QTL analysis of agronomic traits in barley based on the doubled haploid progeny of two elite North American varieties representing different germplasm groups

A better understanding of the genetics of complex traits, such as yield, may be achieved by using molecular tools. This study was conducted to estimate the number, genome location, effect and allele phase of QTLs determining agronomic traits in the two North American malting barley (Hordeum vulgare L.) quality variety standards. Using a doubled haploid population of 140 lines from the cross of two-rowed Harrington×six-rowed Morex, agronomic phenotypic data sets from nine environments, and a 107-marker linkage map, we performed QTL analyses using simple interval mapping and simplified composite interval mapping procedures. Thirty-five QTLs were associated, either across environments or in individual environments, with five grain and agronomic traits (yield, kernel plumpness, test weight, heading date, and plant height). Significant QTL×environment interaction was detected for all traits. These interactions resulted from both changes in the magnitude of response and changes in the sign of the allelic effect. QTLs for multiple traits were coincident. The vrs1 locus on chromosome 2 (2H), which determines inflorescence row type, was coincident with the largest-effect QTL determining four traits (yield, kernel plumpness, test weight, and plant height). QTL analyses were also conducted separately for each sub-population (six-rowed and two-rowed). Seven new QTLs were detected in the sub-populations. Positive transgressive segregants were found for all traits, but they were more prevalent in the six-rowed sub-population.QTL analysis should be useful for identifying candidate genes and introgressing favorable alleles between germplasm groups. Received: 18 August 2000 / Accepted: 15 December 2000     

Identifying regions of the wheat genome controlling seed development by mapping expression quantitative trait loci

Statistical methods established for the genetic analysis of quantitative traits can be applied to gene expression data. Quantitative trait locus (QTL) analysis can associate the expression of genes or groups of genes with particular genomic regions, and thereby identify regions regulating gene expression. A segregating population of 41 doubled haploid (DH) lines from the hard red spring wheat cross RL4452 x 'AC Domain' was used to map expression level polymorphisms. This population had previously been mapped with microsatellites, and includes a full QTL analysis for agronomic and seed quality traits. Expression analysis on mRNA from developing seed grown in two field locations was conducted on 39 of the 41 DH lines using the Affymetrix GeneChip Wheat Genome Array. Analysis of the hybridization intensity identified 1484 Affymetrix probe sets in the first location and 10,280 probe sets in the second location, where the hybridization intensity varied significantly between genotypes of the population. A common set of 1455 probe sets differing in intensity between genotypes in both locations was used for mapping, and 542 QTLs were identified that each mapped to a single chromosome interval, illustrating that major gene expression QTLs could be found in wheat. Genomic regions corresponding to multiple gene expression QTLs were identified. Comparison of expression mapping data with physical mapping of wheat expressed sequence tag (EST) sequences using rice synteny, as well as logarithm of odds (LOD) score analysis, showed that both cis- and trans-acting expression QTLs were present. Chromosomes 1D and 4B may contain significant trans-regulatory regions in this population.     

np2QTL: networking phenotypic plasticity quantitative trait loci across heterogeneous environments

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment‐induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part‐whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta?Volterra ordinary differential equations (LVODE) into an R‐based computing platform, np²QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np²QTL constructs a bidirectional, signed and weighted network of QTL?QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype?environment interactions over any range of environmental change. The utility of np²QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np²QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.     

Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley

The additive main effects and multiplicative interaction (AMMI) model has emerged as a powerful analytical tool for genotype x environment studies. The objective of the present study was to assess its value in quantitative trait locus (QTL) mapping. This was done through the analysis of a large two-way table of genotype-by-environment data of barley (Hordeum vulgare L.) grain yields, where the genotypes constituted a genetic population suitable for mapping studies. Grain yield data of 150 doubled haploid lines derived from the Steptoe x Morex cross, and the two parental lines, were taken by the North American Barley Genome Mapping Project (NABGMP) at 16 environments throughout the barley production areas of the USA and Canada. Four regions of the genome were responsible for most of the differential genotypic expression across environments. They accounted for approximately 50% of the genotypic main effect and 30% of the genotype x environment interaction (GE) sums of squares. The magnitude and sign of AMMI scores for genotypes and sites facilitate inferences about specific interactions. The parallel use of classification (cluster analysis of environments) and ordination (principal component analysis of GE matrix) techniques allowed most of the variation present in the genotype x environment matrix to be summarized in just a few dimensions, specifically four QTLs showing differential adaptation to four clusters of environments. Thus, AMMI genotypic scores, when the genotypes constituted a population suitable for QTL mapping, could provide an adequate way of resolving the magnitude and nature of QTL x environment interactions.Ignacio Romagosa was on sabbatical leave from the University of Lleida and the Institut de Recerca i Tecnologia Agroalimentàries, Lleida, Spain, when this study was conducted     

Molecular breeding for grain yield in barley: an evaluation of QTL effects in a spring barley cross

 We report results from a breeding strategy designed to accumulate favorable QTL alleles for grain yield identified in the SteptoeבMorex’ (SM) barley germplasm. Two map lines (SM73 and SM145) from the original mapping population were selected based on their marker genotype and QTL structure. When crossed, these lines would be expected to produce progeny with most favorable QTL alleles. One hundred doubled haploid (DH) lines from the F₁ hybrid of this cross were genotyped with ten RFLP markers and one morphological marker defining grain yield to monitor QTL segregation. A subset of 24 lines representing various combinations of putatively favorable and unfavorable QTL alleles, together with Steptoe, ‘Morex’, SM73, and SM145, were phenotyped for grain yield in five environments. Multiple regression procedures were used to explore phenotype and genotype relationships. Most target QTLs showed significant effects. However, significance and magnitude of QTL effects and favorable QTL allele phase varied across environments. All target QTLs showed significant QTL-by-environment interaction (QTL×E), and the QTL on chromosome 2 expressed alternative favorable QTL alleles in different environments. Digenic epistatic effects were also detected between some QTL loci. For traits such as grain yield, marker-assisted selection efforts may be better targeted at determining optimum combinations of QTL alleles rather than pyramiding alleles detected in a reference mapping population. Received: 2 June 1998 / Accepted: 17 September 1998     

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.     

标记基因型中QTL基因型条件概率分布

随着分子数量遗传学的发展,人们提出了很多统计模型用于QTL定位分析。在这些模型中,首先得确定QTL在标记基因型中的条件概率分布,然后利用适当的统计方法对QTL在基因组中所处的位置进行估计。本文讨论了常见作图群体（如F2和回交群体）中在给定标记基因型下QTL的条件概率分布,提出了用Mathematics软件推导QTL基因型条件概率分布的方法。用该方法能够快速地、准确地推导出比较复杂的标记基因型中QTL基因型的条件概率分布。     

水稻生物学产量及其构成性状的QTL定位

QTL的加性效应、加性×加性上位性效应及它们与环境的互作效应是数量性状的重要遗传分量.利用IR64/Azucena的125个DH品系为群体,分析了水稻生物学产量及其两个构成性状干草产量和谷粒产量的遗传组成.用基于混合模型的复合区间作图(MCIM)方法进行QTL定位.检测到12个位点有加性主效应,27个位点涉及双位点互作,18个位点存在环境互作.结果表明水稻生物学产量和它的两个构成性状普遍存在上位性效应和QE互作效应.此外,还探讨了性状间相关的遗传基础.发现4个QTLs和一对上位性QTLs可能与生物学产量与干草产量之间的正相关有关.3个QTL可能与干草产量与谷粒产量之间的负相关有关.这些结果可能部分地解释了这3个性状相关的遗传原因.通过对水稻生物学产量及其两个构成性状所定位QTL的分析,加深了对数量性状QTL的认识.首先,QTL的上位性效应和QE互作效应是普遍存在的;其次,QTL的多效性或紧密连锁可能是遗传相关的原因,当QTL对两个性状作用的方向相同时可导致正向遗传相关,反之则为负向遗传相关,当有些QTL表现为同向作用而另一些QTL表现为反向作用时,则可削弱性状间的遗传相关性;第三,复合性状的QTL效应可分解为其组成性状的QTL效应,如果QTL对各组成性状的效应方向相反而相互抵消,可使复合性状的QTL效应不易被检测;第四,加性效应的QTL常参预构成上位性效应,而具有上位性效应的QTL并非都有加性主效应,表明忽略上位性的QTL定位方法会降低检测QTL的功效;最后,鉴别不同类型的QTL效应有利于指导育种实践,选择主效QTL适用于多环境,QE互作QTL适用于特定环境,对上位性QTL应强调选择基因组合而并非单个基因.     

