Genetic Map Construction And QTL Mapping For Agronomic Traits In Cultivated Peanut (arachis Hypogaea L.)

Peanut (Arachis hypogea L.) is one of the most important vegetable oil and economiccrop. Most agronomic traits are complex quantitative traits, which controlled by polygenesand easily influenced by environment. For the improvement of agronomic traits, traditionalbreeding methods were considered to be time-consuming and low-efficiency. In order toachieve genetic improvement of agronomic traits and molecular marker-assisted breeding,molecular marker technology has been extensively applied to divide complex quantitativetraits into simple Mendelian genetic factors. In present research, a recombinant inbred line(RIL) population, including251lines, derived from a cross between Silihong (var.fastigiata) and Jinonghei3(var. hypogaea), was used to construct a genetic linkage mapand identify QTLs for18agronomic traits in two years (2012and2013) and twolocations (Baoding and Handan). The main results were as follows:1. A total of1675SSR primers were screened for polymorphism between Silihong andJinonghei3. One hundred and ninety-two SSR markers, with a polymorphism rate of11.46%, were obtained and used to construct linkage map. χ2-test results showed that40loci (34.19%) deviated from the Mendel ratio.2. A genetic linkage map including117SSR markers was constructed. The mapcomprised of22linkage groups and covered1031.6cM with an average distance of8.8cM between adjacent markers. Nineteen linkage groups were anchored on15chromosomes of A01, A03, A04, A05, A06, A09, A10, B01, B02, B03, B05, B06, B07,B08and B10.3. Fifty-seven additive QTL related to16agronomic traits were mapped through thejointed linkage map. They were located on chromosomes A01, A03, A04, A05, A06,A09, B01, B02, B03, B05, B07and linkage group LG1, LG3respectively, with a range of1-7QTL for each agronomic trait, explained3.81%-53.37%of phenotypicvariation. Seven QTL for the total number of branches were identified with4.46%-13.00%of phenotypic variation explaination. Four QTL for length of firstbranches and pod thickness were found, respectively, explaining5.87%-22.74%of thephenotypic variance. Three QTL for seed weight per plant were detected, explaining4.11%-19.06%of the phenotypic variance. Two QTL were detected for pod length,pod width, seed length, pod number per plant, muti-seed number per plant, pod weightand thickness of pod shell, respectively, explaining3.81%-25.23%of the phenotypicvariance. One QTL for height of main stem and seed thickness were identified,respectively, explaining6.36%-10.12%of the phenotypic variance (at Baoding andHandan in2012). Five QTL for seed length/width were found, explaining7.43%-52.71%of the phenotypic variance. Two QTL were detected for length of firstbranches, pod number per plant, pod length, pod width, pod weight per plant andthickness of pod shell, respectively, explaining4.87%-31.24%of the phenotypicvariance. One QTL were identified for seed length, seed width, seed thickness, seedweight per plant, shelling percentage per plant and total branching number,respectively, explaining5.7%-53.37%of the phenotypic variance (at Baoding andHandan in2013).4. Twenty-eight major QTLs were identified, among of which eight QTLs with highercontribution, genetically stability and repeatability.