A., P. Jing, and R. J. Haasl (2010). A genomic portrait of human microsatellite variation. Molecular Biology and Evolution, 2010 Jul 30 [Epub ahead of print]. [pdf]

Haasl, R. J., and B. A. Payseur (2010). The number of alleles at a microsatellite defines the allele frequency spectrum and facilitates fast, accurate estimation of theta. Molecular Biology and Evolution 27(12):2702-2715 [pdf]

Payseur, B. A. (2010). Using differential introgression in hybrid zones to identify genomic regions involved in speciation. Molecular Ecology Resources 10(5): 806-820 [pdf]

Wong, A. K., A. L. Ruhe, B. L. Dumont, K. R. Robertson, G. Guerrero, S. M. Shull, J. S. Ziegle, L. V. Millon, K. W. Broman, B. A. Payseur, and M. W. Neff (2010). A comprehensive linkage map of the dog genome. Genetics 184:595-605. [pdf]

White, M. A., C. Ane, C. N. Dewey, B. R. Larget, and B. A. Payseur (2009). Fine-scale phylogenetic discordance across the house mouse genome. PLOS Genetics 5(11):e1000729. [pdf]

Dumont, B. L., K. W. Broman, and B. A. Payseur (2009). Variation in genomic recombination rates among heterogeneous stock mice. Genetics 182(4):1345-9. [pdf]

Moyle, L. C., and B. A. Payseur (2009). Reproductive isolation grows on trees. Trends in Ecology and Evolution 24(11):591-8. [pdf]

Shim, H., H. Chun, C. D. Engelman, and B. A. Payseur (2009). Genome-wide association studies using SNPs vs. haplotypes: An empirical comparison with data from the North American Rheumatoid Arthritis Consortium. BMC Proceedings, 3(Suppl 7):S35. [pdf]

Payseur, B. A., and P. Jing (2009). A genome-wide comparison of population structure at STRPs and nearby SNPs in humans. Molecular Biology and Evolution 26:1369-1377. [pdf] & [STRP Genotypes]

Vinyard C. J., and B. A. Payseur (2008). Of “mice” and mammals: Utilizing classical inbred mice to study the genetic architecture of organismal-level function and performance in mammals. Integrative and Comparative Biology doi:10.1093/icb/icn063. [pdf]

Payseur B. A., M. Place, and J. L. Weber (2008). Linkage Disequilibrium between STRPs and SNPs across the Human Genome. The American Journal of Human Genetics. 82:1039-1050. [pdf] & [STRP Genotypes]

Dumont B. L., and B. A. Payseur (2008). Evolution of the genomic recombination rate in mammals. Evolution 62(2):276-294. [pdf]

Teeter K. C., B. A. Payseur, L. W. Harris, M. Bakewell, L. M. Thibodeau, J. E. O'Brien, J. G. Krenz, M. A. Sans-Fuentes, M. W. Nachman, and P. T. Tucker (2008). Genome-wide patterns of gene flow across a house mouse hybrid zone. Genome Research 18(1):67-76. [pdf]

Williamson S.H., M. J. Hubisz, A. G. Clark, B. A. Payseur, C. D. Bustamante, and R. Nielsen (2007). Localizing recent adaptive evolution in the human genome. PLoS Genetics 3:e90. [pdf]

Payseur B. A., and M. Place (2007). Searching the genomes of inbred mouse strains for incompatibilities that separate their wild relatives. Journal of Heredity 98:115-122. [pdf]

Payseur B. A., and M. Place (2007). Prospects for association mapping in classical inbred strains of mice. Genetics 175:1999-2008. [pdf]

Payseur B. A., A. G. Clark, J. Hixson, E. Boerwinkle, and C. F. Sing (2006). Contrasting multi-site genotypic distributions among discordant quantitative phenotypes: the APOA1/C3/A4/A5 gene cluster and cardiovascular disease risk factors. Genetic Epidemiology 30:508-518. [pdf]

Payseur B. A., and A. D. Cutter (2006). Integrating patterns of polymorphism at SNPs and STRs. Trends in Genetics 22:424-429. [pdf]

Payseur B. A., and H. E. Hoekstra (2005). Signatures of reproductive isolation in patterns of single nucleotide diversity across inbred strains of house mice. Genetics 171:1905-1916. [pdf]

Payseur B. A., and M. W. Nachman (2005). The genomics of speciation: investigating the molecular correlates of X chromosome introgression across the hybrid zone between Mus domesticus and Mus musculus. Biological Journal of the Linnean Society 84:523-534.

Payseur B. A., J. G. Krenz, and M. W. Nachman (2004). Differential patterns of introgression across the X chromosome in a hybrid zone between two species of house mice. Evolution 58:2064-2078. [pdf]

Storz J. F., B. A. Payseur, and M. W. Nachman (2004). Genome scans of DNA variability in humans reveal evidence of selective sweeps outside of Africa. Molecular Biology and Evolution 21:1800-1811. [pdf]

Jensen-Seaman M. I., T. S. Furey, B. A. Payseur, Y. Lu, K. M. Roskin, C-F Chen, M. A. Thomas, D. Haussler, and H. J. Jacob (2004). Comparative recombination rates in the rat, mouse, and human genomes. Genome Research 14:528-538. [pdf]

Rat Genome Sequencing Project Consortium (2004). Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428:493-521. [pdf]

Cutter A.D., B. A. Payseur, T. Salcedo, A. M. Estes, J. M. Good, E. Wood, T. Hartl, H. Maughan, J. Strempel, B. Wang, A. C. Bryan, and M. Dellos (2003). Molecular correlates of genes exhibiting RNAi phenotypes in Caenorhabditis elegans. Genome Research 13:2651-2657. [pdf]

Cutter A. D., and B. A. Payseur (2003). Rates of deleterious mutation and the evolution of sex in Caenorhabditis. Journal of Evolutionary Biology 16:812-822. [pdf]

Cutter A.D., and B. A. Payseur (2003). Selection at linked sites in the partial selfer Caenorhabditis elegans. Molecular Biology and Evolution 20:665-673. [pdf]

Payseur B. A., and M. W. Nachman (2002). Natural selection at linked sites in humans. Gene 30:31-42. [pdf]

Payseur B. A., A. D. Cutter, and M. W. Nachman (2002). Searching for evidence of positive selection in the human genome using patterns of microsatellite variability. Molecular Biology and Evolution 19:1143-1153. [pdf]

Payseur B. A., and M. W. Nachman (2002). Gene density and human nucleotide polymorphism. Molecular Biology and Evolution 19:336-340. [pdf]

1999 - 2001

Yoder A.D., J. A. Irwin, and B. A. Payseur (2001). Failure of the ILD to determine data combinability for slow loris phylogeny. Systematic Biology 50:408-424.

Payseur B. A., and M. W. Nachman (2000). Microsatellite variation and recombination rate in the human genome. Genetics 156:1285-1298. [pdf]

Payseur B. A., H. H. Covert, C. J. Vinyard, and M. Dagosto (1999). New body mass estimates for Omomys carteri, a middle Eocene primate from North America. American Journal of Physical Anthropology 109:41-52. [pdf]

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DIT holds that genetic and cultural evolution interacted in the evolution of Homo sapiens. DIT recognizes that the natural selection of genotypes is an important component of the evolution of human behavior and that cultural traits can be constrained by genetic imperatives. However, DIT also recognizes that genetic evolution has endowed the human species with a parallel evolutionary process of cultural evolution. DIT makes three main claims: [5]

Culture capacities are adaptations Edit

The human capacity to store and transmit culture arose from genetically evolved psychological mechanisms. This implies that at some point during the evolution of the human species a type of social learning leading to cumulative cultural evolution was evolutionarily advantageous.

Culture evolves Edit

Social learning processes give rise to cultural evolution. Cultural traits are transmitted differently from genetic traits and, therefore, result in different population-level effects on behavioral variation.

