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

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

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...

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Bibliographic Details
Main Authors: Lin, Min, Zhao, Wei, Wu, Rongling
Format: Online
Language:English
Published: Biological Procedures Online 2006
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1622763/
id pubmed-1622763
recordtype oai_dc
spelling pubmed-16227632006-10-25 A statistical framework for genetic association studies of power curves in bird flight Lin, Min Zhao, Wei Wu, Rongling Research Article 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. Biological Procedures Online 2006-10-24 /pmc/articles/PMC1622763/ /pubmed/17066123 http://dx.doi.org/10.1251/bpo125 Text en Copyright © October 10, 2006, M Lin et al. This paper is Open Access and is published in Biological Procedures Online under license from the authors. Copying, printing, redistribution and storage permitted.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Lin, Min
Zhao, Wei
Wu, Rongling
spellingShingle Lin, Min
Zhao, Wei
Wu, Rongling
A statistical framework for genetic association studies of power curves in bird flight
author_facet Lin, Min
Zhao, Wei
Wu, Rongling
author_sort Lin, Min
title A statistical framework for genetic association studies of power curves in bird flight
title_short A statistical framework for genetic association studies of power curves in bird flight
title_full A statistical framework for genetic association studies of power curves in bird flight
title_fullStr A statistical framework for genetic association studies of power curves in bird flight
title_full_unstemmed A statistical framework for genetic association studies of power curves in bird flight
title_sort statistical framework for genetic association studies of power curves in bird flight
description 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.
publisher Biological Procedures Online
publishDate 2006
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1622763/
_version_ 1611390164073971712

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