International Journal of Primatology

, Volume 35, Issue 1, pp 108–128 | Cite as

Applying Quantitative Genetic Methods to Primate Social Behavior

  • Gregory E. BlomquistEmail author
  • Lauren J. N. Brent


Increasingly, behavioral ecologists have applied quantitative genetic methods to investigate the evolution of behaviors in wild animal populations. The promise of quantitative genetics in unmanaged populations opens the door for simultaneous analysis of inheritance, phenotypic plasticity, and patterns of selection on behavioral phenotypes all within the same study. In this article, we describe how quantitative genetic techniques provide studies of the evolution of behavior with information that is unique and valuable. We outline technical obstacles for applying quantitative genetic techniques that are of particular relevance to studies of behavior in primates, especially those living in noncaptive populations, e.g., the need for pedigree information, non-Gaussian phenotypes, and demonstrate how many of these barriers are now surmountable. We illustrate this by applying recent quantitative genetic methods to spatial proximity data, a simple and widely collected primate social behavior, from adult rhesus macaques on Cayo Santiago. Our analysis shows that proximity measures are consistent across repeated measurements on individuals (repeatable) and that kin have similar mean measurements (heritable). Quantitative genetics may hold lessons of considerable importance for studies of primate behavior, even those without a specific genetic focus.


Animal model Behavioral genetics Generalized linear mixed model Heritability Rhesus macaque Spatial proximity 



We thank the Caribbean Primate Research Center (CPRC) for the permission to undertake research on Cayo Santiago, along with Bonn Aure and Jacqueline Buhl, who assisted in data collection, and Elizabeth Maldonado, Angelina Ruiz-Lambides, and Janis Gonzalez-Martinez, who provided access to the CPRC pedigree database. L. J. N. Brent also thanks Michael Platt for mentorship during the collection of these data. L. J. N. Brent was funded by fellowships awarded by the Duke Center for Interdisciplinary Decision Sciences. Additional funds were provided by NIMH grants no. R01-MH096875 and R01-MH089484. The CPRC is supported by a grant no. 8-P40 OD012217-25 from the National Center for Research Resources (NCRR) and the Office of Research Infrastructure Programs (ORIP) of the National Institutes of Health. G. E. Blomquist is supported by the University of Missouri Department of Anthropology and Research Council. G. E. Blomquist also thanks L. J. N. Brent and Noah Snyder-Mackler for the invitation to participate in the International Primatological Society symposium leading to this special issue.