Genes and culture co-evolve Edit

Cultural traits alter the social and physical environments under which genetic selection operates. For example, the cultural adoptions of agriculture and dairying have, in humans, caused genetic selection for the traits to digest starch and lactose, respectively. [6] [7] [8] [9] [10] [11] As another example, it is likely that once culture became adaptive, genetic selection caused a refinement of the cognitive architecture that stores and transmits cultural information. This refinement may have further influenced the way culture is stored and the biases that govern its transmission.

DIT also predicts that, under certain situations, cultural evolution may select for traits that are genetically maladaptive. An example of this is the demographic transition, which describes the fall of birth rates within industrialized societies. Dual inheritance theorists hypothesize that the demographic transition may be a result of a prestige bias, where individuals that forgo reproduction to gain more influence in industrial societies are more likely to be chosen as cultural models. [12] [13]

People have defined the word "culture" to describe a large set of different phenomena. [14] [15] A definition that sums up what is meant by "culture" in DIT is:

Culture is socially learned information stored in individuals' brains that is capable of affecting behavior. [16] [17]

This view of culture emphasizes population thinking by focusing on the process by which culture is generated and maintained. It also views culture as a dynamic property of individuals, as opposed to a view of culture as a superorganic entity to which individuals must conform. [18] This view's main advantage is that it connects individual-level processes to population-level outcomes. [19]

Genes affect cultural evolution via psychological predispositions on cultural learning. [20] Genes encode much of the information needed to form the human brain. Genes constrain the brain's structure and, hence, the ability of the brain to acquire and store culture. Genes may also endow individuals with certain types of transmission bias (described below).

Culture can profoundly influence gene frequencies in a population.

One of the best known examples is the prevalence of the genotype for adult lactose absorption in human populations, such as Northern Europeans and some African societies, with a long history of raising cattle for milk. Until around 7,500 years ago, [21] lactase production stopped shortly after weaning, [22] and in societies which did not develop dairying, such as East Asians and Amerindians, this is still true today. [23] [24] In areas with lactase persistence, it is believed that by domesticating animals, a source of milk became available while an adult and thus strong selection for lactase persistence could occur, [21] [25] in a Scandinavian population the estimated selection coefficient was 0.09-0.19. [25] This implies that the cultural practice of raising cattle first for meat and later for milk led to selection for genetic traits for lactose digestion. [26] Recently, analysis of natural selection on the human genome suggests that civilization has accelerated genetic change in humans over the past 10,000 years. [27]

Culture has driven changes to the human digestive systems making many digestive organs, like our teeth or stomach, smaller than expected for primates of a similar size, [28] and has been attributed to one of the reasons why humans have such large brains compared to other great apes. [29] [30] This is due to food processing. Early examples of food processing include pounding, marinating and most notably cooking. Pounding meat breaks down the muscle fibres, hence taking away some of the job from the mouth, teeth and jaw. [31] [32] Marinating emulates the action of the stomach with high acid levels. Cooking partially breaks down food making it more easily digestible. Food enters the body effectively partly digested, and as such food processing reduces the work that the digestive system has to do. This means that there is selection for smaller digestive organs as the tissue is energetically expensive, [28] those with smaller digestive organs can process their food but at a lower energetic cost than those with larger organs. [33] Cooking is notable because the energy available from food increases when cooked and this also means less time is spent looking for food. [29] [34] [35]

Humans living on cooked diets spend only a fraction of their day chewing compared to other extant primates living on raw diets. American girls and boys spent on average 8 and 7 percent of their day chewing respectively, compared to chimpanzees who spend more than 6 hours a day chewing. [36] This frees up time which can be used for hunting. A raw diet means hunting is constrained since time spent hunting is time not spent eating and chewing plant material, but cooking reduces the time required to get the day's energy requirements, allowing for more subsistence activities. [37] Digestibility of cooked carbohydrates is approximately on average 30% higher than digestibility of non cooked carbohydrates. [34] [38] This increased energy intake, more free time and savings made on tissue used in the digestive system allowed for the selection of genes for larger brain size.

Despite its benefits, brain tissue requires a large amount of calories, hence a main constraint in selection for larger brains is calorie intake. A greater calorie intake can support greater quantities of brain tissue. This is argued to explain why human brains can be much larger than other apes, since humans are the only ape to engage in food processing. [29] The cooking of food has influenced genes to the extent that, research suggests, humans cannot live without cooking. [39] [29] A study on 513 individuals consuming long term raw diets found that as the percentage of their diet which was made up of raw food and/or the length they had been on a diet of raw food increased, their BMI decreased. [39] This is despite access to many non thermal processing, like grinding, pounding or heating to 48 deg. c. (118 deg. F). [39] With approximately 86 billion neurons in the human brain and 60–70 kg body mass, an exclusively raw diet close to that of what extant primates have would be not viable as, when modelled, it is argued that it would require an infeasible level of more than nine hours of feeding everyday. [29] However, this is contested, with alternative modelling showing enough calories could be obtained within 5–6 hours per day. [40] Some scientists and anthropologists point to evidence that brain size in the Homo lineage started to increase well before the advent of cooking due to increased consumption of meat [28] [40] [41] and that basic food processing (slicing) accounts for the size reduction in organs related to chewing. [42] Cornélio et al. argues that improving cooperative abilities and a varying of diet to more meat and seeds improved foraging and hunting efficiency. It is this that allowed for the brain expansion, independent of cooking which they argue came much later, a consequence from the complex cognition that developed. [40] Yet this is still an example of a cultural shift in diet and the resulting genetic evolution. Further criticism comes from the controversy of the archaeological evidence available. Some claim there is a lack of evidence of fire control when brain sizes first started expanding. [40] [43] Wrangham argues that anatomical evidence around the time of the origin of Homo erectus (1.8 million years ago), indicates that the control of fire and hence cooking occurred. [34] At this time, the largest reductions in tooth size in the entirety of human evolution occurred, indicating that softer foods became prevalent in the diet. Also at this time was a narrowing of the pelvis indicating a smaller gut and also there is evidence that there was a loss of the ability to climb which Wrangham argues indicates the control of fire, since sleeping on the ground needs fire to ward off predators. [44] The proposed increases in brain size from food processing will have led to a greater mental capacity for further cultural innovation in food processing which will have increased digestive efficiency further providing more energy for further gains in brain size. [45] This positive feedback loop is argued to have led to the rapid brain size increases seen in the Homo lineage. [46] [40]

In DIT, the evolution and maintenance of cultures is described by five major mechanisms: natural selection of cultural variants, random variation, cultural drift, guided variation and transmission bias.

Natural selection Edit

Cultural differences among individuals can lead to differential survival of individuals. The patterns of this selective process depend on transmission biases and can result in behavior that is more adaptive to a given environment.

Random variation Edit

Random variation arises from errors in the learning, display or recall of cultural information, and is roughly analogous to the process of mutation in genetic evolution.

Cultural drift Edit

Cultural drift is a process roughly analogous to genetic drift in evolutionary biology. [47] [48] [49] In cultural drift, the frequency of cultural traits in a population may be subject to random fluctuations due to chance variations in which traits are observed and transmitted (sometimes called "sampling error"). [50] These fluctuations might cause cultural variants to disappear from a population. This effect should be especially strong in small populations. [51] A model by Hahn and Bentley shows that cultural drift gives a reasonably good approximation to changes in the popularity of American baby names. [50] Drift processes have also been suggested to explain changes in archaeological pottery and technology patent applications. [49] Changes in the songs of song birds are also thought to arise from drift processes, where distinct dialects in different groups occur due to errors in songbird singing and acquisition by successive generations. [52] Cultural drift is also observed in an early computer model of cultural evolution. [53]

Guided variation Edit

Cultural traits may be gained in a population through the process of individual learning. Once an individual learns a novel trait, it can be transmitted to other members of the population. The process of guided variation depends on an adaptive standard that determines what cultural variants are learned.

