Epistatic Quantitative Trait Loci Research Articles

New Stably Expressed Loci Responsible for Panicle Angle Trait in Japonica Rice in Four Environments

Panicle angle (PA) of 254 recombinant inbred lines derived from a cross between two japonica varieties Xiushui 79 and C Bao was investigated under four environments, and a genetic linkage map including 111 SSR markers was constructed. Genetic analysis was conducted by mixed major gene plus polygene inheritance models, and quantitative trait loci (QTLs) identification by the QTLNetwork 2.0 and the composite interval mapping approach of WinQTLCart 2.5 software. Results showed that the PA trait was controlled by two major genes plus polygenes, mainly by major genes. Eight QTLs for PA were detected by the QTLNetwork 2.0 software, and each locus explained 0.01% to 39.89% of the phenotypic variation. Twelve QTLs for PA were detected by the WinQTLCart 2.5 software, with each locus explaining 2.83% to 30.60% of the phenotypic variation. Two major QTLs (qPA9.2 and qPA9.5) distributed between RM3700 and RM3600 and between RM5652 and RM410, respectively, and a moderate QTL (qPA9.7) distributed between RM257 and OSR28, were both detected by the two methods in all of the four environments. The negative effect alleles of the three QTLs were from Xiushui 79. In addition, eight pairs of epistatic QTLs with minor effects were also detected. QTL × environment interactions were not significant for additive QTLs and epistatic QTL pairs.

QTL mapping and epistatic interaction analysis in asparagus bean for several characterized and novel horticulturally important traits

BackgroundAsparagus bean (Vigna. unguiculata. ssp sesquipedalis) is a subspecies and special vegetable type of cowpea (Vigna. unguiculata L. Walp.) important in Asia. Genetic basis of horticulturally important traits of asparagus bean is still poorly understood, hindering the utilization of targeted, DNA marker-assisted breeding in this crop. Here we report the identification of quantitative trait loci (QTLs) and epistatic interactions for four horticultural traits, namely, days to first flowering (FLD), nodes to first flower (NFF), leaf senescence (LS) and pod number per plant (PN) using a recombinant inbred line (RIL) population of asparagus bean.ResultsA similar genetic mode of one major QTL plus a few minor QTLs was found to dominate each of the four traits, with the number of QTLs for individual traits ranging from three to four. These QTLs were distributed on 7 of the 11 chromosomes. Major QTLs for FLD, NFF and LS were co-localized on LG 11, indicative of tight linkage. Genome wide epistasis analysis detected two and one interactive locus pairs that significantly affect FLD and LS, respectively, and the epistatic QTLs for FLD appeared to work in different ways. Synteny based comparison of QTL locations revealed conservation of chromosome regions controlling these traits in related legume crops.ConclusionMajor, minor, and epistatic QTLs were found to contribute to the inheritance of the FLD, NFF, LS, and PN. Positions of many of these QTLs are conserved among closely related legume species, indicating common mechanisms they share. To our best knowledge, this is the first QTL mapping report using an asparagus bean × asparagus bean intervarietal population and provides marker-trait associations for marker-assisted approaches to selection.

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 (AIL), 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.

QTL Mapping and Interaction Analysis of Seed Weight per Plant in Soybean among Different Environments

The objectives of this study were to map QTLs, which stably controlled seed weight per plant in soybean and analyze their epistatic effects and QTL environment (QE) interactions. A set of F 2:14-F 2:18 RIL populations with 147 lines were planted in two sites in Heilongjiang Province, China from 2006 to 2010, and the QTLs were deduced using CIM and MIM models simultaneously. Seventeen QTLs for seed weight per plant were located in D1a, B1, B2, C2, F, G, and A1 linkage groups, which explained 6.0–47.9% of the phenotypic variation. Three QTLs could be detected by both methods, with phenotypic contributions ranging from 6.3% to 38.3%. Four QTLs could be found in at least two years, which explained the variation in seed weight per plant by 8.1–47.9%. Data from seven environments were used to detect QE interaction effects and epistatic effects using QTLMapper. One QE QTL and four pairs of epistatic QTLs were detected, but their additive effects and phenotypic contributions were small. These results indicated that both major and minor QTLs with epistatic and QE interaction effects should be considered in soybean breeding aiming at seed weight improvement.

