Identification of high yielding stable genotypes is an integral objective of plant breeding programs. Testing of genotypes across environments is required to determine yield stability of genotypes. The specific objective of the current study was to analyze genotype by environment interaction (GEI) of grain yield for 50 genotypes using the additive main effects and multiplicative interaction (AMMI) model. Experiments were planted in an alpha lattice design with two replicates in Peshawar (E-1 and E-3), Hangu (E-2 and E-4) and Kohat (E-5) Khyber Pakhtunkhwa province, Pakistan during 2011/12 and 2012/13. Analysis of variance revealed significant differences among genotypes for all traits, while interactions due to genotype by environment were significant for all traits except days to emergence and 1000-grain weight. Significant GEI justified environment-specific as well as AMMI analysis to identify genotypes with specific and wider adaptation. The AMMI analysis revealed that the first interaction principal component analysis (IPCA 1) captured 64.0% of GEI sum of squares while the second interaction principal component analysis (IPCA 2) explained 25.8% of the interaction sum of square. The AMMI biplot identified G30 as a high yielding genotype followed by G19 and G49, whereas low yielding genotypes were G13, G8 and G7. Being close to IPCA1 axis, the most stable genotype with wider adaptability was G30 followed by G31 and G25. Based on AMMI stability value (ASV), genotypes G18 (2.15), G5 (2.78), G27 (3.72), G44 (4.31), G25 (4.43), G42 (4.57), G43 (5.78), G11 (5.82), G1 (7.66) and G29 (7.81) were found in the given order of relative stability. GGE biplot analysis explained 79.9% (PCA1=56.6 and PCA2= 23.3%) of the total variation. Genotype G19 positioning on vertex in sector E-3, E-4 and E-5, while G30 in sector E-1 and E-2 revealed their specific suitability to respective environments. GGE biplot identified environment E-4 as the most representative environment, whereas G49, G30, G22 and G45 as the high yielding genotypes. Shifted multiplicative model (SHMM) grouped genotypes into four clusters based on similarity/dissimilarity index for grain yield. High yielding and stable genotypesG19, G49 and G30 were placed in group B. Grain yield had positive association with tillers m-2 (r =0.73**), grain weight spike-1 (r =0.57**), biological growth rate (r =0.44**), grain growth rate (r = 0.80**), biological yield (r = 0.41**) and harvest index (r = 0.55*). The SHMM clustering and correlations of yield with other traits inferred that tillers m-2, grain weight spike-1, biological growth rate, grain growth rate, biological yield and harvest index contributed towards higher grain yield. Therefore, these traits could be used as selection criteria for the improvement of grain yield in bread wheat. Stability analysis identified G49 (Wafaq × Ghaznavi-98-3) as a high yielding stable genotype among breeding lines which can be commercialized after fulfilling procedural requirements
The aim of this study is to recognize that how many divorced men and women are agreed that forced marriages and uneducated spouse are the causes of divorce in Hyderabad district. This study is based on primary data, and the data are collected through questionnaires from 400 respondents (200 divorced men and 200 divorced women) by using stratified sampling. Results indicate that both men and women are highly agreed that divorce occurs due to forced marriages and uneducated spouse in Hyderabad district. The hypotheses of this study have been accepted and there is no association between the variables of chi-square test.
Biological sequences consist of A C G and T in a DNA structure and contain vital information of living organisms. This information is used in many applications such as drug design, microarray analysis and phylogenetic trees. Advances in computing technologies, specifically Next Generation Sequencing technologies have increased genomic data at a rapid rate. The increase in genomic data presents significant research challenges in bioinformatics, such as sequence alignment, short read error correction, phylogenetic inference etc. Various tools and algorithms have been proposed for phylogenetic inference. Early algorithms used sequential programs to solve the problem of phylogenetic inference. Improvements were gained in terms of tree accuracy and execution time, however; the programs were still slow, and improvements were needed to infer correct phylogeny in short times. This challenge introduced parallel and distributed processing to the field of bioinformatics. Many tools and programs have been developed based on parallel and distributed computing. This thesis presents algorithmic solutions for phylogenetic inference. Solutions include ‘PhyloDoop’ and ‘SeqCompress’ algorithms. PhyloDoop algorithm is used for inference of phylogenetic trees. The algorithm is based on Maximum Likelihood method, implemented on Hadoop Map/Reduce framework. PhyloDoop is based on clusters i.e. divides the input alignment to clusters, builds trees for each cluster, merges and optimizes all sub-trees and the final tree is also optimized. PhyloDoop is compared to well-known algorithms both on real and simulated datasets. Experiments on real datasets were performed to test likelihood values, execution time, and speedup in distributed environment. The results show better accuracy as compared to other algorithms on most of the datasets. Execution time is also short on most datasets. The proposed algorithm yields better speed up on large datasets. Simulated datasets were used to measure topological accuracy. PhyloDoop is topologically accurate on most datasets with short execution time in comparison to other algorithms. SeqCompress is used to compress DNA sequences in order to reduce memory requirements and execution time. Impressive results are shown in comparison to other algorithms. These results show a gap for efficient usage of compression techniques to infer correct phylogeny with low memory requirements as well as execution time.