Biased transmission Edit

Understanding the different ways that culture traits can be transmitted between individuals has been an important part of DIT research since the 1970s. [54] [55] Transmission biases occur when some cultural variants are favored over others during the process of cultural transmission. [56] Boyd and Richerson (1985) [56] defined and analytically modeled a number of possible transmission biases. The list of biases has been refined over the years, especially by Henrich and McElreath. [57]

Content bias Edit

Content biases result from situations where some aspect of a cultural variant's content makes them more likely to be adopted. [58] Content biases can result from genetic preferences, preferences determined by existing cultural traits, or a combination of the two. For example, food preferences can result from genetic preferences for sugary or fatty foods and socially-learned eating practices and taboos. [58] Content biases are sometimes called "direct biases." [56]

Context bias Edit

Context biases result from individuals using clues about the social structure of their population to determine what cultural variants to adopt. This determination is made without reference to the content of the variant. There are two major categories of context biases: model-based biases, and frequency-dependent biases.

Model-based biases Edit

Model-based biases result when an individual is biased to choose a particular "cultural model" to imitate. There are four major categories of model-based biases: prestige bias, skill bias, success bias, and similarity bias. [5] [59] A "prestige bias" results when individuals are more likely to imitate cultural models that are seen as having more prestige. A measure of prestige could be the amount of deference shown to a potential cultural model by other individuals. A "skill bias" results when individuals can directly observe different cultural models performing a learned skill and are more likely to imitate cultural models that perform better at the specific skill. A "success bias" results from individuals preferentially imitating cultural models that they determine are most generally successful (as opposed to successful at a specific skill as in the skill bias.) A "similarity bias" results when individuals are more likely to imitate cultural models that are perceived as being similar to the individual based on specific traits.

Frequency-dependent biases Edit

Frequency-dependent biases result when an individual is biased to choose particular cultural variants based on their perceived frequency in the population. The most explored frequency-dependent bias is the "conformity bias." Conformity biases result when individuals attempt to copy the mean or the mode cultural variant in the population. Another possible frequency dependent bias is the "rarity bias." The rarity bias results when individuals preferentially choose cultural variants that are less common in the population. The rarity bias is also sometimes called a "nonconformist" or "anti-conformist" bias.

In DIT, the evolution of culture is dependent on the evolution of social learning. Analytic models show that social learning becomes evolutionarily beneficial when the environment changes with enough frequency that genetic inheritance can not track the changes, but not fast enough that individual learning is more efficient. [60] For environments that have very little variability, social learning is not needed since genes can adapt fast enough to the changes that occur, and innate behaviour is able to deal with the constant environment. [61] In fast changing environments cultural learning would not be useful because what the previous generation knew is now outdated and will provide no benefit in the changed environment, and hence individual learning is more beneficial. It is only in the moderately changing environment where cultural learning becomes useful since each generation shares a mostly similar environment but genes have insufficient time to change to changes in the environment. [62] While other species have social learning, and thus some level of culture, only humans, some birds and chimpanzees are known to have cumulative culture. [63] Boyd and Richerson argue that the evolution of cumulative culture depends on observational learning and is uncommon in other species because it is ineffective when it is rare in a population. They propose that the environmental changes occurring in the Pleistocene may have provided the right environmental conditions. [62] Michael Tomasello argues that cumulative cultural evolution results from a ratchet effect that began when humans developed the cognitive architecture to understand others as mental agents. [64] Furthermore, Tomasello proposed in the 80s that there are some disparities between the observational learning mechanisms found in humans and great apes - which go some way to explain the observable difference between great ape traditions and human types of culture (see Emulation (observational learning)).

Although group selection is commonly thought to be nonexistent or unimportant in genetic evolution, [65] [66] [67] DIT predicts that, due to the nature of cultural inheritance, it may be an important force in cultural evolution. Group selection occurs in cultural evolution because conformist biases make it difficult for novel cultural traits to spread through a population (see above section on transmission biases). Conformist bias also helps maintain variation between groups. These two properties, rare in genetic transmission, are necessary for group selection to operate. [68] Based on an earlier model by Cavalli-Sforza and Feldman, [69] Boyd and Richerson show that conformist biases are almost inevitable when traits spread through social learning, [70] implying that group selection is common in cultural evolution. Analysis of small groups in New Guinea imply that cultural group selection might be a good explanation for slowly changing aspects of social structure, but not for rapidly changing fads. [71] The ability of cultural evolution to maintain intergroup diversity is what allows for the study of cultural phylogenetics. [72]

The idea that human cultures undergo a similar evolutionary process as genetic evolution goes back at least to Darwin. [73] In the 1960s, Donald T. Campbell published some of the first theoretical work that adapted principles of evolutionary theory to the evolution of cultures. [74] In 1976, two developments in cultural evolutionary theory set the stage for DIT. In that year Richard Dawkins's The Selfish Gene introduced ideas of cultural evolution to a popular audience. Although one of the best-selling science books of all time, because of its lack of mathematical rigor, it had little effect on the development of DIT. Also in 1976, geneticists Marcus Feldman and Luigi Luca Cavalli-Sforza published the first dynamic models of gene–culture coevolution. [75] These models were to form the basis for subsequent work on DIT, heralded by the publication of three seminal books in the 1980s.

The first was Charles Lumsden and E.O. Wilson's Genes, Mind and Culture. [76] This book outlined a series of mathematical models of how genetic evolution might favor the selection of cultural traits and how cultural traits might, in turn, affect the speed of genetic evolution. While it was the first book published describing how genes and culture might coevolve, it had relatively little effect on the further development of DIT. [77] Some critics felt that their models depended too heavily on genetic mechanisms at the expense of cultural mechanisms. [78] Controversy surrounding Wilson's sociobiological theories may also have decreased the lasting effect of this book. [77]

The second 1981 book was Cavalli-Sforza and Feldman's Cultural Transmission and Evolution: A Quantitative Approach. [48] Borrowing heavily from population genetics and epidemiology, this book built a mathematical theory concerning the spread of cultural traits. It describes the evolutionary implications of vertical transmission, passing cultural traits from parents to offspring oblique transmission, passing cultural traits from any member of an older generation to a younger generation and horizontal transmission, passing traits between members of the same population.

The next significant DIT publication was Robert Boyd and Peter Richerson's 1985 Culture and the Evolutionary Process. [56] This book presents the now-standard mathematical models of the evolution of social learning under different environmental conditions, the population effects of social learning, various forces of selection on cultural learning rules, different forms of biased transmission and their population-level effects, and conflicts between cultural and genetic evolution. The book's conclusion also outlined areas for future research that are still relevant today. [79]

In their 1985 book, Boyd and Richerson outlined an agenda for future DIT research. This agenda, outlined below, called for the development of both theoretical models and empirical research. DIT has since built a rich tradition of theoretical models over the past two decades. [80] However, there has not been a comparable level of empirical work.

In a 2006 interview Harvard biologist E. O. Wilson expressed disappointment at the little attention afforded to DIT:

". for some reason I haven't fully fathomed, this most promising frontier of scientific research has attracted very few people and very little effort." [81]

Kevin Laland and Gillian Ruth Brown attribute this lack of attention to DIT's heavy reliance on formal modeling.