Development of Gossypium barbadense Chromosome 18 Segment Substitution Lines in the Genetic Standard Line TM-1 of Gossypium hirsutum and Mapping of QTLs Related to Agronomic Traits

Substitution line 18 (Sub18) has the background of genetic standard line of G. hirsutum acc. TM-1 with the 18th chromosome of G. barbadense accession 3-79. In the study, a set of G. barbadense accession 3-79 chromosome 18 segment substitution lines (CSSL18) were developed via molecular marker-assisted selection (MAS), with TM-1 as a recipient parent and Sub18 as a donor. The developed CSSLs consisted of 45 lines and 78 introgressed segments in total. Of them, 27 were introgressed with single segment, accounting for 60% of the total lines; 9 were introgressed with two segments, accounting for 20%; and 9 were introgressed with three or more segments, accounting for 20%. The total length of introgression segments was 467.6 cM, which is 4 times of the genetic length of the chromosome 18. The substituted segment length varied in each line, ranging from the shortest of 0.9 cM to the longest of 20.35 cM, with an average length of 5.99 cM. Further, the opening bud trait was detected in 13 substitution lines, with the shortest introgressed segment of 5.05 cM. Using TM-1, Sub18 and 45 substituted lines as materials, we investigated agronomic traits and carried out QTL tagging by joint positioning analysis. Seven additive effect QTLs for fiber strength (qFS-C18-1), uniformity (qFU-C18-1), micronaire (qFMi-C18-1), maturity (qFMa-C18-1), lint weight (qLW-C18-1), seed index (qSI-C18-1), and lint percentage (qLP-C18-1) and five epistatic effect QTLs were identified, respectively. The results will lay a foundation for the fine mapping and cloning of target QTLs, and the molecular breeding by design for pyramiding multi-traits in cotton.

Properties of the Test Statistic under Null Hypothesis and the Calculation of LOD Threshold in Quantitative Trait Loci (QTL) Mapping

Selecting an appropriate LOD threshold is of great interest in QTL mapping studies. Many approaches can be consid- ered to calculate the critical value throughout a genome, such as simulation-based method, analytical approximation, and empiri- cal method based on permutation test. Many tests are conducted in QTL mapping, which are not mutually independent because the linkage relationship of adjacent markers on chromosomes. In order to declare a significant QTL at a genome-wide significance level, it is necessary to understand the behavior of test statistic under null hypothesis in QTL mapping and to deal with the de- pendent multiple-test problem arising in the genome-wide test. Our objectives in this study were (1) to investigate the properties of LRT (likelihood ratio test) statistic of one-point scanning under null hypothesis in QTL mapping, (2) to determine the factors affecting the cumulative distribution of maximum LOD score, and (3) to identify the relationship between the effective number of independent tests and the length of chromosome by simulation method. Results indicated that the LRT test statistic in one-dimensional scanning of additive-dominant QTL and two-dimensional scanning of epistatic QTL followed chi-square distri- butions, and the degree of freedom (df) was equal to the number of genetic parameters to be estimated. For example, degree of freedom in recombinant inbred lines (RIL) population was equal to 1 in one dimensional or two dimensional scanning. Degree of freedom in F2 populations was equal to 2 in one-dimensional scanning and 4 in two-dimensional scanning. Number of chromo- some, population size and phenotyping error variance did not have any effect on the distribution of LRT under null hypothesis, and therefore will not affect the selection of LOD threshold. On the contrary, population type, genome size and marker density had significant impacts. For BC1, RIL, and F2 populations, the threshold was the smallest in BC1 population and the highest in F2 population. Higher marker density and longer chromosome resulted in higher LOD threshold. It was identified that the effective