  1. Adams, M. J. (2011). Evolutionary genetics of personality in nonhuman primates. In M. Inoue-Murayama, S. Kawamura, & A. Weiss (Eds.), From genes to animal behavior (pp. 137–164). New York: Springer.CrossRefGoogle Scholar
  2. Adams, M. J., King, J. E., & Weiss, A. (2012). The majority of genetic variation in orangutan personality and subjective well-being is nonadditive. Behavior Genetics, 42, 675–686.PubMedCrossRefGoogle Scholar
  3. Arnold, S. J. (1994). Multivariate inheritance and evolution: A review of concepts. In C. R. B. Boake (Ed.), Quantitative genetic studies of behavioral evolution (pp. 17–48). Chicago: University of Chicago Press.Google Scholar
  4. Bell, A. M., Hankison, S. J., & Laskowski, K. L. (2009). The repeatability of behaviour: A meta-analysis. Animal Behaviour, 77(4), 771–783.CrossRefGoogle Scholar
  5. Bennett, A. J., & Pierre, P. J. (2010). Nonhuman primate research contributions to understanding genetic and environmental influences on phenotypic outcomes across development. In K. E. Hood, C. T. Halpern, G. Greenberg, & R. M. Lerner (Eds.), Handbook of developmental science, behavior, and genetics (pp. 353–399). New York: Blackwell.CrossRefGoogle Scholar
  6. Blomquist, G. E. (2009a). Environmental and genetic causes of maturational differences among rhesus macaque matrilines. Behavioral Ecology and Sociobiology, 63(9), 1345–1352.CrossRefGoogle Scholar
  7. Blomquist, G. E. (2009b). Fitness-related patterns of genetic variation in rhesus macaques. Genetica, 135, 209–219.PubMedCrossRefGoogle Scholar
  8. Boake, C. R. B. (1994). Quantitative genetic studies of behavioral evolution. Chicago: University of Chicago Press.Google Scholar
  9. Boake, C. R. B., Arnold, S. J., Breden, F., Meffert, L. M., Ritchie, M. G., Taylor, B. J., Wolf, J. B., & Moore, A. J. (2002). Genetic tools for studying adaptation and the evolution of behavior. American Naturalist, 160, S143–S159.PubMedCrossRefGoogle Scholar
  10. Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., Simone, S., & White, J. (2009). Generalized linear mixed models: A practical guide for ecology and evolution. Trends in Ecology and Evolution, 24(3), 127–135.PubMedCrossRefGoogle Scholar
  11. Bradley, B. J., & Lawler, R. R. (2011). Linking genotypes, phenotypes, and fitness in wild primate populations. Evolutionary Anthropology, 20(3), 104–119.PubMedCrossRefGoogle Scholar
  12. Brent, L. J. N., Heilbronner, S. R., Horvath, J. E., Gonzalez-Martinez, J., Ruiz-Lambides, A., Robinson, A. G., Skene, J. H. P., & Platt, M. L. (2013a). Genetic origins of social networks in rhesus macaques. Scientific Reports, 3, 1042.PubMedCentralPubMedCrossRefGoogle Scholar
  13. Brent, L. J. N., MacLarnon, A., Platt, M. L., & Semple, S. (2013b). Seasonal changes in the structure of rhesus macaque social networks. Behavioral Ecology and Sociobiology, 67, 349–359.PubMedCrossRefGoogle Scholar
  14. Brommer, J. E. (2011). Whither P ST ? the approximation of Q ST by P ST in evolutionary and conservation biology. Journal of Evolutionary Biology, 24, 1160–1168.PubMedCrossRefGoogle Scholar
  15. Brommer, J. E., Kontiainen, P., & Pietiäinen, H. (2012). Selection on plasticity of seasonal life-history traits using random regression mixed model analysis. Ecology and Evolution, 2, 695–704.PubMedCrossRefGoogle Scholar
  16. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Springer.Google Scholar
  17. Campbell, C. J., Fuentes, A., MacKinnon, K. C., Bearder, S., & Stumpf, R. (Eds.). (2011). Primates in perspective. New York: Oxford University Press.Google Scholar
  18. Carrasco, J. L. (2010). A generalized concordance correlation coefficient based on the variance components generalized linear mixed models for overdispersed count data. Biometrics, 66, 897–904.PubMedCrossRefGoogle Scholar
  19. Charmantier, A., & Garant, D. (2005). Environmental quality and evolutionary potential: Lessons from wild populations. Proceedings of the Royal Society of London B: Biological Sciences, 272, 1415–1425.CrossRefGoogle Scholar
  20. Cheverud, J. M., & Dittus, W. P. J. (1992). Primate population studies at Polonnaruwa II. Heritability of body measurements in a natural population of toque macaques. American Journal of Primatology, 27, 145–156.CrossRefGoogle Scholar
  21. Cheverud, J. M., & Moore, A. J. (1994). Quantitative genetics and the role of the environment provided by relatives in behavioral evolution. In C. R. B. Boake (Ed.), Quantitative genetic studies of behavioral evolution (pp. 67–100). Chicago: University of Chicago Press.Google Scholar
