QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Identification of QTL for maize grain yield and kernel-related traits

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2-236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.     

QTL mapping of yield, agronomic and quality traits in tetraploid potato (Solanum tuberosum subsp. tuberosum)

Interval mapping of quantitative trait loci (QTL) for 16 yield, agronomic and quality traits in potato was performed on a tetraploid full-sib family comprising 227 clones from a cross between processing clone 12601ab1 and table cultivar Stirling. Thirty-eight AFLP primer combinations and six SSRs provided 514 informative markers which formed a molecular marker map comprising 12 linkage groups (LGs) in 12601ab1 (nine with four homologous chromosomes) which were aligned with 12 in Stirling (11 with four homologous chromosomes), with four partial groups remaining in 12601ab1. Two LGs were identified unequivocally as chromosomes IV and V and eight others were tentatively assigned with chromosomes VII and X unidentified. All of the traits scored had moderately high heritabilities with 54–92% of the variation in clone means over 3 years and two replicates being due to genetic differences. A total of 39 QTLs were identified. A QTL for maturity was identified on chromosome V which explained 56% of the phenotypic variance, whereas the other QTLs individually explained between 5.4 and 16.5%. However, six QTLs were detected for after-cooking blackening and four for each of regularity of tuber shape, fry colour both after storage at 4 and 10°C and sprouting. Just two QTLs were found for each of yield, the two ‘overall’ scores, crop emergence, tuber size and common scab and just one QTL was detected for each of dry matter content, keeping quality, growth cracks and internal condition. The implications for practical potato breeding and for practical linkage and QTL analysis in autotetraploids are discussed.     

Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench]

Sorghum, a cereal of economic importance ensures food and fodder security for millions of rural families in the semi-arid tropics. The objective of the present study was to identify and validate quantitative trait loci (QTL) for grain yield and other agronomic traits using replicated phenotypic data sets from three post-rainy dry sorghum crop seasons involving a mapping population with 245 F₉ recombinant inbred lines derived from a cross of M35-1 × B35. A genetic linkage map was constructed with 237 markers consisting of 174 genomic, 60 genic and 3 morphological markers. The QTL analysis for 11 traits following composite interval mapping identified 91 QTL with 5–12 QTL for each trait. QTL detected in the population individually explained phenotypic variation between 2.5 and 30.3 % for a given trait and six major genomic regions with QTL effect on multiple traits were identified. Stable QTL across seasons were identified. Of the 60 genic markers mapped, 21 were found at QTL peak or tightly linked with QTL. A gene-based marker XnhsbSFCILP67 (Sb03g028240) on SBI-03, encoding indole-3-acetic acid-amido synthetase GH3.5, was found to be involved in QTL for seven traits. The QTL-linked markers identified for 11 agronomic traits may assist in fine mapping, map-based gene isolation and also for improving post-rainy sorghum through marker-assisted breeding.     

Whole genome mapping in a wheat doubled haploid population using SSRs and TRAPs and the identification of QTL for agronomic traits

Genetic maps are useful for detecting quantitative trait loci (QTL) associated with quantitative traits and for marker-assisted selection (MAS) in breeding. In this research, we used the wheat × maize method to develop a doubled haploid (DH) population derived from the synthetic hexaploid wheat (SHW) line TA4152-60 and the North Dakota hard red spring wheat line ND495. The population consisted of 213 lines, of which a subset of 120 lines was randomly selected and used to construct linkage maps of all 21 chromosomes and for QTL detection. The whole genome maps consisted of 632 markers including 410 SSRs, 218 TRAPs, 1 RFLP, and 3 phenotypic markers, and spanned 3,811.5 cM with an average density of one marker per 6.03 cM. Telomere sequence-based TRAPs allowed us to define the ends of seven linkage groups. Analysis revealed major QTLs associated with the traits of days to heading on chromosomes 5A and 5B, plant height on chromosomes 4D and 5A, and spike characteristics on chromosomes 3D, 4A, 4D, 5A and 5B. The DH population and genetic map will be a useful tool for the identification of disease resistance QTL and agronomically important loci, and will aid in the identification and development of markers for MAS. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.     

Population structure and association mapping of yield contributing agronomic traits in foxtail millet

Key message

Association analyses accounting for population structure and relative kinship identified eight SSR markers ( p < 0.01) showing significant association ( R ²  = 18 %) with nine agronomic traits in foxtail millet.