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice (<i>Oryza sativa</i> L.)

Grain size traits are critical agronomic traits which directly determine grain yield, but the genetic bases of these traits are still not well understood. In this study, a total of 154 chromosome segment substitution lines (CSSLs) population derived from a cross between a japonica variety Koshihikari and an indica variety Nona Bokra was used to investigate grain length (GL), grain width (GW), length-width ratio (LWR), grain perimeter (GP), grain area (GA), and thousand grain weight (TGW) under four environments. QTL mapping analysis of six grain size traits was performed by QTL IciMapping 4.2 with an inclusive composite interval mapping (ICIM) model. A total of 64 QTLs were identified for these traits, which mapped to chromosomes 1, 2, 3, 4, 6, 7, 8, 10, 11, and 12 and accounted for 1.6%–27.1% of the total phenotypic variations. Among these QTLs, thirty-six loci were novel and seven QTLs were identified under four environments. One locus containing the known grain size gene, qGL3/GL3.1/OsPPKL1, also have been found. Moreover, five pairs of digenic epistatic interactions were identified except for GL and GP. These findings will facilitate fine mapping of the candidate gene and QTL pyramiding to genetically improve grain yield in rice.     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

The genetic basis of adaptive population differentiation: a quantitative trait locus analysis of fitness traits in two wild barley populations from contrasting habitats

We used a quantitative trait locus (QTL) approach to study the genetic basis of population differentiation in wild barley, Hordeum spontaneum. Several ecotypes are recognized in this model species, and population genetic studies and reciprocal transplant experiments have indicated the role of local adaptation in shaping population differences. We derived a mapping population from a cross between a coastal Mediterranean population and a steppe inland population from Israel and assessed F3 progeny fitness in the natural growing environments of the two parental populations. Dilution of the local gene pool, estimated as the proportion of native alleles at 96 marker loci in the recombinant lines, negatively affected fitness traits at both sites. QTLs for fitness traits tended to differ in the magnitude but not in the direction of their effects across sites, with beneficial alleles generally conferring a greater fitness advantage at their native site. Several QTLs showed fitness effects at one site only, but no opposite selection on individual QTLs was observed across the sites. In a common-garden experiment, we explored the hypothesis that the two populations have adapted to divergent nutrient availabilities. In the different nutrient environments of this experiment, but not under field conditions, fitness of the F3 progeny lines increased with the number of heterozygous marker loci. Comparison of QTL-effects that underlie genotype x nutrient interaction in the common-garden experiment and genotype x site interaction in the field suggested that population differentiation at the field sites may have been driven by divergent nutrient availabilities to a limited extent. Also in this experiment no QTLs were observed with opposite fitness effects in contrasting environments. Our data are consistent with the view that adaptive differentiation can be based on selection on multiple traits changing gradually along ecological gradients. This can occur without QTLs showing opposite fitness effects in the different environments, that is, in the absence of genetic trade-offs in performance between environments.     