  22. Cheverud, J. M., & Wolf, J. B. (2009). The genetics and evolutionary consequences of maternal effects. In D. Maestripieri & J. M. Mateo (Eds.), Maternal effects in mammals (pp. 11–37). Chicago: University of Chicago Press.CrossRefGoogle Scholar
  23. Clutton-Brock, T., & Janson, C. (2012). Primate socioecology at the crossroads: Past, present, and future. Evolutionary Anthropology, 21, 136–150.PubMedCrossRefGoogle Scholar
  24. Dingemanse, N. J., & Dochtermann, N. A. (2013). Quantifying individual variation in behaviour: Mixed-effect modelling approaches. Journal of Animal Ecology, 82, 39–54.PubMedCrossRefGoogle Scholar
  25. Dingemanse, N. J., Kazem, A. J. N., Réale, D., & Wright, J. (2010). Behavioural reaction norms: Animal personality meets individual plasticity. Trends in Ecology and Evolution, 25, 81–89.PubMedCrossRefGoogle Scholar
  26. Dingemanse, N. J., & Réale, D. (2005). Natural selection and animal personality. Behaviour, 142, 1165–1190.CrossRefGoogle Scholar
  27. Elston, D. A., Moss, R., Boulinier, T., Arrowsmith, C., & Lambin, X. (2001). Analysis of aggregation, a worked example: Numbers of ticks on red grouse chicks. Parasitology, 122, 563–569.PubMedCrossRefGoogle Scholar
  28. Foulley, J. L., Gianola, D., & Im, S. (1987). Genetic evaluation of traits distributed as poisson-binomial with reference to reproductive characters. Theoretical and Applied Genetics, 73, 870–877.PubMedCrossRefGoogle Scholar
  29. Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software, 8(15), 1–27.Google Scholar
  30. Fox, J. (2008). Applied regression analysis and generalized linear models (2nd ed.). Thousand Oaks, CA: SAGE.Google Scholar
  31. Fox, J., & Hong, J. (2009). Effect displays in R for multinomial and proportional-odds logit models: Extensions to the effects package. Journal of Statistical Software, 32(1), 1–24.Available at:
  32. Freckleton, R. P. (2009). The seven deadly sins of comparative analysis. Journal of Evolutionary Biology, 22(7), 1367–1375.PubMedCrossRefGoogle Scholar
  33. Frentiu, F. D., Clegg, S. M., Chittock, J., Burke, T., Blows, M. W., & Owens, I. P. F. (2008). Pedigree-free animal models: The relatedness matrix reloaded. Proceedings of the Royal Society of London B: Biological Sciences, 275, 639–647.CrossRefGoogle Scholar
  34. Gelman, A., & Shirley, K. (2011). Inference from simulations and monitoring convergence. InS. Brooks, Gelman A, Jones GL, Meng X (eds) Handbook of Markov Chain Monte Carlo, CRC Press, London, chap 6, pp 163–174Google Scholar
  35. Geyer, C. J. (2011). Introduction to Markov chain Monte Carlo. In S. Brooks, A. Gelman, G. L. Jones, & X. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 3–48). London: CRC Press.Google Scholar
  36. Grafen, A. (1984). Natural selection, kin selection and group selection. In J. R. Krebs & N. B. Davies (Eds.), Behavioural ecology: An evolutionary approach (2nd ed., pp. 62–84). New York: Blackwell.Google Scholar
  37. Hadfield, J. D. (2010). MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. Journal of Statistical Software, 33(2), 1–22.Google Scholar
  38. Hadfield, J. D., & Nakagawa, S. (2010). General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. Journal of Evolutionary Biology, 23(3), 494–508.PubMedCrossRefGoogle Scholar
  39. Hadfield, J. D., Nutall, A., Osorio, D., & Owens, I. P. (2007). Testing the phenotypic gambit: Phenotypic, genetic and environmental correlations of colour. Journal of Evolutionary Biology, 20, 549–557.PubMedCrossRefGoogle Scholar
  40. Hilbe, J. M. (2011). Negative binomial regression (2nd ed.). New York: Cambridge University Press.CrossRefGoogle Scholar
  41. Housworth, E. A., Martins, E. P., & Lynch, M. (2004). The phylogenetic mixed model. American Naturalist, 163(1), 84–96.PubMedCrossRefGoogle Scholar
  42. Jones, C. B. (2005). Behavioral flexibility in primates: Causes and consequences. New York: Springer.CrossRefGoogle Scholar
  43. Klingenberg, C. P. (1996). Multivariate allometry. In L. F. Marcus, M. Corti, A. Loy, G. J. P. Naylor, & D. E. Slice (Eds.), Advances in morphometrics (pp. 23–49). New York: Plenum Press.CrossRefGoogle Scholar
  44. Korsgaard, I. R., Andersen, A. H., & Jensen, J. (2002). Prediction error variance and expected response to selection, when selection is based on the best predictor—for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits. Genetics, Selection, Evolution, 34, 307–333.PubMedCrossRefGoogle Scholar