"In many ways the most complex and potentially rewarding of all approaches, [DIT], with its multiple processes and cerebral onslaught of sigmas and deltas, may appear too abstract to all but the most enthusiastic reader. Until such a time as the theoretical hieroglyphics can be translated into a respectable empirical science most observers will remain immune to its message." [82]

Economist Herbert Gintis disagrees with this critique, citing empirical work as well as more recent work using techniques from behavioral economics. [83] These behavioral economic techniques have been adapted to test predictions of cultural evolutionary models in laboratory settings [84] [85] [86] as well as studying differences in cooperation in fifteen small-scale societies in the field. [87]

Since one of the goals of DIT is to explain the distribution of human cultural traits, ethnographic and ethnologic techniques may also be useful for testing hypothesis stemming from DIT. Although findings from traditional ethnologic studies have been used to buttress DIT arguments, [88] [89] thus far there have been little ethnographic fieldwork designed to explicitly test these hypotheses. [71] [87] [90]

Herb Gintis has named DIT one of the two major conceptual theories with potential for unifying the behavioral sciences, including economics, biology, anthropology, sociology, psychology and political science. Because it addresses both the genetic and cultural components of human inheritance, Gintis sees DIT models as providing the best explanations for the ultimate cause of human behavior and the best paradigm for integrating those disciplines with evolutionary theory. [91] In a review of competing evolutionary perspectives on human behavior, Laland and Brown see DIT as the best candidate for uniting the other evolutionary perspectives under one theoretical umbrella. [92]

Sociology and cultural anthropology Edit

Two major topics of study in both sociology and cultural anthropology are human cultures and cultural variation. However, Dual Inheritance theorists charge that both disciplines too often treat culture as a static superorganic entity that dictates human behavior. [93] [94] Cultures are defined by a suite of common traits shared by a large group of people. DIT theorists argue that this doesn't sufficiently explain variation in cultural traits at the individual level. By contrast, DIT models human culture at the individual level and views culture as the result of a dynamic evolutionary process at the population level. [93] [95]

Human sociobiology and evolutionary psychology Edit

Evolutionary psychologists study the evolved architecture of the human mind. They see it as composed of many different programs that process information, each with assumptions and procedures that were specialized by natural selection to solve a different adaptive problem faced by our hunter-gatherer ancestors (e.g., choosing mates, hunting, avoiding predators, cooperating, using aggression). [96] These evolved programs contain content-rich assumptions about how the world and other people work. As ideas are passed from mind to mind, they are changed by these evolved inference systems (much like messages get changed in a game of telephone). But the changes are not random. Evolved programs add and subtract information, reshaping the ideas in ways that make them more "intuitive", more memorable, and more attention-grabbing. In other words, "memes" (ideas) are not like genes. Genes are copied faithfully as they are replicated, but ideas are not. It’s not just that ideas mutate every once in awhile, like genes do. Ideas are transformed every time they are passed from mind to mind, because the sender's message is being interpreted by evolved inference systems in the receiver. [97] [98] There is no necessary contradiction between evolutionary psychology and DIT, but evolutionary psychologists argue that the psychology implicit in many DIT models is too simple evolved programs have a rich inferential structure not captured by the idea of a "content bias". They also argue that some of the phenomena DIT models attribute to cultural evolution are cases of "evoked culture"—situations in which different evolved programs are activated in different places, in response to cues in the environment. [99]

Human sociobiologists try to understand how maximizing genetic fitness, in either the modern era or past environments, can explain human behavior. When faced with a trait that seems maladaptive, some sociobiologists try to determine how the trait actually increases genetic fitness (maybe through kin selection or by speculating about early evolutionary environments). Dual inheritance theorists, in contrast, will consider a variety of genetic and cultural processes in addition to natural selection on genes.

Human behavioral ecology Edit

Human behavioral ecology (HBE) and DIT have a similar relationship to what ecology and evolutionary biology have in the biological sciences. HBE is more concerned about ecological process and DIT more focused on historical process. [100] One difference is that human behavioral ecologists often assume that culture is a system that produces the most adaptive outcome in a given environment. This implies that similar behavioral traditions should be found in similar environments. However, this is not always the case. A study of African cultures showed that cultural history was a better predictor of cultural traits than local ecological conditions. [101]

Memetics Edit

Memetics, which comes from the meme idea described in Dawkins's The Selfish Gene, is similar to DIT in that it treats culture as an evolutionary process that is distinct from genetic transmission. However, there are some philosophical differences between memetics and DIT. [102] One difference is that memetics' focus is on the selection potential of discrete replicators (memes), where DIT allows for transmission of both non-replicators and non-discrete cultural variants. DIT does not assume that replicators are necessary for cumulative adaptive evolution. DIT also more strongly emphasizes the role of genetic inheritance in shaping the capacity for cultural evolution. But perhaps the biggest difference is a difference in academic lineage. Memetics as a label is more influential in popular culture than in academia. Critics of memetics argue that it is lacking in empirical support or is conceptually ill-founded, and question whether there is hope for the memetic research program succeeding. Proponents point out that many cultural traits are discrete, and that many existing models of cultural inheritance assume discrete cultural units, and hence involve memes. [103]

Psychologist Liane Gabora has criticised DIT. [104] [105] [106] She argues that use of the term ‘dual inheritance’ to refer to not just traits that are transmitted by way of a self-assembly code (as in genetic evolution) but also traits that are not transmitted by way of a self-assembly code (as in cultural evolution) is misleading, because this second use does not capture the algorithmic structure that makes an inheritance system require a particular kind of mathematical framework. [107]

Other criticisms of the effort to frame culture in Darwinian terms have been leveled by Richard Lewontin, [108] Niles Eldredge, [109] and Stuart Kauffman. [110]

Application to association studies

Whole-genome sequencing enables all genetic variants present in a sample set to be tested directly for association with a given disease or trait. To quantify the benefit of having more complete ascertainment of genetic variation beyond that achievable with genotyping arrays, we carried out expression quantitative trait loci (eQTL) association tests on the 142 low-coverage samples for which expression data are available in the cell lines 25 . When association analysis (Spearman rank correlation, FDR <5%, eQTLs within 50 kb of probe) was performed using all sites discovered in the low-coverage project, a larger number of significant eQTLs (increase of ∼ 20% to 50%) was observed as compared to association analysis restricted to sites present on the Illumina 1M chip (Supplementary Table 6). The increase was lower in the CHB+JPT and CEU samples, where greater LD exists between previously examined and newly discovered variants, and higher in the YRI samples, where there are more novel variants and less LD. These results indicate that, while modern genotyping arrays capture most of the common variation, there remain substantial additional contributions to phenotypic variation from the variants not well captured by the arrays.

Population sequencing of large phenotyped cohorts will allow direct association tests for low-frequency variants, with a resolution determined by the LD structure. An alternative that is less expensive, albeit less accurate, is to impute variants from a sequenced reference panel into previously genotyped samples 26,27 . We evaluated the accuracy of imputation that uses the current low-coverage project haplotypes as the reference panel. Specifically, we compared genotypes derived by deep sequencing of one individual in each trio (the fathers) with genotypes derived using the HapMap 3 genotype data (which combined data from the Affymetrix 6.0 and Illumina 1M arrays) in those same two individuals and imputation based on the low-coverage project haplotypes to fill in their missing genotypes. At variant sites (that is, where the father was not homozygous for the reference sequence), imputation accuracy was highest for SNPs at which the minor allele was observed at least six times in our low-coverage samples, with an error rate of ∼ 4% in CEU and ∼ 10% in YRI, and became progressively worse for rarer SNPs, with error rates of 35% for sites where the minor allele was observed only twice in the low-coverage samples (Fig. 4a).

a, Accuracy of imputing variant genotypes using HapMap 3 sites to impute sites from the low-coverage (LC) project into the trio fathers as a function of allele frequency. Accuracy of imputing genotypes from the HapMap II reference panels 4 is also shown. Imputation accuracy for common variants was generally a few per cent worse from the low-coverage project than from HapMap, although error rates increase for less common variants. b, An example of imputation in a cis-eQTL for TIMM22, for which the original Ilumina 300K genotype data gave a weak signal 28 . Imputation using HapMap data made a small improvement, and imputation using low-coverage haplotypes provided a much stronger signal.

Although the ability to impute rare variants accurately from the 1000 Genomes Project resource is currently limited, the completeness of the resource nevertheless increases power to detect association signals. To demonstrate the utility of imputation in disease samples, we imputed into an eQTL study of ∼ 400 children of European ancestry 28 using the low-coverage pilot data and HapMap II as reference panels. By comparison to directly genotyped sites we estimated that the effective sample size at variants imputed from the pilot CEU low-coverage data set is 91% of the true sample size for variants with allele frequencies above 10%, 76% in the allele frequency range 4–6%, and 54% in the range 1–2%. Imputing over 6 million variants from the low-coverage project data increased the number of detected cis-eQTLs by ∼ 16%, compared to a 9% increase with imputing from HapMap II (FDR 5%, signal within 50 kb of transcript for an example see Fig. 4b).