Abstract

Association mapping is an efficient tool for identifying genes regulating complex traits. Although association mapping using genomic simple sequence repeat (SSR) markers has been successfully demonstrated in many agronomically important crops, very few reports are available on marker-trait association analysis in foxtail millet. In the present study, 184 foxtail millet accessions from diverse geographical locations were genotyped using 50 SSR markers representing the nine chromosomes of foxtail millet. The genetic diversity within these accessions was examined using a genetic distance-based and a general model-based clustering method. The model-based analysis using 50 SSR markers identified an underlying population structure comprising five sub-populations which corresponded well with distance-based groupings. The phenotyping of plants was carried out in the field for three consecutive years for 20 yield contributing agronomic traits. The linkage disequilibrium analysis considering population structure and relative kinship identified eight SSR markers (p < 0.01) on different chromosomes showing significant association (R ² = 18 %) with nine agronomic traits. Four of these markers were associated with multiple traits. The integration of genetic and physical map information of eight SSR markers with their functional annotation revealed strong association of two markers encoding for phospholipid acyltransferase and ubiquitin carboxyl-terminal hydrolase located on the same chromosome (5) with flag leaf width and grain yield, respectively. Our findings on association mapping is the first report on Indian foxtail millet germplasm and this could be effectively applied in foxtail millet breeding to further uncover marker-trait associations with a large number of markers.     

Root-ABA1 QTL affects root lodging, grain yield, and other agronomic traits in maize grown under well-watered and water-stressed conditions

A major QTL affecting root traits and leaf ABA concentration was identified in maize (Zea mays L.) and named root-ABA1. For this QTL, back-cross-derived lines (BDLs) homozygous either for the (+) or for the (-) allele increasing or decreasing, respectively, root size and leaf ABA concentration, were developed. This study was conducted to evaluate the QTL effects in various genetic backgrounds and at different water regimes. The (+/+) and (-/-) BDLs were crossed with five or 13 inbred tester lines of different origin, thus producing two sets of test-crosses that were evaluated in Italy and China, respectively. Testing was conducted under both well-watered and water-stressed conditions. In Italy, the test-crosses derived from (+/+) BDLs, as compared with those derived from (-/-) BDLs, showed, across both water regimes, higher leaf ABA concentration (on average 384 versus 351 ng g(-1) DW) and lower root lodging (28.0 versus 52.5%), and lower grain yield under water-stressed conditions (4.88 versus 6.27 Mg ha(-1)). In China, where root lodging did not occur, the test-crosses derived from (+/+) BDLs were less productive at both water regimes (on average, 6.83 versus 7.49 Mg ha(-1)). The lower grain yield of the test-crosses derived from (+/+) BDLs was due to a lower number of ears per plant and to lower kernel weight. The results indicate that the (+) root-ABA1 allele confers not only a consistently lower susceptibility to root lodging but also a lower grain yield, especially when root lodging does not occur.     

Modeling and mapping QTL for senescence-related traits in winter wheat under high temperature

Senescence is a genetically programmed and environmentally influenced process resulting in the destruction of chlorophyll and remobilization of nutrients to younger or reproductive parts of plants. Delayed senescence, or stay-green, contributes to a long grain-filling period and stable yield under stress. To model senescence and identify quantitative trait loci (QTL) for the trait, a population of recombinant inbred lines (RIL) from a cross between winter wheat cultivars, ‘Ventnor’ and ‘Karl 92’ was evaluated for heat tolerance under optimum temperature of 20/15°C (day/night) and continuous heat stress of 30/25°C from 10 days after anthesis (DAA) until maturity. Ventnor is a heat-tolerant cultivar and Karl 92 is a relatively heat-susceptible cultivar. Green leaf area was measured and used to model percent greenness retained over the reproductive period. Chlorophyll content and chlorophyll fluorescence were recorded on flag leaves. Senescence was converted to a quantitative trait using the model. Based on the modeled parameters, the RILs were categorized into three groups. When senescence-related traits were evaluated, nine QTL for heat tolerance were found on chromosome 2A, two each on chromosomes 6A and 6B and one each on chromosome 3A, 3B, and 7A. Both parents contributed favorable alleles for most of the senescence-related traits. Microsatellite markers Xgwm356 and Xgwm5 prominently linked to the senescence-related traits may be useful in marker-assisted breeding. These and the linked AFLP (amplified fragment length polymorphism) markers XCGT.TGCG-349, XCGT.GTG-343, and XCGT.CTCG-406, if converted to STS (sequence tagged sites), can be used for further molecular dissection of the QTL for post-anthesis heat tolerance.     

