

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 mixedmodel analysis of variance has facilitated many recent studies
in evolutionary quantitative genetics. Results: In this twoway 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 yieldrelated
traits on a series of nearhomozygous 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 variancecovariance
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 variancecovariance 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 (σ^{2}_{G}) and thereby estimate the genetic gain after two cycles of selection within two sweet corn source populations, BC110xSynII and BC210. 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 (BC210 and BC110xSynII). The two base populations showed varied average realized responses to MS and SPS. In BC210 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 BC110xSynII 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 BC210, 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. 





