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A statistical framework for genetic association studies of power curves in bird flight

Abstract

How the power required for bird flight varies as a function of forward speed can be used to predict the flight style and behavioral strategy of a bird for feeding and migration. A U-shaped curve was observed between the power and flight velocity in many birds, which is consistent to the theoretical prediction by aerodynamic models. In this article, we present a general genetic model for fine mapping of quantitative trait loci (QTL) responsible for power curves in a sample of birds drawn from a natural population. This model is developed within the maximum likelihood context, implemented with the EM algorithm for estimating the population genetic parameters of QTL and the simplex algorithm for estimating the QTL genotype-specific parameters of power curves. Using Monte Carlo simulation derived from empirical observations of power curves in the European starling (Sturnus vulgaris), we demonstrate how the underlying QTL for power curves can be detected from molecular markers and how the QTL detected affect the most appropriate flight speeds used to design an optimal migration strategy. The results from our model can be directly integrated into a conceptual framework for understanding flight origin and evolution.

References

  1. Rayner JMV. Estimating power curves of flying vertebrates. J Exp Biol 1999; 202:449–461.

    Google Scholar 

  2. Poore SO, Sanchez-Haiman A, Goslow GE. Wing upstroke and the evolution of flapping flight. Nature 1997; 387:799–802.

    Article  CAS  Google Scholar 

  3. Bryant DM. Energy expenditure in wild birds. Proc Nutr Soc 1997; 56:1025–1039.

    Article  PubMed  CAS  Google Scholar 

  4. Ward WS, Möller U, Rayner JMV, Jackson DM, Bilo D, Nachtigall W, Speakman JR. Metabolic power, mechanical power and efficiency during wind tunnel flight by the European starling Sturnus vulgaris. J Exp Biol 2001; 204:3311–3322.

    PubMed  CAS  Google Scholar 

  5. Dial KP, Biewener AA, Tobalske BW, Warrick DR. Mechanical power output of bird flight. Nature 1997; 390:67–70.

    Article  CAS  Google Scholar 

  6. Alexander RM. The U, J and L of bird flight. Nature 1997; 390:13–13.

    Article  CAS  Google Scholar 

  7. Tobalske BW, Hedrick TL, Dial KP, Biewener AA. Comparative power curves in bird flight. Nature 2003; 421:363–366.

    Article  PubMed  CAS  Google Scholar 

  8. Hedenström A. Aerodynamics, evolution and ecology of avian flight. Trends Eco Evo 2002; 17:415–422.

    Article  Google Scholar 

  9. Kvist A, Klaasen M, Lindström A. Energy expenditure in relation to flight speed: what is the power of mass loss rate estimates? J Avian Biol 1998; 29:485–498.

    Article  Google Scholar 

  10. Pennycuick CJ. Bird Flight Performance: A Practical Calculation Manual. Oxford University Press (1989).

  11. Ellington CP. Limitations on animal flight performance. J Exp Biol 1991; 160:71–91.

    Google Scholar 

  12. Ma CX, Casella G, Wu RL. Functional mapping ofquantitative trait loci underlying the character process: A theoretical framework. Genetics 2002; 161:1751–1762.

    PubMed  Google Scholar 

  13. Lou X-Y, Casella G, Littell RC, Yang MCK, Johnson JA, Wu RL. A haplotype-based algorithm for multilocus linkage disequilibrium mapping of quantitative trait loci with epistasis. Genetics 2003; 163:1533–1548.

    PubMed  CAS  Google Scholar 

  14. Lynch M, Walsh B. Genetics and Analysis of Quantitative Traits. Sinauer, Sunderland, MA (1998).

    Google Scholar 

  15. Weiss KM, Clark AG. Linkage disequilibrium and the mapping of complex human traits. Trends Genet 2002; 18:19–24.

    Article  PubMed  CAS  Google Scholar 

  16. Hästbacka J, de la Chapelle A, Mahtani M, Clines G, Reeve-Daly MP, Daly M et al. The diastropic dysplasia gene encodes a novel sulfate transporter: positional cloning by fine-structure linkage disequilibrium mapping. Cell 1994; 78:1073–1087.

    Article  PubMed  Google Scholar 

  17. Wang ZH, Wu RL. A statistical model for highresolution mapping of quantitative trait loci determining HIV dynamics. Stat Med 2004; 23:3033–3051.

    Article  PubMed  Google Scholar 

  18. Wu RL, Ma CX, Chang M, Littell RC, Wu SS, Huang M, Wang M, Casella G. A logistic mixture model for detecting major genes governing growth trajectories. Genet Res 2002; 79:235–245.

    PubMed  Google Scholar 

  19. Kirkpatrick M, Lofsvold D, Bulmer M. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 1999; 124:979–993.

    Google Scholar 

  20. Pletcher SD, Geyer CJ. The genetic analysis of agedependent traits: Modeling the character process. Genetics 1999; 153:825–835.

    PubMed  CAS  Google Scholar 

  21. Jaffrezix F, Pletcher SD. Statistical models for estimating the genetic basis of repeated measures and other function-valued traits. Genetics 2002; 156:913–922.

    Google Scholar 

  22. Sillanpaa MJ, Arjas E. Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data. Genetics 1999; 151:1605–1619.

    PubMed  CAS  Google Scholar 

  23. Zhao W, Wu RL, Ma C-X, Casella G. A fast algorithm for functional mapping of complex traits. Genetics 2004; 167:2133–2137.

    Article  PubMed  CAS  Google Scholar 

  24. Patil N, Berno AJ, Hinds DA, Barrett WA et al. Blocks of limited haplotype diversity revealed by highresolution scanning of human chromosome 21. Science 2001; 294:1719–1723.

    Article  PubMed  CAS  Google Scholar 

  25. Dawson E, Abecasis GR, Bumpstead S, Chen Y et al. A first-generation linkage disequilibrium map of human chromosome 22. Nature 2002; 418:544–548.

    Article  PubMed  CAS  Google Scholar 

  26. Gabriel SB, Schaffner SF, Nguyen H, Moore JM et al. The structure of haplotype blocks in the human genome. Science 2002; 296:2225–2229.

    Article  PubMed  CAS  Google Scholar 

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Correspondence to Rongling Wu.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lin, M., Zhao, W. & Wu, R. A statistical framework for genetic association studies of power curves in bird flight. Biol. Proced. Online 8, 164–174 (2006). https://doi.org/10.1251/bpo125

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  • DOI: https://doi.org/10.1251/bpo125

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