Quantitative Trait Loci Cartographer Research Articles

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software. Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits. The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.

QTL mapping for resistance to southern leaf blight in sweet corn

To identify quantitative trait loci (QTL) associated with southern leaf blight (SLB) in sweet corn, a population consisting of 330 F2:3 families was developed from a T14 (resistant) × T4 (susceptible) cross. The genetic linkage map was constructed based on the F2 population, with 192 SSR markers covering a total map distance of 1260 cm, and an average interval length of 6.56 cm. These 330 F2:3 families were evaluated in 2009 and 2010 for SLB resistance following inoculation at the seedling stage and milk stage. Using composite interval mapping (CIM) in QTL Cartographer v.2.5, 7 QTL out of 18 total QTL were identified at the seedling stage on chromosomes 3, 4, 6 and 9, and the phenotypic variance explained by each QTL ranged from 4.02 to 35.4%. The 11 other QTL were identified at the milk stage on chromosomes 3, 4, 6, 8, 9 and 10 and accounted for 3.50 to 13.65% of the phenotypic variance. Two QTL were detected in both years; these were located on chromosome 3 and 4 in the marker intervals umc2275 to umc2008 and umc2287 to bnlg2162, respectively. One QTL, located on chromosome 3 between umc2275 and umc2008, was detected at both developmental stages. The findings in this study may provide useful information for marker-assisted selection (MAS) and with further genetic and breeding studies for maize SLB. Key words: Sweet corn, southern leaf blight (SLB), composite interval mapping (CIM), quantitative trait loci(QTL).

Analysis of Quantitative Trait Loci for Resistance to Rice False Smut

A recombinant inbred line (RILs) population with 157 lines derived from an inter-subspecies cross of Daguandao/IR28 by the single seed descent method was used to detect quantitative trait loci (QTLs) conferring resistance to rice false smut (caused by Ustilaginoidea virens (Cooke) Takahashi) in 2007 and 2009. The disease rate index of the two parents and 157 RILs caused by rice false smut were scored and the QTLs for rice false smut resistance were detected accordingly by QTL Cartographer software. Seven QTLs controlling false smut resistance were detected on chromosomes 1, 2, 4, 8, 10, 11, and 12, respectively, with the phenotypic variance of 9.8-22.5%. There were five and four QTLs detected in 2007 and 2009, respectively, and only two QTLs were found in both two years, the phenotypic variation was explained by individual QTL ranged from 18.0% to 19.3% for these two QTLs, and the additive effects of these two QTLs contributed to the 8.0-16.3% decrease of disease index and therefore the disease resistance increased. The direction of the additive effects at six loci qFsr1, qFsr2, qFsr8, qFsr10, qFsr11, and qFsr12 coincided with that predicted by phenotypes of the parents, and the IR28 alleles at these loci had positive effect against rice false smut while the negative effects were found in Daguandao alleles at qFsr4. Both qFsr11 and qFsr12 should be useful in rice breeding for resistance to rice false smut in marker-assisted selection (MAS) program.

Mapping and validation of quantitative trait loci associated with wheat yellow mosaic bymovirus resistance in bread wheat

Wheat yellow mosaic (WYM) caused by wheat yellow mosaic bymovirus (WYMV) has been growing as one of the most serious diseases affecting wheat production in China. In this study, the association of quantitative trait loci (QTLs) governing WYMV resistance with molecular markers was established using 164 recombinant inbred lines (RILs) derived from 'Xifeng Wheat' (highly resistant)×'Zhen 9523' (highly susceptible). Phenotypic data of WYMV resistance of the RILs were collected from 4-year, two-location replicated field trials. A molecular marker-based linkage map, which was comprised of 273 non-redundant loci and represented all the 21 wheat chromosomes, was constructed with the JoinMap 4.0 software. Using the Windows QTL Cartographer V2.5 software, three QTLs associated with WYMV resistance, QYm.njau-3B.1, QYm.njau-5A.1 and QYm.njau-7B.1, were detected on chromosomes 3BS, 5AL, and 7BS, respectively. The favorable allele effects were all contributed by 'Xifeng Wheat'. Among the three QTLs, QYm.njau-3B.1 and QYm.njau-5A.1 were detected in all the four trials and the overall mean, and could explain 3.3-10.2% and 25.9-53.7% of the phenotypic variation, respectively, while QYm.njau-7B.1 was detected in one trial and the overall mean and explained 4.9 and 3.3% of the phenotypic variation, respectively. A large portion of the variability for WYMV response was explained by a major QTL, QYm.njau-5A.1. The relationship of the molecular markers linked with QYm.njau-5A.1 and the WYMV resistance was further validated using a secondary F(2) population. The results showed that three markers, i.e., Xwmc415.1, CINAU152, and CINAU153, were closely linked to QYm.njau-5A.1 with the genetic distances of 0.0, 0.0, and 0.1cM, respectively, indicating they should be useful in marker-assisted selection (MAS) wheat breeding for WYMV resistance. A panel of germplasm collection consisting of 46 wheat varieties with known WYMV response phenotypes was further used to validate the presence and effects of QYm.njau-5A.1 and the above three markers. It was found that QYm.njau-5A.1 was present in 12 of the 34 WYMV-resistant varieties.

