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Simple Tests for Detecting Segregation of Major Genes with Phenotypic Data from a Diallel Mating

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Abstract:



Three simple statistical tests (the Bartlett test, the log-ANOVA test, and the Fain test) were investigated for detecting the segregation of major genes controlling a quantitative trait in half-diallel mating populations. Eight experimental diallel progeny populations of loblolly pine (Pinus taeda L.) from a breeding program were analyzed to detect major gene segregation for 6 yr tree height. The results from three simple tests indicated that there could be major gene segregation for 6 yr height in three diallels. Data with different degrees of major and polygenic effects were simulated with the same mating and field experimental design for further evaluation of these simple tests. Statistical powers of detection were assessed for a major gene with two alleles plus the polygenic background of general combining ability (GCA) and specific combining ability (SCA) effects. The robustness of methods to the presence of half-sib relationships, a skewed phenotypic distribution, and unbalanced data were evaluated. The Bartlett test was the most powerful, but was sensitive to half-sib relationship and skewness. The log-ANOVA test was as powerful as the Bartlett test, but was robust to half-sib relationship and skewness. The Fain test was less powerful but less sensitive to skewness than the Bartlett test. The major gene effect (percent of total phenotypic variance) and dominance level of the major gene were the two most important factors affecting the power of detection. The power for the Bartlett and log-ANOVA test increased with the increment of polygenic heritability. The ratio of dominance to additive variance for polygenic inheritance had little effect on the power for the Bartlett and log-ANOVA tests. The log-ANOVA test is the most desired simple test for detecting major gene segregation in a diallel population with half-sib structures. Simple tests can be used to screen breeding populations for major genes affecting tree growth, and the information can be useful to speed up traditional tree breeding programs. FOR. SCI. 49(2):268–278.

Keywords: Polygenes; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; height; heritability; natural resource management; natural resources; robustness; segregation; statistical power

Document Type: Miscellaneous

Affiliations: 1: Former Graduate Research Assistant North Carolina State University, Current Address: Gene Logic, Inc., 708 Quince Orchard Road, Gaithersburg, MD, 20878, 2: Associate Professor Department of Forestry, North Carolina State University, Box 8002 Raleigh, NC, 27695-8002, Phone: (919) 515-6845; Fax: (919) 515-3169 bailian_li@ncsu.edu

Publication date: 2003-04-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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