The term QTL mapping has been used in a number of scientific disciplines. As the fast advancernin molecular genetics, it is much easy to get well-distributed genetic markers in almost everyrnorganism nowadays. Quantitative trait loci (QTL) mapping can provide useful information forrnbreeding programs, since it allows the estimation of genomic locations and genetic effects ofrnchromosomal regions related to the expression of quantitative traits. Therefore, as the majorrndirection of quantitative genetics, various statistical methods have been developed to detect orrnmap quantitative trait loci (QTL) by using the genetic marker information. In Ethiopia, till date,rnresearchers have often been challenged by such type of data. Thus, this study is aimed tornpopularize the principles and methods for QTL mapping in this country. Interval mapping (IM)rnand mixed model based QTL mapping were included in this study. To realize the genetic basis ofrngrain yield of maize (Zea mays L.), an F2 intercross population with SNP markers coveringrn201.6cM, which was obtained from EIAR, were used to detect the QTLs for grain yield in maize.rnAs a result, by analyzing the LOD profile in interval mapping, three QTLs associated with grainrnyield were identified on chromosomes 1, 3 and 4; and these QTLs explain 35.15% of phenotypicrnvariance for grain yield in maize. Whereas, mixed model approach of QTL mapping method byrnconsidering localization and detection stages, seven QTLs were detected on chromosomes 1rn(two), 2 (two), 3 (two) and 4 (one) and six of them have statistical significant effects on the maizerngrain yield. The six significant QTLs explained 64.7% of the phenotypic variance. The prevailingrnmode of gene action revealed overdominance effect in both statistical methods. On the basis ofrnthe findings, we conclude that mixed model approach can detect more number of QTLs thanrninterval mapping. Finally, we recommend that statisticians/biometricians in this country need torncollaborate with molecular geneticists to promote applications of statistical methods on QTLrnmapping, molecular data analysis and related areas using recent methods and neutral markers.rnKey words: Marker, QTL, QTL mapping, interval mapping, mixed linear model, maize grainrnyield