QTL Mapping of Pubescence Density and Length on Leaf Surface of Soybean

Soybean pubescences are known to play important roles in resistance to pests and tolerance to drought stress. QTL mapping of leaf pubescence density and length was conducted in recombinant inbred line populations of NJRIKY (KY) and NJRIXG (XG). The results obtained were as follows: (1) There existed great variation and certain transgressive segregation in leaf pubescence density and length among lines; highly significant negative correlations (r = 0.49 and 0.62, respectively) between the two traits were observed; the heritability values for pubescence density ranged from 75.7% to 76.8%, higher than that for pubescence length ranged from 45.2% to 62.9% in the two populations. (2) Two major QTL for pubescence density detected were PD1-1 accounted for 20.7% of phenotypic variation in XG, and PD12-1 contributed 21.7% of phenotypic variation in KY. The genetic constitution of pubescence density was composed of additive QTL (20.7 36.2% of phenotypic variation), epistatic QTL pairs (0 1.4%) and collective unmapped minor QTL (38.1 56.1%) in the two populations. Here the unmapped minor QTL ac- counted for the most part for the trait, which was not recognized if only using mapping procedures without the consideration of the total genetic variation among the lines. (3) The phenotypic variation of pubescence length in KY was accounted for by epistatic QTL pairs (4.2%) and collective unmapped minor QTL (58.7%) without additive QTL (0%), while that in XG mainly byadditive QTL, including PL1-1 and PL12-1 on chromosomes 1 and 12 accounting for 18.3% and 22.5% of phenotypic variation, respectively, with very small contribution by epistatic QTL pair and collective unmapped minor QTL. Therefore, the genetic constitutions of pubescence length in the two populations were different from each other. The genetic mechanisms of leaf pubescence density and length in soybean are complicated and involve many genes/QTL with different effects.

QTL analysis of Na+ and K+ concentrations in roots and shoots under different levels of NaCl stress in rice (Oryza sativa L.).

The key to plant survival under NaCl salt stress is maintaining a low Na+ level or Na+/K+ ratio in the cells. A population of recombinant inbred lines (RILs, F2∶9) derived from a cross between the salt-tolerant japonica rice variety Jiucaiqing and the salt-sensitive indica variety IR26, was used to determine Na+ and K+ concentrations in the roots and shoots under three different NaCl stress conditions (0, 100 and 120 mM NaCl). A total of nine additive QTLs were identified by QTL Cartographer program using single-environment phenotypic values, whereas eight additive QTLs were identified by QTL IciMapping program. Among these additive QTLs, five were identified by both programs. Epistatic QTLs and QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values, and one additive QTL and nine epistatic QTLs were identified. There were three epistatic QTLs identified for Na+ in roots (RNC), three additive QTLs and two epistatic QTLs identified for Na+ in shoots (SNC), four additive QTLs identified for K+ in roots (RKC), four additive QTLs and three epistatic QTLs identified for K+ in shoots (SKC) and one additive QTL and one epistatic QTL for salt tolerance rating (STR). The phenotypic variation explained by each additive, epistatic QTL and QTL×environment interaction ranged from 8.5 to 18.9%, 0.5 to 5.3% and 0.7 to 7.5%, respectively. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSNC9, qSKC1, qSKC9, qRKC4 and qSTR7, might represent novel salt tolerance loci. The identification of salt tolerance in selected RILs showed that a major QTL qSNC11 played a significant role in rice salt tolerance, and could be used to improve salt tolerance of commercial rice varieties with marker-assisted selection (MAS) approach.