QTL mapping of root traits in phosphorus-deficient soils reveals important genomic regions for improving NDVI and grain yield in barley

Key message

Major QTLs for root rhizosheath size are not correlated with grain yield or yield response to phosphorus. Important QTLs were found to improve phosphorus efficiency.

Abstract

Root traits are important for phosphorus (P) acquisition, but they are often difficult to characterize and their breeding values are seldom assessed under field conditions. This has shed doubts on using seedling-based criteria of root traits to select and breed for P efficiency. Eight root traits were assessed under controlled conditions in a barley doubled-haploid population in soils differing in P levels. The population was also phenotyped for grain yield, normalized difference vegetation index (NDVI), grain P uptake and P utilization efficiency at maturity (PutE_GY) under field conditions. Several quantitative traits loci (QTLs) from the root screening and the field trials were co-incident. QTLs for root rhizosheath size and root diameter explained the highest phenotypic variation in comparison to QTLs for other root traits. Shared QTLs were found between root diameter and grain yield, and total root length and PutE_GY. A common major QTL for rhizosheath size and NDVI was mapped to the HvMATE gene marker on chromosome 4H. Collocations between major QTLs for NDVI and grain yield were detected on chromosomes 6H and 7H. When results from BIP and MET were combined, QTLs detected for grain yield were also those QTLs found for NDVI. QTLs qGY5H, qGY6H and qGY7Hb on 7H were robust QTLs in improving P efficiency. A selection of multiple loci may be needed to optimize the breeding outcomes due to the QTL x Environment interaction. We suggest that rhizosheath size alone is not a reliable trait to predict P efficiency or grain yield.

     

Association between line per se and testcross performance for eight agronomic and quality traits in winter rye

Key message

We investigated associations between line per se and testcross performance in rye and suggested that selection for per se performance is valuable for several traits in multi-stage selection programs.

Abstract

Genotypic correlation between line per se and testcross performance is an important quantitative-genetic parameter for optimizing hybrid breeding programs. The main goal of this survey was to study the association of line per se and testcross performance at the phenotypic level. We used experimental data from the line per se and testcross performance of two segregating winter rye populations (A, B) with each of 220 progenies tested in six environments for eight agronomic and quality traits. Genotypic variances were considerably larger for per se than for testcross performance of all investigated traits resulting in higher heritabilities of the former in most instances. Genotypic correlations (r _g) between testcross and line per se performance decreased with increasing complexity of the trait as shown by the respective heritabilities. They were highest (r _g ≥ 0.7) for plant height and test weight in population B, and thousand-kernel weight, falling number and starch content in both populations. A selection of these traits for line per se performance in early generations will save field plots in further testing testcross performance and increase efficiency of hybrid breeding.     

我国旱地春小麦产量及主要农艺指标的变异分析

采用4年、13个品种(系)、18个试点组成的全国旱地春小麦区域试验产量资料,通过联合方差分析和基因型及其与环境互作（GGE）双标图分析,研究了基因型、环境、基因型与环境互作效应（GEI）对产量变异的影响及品种的产量稳定性.结果表明：环境对产量变异的影响远大于基因型和GEI,环境引起的产量变异占87.5%～92.0%．互作因素中以地点×基因型的互作效应最大,基因型×年份的互作效应最小.我国旱地春小麦基因型多年多点的平均产量水平为2550 kg·hm^-2.产量三要素中,千粒重受环境的影响最小.影响产量变异的主要环境因子有：≥10 ℃年积温、生育期降雨量、平均气温、海拔、年降雨量和无霜期.产量与单位面积穗数（0.675^**）、穗粒数（0.581^**）、千粒重（0.456^**）呈极显著正相关,产量三要素间也呈正相关（0.244～0.480^**）,处于可同步提高范围.     

