Qtl Analysis of Ethiopian Maize Data Using Molecular Markers: Interval Mapping and Linear Mixed Model Approaches
| dc.contributor.advisor | Taye, Girma (PhD) | |
| dc.contributor.author | Biyadgie, Worku | |
| dc.date.accessioned | 2018-06-28T08:51:11Z | |
| dc.date.accessioned | 2023-11-09T14:29:17Z | |
| dc.date.available | 2018-06-28T08:51:11Z | |
| dc.date.available | 2023-11-09T14:29:17Z | |
| dc.date.issued | 2014-06 | |
| dc.description.abstract | The term QTL mapping has been used in a number of scientific disciplines. As the fast advance in molecular genetics, it is much easy to get well-distributed genetic markers in almost every organism nowadays. Quantitative trait loci (QTL) mapping can provide useful information for breeding programs, since it allows the estimation of genomic locations and genetic effects of chromosomal regions related to the expression of quantitative traits. Therefore, as the major direction of quantitative genetics, various statistical methods have been developed to detect or map quantitative trait loci (QTL) by using the genetic marker information. In Ethiopia, till date, researchers have often been challenged by such type of data. Thus, this study is aimed to popularize the principles and methods for QTL mapping in this country. Interval mapping (IM) and mixed model based QTL mapping were included in this study. To realize the genetic basis of grain yield of maize (Zea mays L.), an F2 intercross population with SNP markers covering 201.6cM, which was obtained from EIAR, were used to detect the QTLs for grain yield in maize. As a result, by analyzing the LOD profile in interval mapping, three QTLs associated with grain yield were identified on chromosomes 1, 3 and 4; and these QTLs explain 35.15% of phenotypic variance for grain yield in maize. Whereas, mixed model approach of QTL mapping method by considering localization and detection stages, seven QTLs were detected on chromosomes 1 (two), 2 (two), 3 (two) and 4 (one) and six of them have statistical significant effects on the maize grain yield. The six significant QTLs explained 64.7% of the phenotypic variance. The prevailing mode of gene action revealed overdominance effect in both statistical methods. On the basis of the findings, we conclude that mixed model approach can detect more number of QTLs than interval mapping. Finally, we recommend that statisticians/biometricians in this country need to collaborate with molecular geneticists to promote applications of statistical methods on QTL mapping, molecular data analysis and related areas using recent methods and neutral markers. Key words: Marker, QTL, QTL mapping, interval mapping, mixed linear model, maize grain yield | en_US |
| dc.identifier.uri | http://10.90.10.223:4000/handle/123456789/4574 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Abeba university | en_US |
| dc.subject | Marker | en_US |
| dc.subject | QTL | en_US |
| dc.subject | QTL Mapping | en_US |
| dc.subject | Interval Mapping | en_US |
| dc.subject | Mixed Linear Model | en_US |
| dc.subject | Maize Grain Yield | en_US |
| dc.title | Qtl Analysis of Ethiopian Maize Data Using Molecular Markers: Interval Mapping and Linear Mixed Model Approaches | en_US |
| dc.type | Thesis | en_US |