Mapping QTLs for tissue culture response in soybean (Glycine max (L.) Merr.).

Quantitative trait loci (QTLs) that control the tissue culture response in soybean were detected by using 184 recombinant inbred lines (RILs) derived from two varieties: Kefeng No.1 and Nannong 1138-2. The molecular map consisting of 834 molecular markers using this population covered space 2307.83 cM of the genome throughout 24 linkage groups. The performance of tissue culture in soybean was evaluated by two indices: callus induction frequency (CIF) and somatic embryos initiation frequency (SEIF). They were expressed as the number of explants producing callus/ the number of total explants and the number of explants producing somatic embryos/ the number of total explants, respectively. The RIL lines showed continuous segregation for both indices. With the composite interval mapping (CIM) described in Windows QTL Cartographer Version 2.5, three quantitative trait loci (QTLs) were identified for the frequency of callus induction, on chromosomes B2 and D2, accounting for phenotypic variation from 5.84% to 16.60%; four QTLs on chromosome G were detected for the frequency of somatic embryos initiation and explained the phenotypic variation from 7.79% to 14.16%. The information of new QTLs identified in the present study will contribute to genetic improvement of regeneration traits with marker-assisted selection (MAS) in soybean.

Identification of quantitative trait loci for partial resistance to Phytophthora sojae in soybean

With 3 figures and 3 tables AbstractPartial resistance to Phytophthora sojae in soybean may provide effective protection against all isolates of the pathogen, thus reducing yield losses. Better understanding of partial resistance has, therefore, become an important goal for the improvement of soybean (Glycine max). The aim of the present study was to identify the potential quantitative trait loci (QTLs) for partial resistance to P. sojae. A population of 176 F7:11 recombinant inbred lines derived from the cross of Su88‐M21 × Xinyixiaoheidou, was evaluated for Phytophthora partial resistance using slant board assays. The heritability estimate on an entry basis was 0.87. Two QTLs (qPR‐15‐1 and qPR‐10‐2) were detected in the same flanking marker region, Satt598–Satt651 on chromosome (Chr) 15 with molecular linkage group (MLG) E and Satt420–Sat_274 on Chr 10 with MLG O, respectively, by both composite interval mapping and multiple interval mapping (MIM) of Windows QTL Cartographer (version 2.5). One QTL (qPR‐6‐3) on Chr 6 with MLG C2, which was only identified under MIM, was closely linked to marker Satt520, and was positioned 0.01 cM away from Satt520. The newly identified QTLs would be beneficial for marker assistant selection aimed at improving the resistance to P. sojae and reducing disease‐related mortality in soybean.

