Mapping of quantitative trait loci using the skew-normal distribution

In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.

Mapping quantitative trait loci associated with starch paste viscosity attributes by using double haploid populations of rice (Oryza sativa L.)

The paste viscosity attributes of starch,measured by rapid visco analyzer(RVA),are important factors for the evaluation of the cooking and eating qualities of rice in breeding programs.To determine the genetic roots of the paste viscosity attributes of rice grains,quantitative trait loci(QTLs)associated with the paste viscosity attributes were mapped,using a double haploid(DH)population derived from Zhongjiazao 17(YK17),a super rice variety,crossed with D50,a tropic japonica variety.Fifty-four QTLs,for seven parameters of the RVA profiles,were identified in three planting seasons.The 54 QTLs were located on all of the 12 chromosomes,with a single QTL explaining 5.99 to 47.11%of phenotypic variation.From the QTLs identified,four were repeatedly detected under three environmental conditions and the other four QTLs were repeated under two environments.Most of the QTLs detected for peak viscosity(PKV),trough viscosity(TV),cool paste viscosity(CPV),breakdown viscosity(BDV),setback viscosity(SBV),and peak time(PeT)were located in the interval of RM 6775-RM 3805 under all three environmental conditions,with the exception of pasting temperature(PaT).For digenic interactions,eight QTLs with six traits were identified for additivexenvironment interactions in all three planting environments.The epistatic interactions were estimated only for PKV,SBV and PaT.The present study will facilitate further understanding of the genetic architecture of eating and cooking quality(ECQ)in the rice quality improvement program.

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.

Dissection of Genetic Effects of Quantitative Trait Loci(QTL) in Transgenic Cotton

When alien DNA inserts into cotton genome in multi-copy manner,several QTL in cotton genome are disrupted,which are called dQTL in this study.Transgenic mutant line is near-isogenic to its recipient which is divergent for the dQTL from remaining QTL.So,a set of data from a

Identification of Quantitative Trait Loci for Anthesis-Silking Interval and Yield Components Under Drought Stress in Maize

A genetic linkage map with 89 SSR marker loci was constructed based on a maize (Zea mays L.) population consisting of 184 F-2 individuals from the cross, Huangzao 4 X Ye 107. The 184 F-3 families were evaluated in the field under well-watered and drought-stressed regimes in Shanxi Province of China. The objectives of the study were to identify genetic segments responsible for the expression of anthesis-silking interval (ASI), ear setting and grain yield, and to examine if the quantitative trait loci (QTLs) for ASI or yield components can be used in marker-assisted selection (MAS) to improve grain yield under drought conditions. Results showed that under well-watered and drought-stressed regimes, three and two QTLs involved in the expression of ASI were detected on chromosomes 1, 2 and 3, and 2 and 5, respectively. Under well-watered regime, two QTLs for ear setting were detected on chromosomes 3 and 6, explaining about 19.9% of the phenotypic variance, and displayed additive and partial dominant effects, respectively. Under drought-stressed condition, four QTLs for ear setting were detected on chromosomes 3, 7 and 10, which were responsible for interpreting 60.4% of the phenotypic variance, and showed dominant or partial dominant effects. Under well-watered condition, four QTLs controlling grain yield were identified on chromosomes 3, 6 and 7, while five QTLs were identified under drought stress on chromosomes 1, 2, 4 and 8. The gene action was of additive or partial dominant effects, and each QTL could explain 7.3% to 22.0% of the phenotypic variance, respectively. Under drought conditions, ASI and ear setting percentage were highly correlated with grain yield, which can be used as secondary traits for grain yield selection. Based on linked markers detected and gene action analyzed, an MAS strategy for yield improvement under drought condition could be established, which consists of QTLs contributing to decreased ASI and to increased ear setting and grain yield, respectively.

Detection of Quantitative Trait Loci Associated with Several Internal Organ Traits and Teat Number Trait in a Pig Population