In addition to this modest increase in the number of discoveries, testing almost all common variants allows identification of many additional candidate variants that might underlie each association. For example, we find that rs11078928, a variant in a splice site for GSDMB, is in strong LD with SNPs near ORMDL3, previously associated with asthma, Crohn’s disease, type 1 diabetes and rheumatoid arthritis, thus leading to the hypothesis that GSDMB could be the causative gene in these associations. Although rs11078928 is not newly discovered, it was not included in HapMap or on commercial SNP arrays, and thus could not have been identified as associated with these diseases before this project. Similarly, a recent study 29 used project data to show that coding variants in APOL1 probably underlie a major risk for kidney disease in African-Americans previously attributed (at a lower effect size) to MYH9. These examples demonstrate the value of having much more complete information on LD, the almost complete set of common variants, and putative functional variants in known association intervals.

Testing almost all common variants also allows us to examine general properties of genetic association signals. The NHGRI GWAS catalogue (, accessed 15 July 2010) described 1,227 unique SNPs associated with one or more traits (P < 5 × 10 −8 ). Of these, 1,185 (96.5%) are present in the low-coverage CEU data set. Under 30% of these are either annotated as non-synonymous variants (77, 6.5%) or in substantial LD (r 2 > 0.5) with a non-synonymous variant (272, 23%). In the latter group, only 93 (8.4%) are in strong LD (r 2 > 0.9) with a non-synonymous variant. Because we tested ∼ 95% of common variation, these results indicate that no more than one-third of complex trait association signals are likely to be caused by common coding variation. Although it remains to be seen whether reported associations are better explained through weak LD to coding variants with strong effects, these results are consistent with the view that most contributions of common variation to complex traits are regulatory in nature.


Population Genetics Statistics

Across the Eastern United States, all populations had inbreeding coefficients (FIS) significantly >0. The average FIS was 0.296 ( table 1), consistent with a high degree of inbreeding in these populations. However, positive FIS values could also result from subpopulation structure within populations, known as the Wahlund effect ( Wahlund 1928 Sinnock 1975). Mean whole-genome nucleotide diversity (π) was similar across populations (range: 0.0005–0.0008). Central New York and Massachusetts showed a slight decrease in mean nucleotide diversity of mitochondrial genes (πmt) relative to the other populations. All population genetic statistics were similar between the full and the downsampled data sets.

Population Genetic Statistics for Each Study Region and across All Regions in Eastern United States

Location . Latitude . Longitude . r (km) . N . π . πmt . He . Ho . FIS .
C. NY (all) 42 −77 20 174 0.0007 0.0025 0.0961 0.0718 0.2748
C. NY (subset) 42 −77 20 8 0.0007 0.0038 0.0935 0.0822 0.2500
N. NY 43 −74 2.2 5 0.0008 0.0070 0.0814 0.0837 0.0802
NC 36 −79 3.5 6 0.0007 0.0077 0.0808 0.0681 0.2296
MD 39 −77 67 8 0.0005 0.0072 0.0742 0.0485 0.4249
MA (all) 42 −71 15 11 0.0006 0.0027 0.0799 0.0618 0.2745
MA (subset) 42 −71 15 8 0.0007 0.0038 0.0806 0.0667 0.2223
All regions (all) NA NA 750 204 0.0007 0.0026 0.0961 0.0706 0.2858
All regions (subset) NA NA 750 33 0.0007 0.0039 0.0936 0.0691 0.2955
Location . Latitude . Longitude . r (km) . N . π . πmt . He . Ho . FIS .
C. NY (all) 42 −77 20 174 0.0007 0.0025 0.0961 0.0718 0.2748
C. NY (subset) 42 −77 20 8 0.0007 0.0038 0.0935 0.0822 0.2500
N. NY 43 −74 2.2 5 0.0008 0.0070 0.0814 0.0837 0.0802
NC 36 −79 3.5 6 0.0007 0.0077 0.0808 0.0681 0.2296
MD 39 −77 67 8 0.0005 0.0072 0.0742 0.0485 0.4249
MA (all) 42 −71 15 11 0.0006 0.0027 0.0799 0.0618 0.2745
MA (subset) 42 −71 15 8 0.0007 0.0038 0.0806 0.0667 0.2223
All regions (all) NA NA 750 204 0.0007 0.0026 0.0961 0.0706 0.2858
All regions (subset) NA NA 750 33 0.0007 0.0039 0.0936 0.0691 0.2955

Note .—r, radius of specified population in kilometers N, number of individuals πmt, mitochondrial gene nucleotide diversity π, mean whole-genome nucleotide diversity FIS, inbreeding coefficient.

Population Genetic Statistics for Each Study Region and across All Regions in Eastern United States

Location . Latitude . Longitude . r (km) . N . π . πmt . He . Ho . FIS .
C. NY (all) 42 −77 20 174 0.0007 0.0025 0.0961 0.0718 0.2748
C. NY (subset) 42 −77 20 8 0.0007 0.0038 0.0935 0.0822 0.2500
N. NY 43 −74 2.2 5 0.0008 0.0070 0.0814 0.0837 0.0802
NC 36 −79 3.5 6 0.0007 0.0077 0.0808 0.0681 0.2296
MD 39 −77 67 8 0.0005 0.0072 0.0742 0.0485 0.4249
MA (all) 42 −71 15 11 0.0006 0.0027 0.0799 0.0618 0.2745
MA (subset) 42 −71 15 8 0.0007 0.0038 0.0806 0.0667 0.2223
All regions (all) NA NA 750 204 0.0007 0.0026 0.0961 0.0706 0.2858
All regions (subset) NA NA 750 33 0.0007 0.0039 0.0936 0.0691 0.2955
Location . Latitude . Longitude . r (km) . N . π . πmt . He . Ho . FIS .
C. NY (all) 42 −77 20 174 0.0007 0.0025 0.0961 0.0718 0.2748
C. NY (subset) 42 −77 20 8 0.0007 0.0038 0.0935 0.0822 0.2500
N. NY 43 −74 2.2 5 0.0008 0.0070 0.0814 0.0837 0.0802
NC 36 −79 3.5 6 0.0007 0.0077 0.0808 0.0681 0.2296
MD 39 −77 67 8 0.0005 0.0072 0.0742 0.0485 0.4249
MA (all) 42 −71 15 11 0.0006 0.0027 0.0799 0.0618 0.2745
MA (subset) 42 −71 15 8 0.0007 0.0038 0.0806 0.0667 0.2223
All regions (all) NA NA 750 204 0.0007 0.0026 0.0961 0.0706 0.2858
All regions (subset) NA NA 750 33 0.0007 0.0039 0.0936 0.0691 0.2955

Note .—r, radius of specified population in kilometers N, number of individuals πmt, mitochondrial gene nucleotide diversity π, mean whole-genome nucleotide diversity FIS, inbreeding coefficient.

Population Structure

MDS plots grouped P. fuscatus individuals by region at the three different spatial scales. Across the Eastern United States, the first two MDS axes (C1 and C2) grouped individuals broadly by region, with some overlap among regions ( fig. 1A and B). Within central New York ( fig. 1C and D), C1 separates individuals collected from Erin and Slaterville Springs from the other populations, whereas C2 roughly correlates with latitude. Remarkably, for P. fuscatus within the same site in Arnot Forest, the C1 axis largely separated individuals nesting on buildings A–B from individuals nesting on buildings E–G, with individuals nesting on buildings C–D showing intermediate values ( fig. 1E and F). Individuals sampled across multiple years clustered by location across the Eastern United States and within Central New York. Within Arnot Forest, individuals sampled in 2016 tended to favor the same general location as those sampled in 2015. In several cases, closely related individuals nested on the same building across multiple years, suggesting the possibility of fine-scale philopatry for some foundresses. Eigenvalues for all MDS plots are given in supplementary table S3 , Supplementary Material online.

