IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v78y2022i3p1056-1066.html
   My bibliography  Save this article

Optimization of sampling designs for pedigrees and association studies

Author

Listed:
  • Olivier David
  • Arnaud Le Rouzic
  • Christine Dillmann

Abstract

In many studies, related individuals are phenotyped in order to infer how their genotype contributes to their phenotype, through the estimation of parameters such as breeding values or locus effects. When it is not possible to phenotype all the individuals, it is important to properly sample the population to improve the precision of the statistical analysis. This article studies how to optimize such sampling designs for pedigrees and association studies. Two sampling methods are developed, stratified sampling and D optimality. It is found that it is important to take account of mutation when sampling pedigrees with many generations: as the size of mutation effects increases, optimized designs sample more individuals in late generations. Optimized designs for association studies tend to improve the joint estimation of breeding values and locus effects, all the more as sample size is low and the genetic architecture of the trait is simple. When the trait is determined by few loci, they are reminiscent of classical experimental designs for regression models and tend to select homozygous individuals. When the trait is determined by many loci, locus effects may be difficult to estimate, even if an optimized design is used.

Suggested Citation

  • Olivier David & Arnaud Le Rouzic & Christine Dillmann, 2022. "Optimization of sampling designs for pedigrees and association studies," Biometrics, The International Biometric Society, vol. 78(3), pages 1056-1066, September.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:3:p:1056-1066
    DOI: 10.1111/biom.13476
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13476
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13476?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Peter Muller & Bruno Sanso & Maria De Iorio, 2004. "Optimal Bayesian Design by Inhomogeneous Markov Chain Simulation," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 788-798, January.
    2. David, Olivier & van Frank, Gaëlle & Goldringer, Isabelle & Rivière, Pierre & Turbet Delof, Michel, 2020. "Bayesian inference of natural selection from spatiotemporal phenotypic data," Theoretical Population Biology, Elsevier, vol. 131(C), pages 100-109.
    3. Olivier David & Hervé Monod & Joël Amoussou, 2000. "Optimal Complete Block Designs to Adjust for Interplot Competition with a Covariance Analysis," Biometrics, The International Biometric Society, vol. 56(2), pages 389-393, June.
    4. Kobilinsky, André & Monod, Hervé & Bailey, R.A., 2017. "Automatic generation of generalised regular factorial designs," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 311-329.
    5. Barton, N.H. & Etheridge, A.M. & Véber, A., 2017. "The infinitesimal model: Definition, derivation, and implications," Theoretical Population Biology, Elsevier, vol. 118(C), pages 50-73.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barton, N.H. & Etheridge, A.M., 2018. "Establishment in a new habitat by polygenic adaptation," Theoretical Population Biology, Elsevier, vol. 122(C), pages 110-127.
    2. Ekin, Tahir, 2018. "Integrated maintenance and production planning with endogenous uncertain yield," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 52-61.
    3. Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.
    4. Steiner, Ulrich K. & Tuljapurkar, Shripad, 2020. "Drivers of diversity in individual life courses: Sensitivity of the population entropy of a Markov chain," Theoretical Population Biology, Elsevier, vol. 133(C), pages 159-167.
    5. Manuel Plate & Richard Bernstein & Andreas Hoppe & Kaspar Bienefeld, 2019. "Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
    6. Dekens, L. & Otto, S.P. & Calvez, V., 2022. "The best of both worlds: Combining population genetic and quantitative genetic models," Theoretical Population Biology, Elsevier, vol. 148(C), pages 49-75.
    7. Rouger, Baptiste & Goldringer, Isabelle & Barbillon, Pierre & Miramon, Anne & Naino Jika, Abdel Kader & Thomas, Mathieu, 2023. "Sensitivity analysis of a crop metapopulation model," Ecological Modelling, Elsevier, vol. 475(C).
    8. Yengo, Loic & Visscher, Peter M., 2018. "Assortative mating on complex traits revisited: Double first cousins and the X-chromosome," Theoretical Population Biology, Elsevier, vol. 124(C), pages 51-60.
    9. Ludkovski, Michael, 2009. "A simulation approach to optimal stopping under partial information," Stochastic Processes and their Applications, Elsevier, vol. 119(12), pages 4061-4087, December.
    10. Bayraktar, Erhan & Ludkovski, Michael, 2009. "Sequential tracking of a hidden Markov chain using point process observations," Stochastic Processes and their Applications, Elsevier, vol. 119(6), pages 1792-1822, June.
    11. Herbert Lee & Matthew Taddy & Genetha Gray, 2010. "Selection of a Representative Sample," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 41-53, March.
    12. Ekin, Tahir & Naveiro, Roi & Ríos Insua, David & Torres-Barrán, Alberto, 2023. "Augmented probability simulation methods for sequential games," European Journal of Operational Research, Elsevier, vol. 306(1), pages 418-430.
    13. Tahir Ekin & Nicholas G. Polson & Refik Soyer, 2017. "Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(8), pages 613-627, December.
    14. David, Olivier & van Frank, Gaëlle & Goldringer, Isabelle & Rivière, Pierre & Turbet Delof, Michel, 2020. "Bayesian inference of natural selection from spatiotemporal phenotypic data," Theoretical Population Biology, Elsevier, vol. 131(C), pages 100-109.
    15. Grömping, Ulrike & Fontana, Roberto, 2019. "An algorithm for generating good mixed level factorial designs," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 101-114.
    16. Tevfik Aktekin & Tahir Ekin, 2016. "Stochastic call center staffing with uncertain arrival, service and abandonment rates: A Bayesian perspective," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(6), pages 460-478, September.
    17. Ekin, Tahir & Aktekin, Tevfik, 2021. "Decision making under uncertain and dependent system rates in service systems," European Journal of Operational Research, Elsevier, vol. 291(1), pages 335-348.
    18. Tahir Ekin & Nicholas G. Polson & Refik Soyer, 2014. "Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse," Decision Analysis, INFORMS, vol. 11(4), pages 250-264, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:78:y:2022:i:3:p:1056-1066. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.