Quantitative trait loci （QTL） were detected for 8 internal organ traits, 3 carcass length traits, and teat number trait in 214 pigs in a resource population that included 180 F2 individuals. A total of 39 microsatellite markers were examined on SSC4, SSC6, SSC7, SSC8, and SSC13. The genetic traits included heart weight （HW）, lung weight （LW）, liver and gallbladder weight （LGW）, spleen weight （SPW）, stomach weight （STW）, small intestine weight （S1W）, large intestine weight （LIW）, kidney weight （KW）, carcass length to the first cervical vertebra （CL1）, carcass length to the first thoracic vertebra （CL2）, rib numbers （RNS）, and teat numbers （TNS）. Results indicated that, 3 highly significant QTL （P≤0.01 at chromosome-wise level） for HW （at 30 cM on SSC6）, RNS （at 115 cM on SSC7）, TNS （at 110 cM on SSC7）, and 6 significant QTL （P≤0.05 at chromosome-wise level） for LW （at 119 cM on SSC13）, LGW （at 94 cM on SSC6）, SPW （at 106 cM on SSC8）, SIW （0 cM on SSC4）, LIW （170 cM on SSC 4）, and TNS （at 95 cM on SSC6） were detected. The phenotypic variances for which these QTL were accounted ranged from 0.04 % to 14.06 %. Most of these QTL had not been previously reported.

High-density SNP genetic linkage map construction and quantitative trait locus mapping for resistance to cucumber mosaic virus in tobacco(Nicotiana tabacum L.)

Genetic linkage maps are essential for studies of genetics, genomic structure, and genomic evolution, and for mapping quantitative trait loci (QTL). Identification of molecular markers and construction of genetic linkage maps in tobacco (Nicotiana tabacum L.), a classical model plant and important economic crop, have remained limited. In the present study we identified a large number of single nucleotide polymorphism (SNP) markers and constructed a high-density SNP genetic map for tobacco using restriction site-associated DNA sequencing. In 1216.30 Gb of clean sequence obtained using the Illumina HiSeq 2000 sequencing platform, 99,647,735 SNPs were identified that differed between 203 sequenced plant genomes and the tobacco reference genome. Finally, 13,273 SNP markers were mapped on 24 high-density tobacco genetic linkage groups. The entire linkage map spanned 3421.80 cM, with a mean distance of 0.26 cM between adjacent markers. Compared with genetic linkage maps published previously, this version represents a considerable improvement in the number and density of markers. Seven QTL for resistance to cucumber mosaic virus (CMV) in tobacco were mapped to groups 5 and 8. This high-density genetic map is a promising tool for elucidation of the genetic bases of QTL and for molecular breeding in tobacco.

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.

Quantitative trait locus mapping of yield and plant height in autotetraploid alfalfa(Medicago sativa L.)

Alfalfa(Medicago sativa L.)is the most widely grown forage legume crop worldwide.Yield and plant height are important agronomic traits influenced by genetic and environmental factors.The objective of this study was to identify quantitative trait loci(QTL)and molecular markers associated with alfalfa yield and plant height.To understand the genetic basis of these traits,a full-sib F1 population composed of 392 individuals was developed by crossing a low-yielding precocious alfalfa genotype(male parent)with a high-yielding latematuring alfalfa cultivar(female parent).The linkage maps were constructed with 3818 single-nucleotide polymorphism(SNP)markers on 64 linkage groups.QTL for yield and plant height were mapped using phenotypic data for three years.Sixteen QTL associated with yield and plant height were identified on chromosomes 1 to 8.Six QTL explained more than 10%of phenotypic variation,representing major loci controlling yield and plant height.One locus on chromosome 1 controlling yield traits had not been identified in previous studies.Three QTL co-located with other QTL(qyield-1 and qheight-7,qheight-5 and qyield-4,qheight-6,and qyield-6).With further validation,the markers closely linked with these QTL may be used for marker-assisted selection in breeding new alfalfa varieties with high yield.

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

Quantitative trait loci detection of E dwardsiella tarda resistance in Japanese flounder Paralichthys olivaceus using bulked segregant analysis

In recent years, Edwardsiella tarda has become one of the most deadly pathogens of Japanese fl ounder( Paralichthys olivaceus), causing serious annual losses in commercial production. In contrast to the rapid advances in the aquaculture of P. o livaceus, the study of E. tarda resistance-related markers has lagged behind, hindering the development of a disease-resistant strain. Thus, a marker-trait association analysis was initiated, combining bulked segregant analysis(BSA) and quantitative trait loci(QTL) mapping. Based on 180 microsatellite loci across all chromosomes, 106 individuals from the F1333(♀: F0768 ×♂: F0915)(Nomenclature rule: F+year+family number) were used to detect simple sequence repeats(SSRs) and QTLs associated with E. tarda resistance. After a genomic scan, three markers(Scaffold 404-21589, Scaffold 404-21594 and Scaffold 270-13812) from the same linkage group(LG)-1 exhibited a signifi cant difference between DNA, pooled/bulked from the resistant and susceptible groups( P <0.001). Therefore, 106 individuals were genotyped using all the SSR markers in LG1 by single marker analysis. Two different analytical models were then employed to detect SSR markers with different levels of signifi cance in LG1, where 17 and 18 SSR markers were identifi ed, respectively. Each model found three resistance-related QTLs by composite interval mapping(CIM). These six QTLs, designated q E1–6, explained 16.0%–89.5% of the phenotypic variance. Two of the QTLs, q E-2 and q E-4, were located at the 66.7 c M region, which was considered a major candidate region for E. tarda resistance. This study will provide valuable data for further investigations of E. tarda resistance genes and facilitate the selective breeding of disease-resistant Japanese fl ounder in the future.