玉米雄穗分枝数与主轴长的QTL鉴定

在包含103个SSR标记的连锁图谱基础上, 运用复合区间作图法检测玉米组合(N87-1×9526 )F3家系在正常与干旱胁迫环境下的雄穗分枝数与主轴长性状QTL。雄穗分枝数在正常环境下被检测到2个QTL座位, 分别位于第5和7连锁群上; 在胁迫环境下被检测到4个QTL座位分别位于 2、5、7和10连锁群上, 其中位于第5和7连锁群上的QTL不仅具有一致性而且与本作图群体中曾检测到的耐旱相关性状QTL存在连锁。雄穗主轴长在正常环境下被检测到2个QTL位于第2和第6连锁群上, 在干旱胁迫环境下被检测到了3个QTL分别于第2、4和10连锁群上, 其中位于第2染色体上的QTL是两种环境下所共同检测到的QTL。分析QTL的遗传作用方式表明, 雄穗分枝数以部分加性效应为主, 而雄主轴长全部表现为显性和超显性。     

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 Rht1 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).     

The effect of semi-dwarf genes on root system size in field-grown barley

Root system size (RSS) was measured in 12 diverse barley genotypes and 157 double-haploid lines (DHs), using electric capacitance. The parents of the DHs, Derkado and B83-12/21/5, carry different semi-dwarfing genes, sdw1 and ari-e.GP, respectively. Estimates of RSS were taken in the field thrice during plant development: stem elongation (RSS1), heading (RSS2) and grain filling (RSS3). The 12 barley genotypes were assessed over 3 years and at two or three locations each year; the DH mapping population was assessed at two locations in 2002. Among the 12 barley genotypes, those with the semi-dwarf genes had greater RSS values in all 3 years (28.9, 24.6 and 15.0% in years 1, 2 and 3, respectively) compared to non-semi-dwarf controls. The DH population showed transgressive segregation on both sides of the parent means, indicating polygenic control of RSS. Quantitative trait loci (QTLs) for RSS were found on five of the seven chromosomes: 1H, 3H, 4H, 5H and 7H and these were compared with previously mapped agronomic traits. The TotalRSS QTL on 3H was associated with sdw1 and QTLs for height, plant yield and plant weight. The RSS3 QTL on 5H was associated with ari-e.GP and QTLs for height, plant yield, plant weight, harvest index and tiller number. The RSS3 QTL on 7H was also associated with a TotalRSS QTL and QTLs for plant weight and harvest index. Other RSS QTLs were not associated with any other trait studied. RSS is considered to be a polygenic trait linked to important traits, in particular to yield. The study highlights the effects of semi-dwarfing genes and discusses the potential for breeding for root traits.     

Mapping QTL for Biomass Yield and Its Components in Rice (Oryza sativa L.)

Addicive effects, additive by additive epistatic effects, and their environmental interactions of QTLs are important genetic components of quantitative traits. Genetic architecture underlying rice biomass yield and its two component traits (straw yield and grain yield) were analyzed for a population of 125 DH lines from an inter-subspecific cross of IR64/Azucena. The mixed-model based composite interval mapping approach (MCIM) was used to detect QTLs, There were 12 QTLs detected with additive main effects, 27 QTLs involved in digenic interaction with aa and/or aae effects, and 18 QTLs affected by environments with ae and/or aae effects. It was revealed that epistatic effects and QE interaction effects existed on biomass yield and its component traits in rice. In addition, the genetic basis of relationships among these traits were investigated. Four QTLs and one pair of epistatic QTLs were detected to be responsible for the positive correlation between biomass yield and straw yield. Three QTLs might be responsible for the negative correlation between straw yield and grain yield. This result could partially explain the genetic basis of correlation among the three traits, and provide useful information for genetic improvement of these traits by marker-assisted selection.     