Bacterial Panicle Blight Resistance QTLs in Rice and Their Association with Other Disease Resistance Loci and Heading Date

Bacterial panicle blight (BPB) of rice (Oryza sativa L.) occurs when the bacterium Burkholderia glumae Kurita and Tabei infects emerging and flowering panicles, causing kernels to abort. To identify quantitative trait loci (QTLs) for BPB resistance, a population of 300 recombinant inbred lines (RILs) derived from a cross between ‘Lemont’ and ‘TeQing’ were evaluated in 2001 and 2002 in field plots spray‐inoculated with B. glumae at the time of flowering. Because this RIL population had been previously used to map QTLs for three other diseases, present use of this population allowed direct comparison between the various disease resistance QTLs. Multiple interval mapping using QTL Cartographer v2.5 putatively identified 12 BPB QTLs, three of which were statistically significant in both years and found to have epistatic effects in 2002. TeQing was the source of resistance for eight QTLs; Lemont for four. Four BPB QTLs colocated with QTLs previously identified as providing resistance to one or multiple other diseases. Three BPB QTLs were also associated with late flowering. Because late flowering panicles are subjected to cooler temperatures that are less conducive to disease development during grain fill, it is possible that the genetic effects of the heading‐related QTLs were biased. The present data could not distinguish between pleiotropy and close linkage of the BPB QTLs with the previously identified heading and disease resistance QTLs.

QTL Analysis for Dynamic Expression of Chlorophyll Content in Soybean ( Glycine max L. Merr.)

The objective of this study was to map quantitative trait loci (QTLs) for chlorophyll content in the F 2:3 and F 2:4 populations derived from soybean ( Glycine max L. Merr.) varieties Lishuizhongzihuangdou and Nannong 493-1. Chlorophyll contents for the function leaf in vivo were measured from seedling to blooming stage in the 244 F 2:3 and F 2:4 families growing in 2 environments. The developmental behaviors of chlorophyll content across environments were analyzed using composite interval mapping in the software of Windows QTL Cartographer v2.5. A total of 20 QTLs were detected at various stages in 2 environments, of which only several QTLs were common across stages, years, or test sites. QTLs distributing in linkage groups N, D1a, F, and K were repeatedly detected for 3 to 4 times. QTL qchl-D1a-1, which was located on linkage group D1a between markers sat_160 and satt147, was only identified at Jiangpu site in both years. Three common QTLs were mapped on linkage groups K, M, and N at both Jiangpu and Linyi sites. These results provide a theoretical basis for genetic analysis of chlorophyll traits and marker-assisted breeding in soybean.

Linkage mapping of femoral material properties in a reciprocal intercross of HcB-8 and HcB-23 recombinant mouse strains

Skeletal fragility is an important health problem with a large genetic component. We performed a 603 animal F2 reciprocal intercross of the recombinant congenic strains HcB-8 and HcB-23 to genetically map quantitative trait loci (QTLs) for tissue-level femoral biomechanical performance. These included elastic and post-yield strain, Young's modulus, elastic and maximum stress, and toughness and were calculated from 3-point bend testing of femora by the application of standard beam equations. We mapped these with R/qtl and QTL Cartographer and established significance levels empirically by permutation testing. Significant QTLs for at least one trait are present on chromosomes 1, 6, and 10 in the full F2 population, with additional QTLs evident in subpopulations defined by sex and cross direction. On chromosome 10, we find a QTL for post-yield strain and toughness, phenotypes that have not been mapped previously. Notably, the HcB-8 allele at this QTL increases post-yield strain and toughness, but decreases bone mineral density (BMD), while the material property QTLs on chromosomes 1, 6, and at a second chromosome 10 QTL are independent of BMD. We find significant sex×QTL and cross direction×QTL interactions. A robust, pleiotropic chromosome 4 QTL that we previously reported at the whole-bone level showed no evidence of linkage at the tissue-level, supporting our interpretation that modeling capacity is its primary phenotype. Our data demonstrate an inverse relationship between femoral perimeter and Young's modulus, with R2=0.27, supporting the view that geometric and material bone properties are subject to an integrated set of regulatory mechanisms. Mapping QTLs for tissue-level biomechanical performance advances understanding of the genetic basis of bone quality.