Main and Epistatic Quantitative Trait Loci Associated with Seed Size in Watermelon

Watermelon (Citrullus lanatus) is an important crop grown for both its edible flesh and seeds. Seed size is an important trait in watermelon breeding, with large seeds preferred for planting and edible seeds but small seeds preferred in fruit sold for consumption. Large, medium, and small seed sizes are thought to be controlled by two genes with epistatic interaction. A ‘Klondike Black Seeded’ × ‘New Hampshire Midget’ (KBS × NHM) recombinant inbred line population and a ZWRM 50 × PI 244019 (ZWRM × Citroides) F2 population were used to identify main effect quantitative trait loci (M-QTL) and epistatic QTL (E-QTL) associated with 100 seed weight (100SWT), seed length (SL), and seed width (SWD). Thirteen M-QTL were identified on four linkage groups (LGs) for the three traits in the two populations. Major M-QTL (R2 = 26.9% to 73.6%) were identified at the same location on LG 2 in both populations for all three traits. M-QTL for all three traits also colocalized on LG 9 in the ZWRM × Citroides population and on LG 4 for 100SWT and SL in the KBS × NHM population. Significant epistatic effects were found between the M-QTL on LG 2 and LG 4 in the KBS × NHM population and between LG 2 and LG 9 in the ZWRM × Citroides population. The phenotypic variance explained by the E-QTL was generally small. The stable, major M-QTL on LG 2 is a candidate for marker-assisted selection for seed size in watermelon.

Molecular mapping of QTLs for grain zinc, iron and protein concentration of wheat across two environments

Low concentration or deficiencies of bioavailable micronutrients in staple food result in micronutrient malnutrition. The understanding of the genetic basis for micronutrient concentration in wheat grain can provide useful information for wheat biofortification breeding by marker-assisted selection (MAS). In the present study, grain zinc (Zn), iron (Fe) and protein concentrations were analyzed in a recombinant inbred line (RIL) population under two environments to detect quantitative trait loci (QTLs) and their interactions. Nine additive and four epistatic QTLs were identified, among which six and four, respectively, were effective at the two environments. Three intervals that affected two or three traits were found on chromosomes 4B and 5A, which indicated a common genetic basis among grain Zn, Fe and protein concentrations. Multi-trait composite interval mapping analysis detected five intervals that influenced more than one trait, among which three were identified at both the environments and two could affect all of the three traits. The interval on chromosome 5A for grain Fe and protein concentrations was co-located with QTLs controlling the same traits in a previous study, both near the marker Xgwm154. The molecular markers closely linked to the QTLs, e.g., Xgwm154, may be useful in wheat biofortification breeding by MAS. The interval Xcfd21641-NP21.1 on chromosome 6A which was effective for all the three traits may be an ortholog of Gpc-B1.

Genetic dissection of developmental behavior of grain weight in wheat under diverse temperature and water regimes

As a quantitatively inherited trait related to high yield potential, grain weight (GW) development in wheat is constrained by abiotic stresses such as limited water supply and high temperature. Data from a doubled haploid population, derived from a cross of (Hanxuan 10 × Lumai 14), grown in four environments were used to explore the genetic basis of GW developmental behavior in unconditional and conditional quantitative trait locus (QTL) analyses using a mixed linear model. Thirty additive QTLs and 41 pairs of epistatic QTLs were detected, and were more frequently observed on chromosomes 1B, 2A, 2D, 4A, 4B and 7B. No single QTL was continually active during all stages or periods of grain growth. The QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the flowering to the seventh day after) formed a foundation for GW development. GW development at these stages can be used as an index for screening superior genotypes under diverse abiotic stresses in a wheat breeding program. One QTL, i.e. Qgw.cgb-6A.2, showed high adaptability for water-limited and heat-stress environments. Many A-QTLs interacted with more than one other QTL in the two genetic models, such as Qgw.cgb-4B.2 interacted with five QTLs, showing that the genetic architecture underlying GW development involves a collective expression of genes with additive and epistatic effects.

Marker-based linkage map of Andean common bean (Phaseolus vulgarisL.) and mapping of QTLs underlying popping ability traits

