The Statistical Power of Inclusive Composite Interval Mapping in Detecting Digenic Epistasis Showing Common F_2 Segregation Ratios

Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping （ICIM） is able to identify epistatic quantitative trait loci （QTLs） no matter whether the two interacting QTLs have any additive effects. In this article, we conducted a simulation study to evaluate detection power and false discovery rate （FDR） of ICIM epistatic mapping, by considering F2 and doubled haploid （DH） populations, different F2 segregation ratios and population sizes. Results indicated that estimations of QTL locations and effects were unbiased, and the detection power of epistatic mapping was largely affected by population size, heritability of epistasis, and the amount and distribution of genetic effects. When the same likelihood of odd （LOD） threshold was used, detection power of QTL was higher in F2 population than power in DH population; meanwhile FDR in F2 was also higher than that in DH. The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR. In simulated populations, ICIM achieved better mapping results than multiple interval mapping （MIM） in estimation of QTL positions and effect. At the end, we gave epistatic mapping results of ICIM in one actual population in rice （Oryza sativa L.）.

QTL Analysis of Agronomic Traits in Soybean

[Objective]The aim was to analyze QTL of agronomic traits in soybean and provide reference for a discussion on soybean genetic mechanism and genetic breeding. [Method]The composite interval mapping method was used for QTL location and genetic effects analysis on 5 quantitative traits including protein content,fat content,yield,100-grain weight and growth period. [Result]The control of these traits 4,4,1,2,5,a total of 16 QTL loci was detected. The genetic contribution rate was in 7.4%-33.7%,among which,a large main-effect QTL of the genetic contribution rate were located in linkage group I Satt562-Sat_219,Sat_219-Satt496,Sat_219-Satt496 interval of the three control protein content QTL sites,their genetic contribution rates were 29.15%,33.7 % and 31.67% respectively,all from the female parent Hefeng 25 plus minor gene; still in O linkage group Satt477-Satt331,Satt331-Satt153 interval of two control growing period QTL loci,their genetic contribution rates were up to 24.69% and 24.96%,also from the female parent Hefeng 25 plus minor gene. In addition,six QTL sites from M linkage group Satt175 （protein）,A1 linkage group Satt684 （oil）,F linkage group Satt348 （oil）,J linkage group Sat_412 （oil）,C1 linkage group Sat_416 （100-grain weight） and C1 linkage group Sat_416 （growth period） marks only 0.01 cm were detected. [Conclusion]QTL sites which had effects on the 5 important agronomic traits in soybean were located.

Identification of additional QTLs for flowering time by removing the effect of the maturity gene E1 in soybean

The adaptability of soybean to be grown at a wide range of latitudes is attributed to natural variation in the major genes and quantitative trait loci （QTLs） that control flowering time and maturity. Thus, the identification of genes controlling flowering time and maturity and the understanding of their molecular basis are critical for improving soybean productivity. However, due to the great effect of the major maturity gene E1 on flowering time, it is difficult to detect other small-effect QTLs. In this study, aiming to reduce the effect of the QTL, associated with the E1 gene, on the detection of other QTLs, we divided a population of 96 recombinant inbred lines （RILs） into two sub-populations： one with the E1 allele and another with the elns allele. Compared with the results of using all 96 recombinant inbred lines, additional QTLs for flowering time were identified in the sub-populations, two （qFT-B1 and qFT-H） in RILs with the E1 allele and one （qFT-J-2） in the RILs with the elnl allele, respectively. The three QTLs, qFT-B1, qFT-H and qFT-J-2 were true QTLs and played an important role in the regulation of growth period. Our data provides valuable information for the genetic mapping and gene cloning of traits controlling flowering time and maturity and will help a better understanding of the mechanism of photoperiod-regulated flowering and molecular breeding in soybean.

Identification of QTLs for Yield and Associated Traits in F_(2) Population of Rice

Identification of quantitative trait loci(QTLs)controlling yield and yield-related traits in rice was performed in the F_(2) mapping population derived from parental rice genotypes DHMAS and K343.A total of 30 QTLs governing nine different traits were identified using the composite interval mapping(CIM)method.Four QTLs were mapped for number of tillers per plant on chromosomes 1(2 QTLs),2 and 3;three QTLs for panicle number per plant on chromosomes 1(2 QTLs)and 3;four QTLs for plant height on chromosomes 2,4,5 and 6;one QTL for spikelet density on chromosome 5;four QTLs for spikelet fertility percentage(SFP)on chromosomes 2,3 and 5(2 QTLs);two QTLs for grain length on chromosomes 1 and 8;three QTLs for grain width on chromosomes1,3 and 8;three QTLs for 1000-grain weight(TGW)on chromosomes 1,4 and 8 and six QTLs for yield per plant(YPP)on chromosomes 2(3 QTLs),4,6 and 8.Most of the QTLs were detected on chromosome 2,so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety.Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection(MAS)breeding.Further,the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.