Verification of yield QTL through realized molecular marker- assisted selection responses in a barley cross

Verification of putative quantitative trait loci (QTL) is an essential step towards implementing the use of marker-assisted selection (MAS) in cultivar improvement. In a previous study with 150 doubled haploid lines derived from the 6-row cross Steptoe/Morex (S/M), four regions (QTL1–4) of the barley genome were associated with differential genotypic expression for grain yield across environments. The objectives of this study were to verify the value of these four QTL for selection and to compare the efficiency of alternative MAS strategies using these QTL vs. conventional phenotypic selection for grain yield. A total of 92 DHLs derived from the S/M cross that were not used in the original mapping efforts were used for QTL verification. Confirmation of QTL effects was first accomplished by assessing yield differences between individuals carrying alternative alleles at each putative locus in three environments. QTL1 on chromosome 3 was confirmed as the most important and consistent locus to determine yield across sites, with the S allele being favorable. The M allele at QTL3 on chromosome 6 was beneficial for grain yield across sites, but to a lesser degree than QTL1. Magnitudes of allele effects at QTL2 (chromosome 2) and QTL4 (chromosome 7) were highly influenced by the environment where the genotypes were grown. Verification of QTL effects was best achieved by comparing realized selection response. Genotypic (MAS) and tandem genotypic and phenotypic selection were at least as good as phenotypic selection. Consistent selection responses were detected for QTL1 alone and together with QTL3. Genotypic selection for lines carrying the S allele at QTL1 resulted in the identification of high-yielding genotypes. Selection responses increased when the M allele at QTL3 was combined with the S allele at QTL1. Significant qualitative QTL × environment interactions for QTL2 and QTL4 were detected through differential realized selection responses at different sites. Without a thorough understanding of the physiological and agronomic particulars of any QTL and the target environment, MAS for QTL showing qualitative interactions should be minimized This revised version was published online in June 2006 with corrections to the Cover Date.     

Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture

A previous genetic map containing 117 microsatellite loci and 400 F(2) plants was used for quantitative trait loci (QTL) mapping in tropical maize. QTL were characterized in a population of 400 F(2:3) lines, derived from selfing the F(2) plants, and were evaluated with two replications in five environments. QTL determinations were made from the mean of these five environments. Grain yield (GY), plant height (PH), ear height (EH) and grain moisture (GM) were measured. Variance components for genotypes (G), environments (E) and GxE interaction were highly significant for all traits. Heritability was 0.69 for GY, 0.66 for PH, 0.67 for EH and 0.23 for GM. Using composite interval mapping (CIM), a total of 13 distinct QTLs were identified: four for GY, four for PH and five for EH. No QTL was detected for GM. The QTL explained 32.73 % of the phenotypic variance of GY, 24.76 % of PH and 20.91 % of EH. The 13 QTLs displayed mostly partial dominance or overdominance gene action and mapped to chromosomes 1, 2, 7, 8 and 9. Most QTL alleles conferring high values for the traits came from line L-14-4B. Mapping analysis identified genomic regions associated with two or more traits in a manner that was consistent with correlation among traits, supporting either pleiotropy or tight linkage among QTL. The low number of QTLs found, can be due to the great variation that exists among tropical environments.     

植物抗病遗传育种中的QTL定位和克隆(综述)

对植物抗病遗传育种中QTL定位与克隆研究进行综述。主要阐述了数量抗性的遗传学基础、作物抗病性QTL的定位作图、QTL作图的可靠性及应对措施、QTLs候选基因的证实和定位克隆等,并对植物抗病遗传育种未来的研究方向予以讨论。     

胚乳性状数量基因的定位方法

将三倍体胚乳性状的数量遗传模型和二倍体性状数量基因（QTL）图构建方法相结合,导出双侧标记基因型下有关胚乳性状QTL的遗传组成、平均数和遗传方差分量,据之提出以某一区间双侧标记基因型胚乳性状的平均值为依变数,以该区间内任一点假定存在的QTL的加性效应d、显性效应h1和／或h2的系数为自变数,进行有重复观察值的多元线性回归分析,根据多元线性回归的显著性测验该点是否存在QTL,并估计出QTL的遗传效应。给定区间内任一点,皆可以此进行分析,从而可在整条染色体上作图,并以之确定QTL的数目和最可能位置,同时,在检测某一区间时,利用多元线性回归方法将该区间外可能存在的QTL的干扰进行统计控制,以提高QTL检测的精度。此外,还讨论了如何将之推广应用于其他类型的DNA不对应资料以及具复杂遗传模型的胚乳性状资料。