—Plots of the first two axes from MDS analyses of whole-genome sequences reveal population genetic structure at three different spatial scales. (A) Location of all sampling sites across the Eastern United States. (B) MDS plot of individuals collected across the Eastern United States. Populations have been subsampled to include the same number of individuals across regions. (C) Inset showing sampling sites within the Central New York region. (D) MDS plot of Central New York individuals. Sample locations are indicated by color. (E) Inset showing sampling sites within Arnot Forest in Van Etten New York with approximate location of building indicated by the colored boxes. (F) MDS plot of individuals within Arnot Forest. The color of points corresponds to the building where the individual was collected. Sampling locations and abbreviations are given in supplementary table S2 , Supplementary Material online. Eigenvalues are given in supplementary table S3 , Supplementary Material online.

—Plots of the first two axes from MDS analyses of whole-genome sequences reveal population genetic structure at three different spatial scales. (A) Location of all sampling sites across the Eastern United States. (B) MDS plot of individuals collected across the Eastern United States. Populations have been subsampled to include the same number of individuals across regions. (C) Inset showing sampling sites within the Central New York region. (D) MDS plot of Central New York individuals. Sample locations are indicated by color. (E) Inset showing sampling sites within Arnot Forest in Van Etten New York with approximate location of building indicated by the colored boxes. (F) MDS plot of individuals within Arnot Forest. The color of points corresponds to the building where the individual was collected. Sampling locations and abbreviations are given in supplementary table S2 , Supplementary Material online. Eigenvalues are given in supplementary table S3 , Supplementary Material online.

In contrast to the fine-scale spatial segregation detected in MDS plots, population differentiation among regions was not observed in the fastSTRUCTURE analysis ( fig. 2). The best-supported model was K = 1 or K = 2. The model output for K = 2 demes indicates near panmictic levels of homogeneity for most individuals, but with a few individuals from Central New York, Massachusetts, and Maryland assigned to a second population. Model outputs for K = 3–6 showed no evidence of regional population differentiation although the prediction of a second and third population within the data remains consistent with increasing values of K ( supplementary fig. S1 , Supplementary Material online). Similar results were obtained analyzing data with STRUCTURE ( supplementary fig. S2 , Supplementary Material online).

—Cluster analysis of populations using fastSTRUCTURE for the most highly supported model K = 2. Color within a column indicates the inferred posterior probability that the individual is a member of a particular cluster. Each individual is represented by a single column. Regions represented are Northern New York (N. NY), central New York, Massachusetts (MA), Maryland (MD), and North Carolina (NC).

—Cluster analysis of populations using fastSTRUCTURE for the most highly supported model K = 2. Color within a column indicates the inferred posterior probability that the individual is a member of a particular cluster. Each individual is represented by a single column. Regions represented are Northern New York (N. NY), central New York, Massachusetts (MA), Maryland (MD), and North Carolina (NC).

Isolation by Distance

Whole-genome analyses revealed significant patterns of IBD in P. fuscatus wasps both across the Eastern United States and within Central New York ( fig. 3A and B). Linear models showed significant correlation between linearized genetic and geographic distance across the Eastern United States (y = 1.27 × 10 −4 x – 0.01, R 2 = 0.60, P < 0.005) but a poor correlation between these values in Central New York (y = 0.0021x – 0.05, R 2 = 0.05, P = 0.26). The mean pairwise divergence across all subpopulation comparisons was FST = 0.0525 (pairwise comparisons given in supplementary table S4 , Supplementary Material online). Results from mitochondrial data were similar ( fig. 3C and D) with significant patterns of IBD detected in mitochondrial sequence across the Eastern United States (y = 8.8 × 10 −4 x – 0.1, R 2 =0.64, P = 0.003) and a weaker correlation within Central New York (y = 0.13x − 0.93, R 2 = 0.27, P = 0.072). The mean pairwise mitochondrial divergence across all comparisons was FST = 0.24 (pairwise comparisons given in supplementary table S4 , Supplementary Material online).

—Linearized pairwise whole-genome divergence versus geographic distance across the Eastern United States (A) and within the Central New York region (C. NY) (B) and pairwise mitochondrial divergence versus geographic distance across the Eastern United States (C) and within the Central New York region (D). Patterns of IBD are evident at both scales and for both types of markers. Nonsignificant regressions are illustrated with dotted lines.

—Linearized pairwise whole-genome divergence versus geographic distance across the Eastern United States (A) and within the Central New York region (C. NY) (B) and pairwise mitochondrial divergence versus geographic distance across the Eastern United States (C) and within the Central New York region (D). Patterns of IBD are evident at both scales and for both types of markers. Nonsignificant regressions are illustrated with dotted lines.

To further investigate patterns of genetic divergence, we constructed mitochondrial haplotype networks for the Eastern United States, Central New York, and within Arnot Forest ( fig. 4). The distribution of haplotypes supports the findings from the MDS analysis. At the continental scale, haplotypes were not shared among Eastern US populations. Within Central New York, haplotypes were only shared among neighboring populations. At the local scale, mitochondrial differentiation was evident among buildings within a single clearing in the Arnot Forest.

—Haplotype network of concatenated mitochondrial sequence across the (A) Eastern United States, (B) within Central New York, and (C) at a single site in Arnot Forest. Circle sizes correspond to the number of individuals with that haplotype. Colors indicate the sampling locations. Lines connect haplotypes to their most closely related neighbor. Bars represent mutational differences between neighboring haplotypes.

—Haplotype network of concatenated mitochondrial sequence across the (A) Eastern United States, (B) within Central New York, and (C) at a single site in Arnot Forest. Circle sizes correspond to the number of individuals with that haplotype. Colors indicate the sampling locations. Lines connect haplotypes to their most closely related neighbor. Bars represent mutational differences between neighboring haplotypes.


To look for evidence of differences in male and female dispersal distances, we compared nuclear and mitochondrial genetic diversity statistics. We calculated the difference between the observed and expected values for each pairwise comparison across the Eastern United States. The average expected value of FST(mito) was estimated as 0.24. This value was greater than the average observed value of 0.172 (one sample t-test, t = 2.49, df = 9, P = 0.03) indicating likely male-biased dispersal in these populations.

We estimated the mean parent–offspring axial dispersal distance using the slope of the regression line for the Eastern US comparison of FST/(1 − FST) against pairwise distance. This yields an estimated dispersal distance of σ = 761 m using the measurements of nest density from Arnot Forest and σ = 501 m using nest density estimates from Tsuchida et al. (2014). We repeated this measurement using the slope of the regression line from the Central New York comparison, although this regression line had a poor goodness of fit for our data. Using only the Central New York data, we estimate σ = 188 m based on the nest density from Arnot Forest and σ = 124 m using the nest density from Tsuchida et al. (2014).

Comparison of P. fuscatus Genetic Variation with Additional Polistes Species

To further investigate the second genetic population identified in the fastSTRUCTURE analysis, we performed a second MDS analysis using all 204 samples collected across the Eastern United States ( supplementary fig. S3 , Supplementary Material online). Eigenvalues are given in supplementary table S3 , Supplementary Material online. The MDS axis (C1) separated 24 samples from the remaining P. fuscatus individuals. Upper level MDS axes (2–10) were driven by variation within Central New York samples, likely caused by the overrepresentation of individuals from this region relative to the other regions in the analysis. These 24 samples were geographically widespread, and largely, but not entirely, corresponded with samples assigned >10% membership probability in group 2 or group 3 in the fastSTRUCTURE analysis ( supplementary fig. S1 , Supplementary Material online).