BackgroundNuña bean is a type of ancient common bean (Phaseolus vulgaris L.) native to the Andean region of South America, whose seeds possess the unusual property of popping. The nutritional features of popped seeds make them a healthy low fat and high protein snack. However, flowering of nuña bean only takes place under short-day photoperiod conditions, which means a difficulty to extend production to areas where such conditions do not prevail. Therefore, breeding programs of adaptation traits will facilitate the diversification of the bean crops and the development of new varieties with enhanced healthy properties. Although the popping trait has been profusely studied in maize (popcorn), little is known about the biology and genetic basis of the popping ability in common bean. To obtain insights into the genetics of popping ability related traits of nuña bean, a comprehensive quantitative trait loci (QTL) analysis was performed to detect single-locus and epistatic QTLs responsible for the phenotypic variance observed in these traits.ResultsA mapping population of 185 recombinant inbred lines (RILs) derived from a cross between two Andean common bean genotypes was evaluated for three popping related traits, popping dimension index (PDI), expansion coefficient (EC), and percentage of unpopped seeds (PUS), in five different environmental conditions. The genetic map constructed included 193 loci across 12 linkage groups (LGs), covering a genetic distance of 822.1 cM, with an average of 4.3 cM per marker. Individual and multi-environment QTL analyses detected a total of nineteen single-locus QTLs, highlighting among them the co-localized QTLs for the three popping ability traits placed on LGs 3, 5, 6, and 7, which together explained 24.9, 14.5, and 25.3% of the phenotypic variance for PDI, EC, and PUS, respectively. Interestingly, epistatic interactions among QTLs have been detected, which could have a key role in the genetic control of popping.ConclusionsThe QTLs here reported constitute useful tools for marker assisted selection breeding programs aimed at improving nuña bean cultivars, as well as for extending our knowledge of the genetic determinants and genotype x environment interaction involved in the popping ability traits of this bean crop.

Identification of positive yield QTL alleles from exotic soybean germplasm in two backcross populations

Increasing seed yield is an important breeding goal of soybean [Glycine max (L.) Merr.] improvement efforts. Due to the small number of ancestors and subsequent breeding and selection, the genetic base of current soybean cultivars in North America is narrow. The objective of this study was to map quantitative trait loci (QTL) in two backcross populations developed using soybean plant introductions as donor parents. The first population included 116 BC(2)F(3)-derived lines developed using "Elgin" as the recurrent parent and PI 436684 as the donor parent (E population). The second population included 93 BC(3)F(3)-derived lines developed with "Williams 82" as the recurrent parent and PI 90566-1 as the donor parent (W population). The two populations were evaluated with 1,536 SNP markers and during 2years for seed yield and other agronomic traits. Genotypic and phenotypic data were analyzed using the programs MapQTL and QTLNetwork to identify major QTL and epistatic QTL. In the E population, two yield QTL were identified by both MapQTL and QTLNetwork, and the PI 436684 alleles were associated with yield increases. In the W population, a QTL allele from PI 90566-1 accounted for 30% of the yield variation; however, the PI region was also associated with later maturity and shorter plant height. No epistasis for seed yield was identified in either population. No yield QTL was previously reported at the regions where these QTL map indicating that exotic germplasm can be a source of new alleles that can improve soybean yield.

Analysis of the epistatic and QTL×environments interaction effects of plant height in maize (Zea mays L.)

A genetic map containing 103 microsatellite loci and 200 F2 plants derived from the cross R15 × Ye478 were used for mapping of quantitative trait loci (QTL) in maize (Zea mays L.). QTLs were characterized in a population of 200 F2 4 lines, derived from selfing the F2 plants, and were evaluated with two replications in two environments. QTL mapping analysis of plant height was performed by using QTLMapper 2.0 software. Three main-effect QTLs and seven pairs of distinctly epistatic loci included a total of fourteen QTLs were located on seven chromosomes. QTL main effects of additive, dominance, and additive × additive, additive × dominance, and dominance×dominance interactions were estimated. Interaction effects between QTL main effects and QTL×environments (QE) were also predicted. Less than 30% of single effects for identified QTLs were significant at 5% level. The information about epistatic QTLs and QE interaction will facilitate marker assisted selection for plant height breeding programs in maize.

