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     International Journal of Fisheries and Aquatic Sciences

    Abstract
2012(Vol.1, Issue:1)
Article Information:

Modeling Quantitative Traits and Evolution by Natural Selection Using Cichlids of Lake Victoria Fish

Tsegazeabe H. Haileselasie
Corresponding Author:  Tsegazeabe H. Haileselasie 
Submitted: April 08, 2012
Accepted: April 30, 2012
Published: July 25, 2012
Abstract:
Life is a study in contrasts between randomness and determinism. Scientists often reconcile the stochastic and the deterministic by appealing to the statistics of large numbers for variations among individuals in a population. And in natural populations, variation in most characters takes the form of a continuous phenotypic range rather than discrete phenotypic classes. In other words, the variation is quantitative, not qualitative. Mendelian genetic analysis is extremely difficult to apply to such continuous phenotypic distributions, so statistical techniques are employed instead. A major task of quantitative genetics is therefore to determine the ways in which genes interact with the environment to contribute to the formation of a given quantitative trait distribution. Furthermore, the genetic variation underlying a continuous character distribution can be the result of segregation at a single genetic locus or at numerous interacting loci that produce cumulative effects on the phenotype. Thus, this study was aimed at modeling quantitative traits and evolution by natural selection using some traits related to trophic adaptation of Lake Victoria cichlid fish. And we showed that disruptive natural selection is acting upon the traits investigated (i.e., Lower Jaw Length (LJL), Lower Jaw Length (LJW), Snout Length (SnL) and Snout Width (SnW).

Key words:  Evolution, natural selection, quantitative genetics, , , ,
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Cite this Reference:
Tsegazeabe H. Haileselasie, . Modeling Quantitative Traits and Evolution by Natural Selection Using Cichlids of Lake Victoria Fish. International Journal of Fisheries and Aquatic Sciences, (1): 1-4.
ISSN (Online):  2049-842X
ISSN (Print):   2049-8411
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