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Articles by Ghizan Saleh
Total Records ( 2 ) for Ghizan Saleh
  Pedram Kashiani and Ghizan Saleh
  Problem statement: Genetic correlations among traits refer to the extent of relatedness among them due to http://en.wikipedia.org/wiki/Gene/oGene">genetic causes. Estimating genetic correlations for quantitative traits is tedious if done manually. Approach: However, the use of the computer software SAS, applying mixed-model analysis of variance has facilitated many recent studies in evolutionary quantitative genetics. Results: In this two-way statistical model, the variance component corresponding to the random statement is the covariance associated with a level of the random factor across levels of the fix factor. Therefore, the SAS model has a natural application for estimating genetic correlations among traits measured. Correlation studies were undertaken for 10 yield-related traits on a series of near-homozygous sweet corn inbred lines obtained from various tropical source populations. The SAS program was used to estimate genetic correlation coefficients among traits observed, where effects of blocks were considered fixed while effects of inbred lines as random. The "asycov" was added to the "PROC MIXED" statement in order to produce the variance-covariance matrix of variance components. The "type = UN" option requested in "RANDOM" statement resulted in an unstructured covariance matrix for each inbred line being estimated, while the "G" and "GCORR" options produced genetic variance-covariance matrix and genetic correlation matrix between traits, respectively. Results showed that there was no significant difference between genetic correlations estimated by SAS MIXED model and those estimated by manual calculation. Conclusion/Recommendations: This indicated that SAS has the natural capability to estimate genetic correlations among traits measured, as opposed to manual methods employing quantitative genetics equations.
  Mandefro Nigussie and Ghizan Saleh
  The objectives of this study were to determine the genetic variability (σ2G) and thereby estimate the genetic gain after two cycles of selection within two sweet corn source populations, BC1-10xSyn-II and BC2-10. Selfed progenies from each of the two source populations were evaluated following the recommended cultural practices. As the progenies derived from the two source populations had sufficient genetic variability for most traits, two cycles of mass selection (MS) and selfed progeny selection (SPS) were conducted on the two sweet corn populations (BC2-10 and BC1-10xSyn-II). The two base populations showed varied average realized responses to MS and SPS. In BC2-10 derived populations, the realized responses to MS were 5.1% in cycle 1 (C1) and 4.8% in Cycle 2 (C2), whereas the realized responses to SPS were 9.1% in C1 and 1.2% in C2. In BC1-10xSyn-II derived populations, the realized responses to MS were 5.5% in C1 and 2.9% in C2, while the realized responses to SPS were 5.6% in C1 and 2.9% in C2. The two selection methods were equally effective in improving the populations for ear length, except in C1 of BC2-10, where SPS was more effective than MS. Both selection methods were also effective in increasing fresh ear yield and number of kernels per row. Response of other correlated traits depended on selection methods used and populations under selection. The improved populations generated could serve as better germplasm sources and further selection in these populations could offer better responses.
 
 
 
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