Quantitative Trait Loci Analysis of Novel Fusarium Head Blight Resistance in Tokai 66

Problem statement: Identification of Quantitative Trait Loci (QTLs) in durable resistant genotype is important for marker-assisted breeding. The study was conducted to identify QTLs linked to FHB resistance in 'Tokai 66', a Japanese genotyp e. Approach: A cross was made between Tokai 66 and Jagalene and the single seed descend was used t o advance the population. The F 2:4 and F 2:5 populations were evaluated by artificially inoculat ing disease in a mist-irrigated nursery in 2006 and 2007. Disease incidence, severity, FDK and DON content were evaluated in the 128 RILs. Map Manager QTX was used to prepare the linkage map and QTL Cartographer 2.0 was used to identify QTLs for FHB responses from the field data respecti vely. Results: Four QTLs for FHB responses were detected, of which one QTL each for severity a nd FDK were stable across two years and were located at the 5B and 3BSc, respectively. Both QTLs conferred resistance to FHB. Two unstable QTLs were detected at the 5B and 3D for FDK and DON content, respectively using the field environment. The 5B QTL for FDK contributed to susceptibility, whereas the 3D QTL for DON content contributed to resistance. Conclusion: The 5B QTL associated with resistance to severity a nd 3B QTL associated with resistance to FDK could be utilized by winter wheat breeding programs selection to enhance FHB resistance.

Quantitative trait loci for biomechanical performance and femoral geometry in an intercross of recombinant congenic mice: restriction of theBmd7candidate interval

Despite steady progress in identifying quantitative trait loci (QTLs) for bone phenotypes, relatively little progress has been made in moving from QTLs to identifying the relevant gene. We exploited the genetic structure of recombinant congenic mouse strains by performing a reciprocal intercross of the strains HcB-8 and HcB-23, phenotyped for body size, femoral biomechanical performance, and femoral diaphyseal geometry and mapped with R/qtl and QTL Cartographer. Significant QTLs are present on chromosomes 1, 2, 3, 4, 6, and 10. We found significant sex x QTL and cross-direction x QTL interactions. The chromosome 4 QTL affects multiple femoral anatomic features and biomechanical properties. The known segregating segment of chromosome 4 contains only 18 genes, among which Ece1, encoding endothelin-converting enzyme 1, stands out as a candidate. Endothelin signaling has been shown to promote the growth of osteoblastic metastases and to potentiate signaling via the Wnt pathway. The colocalizing chromosome 4 QTL Bmd7 (for bone mineral density 7) increases responsiveness to mechanical loading. By exploiting the short informative segment of chromosome 4 and the known biology, we propose that Ece1 is the gene responsible for Bmd7 and that it acts by increasing responsiveness to mechanical loading through modulation of Wnt signaling.

Analysis of QTLs for Resistance to Small Brown Planthopper in Rice Using an F 2 Population from a Cross Between Mudgo and Wuyujing 3

To map quantitative trait loci (QTLs) associated with rice ( Oryza sativa L.) resistance to small brown planthopper (SBPH), this study constructed a genetic linkage map with an F 2 population derived from the cross between indica cultivar Mudgo (high resistance to SBPH) and japonica cultivar Wuyujing 3. The linkage map comprised of 104 simple sequence repeat (SSR) and 3 Indel markers and covered 1409.9 cM of rice genome with an average marker interval of 13.2 cM. One hundred and seventy-seven plants from the F 2:3 families were identified for resistance to SBPH by the modified seedbox screening test. QTL analysis of SBPH resistance was conducted using composite interval mapping implemented in Windows QTL Cartographer 2.5. A total of 3 QTLs conferring resistance to SBPH, Qsbph2b, Qsbph3d, and Qsbph12a, were detected in the regions of RM5791–RM29, RM3199–RM5442, and I12-17–RM3331 on chromosomes 2, 3, and 12, with the LOD scores of 3.25, 3.11, and 6.82, respectively. The resistant alleles of Qsbph2b, Qsbph3d, and Qsbph12a were all from Mudgo and explained 17.3, 15.6, and 35.8% of the total phenotypic variance, respectively. The QTL Qsbph12a tightly linking to markers RM3331 and I12-17 has a potential in breeding for SBPH resistance by marker-assisted selection.

Confirmation of QTLs controlling Ascochyta fabae resistance in different generations of faba bean (Vicia faba L.)