Cytonuclear epistatic quantitative trait locus mapping for plant height and ear height in maize

Plant height (PH) and ear height (EH) are important traits in maize (Zea mays L.) breeding. Previous research has indicated that these traits are influenced by quantitative trait loci (QTL). However, previous studies attempting to identify the genetic bases of PH and EH have ignored the possibility that cytoplasmic effects and cytonuclear interactions may influence these traits. The objectives of this study were to identify the cytonuclear epistatic QTL and to evaluate the contributions of cytoplasm and QTL × cytoplasm interactions to phenotypic variation of PH and EH. A reciprocal mating design was conducted to generate F2 mapping populations comprising 120 F2 plants from the direct cross (JB × Y53) and 120 F2 plants from the reciprocal cross (Y53 × JB). F2:3 mapping populations were further generated with 91 direct F2:3 families and 120 reciprocal F2:3 families (ten plants per family). The PH and EH of the above F2 and F2:3 mapping populations were evaluated in the same field at the same experimental station in 2007 and 2008. A genetic linkage map with 154 microsatellite markers was constructed, which covered 1,735.0 cM of the maize genome with an average marker spacing of 11.3 cM. A joint-analysis method incorporating the cytonuclear interaction mapping approach was proposed and performed to detect cytonuclear interacting QTL affecting PH and EH. We identified six cytonuclear epistatic QTL affecting PH and five affecting EH. The average phenotypic variance explained by the genetic components of the QTL × cytoplasm interaction for each QTL was 18 % for PH and 9 % for EH. In addition, we observed cytoplasmic effects contributing substantially to phenotypic variance, reaching 9 and 40 % of the phenotypic contributions to PH and EH, respectively.

Dissecting anxiety-related QTLs in mice by univariate and multivariate mapping

To detect genes underlying anxiety-related traits in mice, we performed univariate and multivariate QTL mapping analyses of phenotypes obtained from 71 mice of the BXD recombinant inbred (RI) strains (n=528 mice) and their parental strains (C57BL/6J and DBA/2J). Separate and joint mapping analyses were carried out using a linkage map composed of 506 simple sequence repeats (SSRs). The main QTL effects, interactions between pairs of QTLs (epistasis), and their environmental interactions were estimated. The results showed that anxiety-related traits were influenced by multiple QTLs (five main effect QTLs and three epistatic QTLs). Ten potential anxiety-related candidate genes within the QTL intervals on chromosomes 5, 13 and 15 were identified. Some of these genes have been reported previously to be associated with the anxiety response. Based on our results, it is suggested that the multivariate QTL mapping approach improves the statistical power for detecting QTL and the precision of parameter estimation. Moreover, multivariate mapping can also detect pleiotropic QTL effects. multivariate QTL mapping, mouse, BXD strains, pleiotropy

QTL underlying developmental behaviour of 100‐seed weight of soybean

With 4 tablesAbstractThe objective of this study was to determine QTL underlying seed weight from earlier developmental stage to postharvest in soybean, using a F5:8‐derived recombinant inbred line (RIL) (n = 125) population. Twenty‐three unconditional QTL and 17 conditional QTL associated with seed weight were identified in different developmental stages in 2008 and 2009. Four conditional QTL QSW J‐1 (on LG J, Gm 16), QSWF‐1(on LG F, Gm 13), QSWK‐2 (on LG K, Gm 9) and QSWA2‐2 (on LG A2, Gm 8) were identified across multiple stages and the two years. Fifteen QTL with conditional additive main effect (a) and/or conditional additive ×environment interaction effect (ae) at certain stages were identified. Of them, seven conditional QTL possessed significant additive main effect (a), and ten conditional QTL had significant ae effects. Twenty‐six epistatic pairwise QTL (aae) were identified in different developmental stages. Eleven pairs showed aa effect, and twelve pairs showed aae effect in different developmental stages.