  45. Kruschke, J. (2011). Doing Bayesian data analysis: A tutorial introduction with R and BUGS. Burlington, MA: Academic Press.Google Scholar
  46. Kruuk, L. E. B. (2004). Estimating genetic parameters in natural populations using the ‘animal model. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 359(1446), 873–890.PubMedCrossRefGoogle Scholar
  47. Kruuk, L. E. B., Slate, J., & Wilson, A. J. (2008). New answers for old questions: The evolutionary quantitative genetics of wild animal populations. Ecology, 39, 525–548.Google Scholar
  48. Lande, R. (1982). A quantitative genetic theory of life history evolution. Ecology, 63(3), 607–615.CrossRefGoogle Scholar
  49. Lawler, R. R. (2006). Sifaka positional behavior: Ontogenetic and quantitative genetic approaches. American Journal of Physical Anthropology, 131, 261–271.PubMedCrossRefGoogle Scholar
  50. Leigh, S. R. (2001). Evolution of human growth. Evolutionary Anthropology, 10, 223–236.CrossRefGoogle Scholar
  51. Loeys, T., Moerkerke, B., De Smet, O., & Buysse, A. (2012). The analysis of zero-inflated count data: Beyond zero-inflated Poisson regression. British Journal of Mathematical and Statistical Psychology, 65, 163–180.PubMedCrossRefGoogle Scholar
  52. Lynch, M., & Walsh, B. (1998). Genetics and analysis of quantitative traits. Sunderland, MA: Sinauer Associates.Google Scholar
  53. Maestripieri, D. (2009). Maternal influences on offspring growth, reproduction, and behavior in primates. In D. Maestripieri & J. M. Mateo (Eds.), Maternal effects in mammals (pp. 256–291). Chicago: University of Chicago Press.CrossRefGoogle Scholar
  54. Martin, J. G. A., Nussey, D. H., Wilson, A. J., & Réale, D. (2011). Measuring individual differences in reaction norms in field and experimental studies: A power analysis of random regression models. Methods in Ecology and Evolution, 2, 362–374.CrossRefGoogle Scholar
  55. McCulloch, C. E., & Searle, S. R. (2001). Generalized, linear, and mixed models. New York: John Wiley & Sons.Google Scholar
  56. Meyer, K. (2005). Random regression analyses using B-splines to model growth of Australian Angus cattle. Genetics, Selection, Evolution, 37, 473–500.PubMedCrossRefGoogle Scholar
  57. Moore, A. J., Haynes, K. F., Preziosi, R. F., & Moore, P. J. (2002). The evolution of interacting phenotypes: Genetics and evolution of social dominance. American Naturalist, 160, S186–S197.PubMedCrossRefGoogle Scholar
  58. Morrissey, M. B., & Wilson, A. J. (2010). pedantics: An r package for pedigree-based genetic simulation and pedigree manipulation, characterization and viewing. Molecular Ecology Resources, 10, 711–719.PubMedCrossRefGoogle Scholar
  59. Nakagawa, S., & Schielzeth, H. (2010). Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biological Reviews of the Cambridge Philosophical Society, 85, 935–956.PubMedGoogle Scholar
  60. Nee, S., Colegrave, N., West, S. A., & Grafen, A. (2005). The illusion of invariant quantities in life histories. Science, 309, 1236–1239.PubMedCrossRefGoogle Scholar
  61. O’Hara, R. B., Cano, J. M., Ovaskainen, O., Teplitsky, C., & Ahlo, J. S. (2008). Bayesian approaches in evolutionary quantitative genetics. Journal of Evolutionary Biology, 21, 949–957.PubMedCrossRefGoogle Scholar
  62. Olesen, I., Perez-Enciso, M., Gianola, D., & Thomas, D. L. (1994). A comparison of normal and nonnormal mixed models for number of lambs born in Norwegian sheep. Journal of Animal Science, 72, 1166–1173.PubMedGoogle Scholar
  63. Pemberton, J. M. (2008). Wild pedigrees: The way forward. Proceedings of the Royal Society of London B: Biological Sciences, 275, 613–621.CrossRefGoogle Scholar
  64. Peters, R. H. (1991). A critique for ecology. New York: Cambridge University Press.Google Scholar
  65. Plomin, R., DeFries, J. C., McClearn, G. E., & McGuffin, P. (2009). Behavioral genetics (5th ed.). New York: Worth.Google Scholar
  66. Price, T., & Schluter, D. (1991). On the low heritability of life history traits. Evolution, 45, 853–861.CrossRefGoogle Scholar
  67. Quinn, J. L., Charmantier, A., Garant, D., & Sheldon, B. C. (2006). Data depth, data completeness, and their influence on quantitative genetic estimation in two contrasting bird populations. Journal of Evolutionary Biology, 19, 994–1002.PubMedCrossRefGoogle Scholar