Detection of Quantitative Trait Loci Associated with Live Measurement Traits in Pigs

Live measurement growth traits are very important economic traits in pig production and breeding. In this research, quantitative trait loci （QTL） were detected for 11 live estimated growth and carcass traits, including birth weight （BWT）, average daily gain over testing periods （ADG3）, live backfat thickness at last 3-4th lumbar （LBFT3）, live loin eye area （LLEA）, and so on, in 214 pig resource family population, including 180 F2 individual, by 39 microsatellite marker loci on SSC4, SSC6, SSC7, SSC8, and SSC13. The results indicated that 4 chromosome significant level QTL and one suggestive QTL were detected for ADG3 （at position of 50 cM on SSC8）, LBFT3 （at position of 147 cM on SSC4）, LLEA （one highly significant at position of 48 cM on SSC7; another significant at position of 125 cM on SSC8） and BWT （suggestive significant at position of 0 cM, at marker sw489 on SSC4）. The phenotypic variance of these QTL accounted for 0.95% to 16.91%. Most of them were mentioned in previous reports; except the QTL of LLEA at position of sw1953 on SSC8 which maybe a new QTL.

Identification of Quantitative Trait Loci Controlling Seed Physical and Nutrient Traits in Cotton

Cotton(Gossypium spp.) is the leading fiber crop,and an important source of the important edible oil and protein meals in the world.Complex genetics and strong environmental effects hinder

Mapping of QTLs Related to Grain Weight Using Chromosome Single Segment Substitution Lines in Rice

Grain weight, one of the major factors determining rice yield, is a typical quantitative trait control ed by multiple genes. With Guangluai 4 as recipient and Nipponbare as donor, a population of 119 chromosome single segment substitution lines had been developed. Correlation analysis between grain weight and grain shape by SPSS revealed that 1 000-grain weight shared extremely significant posi-tive correlation with grain length and length-width ratio, but no significant correlation with grain width and thickness. The QTL analysis of grain weight was carried out using one-way analysis of variance and Dunnett＇s test. Nineteen stable QTLs re-sponsible for grain weight were identified over two years. Al 19 QTLs were identi-fied on al chromosomes except for chromosome 10 and 12 at a significance level of P≤0.001. Among them, 10 QTLs had a positive effect and were derived from the Nipponbare al ele, the additive effect of these QTLs ranged from 0.49 to 2.74 g, and the contributions of the additive effects ranged from 2.00% to 11.05%. Another 9 QTLs had a negative effect and were al derived from Guangluai 4 al ele, the ad-ditive effect of these QTLs ranged from 0.60 to 2.35 g, and the contributions of the additive effects ranged from 2.40% to 9.84%. The results provide a basis for the fine mapping and gene cloning of novel locus associated with rice grain weight.

QTLs Mapping for Salinity Tolerance at Seedling Stage or Rice(Oryza sativa L.) Using Chromosome Segment Substitution Lines

In this study, a population of chromosome segment substitution lines （CSSLs） derived from the cross between 9311 （indica） and Nipponbare （japonica） was employed to map the quantitative trait loci （QTLs） for salt tolerance under the salt stress simulated with 0.5% NaCI, using survival rate as the index. The data were analyzed by QTL IciMapping v3.1, and the results showed that one QTL （QSsr3） related to salt tolerance was located in the vicinity of the marker RM1350 on chromosome 3, into a genetic interval of 113.2-132.8 cM, with a contribution rate of 17.75%. The additive effect was 10.9, indicating that the QTL derived from the parent Nipponbare improved the salt tolerance of rice at seedling stage. This study will provide a theoretical basis for the selection of salt tolerant rice germplasm.

Mapping QTLs Associated with Sheath Blight Resistance Using Chromosome Segment Substitution Lines of Rice(Oryza sativa L.)