A potential explanation for additional structure unrelated to geography is the inclusion of misidentified species within our samples. The northern range limit of P. dorsalis extends into Central New York and smaller P. fuscatus are commonly misclassified as P. dorsalis ( Buck et al. 2008). Similarly, P. metricus occurs in Maryland and North Carolina and darker bodied P. fuscatus are less commonly misclassified as P. metricus ( Buck et al. 2008). To account for this possibility, we combined the 204 Eastern US P. fuscatus from this study with 93 previously generated whole-genome sequences from four sympatric closely related Polistes species: P. metricus, P. carolina, P. perplexus, and P. dorsalis ( Miller et al. 2020). An MDS analysis including these additional species clearly shows that these 24 individuals were not misclassified P. dorsalis or P. metricus ( fig. 5). Interestingly, these 24 individuals show a slight separation along the second MDS axis (C2) from the other P. fuscatus samples. These individuals are also not recent hybrids between P. fuscatus and other species because they do not have an intermediate value in multidimensional space. There is no clear biological difference associated with these specimens and unraveling the cause of this genetic variance will require future study.

—Plot of the first two axes from a MDS analysis of whole-genome sequences for all P. fuscatus samples in this study and whole-genome sequences from four sympatric species of paper wasp.

—Plot of the first two axes from a MDS analysis of whole-genome sequences for all P. fuscatus samples in this study and whole-genome sequences from four sympatric species of paper wasp.

To test the contribution of these 24 samples to our findings, we repeated our calculations of IBD across the Eastern United States and within Central New York without these samples. Due to the smaller sample sizes of some populations, comparisons between Maryland and North Carolina were dropped from the Eastern US analysis, and all comparisons with Erin New York were dropped from the Central New York analysis. We find significant IBD in the nuclear genome across Eastern United States (y = 9.54 × 10 −5 x – 9.58 × 10 −3 , R 2 = 0.68, P < 0.003) and a similar poor correlation between linearized genetic and geographic distance in Central New York (y = −0.001x + 0.08, R 2 = 0.05, P = 0.32) ( supplementary fig. S4 , Supplementary Material online). There was no significant relationship between genetic distance and geographic distance using mitochondrial markers in the Eastern United States (y = 0.01x + 3.67, R 2 = −0.08, P = 0.54) or in Central New York (y = −0.01x + 1.38, R 2 = −0.26, P = 0.91). Calculating mean parent–offspring axial dispersal using the slope of regression line for the Eastern US comparison yields an estimate of σ = 578–879 m.


The idea that the function of a trait might shift during its evolutionary history originated with Charles Darwin (Darwin 1859). For many years the phenomenon was labeled "preadaptation", but since this term suggests teleology in biology, appearing to conflict with natural selection, it has been replaced by the term exaptation.

The idea had been explored by several scholars [a] when in 1982 Stephen Jay Gould and Elisabeth Vrba introduced the term "exaptation". However, this definition had two categories with different implications for the role of adaptation.

(1) A character, previously shaped by natural selection for a particular function (an adaptation), is coopted for a new use—cooptation. (2) A character whose origin cannot be ascribed to the direct action of natural selection (a nonaptation), is coopted for a current use—cooptation. (Gould and Vrba 1982, Table 1)

The definitions are silent as to whether exaptations had been shaped by natural selection after cooption, although Gould and Vrba cite examples (e.g., feathers) of traits shaped after cooption. Note that the selection pressure upon a trait is likely to change if it is (especially, primarily or solely) used for a new purpose, potentially initiating a different evolutionary trajectory.

To avoid these ambiguities, Buss et al. (1998) suggested the term "co-opted adaptation", which is limited to traits that evolved after cooption. However, the commonly used terms of "exaptation" and "cooption" are ambiguous in this regard.

In some circumstances, the "pre-" in preadaptation can be interpreted as applying, for non-teleological reasons, prior to the adaptation itself, creating a meaning for the term that is distinct from exaptation. [6] [7] For example, future environments (say, hotter or drier ones), may resemble those already encountered by a population at one of its current spatial or temporal margins. [6] This is not actual foresight, but rather the luck of having adapted to a climate which later becomes more prominent. Cryptic genetic variation may have the most strongly deleterious mutations purged from it, leaving an increased chance of useful adaptations, [7] [8] but this represents selection acting on current genomes with consequences for the future, rather than foresight.

Function may not always come before form: developed structures could change or alter the primary functions they were intended for [ by whom? ] due to some structural or historical cause. [9]

Exaptations include the co-option of feathers, which initially evolved for heat regulation, for display, and later for use in bird flight. Another example is the lungs of many basa fish, which evolved into the lungs of terrestrial vertebrates but also underwent exaptation to become the gas bladder, a buoyancy control organ, in derived fish. [10] A third is the repurposing of two of the three bones in the reptilian jaw to become the malleus and incus of the mammalian ear, leaving the mammalian jaw with just one hinge. [11]

A behavioural example pertains to subdominant wolves licking the mouths of lead wolves as a sign of submissiveness. (Similarly, dogs, which are wolves who through a long process were domesticated, lick the faces of their human owners.) This trait can be explained as an exaptation of wolf pups licking the faces of adults to encourage them to regurgitate food. [12]

Arthropods provide the earliest identifiable fossils of land animals, from about 419 million years ago in the Late Silurian, and terrestrial tracks from about 450 million years ago appear to have been made by arthropods. [13] Arthropods were well pre-adapted to colonize land, because their existing jointed exoskeletons provided support against gravity and mechanical components that could interact to provide levers, columns and other means of locomotion that did not depend on submergence in water. [14]

Metabolism can be considered an important part of exaptation. As one of the oldest biological systems and being central to life on the Earth, studies have shown that metabolism may be able to use exaptation in order to be fit, given some new set of conditions or environment. [15] Studies have shown that up to 44 carbon sources are viable for metabolism to successfully take place and that any one adaptation in these specific metabolic systems is due to multiple exaptations. [16] Taking this perspective, exaptations are important in the origination of adaptations in general. A recent example comes from Richard Lenski's E. coli long-term evolution experiment, in which aerobic growth on citrate arose in one of twelve populations after 31,000 generations of evolution. [17] Genomic analysis by Blount and colleagues showed that this novel trait was due to a gene duplication that caused a citrate transporter that is normally expressed only under anoxic conditions to be expressed under oxic conditions, thus exapting it for aerobic use. [18] Metabolic systems have the potential to innovate without adaptive origins.

Gould and Brosius took the concept of exaptation to the genetic level. It is possible to look at a retroposon, originally thought to be simply junk DNA, and deduce that it may have gotten a new function to be termed as an exaptation. [19] [20] [21] Given an emergency situation in the past, a species may have used junk DNA for a useful purpose in order to evolve and be able to survive. This may have occurred with mammalian ancestors when confronted with a large mass extinction about 250 million years ago and substantial increase in the level of oxygen in Earth's atmosphere. More than 100 loci have been found to be conserved only among mammalian genomes and are thought to have essential roles in the generation of features such as the placenta, diaphragm, mammary glands, neocortex, and auditory ossicles. It is believed that as a result of exaptation, or making previously "useless" DNA into DNA that could be used in order to increase survival chance, mammals were able to generate new brain structures as well as behavior to better survive the mass extinction and adapt to new environments. Similarly, viruses and their components have been repeatedly exapted for host functions. The functions of exapted viruses typically involve either defense from other viruses or cellular competitors or transfer of nucleic acids between cells, or storage functions. Koonin and Krupovic suggested that virus exaptation can reach different depths, from recruitment of a fully functional virus to exploitation of defective, partially degraded viruses, to utilization of individual virus proteins. [22]

It was speculated by Gould and Vrba [23] in one of the first papers written about exaptation, that when an exaptation arises, it may not be perfectly suited for its new role and may therefore develop new adaptations to promote its use in a better manner. In other words, the beginning of developing a particular trait starts out with a primary adaptation toward a fit or specific role, followed by a primary exaptation (a new role is derived using the existing feature but may not be perfect for it), which in turn leads to the development of a secondary adaptation (the feature is improved by natural selection for better performance), promoting further development of an exaptation, and so forth.

Once again, feathers are an important example, in that they may have first been adapted for thermoregulation and with time became useful for catching insects, and therefore served as a new feature for another benefit. For instance, large contour feathers with specific arrangements arose as an adaptation for catching insects more successfully, which eventually led to flight, since the larger feathers served better for that purpose.