  68. R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available at:
  69. Rawlins, R. G., & Kessler, M. J. (Eds.). (1986). The Cayo Santiago macaques: History, behavior, and biology. Albany: SUNY Press.Google Scholar
  70. Roff, D. A. (1994). Optimality modeling and quantitative genetics: A comparison of the two approaches. In C. R. B. Boake (Ed.), Quantitative genetic studies of behavioral evolution (pp. 49–66). Chicago: University of Chicago Press.Google Scholar
  71. Roff, D. A. (1997). Evolutionary quantitative genetics. New York: Chapman and Hall.CrossRefGoogle Scholar
  72. Schielzeth, H. (2010). Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution, 1, 103–113.CrossRefGoogle Scholar
  73. Schlichting, C. D., & Pigliucci, M. (1998). Phenotypic evolution: A reaction norm perspective. Sunderland, MA: Sinauer Associates.Google Scholar
  74. Silk, J. B. (1984). Measurement of the relative importance of individual selection and kin selection among females of the genus Macaca. Evolution, 38(3), 553–559.CrossRefGoogle Scholar
  75. Silk, J. B. (2002). Using the “f”-word in primatology. Behaviour, 139, 421–446.CrossRefGoogle Scholar
  76. Sillanpää, M. J. (2011). On statistical methods for estimating heritability in wild populations. Molecular Ecology, 20(7), 1324–1332.PubMedCrossRefGoogle Scholar
  77. Sorensen, D., & Gianola, D. (2002). Likelihood, Bayesian, and MCMC methods in quantitative genetics. New York: Springer.Google Scholar
  78. Spencer, H. G. (2009). Effects of genomic imprinting on quantitative traits. Genetica, 136, 285–293.PubMedCrossRefGoogle Scholar
  79. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society B: Statistical Methodology, 64, 583–639.CrossRefGoogle Scholar
  80. Stirling, D. G., Reale, D., & Roff, D. A. (2002). Selection, structure and the heritability of behaviour. Journal of Evolutionary Biology, 15, 277–289.CrossRefGoogle Scholar
  81. Tung, J., Alberts, S. C., & Wray, G. A. (2010). Evolutionary genetics in wild primates: Combining genetic approaches with field studies of natural populations. Trends in Genetics, 26, 353–362.PubMedCentralPubMedCrossRefGoogle Scholar
  82. van Oers, K., & Sinn, D. L. (2011). Toward a basis for the phenotypic gambit: Advances in the evolutionary genetics of animal personality. In M. Inoue-Murayama, S. Kawamura, & A. Weiss (Eds.), From genes to animal behavior (pp. 165–183). New York: Springer.CrossRefGoogle Scholar
  83. Visscher, P. M., Hill, W. G., & Wray, N. R. (2008). Heritability in the genomics era—concepts and misconceptions. Nature Reviews Genetics, 9, 255–266.PubMedCrossRefGoogle Scholar
  84. Visscher, P. M., McEvoy, B., & Yang, J. (2010). From Galton to GWAS: Quantitative genetics of human height. Genetical Research, 92, 371–379.CrossRefGoogle Scholar
  85. Vitzthum, V. J. (2003). A number no greater than the sum of its parts: The use and abuse of heritability. Human Biology, 75, 539–558.PubMedCrossRefGoogle Scholar
  86. Wainer, H. (1974). The suspended rootogram and other visual displays: An empirical validation. American Statistician, 28, 143–145.Google Scholar
  87. Weiss, A., King, J. E., & Enns, R. M. (2002). Subjective well-being is heritable and genetically correlated with dominance in chimpanzees (Pan troglodytes). Journal of Personality and Social Psychology, 83(5), 1141–1149.PubMedCrossRefGoogle Scholar
  88. Williamson, D. E., Coleman, K., Bacanu, S., Devlin, B. J., Rogers, J., Ryan, N. D., & Cameron, J. L. (2003). Heritability of fearful-anxious endophenotypes in infant rhesus macaques: A preliminary report. Biological Psychiatry, 53, 284–291.PubMedCrossRefGoogle Scholar
  89. Wilson, A. J. (2008). Why h 2 does not always equal V A /V P ? Journal of Evolutionary Biology, 21(3), 647–650.PubMedCrossRefGoogle Scholar
  90. Wilson, A. J., Reale, D., Clements, M. N., Morrissey, M. M., Postma, E., Walling, C. A., Kruuk, L. E. B., & Nussey, D. H. (2010). An ecologist’s guide to the animal model. Journal of Animal Ecology, 79, 13–26.PubMedCrossRefGoogle Scholar
  91. Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York:Springer.Google Scholar

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Authors and Affiliations

  1. 1.Department of AnthropologyUniversity of MissouriColumbiaUSA
  2. 2.Duke Institute of Brain Sciences, Center for Cognitive NeuroscienceDuke UniversityDurhamUSA

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