In this study, a population of 119 chromosome segment substitution lines （CSSLs） derived from backcross between indica 9311 and japonica Nipponbare was employed to map quantitative trait loci （QTL） associated with sheath blight resis-tance in rice with toothpick inoculation method. A total of three sheath blight resis-tance-associated QTLs （qsb8-1, qsb8-2 and qsb8-3） were identified, which were lo-cated on adjacent molecular markers RM3262, RM5485 and RM3496 of chromo-some 8; the genetic interval was 81.7cM-91.7cM, 91.7cM-108.1cM and 108.1cM-119.6cM, respectively. The additive effect of qsb8-2 was negative, indicating that sheath blight resistance of susceptible parent harboring qsb8-2 fragment was en-hanced; additive effects of qsb8-1 and qsb8-3 were positive, indicating that sheath blight resistance of susceptible parent harboring qsb8-1 and qsb8-3 fragments was reduced.

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 Drought Tolerance at Stages of Germination and Seedling in Wheat (Triticum aestivum L.) Using a DH Population

Drought is a major constraint in many wheat( Triticum aestivum L.) production regions. Quantitative trait loci (QTLs) conditioning drought tolerance at stages of germination and seedling in wheat were identified in a double haploid (DH) population derived from the cross, Hanxuan10×Lumai14, using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers. Interval mapping analysis revealed that QTLs for drought tolerance at germination stage were located on chromosomes 1B, 2B, 5A, 6B, 7A and 7B, respectively, and the most effective QTL was mapped on chromosome 2B, explaining 27.2% of phenotypic variance. The QTLs for drought tolerance at seedling stage were located on 1B, 3B and 7B, respectively, and the most effective QTL was mapped on chromosome 3B, explaining 21.6% of phenotypic variance. Their positions were different from those of QTLs conferring drought tolerance at germination stage, indicating that drought tolerance at germination stage and seedling stage was controlled by different loci. Most of the identified QTLs explained 18% or more of phenotypic variance for drought tolerance at germination and seedling stage, and would be useful in future for marker assisted selection programs and cultivar improvement.

QTL Mapping for Partial Resistance to Southern Corn Rust Using RILs of Tropical Sweet Corn

Southern corn rust is one of destructive diseases in maize caused by Puccinia polysora Undrew. A mapping population of tropical sweet corn recombinant inbred lines (RILs) derived from a cross between hA9104 and hA9035 inbred lines were set up to detect quantitative trait loci (QTLs) involved in partial resistance to southern corn rust. Eighty nine RILs were used to evaluate resistance levels using nine-point relative scale (1-9) at Sweet Seeds, Suwan Farm, Thailand include combined analysis. A genetic linkage map was constructed with 157 SSR markers, with a total length of 2123.1 cM, covering 10 chromosomes. Broad-sense heritability of individual location ranged from 0.76 and 0.82 and combined across locations was 0.87. Multiple QTL mapping (MQM) was applied for the identification of the QTLs. Fifteen QTLs were detected on chromosome 1, 2, 5, 6, 9 and 10 in both locations and combined across locations. QTLs on chromosome 1, 5 and 6 were contributed by alleles of resistant parent hA9104 while others were contributed by alleles from the susceptible parent, hA9035. Phenotypic variance of each QTL explained ranged from 6.1% to 41.8% with a total of 69.8% - 81.9%. QTL on chromosome 1, 6 and 10 were stable QTLs detected in both locations.

Fine mapping of qTGW10-20.8, a QTL having important contribution to grain weight variation in rice

Grain weight is one of themost important determinants of grain yield in rice.In this study,QTL analysis for grain weight,grain length,and grainwidthwas performed using populations derived from crosses between major parental lines of three-line indica hybrid rice.A total of 27 QTL for grain weight were detected using three recombinant inbred line populations derived from the crosses Teqing/IRBB lines,Zhenshan 97/Milyang 46,and Xieqingzao/Milyang 46.Of these,10 were found in only a single population and the other 17 in two or all three populations.Nine of the 17 common QTL were located in regions where no QTL associated with grain weight have been cloned and onewas selected for fine-mapping.Eight populations segregating in an isogenic background were derived from one F7 residual heterozygote of Teqing/IRBB52.The target QTL,qTGW10-20.8 controlling grain weight,grain length,and grain width,was localized to a 70.7-kb region flanked by InDel markers Te20811 and Te20882 on the long arm of chromosome 10.The QTL region contains seven annotated genes,ofwhich six encode proteins with known functional domains and one encodes a hypothetical protein.One of the genes,Os10g0536100 encoding the MIKC-type MADS-box protein OsMADS56,is the most likely candidate for qTGW10-20.8.These results provide a basis for cloning qTGW10-20.8,which has an important contribution to grain weight variation in rice.