Combined QTL Mapping Method Based On SNP Markers

In studies of animals and plants, the vast majority of quality traits andeconomic traits are quantitative traits, then studying the genetic mechanism ofquantitative traits and applying to actual production practice will have a greatsignificance on agricultural plant and animal breeding production activities.Quantitative trait locus mapping also namely QTL mapping, which studyingthe genes that affecting the quantitative trait phenotypic variation through thegenetic markers on chromosome and the observed information of phenotypicvalues. Searching the location of these genes on the chromosome and the genes’effect to the traits. QTL mapping studies have been widely used in the field, suchas plant and animal breeding, genetic analysis of complex traits, genetic diseasetreatment and prevention.The essence of QTL is by using the chain relation between markers and QTL,positioning the QTL nearby the markers on chromosome, then completing thepositioning of QTL. Choose the right marks and reasonable and eff icient way tofind the significant marker associated with traits that been studied, speculatelocating QTL on the chromosome more accurately, all these are very important inQTL mapping studies.In order to achieve better results of QTL mapping, we propose a combinationstrategy through use the genetic markers on a genome-wide to conducting QTLmapping. The main research contents are as follows:(1) SNP markers distributed in the genome of high density, good stability,ease of screening and representative and contains a large amount of geneticinformation, and therefore, we choose SNP markers throughout the genome areanalyzed to provide a wealth of genetic information for the QTL mapping.(2) While traditional association analysis can get a lot genetic markers whichhave a strong associated with the quantitative traits that been studied, but there is ahigher false positive, it is difficult to filter out the really linked markers whenthere is a large number of markers. emBayesB method effectively utilizes t he EMalgorithm for missing data characteristics, calculating the posterior probability ofthe linkage relationship between SNP markers and QTL by combined with BayesBmethod. Through these methods screening marker that has strong correlation withQTL and has obvious advantages in dealing with large data quantity.(3) After getting significant SNP markers that have a strong association withthe trait studied, the fixed-interval determination and LD-interval determination just a interval in the approximate range of a chromosome that contains a QTL, cannot accurately calculate the specific location of the QTL. The combination strategyhope by use trait-mark interval determination calculated the location of QTL moreaccurately. Taking into account the influence of genetic background information ofother markers on chromosome for QTL mapping, to improve the positioningaccuracy and increase the locating credibility.