Validation of mega-environment universal and specific QTL associated with seed yield and agronomic traits in soybeans

The value of quantitative trait loci (QTL) is dependant on the strength of association with the traits of interest, allelic diversity at the QTL and the effect of the genetic background on the expression of the QTL. A number of recent studies have identified QTL associated with traits of interest that appear to be independent of the environment but dependant on the genetic background in which they are found. Therefore, the objective of this study was to validate universal and/or mega-environment-specific seed yield QTL that have been previously reported in an independent recombinant inbred line (RIL) population derived from the cross between an elite Chinese and Canadian parent. The population was evaluated at two field environments in China and in five environments in Canada in 2005 and 2006. Of the seven markers linked to seed yield QTL reported by our group in a previous study, four were polymorphic between the two parents. No association between seed yield and QTL was observed. The result could imply that seed yield QTL were either not stable in this particular genetic background or harboured different alleles than the ones in the original mapping population. QTL_U Satt162 was associated with several agronomic traits of which lodging was validated. Both the non-adapted and adapted parent contributed favourable alleles to the progeny. Therefore, plant introductions have been validated as a source of favourable alleles that could increase the genetic variability of the soybean germplasm pool and lead to further improvements in seed yield and other agronomic traits.     

Mapping QTL for agronomic traits in breeding populations

Detection of quantitative trait loci (QTL) in breeding populations offers the advantage that these QTL are of direct relevance for the improvement of crops via knowledge-based breeding. As phenotypic data are routinely generated in breeding programs and the costs for genotyping are constantly decreasing, it is tempting to exploit this information to unravel the genetic architecture underlying important agronomic traits in crops. This review characterizes the germplasm from breeding populations available for QTL detection, provides a classification of the different QTL mapping approaches that are available, and highlights important considerations concerning study design and biometrical models suitable for QTL analysis.     

Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments

A major aim in some plant-based studies is the determination of quantitative trait loci (QTL) for multiple traits or across multiple environments. Understanding these QTL by trait or QTL by environment interactions can be of great value to the plant breeder. A whole genome approach for the analysis of QTL is presented for such multivariate applications. The approach is an extension of whole genome average interval mapping in which all intervals on a linkage map are included in the analysis simultaneously. A random effects working model is proposed for the multivariate (trait or environment) QTL effects for each interval, with a variance-covariance matrix linking the variates in a particular interval. The significance of the variance-covariance matrix for the QTL effects is tested and if significant, an outlier detection technique is used to select a putative QTL. This QTL by variate interaction is transferred to the fixed effects. The process is repeated until the variance-covariance matrix for QTL random effects is not significant; at this point all putative QTL have been selected. Unlinked markers can also be included in the analysis. A simulation study was conducted to examine the performance of the approach and demonstrated the multivariate approach results in increased power for detecting QTL in comparison to univariate methods. The approach is illustrated for data arising from experiments involving two doubled haploid populations. The first involves analysis of two wheat traits, α-amylase activity and height, while the second is concerned with a multi-environment trial for extensibility of flour dough. The method provides an approach for multi-trait and multi-environment QTL analysis in the presence of non-genetic sources of variation.     

Conditional QTL mapping of oil content in rapeseed with respect to protein content and traits related to plant development and grain yield

Oil content in rapeseed (Brassica napus L.) is generally regarded as a character with high heritability that is negatively correlated with protein content and influenced by plant developmental and yield related traits. To evaluate possible genetic interrelationships between these traits and oil content, QTL for oil content were mapped using data on oil content and on oil content conditioned on the putatively interrelated traits. Phenotypic data were evaluated in a segregating doubled haploid population of 282 lines derived from the F₁ of a cross between the old German cultivar Sollux and the Chinese cultivar Gaoyou. The material was tested at four locations, two each in Germany and in China. QTLMapper version 1.0 was used for mapping unconditional and conditional QTL with additive (a) and locus pairs with additive × additive epistatic (aa) effects. Clear evidence was found for a strong genetic relationship between oil and protein content. Six QTL and nine epistatic locus pairs were found, which had pleiotropic effects on both traits. Nevertheless, two QTL were also identified, which control oil content independent from protein content and which could be used in practical breeding programs to increase oil content without affecting seed protein content. In addition, six additional QTL with small effects were only identified in the conditional mapping. Some evidence was apparent for a genetic interrelationship between oil content and the number of seeds per silique but no evidence was found for a genetic relationship between oil content and flowering time, grain filling period or single seed weight. The results indicate that for closely correlated traits conditional QTL mapping can be used to dissect the genetic interrelationship between two traits at the level of individual QTL. Furthermore, conditional QTL mapping can reveal additional QTL with small effects that are undetectable in unconditional mapping.     

