Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci （QTLs） for plant height in wheat （Triticum aestivum L.） were studied using a set of 168 doubled haploid （DH） lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai＇an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions （QEs）. Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rhtl and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection （MAS）.

QTL Mapping for Wheat Flour Color with Additive,Epistatic,and QTL×Environmental Interaction Effects

To investigate genetic factors affecting wheat flour color traits,a linkage map was constructed using a recombinant inbred line （RIL） population derived from Jing 771×Pm 97034.Main,epistatic and QTL×environment （QE） interaction effects of quantitative trait loci （QTLs） controlling wheat flour color were studied by the mixed linear modeling of data collected from wheat RIL plants under three different environmental conditions.13 QTLs with additive effects and 55 pairs of QTLs with epistatic effects were detected for wheat flour color traits.The additive-additive interactions （AA） involved all of the wheat chromosomes except 3D.Epistasis accounted for more of the observed phenotypic variation than did the main effect QTLs （M-QTLs）.Our results suggested that dual-locus interactions are widespread in the wheat genome and play a critical role in determining wheat flour color characteristics.In this study,3 QTLs were identified to have QE interaction effects,one of them showing significant QE interaction in E2 environment.

QTL Mapping for Stalk Related Traits in Maize (Zea mays L.) Under Different Densities

Stalk related traits, comprising plant height （PH）, ear height （EH）, internode number （IN）, average internode length （ALL）, stalk diameter （SD）, and ear height coefficient （EHC）, are significantly correlated with yield, density tolerance, and lodging resistance in maize. To investigate the genetic basis for stalk related traits, a doubled haploid （DH） population derived from a cross between NX531 and NX110 were evauluated under two densities over 2 yr. The additive quantitative trait loci （QTLs）, epistatic QTLs were detected using inclusive composite interval mapping and QTL-by-environment interaction were detected using mixed linear model. Differences between the two densities were significant for the six traits in the DH population. A linkage map that covered 1 721.19 cM with an average interval of 10.50 cM was constructed with 164 simple sequence repeat （SSR）. Two, two, seven, six, two, and eight additive QTLs for PH, IN, AIL, EH, SD, and EHC, respectively. The extend of their contribution to penotypic variation ranged from 10.10 to 31.93%. Seven QTLs were indentified simultaneously under both densities. One pair, two pairs and one pair of epistatic effects were detected for AIL, SD and EHC, respectively. No epistatic effects were detected for PH, EH, and IN. Nineteen QTLs with environment interactions were detected and their contribution to phenotypic variation ranged from 0.43 to 1.89%. Some QTLs were stably detected under different environments or genetic backgrounds comparing with previous studies. These QTLs could be useful for genetic improvement of stalk related traits in maize breeding.

Dissection of the genetic architecture for tassel branch number by QTL analysis in two related populations in maize

Tassel branch number （TBN） is the principal component of maize tassel inflorescence architecture and is a typical quan- titative trait controlled by multiple genes. The main objective of this research was to detect quantitative trait loci （QTLs） for TBN. The maize inbred line SICAU1212 was used as the common parent to develop BC1S1 and recombinant inbred line （RIL） populations with inbred lines 3237 and B73, respectively. The two related populations consisted of 123 and 238 lines, respectively. Each population was grown and phenotyped for TBN in two environments. Eleven QTLs were detected in the BC1S1 population, located on chromosomes 2, 3, 5, and 7, accounted for 4.45-26.58% of the phenotypic variation. Two QTLs （qB11Jtbn2-1, qB12Ctbn2-1, qBJtbn2-1; q11JBtbn5-1, qB12Ctbn5-1, qBJtbn5-1） that accounted for more than 10% of the phenotypic variation were identified. Three QTLs located on chromosomes 2, 3 and 5, exhibited stable expres- sion in the two environments. Ten QTLs were detected in the RIL population, located on chromosomes 2, 3, 5, 8, and 10, accounted for 2.69-13.58% of the TBN variation. One QTL （qR14Dtbn2-2） explained 〉10% of the phenotypic variation. One common QTL （qB12Ctbn2-2, qR14Dtbn2-2, qRJtbn2-2） was detected between the two related populations. Three pairs of epistatic effects were identified between two loci with or without additive effects and accounted for 1.19-4.26% of the phenotypic variance. These results demonstrated that TBN variation was mainly caused by major effects, minor effects and slightly modified by epistatic effects. Thus, identification of QTL for TBN may help elucidate the genetic basis of TBN and also facilitate map-based cloning and marker-assisted selection （MAS） in maize breeding programs.

QTL mapping for leaf area in maize (Zea mays L.) under multienvironments

Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.

Fine mapping of multiple interacting quantitative trait loci using combined linkage disequilibrium and linkage information

Quantitative trait loci (QTL) and their additive, dominance and epistatic effects play a critical role in complex trait variation. It is often infeasible to detect multiple interacting QTL due to main effects often being confounded by interaction effects. Positioning interacting QTL within a small region is even more difficult. We present a variance component approach nested in an empirical Bayesian method, which simultaneously takes into account additive, dominance and epistatic effects due to multiple interacting QTL. The covariance structure used in the variance component approach is based on combined linkage disequilibrium and linkage (LDL) information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously fine map interacting QTL using the proposed approach. The present method combined with LDL information can efficiently detect QTL and their dominance and epistatic effects, making it possible to simultaneously fine map main and epistatic QTL.