Identification of QTLs with main, epistatic and QTL × environment interaction effects for salt tolerance in rice seedlings under different salinity conditions

Salt tolerance of rice (Oryza sativa L.) at the seedling stage is one of the major determinants of its stable establishment in saline soil. One population of recombinant inbred lines (RILs, F (2:9)) derived from a cross between the salt-tolerant variety Jiucaiqing and the salt-sensitive variety IR26 was used to determine the genetic mechanism of four salt tolerance indices, seedling height (SH), dry shoot weight (DSW), dry root weight (DRW) and Na/K ratios (Na/K) in roots after 10 days in three salt concentrations (0.0, 0.5 and 0.7 % NaCl). The main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) were detected by QTL IciMapping program using single environment phenotypic values. Eleven M-QTLs and 11 E-QTLs were identified for the salt tolerance indices. There were six M-QTLs and two E-QTLs identified for SH, three M-QTLs and five E-QTLs identified for DSW, two M-QTLs and one E-QTL identified for DRW, and three E-QTLs identified for Na/K. The phenotypic variation explained by each M-QTL and E-QTL ranged from 7.8 to 23.9 % and 13.3 to 73.7 %, respectively. The QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values. Six M-QTLs and five E-QTLs were identified. The phenotypic variation explained by each QTL and QTL × environment interaction ranged from 0.95 to 6.90 % and 0.02 to 0.50 %, respectively. By comparing chromosomal positions of these M-QTLs with those previously identified, five M-QTLs qSH1.3, qSH12.1, qSH12.2, qDSW12.1 and qDRW11 might represent novel salt tolerance genes. Five selected RILs with high salt tolerance had six to eight positive alleles of the M-QTLs, indicating that pyramiding by marker-assisted selection (MAS) of M-QTLs can be applied in rice salt tolerance breeding programs.

Mapping QTLs with epistatic effects and QTL-by-environment interactions for seed coat cracking in soybeans

Seed coat cracking (SCC) consists of irregular cracks on the surface of seed coat, giving rise to serious effects on the seed quality in soybeans. However, breeding to achieve resistance to SCC has been an arduous task due to the complicated genetic behavior and environmental interactions. Thus, this study aims to map quantitative trait loci (QTLs) controlling irregular SCC in soybeans and to study QTL-by-environment interactions. QTL analysis was conducted using an RIL population derived from Keunolkong × Sinpaldalkong. The SCC was determined at natural conditions and calculated three times by counting cracked seeds with a 100 seeds per plant basis. In the four environments, a total of 10 QTLs with additive effects were identified. Among them, three QTLs (qSCC2-1, qSCC9, and qSCC20) located on chromosomes 2, 9, and 20 showed main-additive effects explaining 9.5–19.4 % of phenotypic variances in two environments. Across environments, seven additive QTLs, four additive × environment (AE) interactions, and two pairs of epistatic QTLs and epistasis × environment interactions were identified for SCC. Similar to the results in individual environments, two major QTLs (qSCC2-1 and qSCC9) showed additive main effects explaining 10.5 and 6.5 % of phenotypic variances, respectively and significant AE interaction in 2006 Suwon. The total QTLs explained 48.0 % of the phenotypic variation, with additive effects of 33.8 %, epistatic effects of 7.6 %, and AE and AAE of 6.6 %. All in all, these results showed that both additive effects and AE interactions can serve as important genetic basis for SCC.

Bayesian Variable Selection to identify QTL affecting a simulated quantitative trait.

BackgroundRecent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies.ResultsEstimated heritability of the simulated quantitative trait was 0.30 (SD = 0.02). Mean posterior probability of SNP modelled having a large effect () was 0.0066 (95%HPDR: 0.0014-0.0132). Mean posterior probability of variance of second distribution was 0.409 (95%HPDR: 0.286-0.589). The genome-wide association analysis resulted in 14 significant and 43 putative SNP, comprising 7 significant QTL on chromosome 1, 2 and 3 and putative QTL on all chromosomes. Assigning single or multiple QTL to significant SNP was not obvious, especially for SNP in the same region that were more or less in LD. Correlation between the simulated and estimated breeding values of 1,000 offspring without phenotypes was 0.91.ConclusionsBayesian Variable Selection using thousands of SNP was successfully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Simulated QTL with Mendelian inheritance were accurately identified, while imprinted and epistatic QTL were only putatively detected. The correlation between simulated and estimated breeding values of offspring without phenotypes was high.