Ascochyta blight, caused by Ascochyta fabae Speg., is a disease of faba bean (Vicia faba L.) of worldwide distribution. In this study we have conducted an experiment on Ascochyta fabae resistance in 165 recombinant inbred lines (RILs) developed by single-seed descent from the cross between resistant and susceptible lines (Vf6 × Vf136) in which A. fabae resistance QTLs (quantitative trait loci) have been previously reported in the original F2 population. Recombinant inbred lines were inoculated under controlled growth chamber conditions and evaluated for disease severity and infection type index. The linkage map was constructed by MAPMAKER V2.0 and the QTL analysis was carried out using QTL Cartographer. Two hundred and seventy-seven markers (238 RAPDs, 4 isozymes, 5 ESTs, 1 SCAR, 6 SSRs, 2 STSs, and 21 intron-spanning markers) mapped into 21 linkage groups covering 2.856.7 cM, with a mean inter-marker distance of 12.72 cM. Composite interval mapping identified two zones of putative QTL action in the RIL population for DSL (disease severity on leaves) and DSS (disease severity on stems) traits. Putative QTLs (Af1 and Af2) were identified on chromosome 3 and chromosome 2, respectively, and jointly explained 24% of the phenotypic variance of DSL and 16% of DSS. With this study we have (1) confirmed the QTLs for ascochyta blight resistance found in F3 families in the derived RILs (F6), (2) re-estimated their position and genetic effects, and (3) assessed the stability of these QTLs in different genetic backgrounds by comparison of the mapping data with a previous QTL study.

Quantitative Trait Loci Mapping and The Genetic Basis of Heterosis in Maize and Rice

Despite its importance to agriculture, the genetic basis of heterosis is still not well understood. The main competing hypotheses include dominance, overdominance, and epistasis. NC design III is an experimental design that has been used for estimating the average degree of dominance of quantitative trait loci (QTL) and also for studying heterosis. In this study, we first develop a multiple-interval mapping (MIM) model for design III that provides a platform to estimate the number, genomic positions, augmented additive and dominance effects, and epistatic interactions of QTL. The model can be used for parents with any generation of selfing. We apply the method to two data sets, one for maize and one for rice. Our results show that heterosis in maize is mainly due to dominant gene action, although overdominance of individual QTL could not completely be ruled out due to the mapping resolution and limitations of NC design III. For rice, the estimated QTL dominant effects could not explain the observed heterosis. There is evidence that additive x additive epistatic effects of QTL could be the main cause for the heterosis in rice. The difference in the genetic basis of heterosis seems to be related to open or self pollination of the two species. The MIM model for NC design III is implemented in Windows QTL Cartographer, a freely distributed software.

Dissection of QTLs for Yield Traits on the Short Arm of Rice Chromosome 6

This study was undertaken to dissect quantitative trait loci (QTLs) controlling yield traits on the short arm of rice chromosome 6. A residual heterozygous line that carries a heterozygous segment extending from RM587 to RM19784 on the short arm of rice chromosome 6 was selected from an F 7 population of the indica rice cross Zhenshan 97B/Milyang 46. An F 2:3 population consisting of 221 lines was derived and grown in two trial sites. Six yield traits including number of panicles per plant, number of filled grains per panicle, total number of spikelets per panicle, spikelet fertility, 1 000-grain weight, and grain yield per plant were measured. An SSR marker linkage map was constructed and employed to determine QTLs for yield traits with Windows QTL Cartographer 2.5. QTLs were detected in the target interval for all the traits analyzed except NP, with phenotypic variance explained by a single QTL ranging between 6.3% and 35.2%. Most of the QTLs for yield components acted as additive QTLs, while the three QTLs for grain yield had dominance degrees of 1.65, 0.84, and −0.42, respectively. It was indicated that three or more QTLs for yield traits were located in the target region. The genetic action mode, the direction of the QTL effect, and the magnitude of the QTL effect varied among different QTLs for a given trait, and among QTLs for different traits that were located in the same interval.

Quantitative genetics and functional-structural plant growth models: simulation of quantitative trait loci detection for model parameters and application to potential yield optimization.

Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization. The GREENLAB model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination. By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits--such as cob weight--and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GREENLAB maize model. The paper discusses the potentials of GREENLAB to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand.

A Modified Algorithm for the Improvement of Composite Interval Mapping

Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.