Evolution of complex traits Edit

One of the challenges to Darwin's theory of evolution was explaining how complex structures could evolve gradually, [24] given that their incipient forms may have been inadequate to serve any function. As George Jackson Mivart (a critic of Darwin) pointed out, 5 percent of a bird wing would not be functional. The incipient form of complex traits would not have survived long enough to evolve to a useful form.

As Darwin elaborated in the last edition of The Origin of Species, [25] many complex traits evolved from earlier traits that had served different functions. By trapping air, primitive wings would have enabled birds to efficiently regulate their temperature, in part, by lifting up their feathers when too warm. Individual animals with more of this functionality would more successfully survive and reproduce, resulting in the proliferation and intensification of the trait.

Eventually, feathers became sufficiently large to enable some individuals to glide. These individuals would in turn more successfully survive and reproduce, resulting in the spread of this trait because it served a second and still more beneficial function: that of locomotion. Hence, the evolution of bird wings can be explained by a shifting in function from the regulation of temperature to flight.

Jury-rigged design Edit

Darwin explained how the traits of living organisms are well-designed for their environment, but he also recognized that many traits are imperfectly designed. They appear to have been made from available material, that is, jury-rigged. [b] Understanding exaptations may suggest hypotheses regarding subtleties in the adaptation. For instance, that feathers evolved initially for thermal regulation may help to explain some of their features unrelated to flight (Buss et al., 1998). However, this is readily explained by the fact that they serve a dual purpose.

Some of the chemical pathways for physical pain and pain from social exclusion overlap. [26] The physical pain system may have been co-opted to motivate social animals to respond to threats to their inclusion in the group.

Evolution of technology Edit

Exaptation has received increasing attention in innovation and management studies inspired by evolutionary dynamics, where it has been proposed as a mechanism that drives the serendipitous expansion of technologies and products in new domains. [27] [1]


Distinguishing which traits have evolved under natural selection, as opposed to neutral evolution, is a major goal of evolutionary biology. Several tests have been proposed to accomplish this, but these either rely on false assumptions or suffer from low power. Here, I introduce an approach to detecting selection that makes minimal assumptions and only requires phenotypic data from ∼10 individuals. The test compares the phenotypic difference between two populations to what would be expected by chance under neutral evolution, which can be estimated from the phenotypic distribution of an F2 cross between those populations. Simulations show that the test is robust to variation in the number of loci affecting the trait, the distribution of locus effect sizes, heritability, dominance, and epistasis. Comparing its performance to the QTL sign test—an existing test of selection that requires both genotype and phenotype data—the new test achieves comparable power with 50- to 100-fold fewer individuals (and no genotype data). Applying the test to empirical data spanning over a century shows strong directional selection in many crops, as well as on naturally selected traits such as head shape in Hawaiian Drosophila and skin color in humans. Applied to gene expression data, the test reveals that the strength of stabilizing selection acting on mRNA levels in a species is strongly associated with that species’ effective population size. In sum, this test is applicable to phenotypic data from almost any genetic cross, allowing selection to be detected more easily and powerfully than previously possible.

Trait-based tests of selection aim to distinguish the effects of two major forces of evolution: natural selection and neutral drift. Because many factors affect trait divergence—e.g., population size, divergence time, and genetic architecture—distinguishing these two forces is seldom straightforward. Several types of trait-based selection tests have been proposed, all of which view neutrality as a null model, but which differ in how they assess this null and in the type of data they require [reviewed in chapter 12 of Walsh and Lynch (1)].

For example, time series tests use phenotypic measurements of a single species over time, typically from the fossil record (a stratophenetic series). If the trait shows departure from the neutral expectation of a random walk—e.g., many more time steps with trait increases than decreases—then neutrality is rejected. The key assumption is that environmental changes do not affect these phenotypic trends, which is difficult to justify considering how much environments can change over the millions of years typically covered in a stratophenetic series.

A more widely used approach is known as QST, where the population structure of phenotypic variance is compared to the analogous genetic metric FST. By utilizing genetic crosses in common garden experiments, the confounding effects of environment can be controlled, allowing selection to be assessed in a wide range of species (2). Limitations of this approach include low power [requiring data from >10 populations (3)] and several assumptions about epistasis and mutation rates (SI Appendix). However, an improved QST-based method has sufficient power to detect selection using only a few populations (4).

Another widely used test is known as the quantitative trait locus (QTL) sign test (5, 6). In this test, QTL are first mapped using genotype and phenotype data from a genetic cross between two divergent parental lines. Under neutrality (and the absence of ascertainment bias), QTL directionality—i.e., which parent’s allele increases the trait at each QTL—is expected to be binomially distributed around 50%, much like a series of coin flips (Fig. 1 A, Left). In contrast, under lineage-specific selection, QTL directions will be biased in one direction (Fig. 1 A, Right). Although this test is quite robust due to its minimal assumptions, it also suffers from low power: A minimum of eight QTL (which is rarely reached in practice see SI Appendix) is required to achieve a nominal P < 0.01.

The sign test and the v test. (A) Illustration of the sign test applied to the trait of mouse size. (Left) Two mice from separate populations that have had no selection acting on size are expected to have approximately equal numbers of QTL (or QTN) alleles increasing size (binomially distributed with expected frequency = 1/2 stabilizing selection on size would result in a similar pattern, but with a smaller expected parental trait divergence). (Right) In contrast, two populations that have experienced lineage-specific directional selection on size will show greater phenotypic divergence and a preponderance of QTL alleles increasing size in the larger strain. A significant deviation from the binomial expectation indicates rejection of the null hypothesis of neutral evolution. (B) Simulation of trait divergence under a simple model of three selection regimes. One exponentially distributed QTL (or QTN) is added per time step, and the number and effect sizes of QTL are identical in each selection regime the only difference is their directionality. Under directional selection, all QTL increase the trait value (as in Fig. 1 A, Right) under neutral evolution, their directionalities are random and under stabilizing selection, their directionalities are whatever will bring the trait closer to the optimum (e.g., if the trait is above the optimum, the next QTL will be negative). Each selection regime has 100 lineages simulated for 100 time steps. (C) Illustration of the v test. Under a simple model, the variance of a neutral trait in two populations is expected to be approximately equal to that of their F2 progeny (Eq. 1). Lineage-specific directional selection will result in higher parental variance, whereas stabilizing selection will lead to lower parental variance (transgressive segregation).

The sign test’s low power is largely due to the fact that it only uses QTL directionality information, while ignoring the phenotypic divergence between the two parental lines. However, the parental traits contain important information: If a trait evolves under directional selection, it will diverge much faster than under neutrality (Fig. 1B). If it were possible to estimate the divergence expected by chance under neutrality, then this could be used as a null hypothesis parental trait divergence significantly greater than this expectation would suggest lineage-specific directional selection, whereas divergence less than this would suggest stabilizing selection.

Indeed, this intuitive logic underlies another class of trait-based methods, “rate tests,” that ask whether the phenotypic divergence of multiple populations is consistent with neutral drift (1, 7). The neutral expectation is estimated from population genetic theory, using parameters such as the effective population size, the mutational variance, and the number of generations since population divergence. Since these parameters and their sampling variances can typically only be roughly estimated (at best), and several strong assumptions must also be made, rate tests are viewed as qualitative guides rather than quantitative tests of neutrality (1, 7) (SI Appendix).

In this work, I sought to develop a trait-based test of selection with the robustness of the sign test, while utilizing the framework of rate tests to increase the power to detect selection.

Example Two

How are sexual reproduction and asexual reproduction different from each other?

A. sexual reproduction requires two parents and asexual reproduction requires only one parent

B. asexual reproduction requires two parents and sexual reproduction requires only one parent

C. mutation rates are lower in sexual reproduction than in asexual reproduction

D. asexual reproduction occurs only in multicellular organisms

Watch the video: Variation. Genetics. Biology. FuseSchool (November 2021).