Correlation and path analysis of morphological and weight traits in marine gastropod Glossaulax reiniana

Liqiang Zhao; Yangyang He; Feng Yang; Hongtao Nie; Xiwu Yan

doi:10.1007/s00343-014-3290-4

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Correlation and path analysis of morphological and weight traits in marine gastropod Glossaulax reiniana

Updated：2023-05-16

- Correlation and path analysis of morphological and weight traits in marine gastropod Glossaulax reiniana
- Journal of Oceanology and Limnology Vol. 32, Issue 4, Pages: 821-827(2014)
- Affiliations：
  
  College of Fisheries and Life Science Dalian Ocean University
- Author bio：
- Funds：
- DOI：10.1007/s00343-014-3290-4
  CLC：
- Online First：11 July 2014，
  
  Published：2014
- Accepted：
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Zhao, L., He, Y., Yang, F. et al. Correlation and path analysis of morphological and weight traits in marine gastropod Glossaulax reiniana, ., Chin. J. Ocean. Limnol. 32, 821–827 (2014). https://doi.org/10.1007/s00343-014-3290-4
DOI：

Zhao, L., He, Y., Yang, F. et al. Correlation and path analysis of morphological and weight traits in marine gastropod Glossaulax reiniana, ., Chin. J. Ocean. Limnol. 32, 821–827 (2014). https://doi.org/10.1007/s00343-014-3290-4 DOI：

摘要

Abstract

Using correlation and path analysis

the genetic correlation between weight traits and morphological traits was determined in the marine gastropod Glossaulax reiniana. A total of 100 G. reiniana individuals from a wild population were used. Shell width (X 1)

shell height (X 2)

umbo-callus height (X 3)

body width (X 4)

operculum length (X 5)

operculum width (X 6)

body weight (Y 1) and soft-tissue weight (Y 2) were measured

and the correlation coefficient matrix calculated. Morphological traits were used as independent variables and weight traits as dependent variables for path coefficient analysis. Path coefficients

correlation indices and determination coefficients were also determined. Results indicate that the correlation coefficients associated with each morphological and weight trait were all highly significant (P <0.01). After deleting redundant independent variables

the following optimum multiple regression equations were obtained using stepwise multiple regression analysis: Y 1 = − 29.317+0.362 X 2 +0.349 X 4 + 1.190 X 5 for body weight; and Y 2 = − 17.292+0.166 X 1 +0.171 X 2 +0.703 X 5

for soft-tissue weight. Operculum height had the highest positive direct correlation with both body weight and soft-tissue weight

which was in accordance with the test results obtained from determinate coefficient analysis. The indication of high genetic correlations between weight traits and morphological traits will provide valuable information for G. reiniana breeding programs.

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references

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