Principal component analysis of quantitative trait loci for immune response to adenovirus in mice

Data on the duration of transgene expression in the liver, the presence of cytotoxic T lymphocytes (CTLs) against adenovirus, and serum cytokines from 18 strains of C57BL/6 x DBA/2 (B x D) recombinant inbred mice were analyzed. Our aim was to detect quantitative trait loci (QTLs) that may have causal relationship with the duration of adenovirus-mediated transgene expression in the liver. Information from beta-galactosidase (LacZ) expression; CTL production; and serum levels of gamma interferon, tumor necrosis factor-alpha, and interleukin-6 30 days after intravenous injection of liver LacZ were summarized by principal component analysis and analyzed using maximum likelihood interval mapping implemented in the QTL cartographer software. Two principal component (PC) scores explained 82.5% of the phenotypic variance in the original variables and identified QTLs not identified by analysis of individual traits. The distribution of original variables among PCs was such that variables in PC1 were predominantly cytokines with little CTL response whereas LacZ and CTL were the predominant contributors to PC2 with practically no contribution from cytokines. PC1 was significantly associated with two QTLs on chromosomes 7 and 9 located at 57.5 cM and 41.01 cM, respectively. Five QTLs were significantly associated with PC2 on chromosomes 12 (23.01 and 31.01 cM) and 15 (29.21, 36.01, and 56.31 cM). These results illustrate the use of principal component analysis in mapping QTLs using multiple correlated traits.

Confirmation and Fine Mapping of Ethanol Sensitivity Quantitative Trait Loci, and Candidate Gene Testing in the LXS Recombinant Inbred Mice

In previous studies, we have mapped quantitative trait loci (QTLs) for hypnotic sensitivity to ethanol using a small recombinant inbred (RI) panel and a large F(2) backcross. Alcohol sensitivity is a major predictor of long-term risk for alcoholism. We remapped hypnotic sensitivity using a new set of 75 RI strains, the LXS, derived from Inbred Long Sleep and Inbred Short Sleep strains. We expected to improve mapping resolution in the QTL regions and to identify novel QTLs for loss of the righting reflex due to ethanol. We used three common mapping algorithms (R/qtl, QTL Cartographer, and WebQTL) to map QTLs in the LXS, and we compared the results. Most mapping studies use only a single algorithm, an approach that may result in failure to identify minor QTLs. We confirmed most of our previously reported QTLs, although one major QTL from earlier work (Lore2) failed to replicate, possibly because it represented multiple linked genes separated by recombination in the RI strains. We also report narrowed confidence intervals, based on mapping with a new genetic resource of more than 4000 polymorphic single-nucleotide polymorphism markers. These narrowed confidence intervals will facilitate candidate gene identification and assessment of overlap with human regions specifying risk for alcoholism. Finally, we present an approach for using these RI strains to assess evidence for candidate genes in the narrowed intervals, and we apply this method to a strong candidate, the serotonin transporter.

Genomic Regions Associated with Amino Acid Composition in Soybean

Soybean [Glycine max (L.) Merr.] is the single largest source of protein in animal feed. However, few studies have been conducted to evaluate genomic regions controlling amino acid composition in soybean. It is important to study the genetics of amino acid composition to achieve improvements through breeding. The objectives of this study were to determine the ratios between essential to non-essential (E:NE) and essential to total (E:T) amino acids, and to identify genomic regions controlling essential and non-essential amino acid composition in soybean seed. To achieve these objectives, 101 F6-derived recombinant inbred lines (RIL) developed from a cross of N87-984-16 × TN93-99 were used. Ground soybean seed samples were analyzed for amino acids using a near infrared spectroscopy (NIRS) instrument. A significant (p < 0.01) difference among the RIL was found for amino acid composition. Heritability estimates on an entry mean basis ranged from 0.13 for His to 0.67 for Tyr. A total of 94 polymorphic simple sequence repeat (SSR) molecular genetic markers were screened in DNA from progenies. Single factor ANOVA was used to identify candidate quantitative trait loci (QTL), which were then confirmed by QTL Cartographer. At least one QTL for each amino acid was detected in this population. QTL linked to molecular markers Satt143, Satt168, Satt203, Satt274 and Satt495 were associated with most of the amino acids. Phenotypic variation explained by an individual QTL ranged from 9.4 to 45.3%. QTL detected for amino acids in soybean in this experiment are expected to be useful for future breeding programs targeting development of improved soybean amino acid composition for human and animal nutrition.