QTL mapping and epistasis analysis of brace root traits in maize

Root architecture is a major factor influencing root lodging, which limits greater yield stability at high planting density. Total brace root tier number (TBRTN) and effective brace root tier number (EBRTN) are the two most important root architecture traits influencing root lodging. However, the genetic mechanisms that underlie these traits remain poorly understood. In this study, quantitative trait loci (QTL) for TBRTN and EBRTN were mapped using a set of 201 recombinant inbred lines (RILs) and 278 immortalized F2 (IF2) populations derived from these RILs, which were evaluated in three environments. Ten QTL in the RILs and 15 QTL in the IF2 population were detected. In the two populations, we identified two coincident major QTL for TBRTN and a single identical major QTL for EBRTN. The QTL for TBRTN showed the largest additive effect, accounting for 16.36 and 17.88% of the phenotypic variance in the RILs and IF2 population, respectively. Additional epistatic effects were identified for all the maize chromosomes, except for chromosome 4. Most epistatic effects involved pairs of loci that were on different chromosomes. At the same time, we found loci that interacted simultaneously with several other loci to affect expression of the traits, which was particularly evident in the IF2 population. For example, qTAR1-2 interacted simultaneously with qTAR2-1, qTAR3-1, qTAR5-1, and qITAR8-2 to affect the expression of TBRTN. Therefore, a complex network controlling the traits was found in maize. These results provide useful information for understanding the molecular mechanisms controlling root architecture.     

橡胶树与胶乳产量相关性状的QTL定位

对橡胶树株产干胶等与胶乳产量相关性状进行了QTL分析。以热研88-13×IAN873杂交授粉子代F1群体为材料,测定其株产干胶、干胶含量、乳管列数、乳管个数和乳管大小等与橡胶树胶乳产量相关的性状,利用FsQtlMap 1.0软件,复合区间作图法(CIM,composite interval mapping),以已构建的SSR连锁图谱为基础进行QTL分析。结果显示,各性状偏度和峰度符合正态分布,除乳管大小外,各性状的平均值和方差有较大变化;株产干胶的变异系数最大,各因子的变异系数顺序为株产干胶>乳管个数>乳管列数>乳管大小>干胶含量;LOD阈值大于2.5,检测到与株产干胶相关的QTL 5个,分别可解释71.3%、69.1%、68.3%、64.4%和62.9%的群体变异,与乳管大小相关的QTL 3个,分别可解释84.5%、81.3%和15.4%的群体变异,未检测到与其他性状相关的QTL。     

Multivariate statistical analysis of grain yield and agronomic characters in Durum wheat

Summary Correlation, stepwise multiple regression and factor analyses were conducted on grain yield and a number of agronomic characters in the parental, F ₁ and F ₂ families originating from a 10 X 10 diallel cross in durum wheat. For the F ₁ diallel, the correlation analysis indicated that the number of spikes and kernels per plant and 1,000 kernel weight had the highest correlations with grain yield; for the F ₂ diallel, the number of spikes and kernels per meter, 1000 kernel weight and plant height showed most striking correlations with same.Stepwise multiple regression analysis indicated that, for the F ₁ diallel, number of kernels per plant, 1000 kernel weight and days to maturity were the most potent predictor variables for grain yield, accounting for 78% of its variability. For the F ₂ diallel, the number of kernels and number of spikes per meter, 1000 kernel weight and number of kernels per spike were the most potent predictors for grain yield, accounting for 91 % of its variability. Five common factors were extracted which explained 98.8% and 98.1% of the total variance in the F ₁ and F ₂ diallel, respectively. However, the importance of each of the five factors and the characters which loaded highly on each of them differed from generation to generation.Contribution No. 323 from Plant Science Department, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada.     

QTL mapping for yield and photosynthetic related traits under different water regimes in wheat

The improvement for drought tolerance requires understanding of the genetic control of wheat (Triticum aestivum L.) reaction to drought. In this study, a set of 131 recombinant inbred lines of wheat were investigated under well-watered (WW) and drought stress (DS) environments across 2 years to map quantitative trait loci (QTLs) for yield and physiological traits. A total of 225 QTLs were detected, including 32 non-environment-specific loci that were significant in both DS and WW, one drought-specific locus and two watering-specific loci. Three consistently-expressed QTLs (QTkw-3A.2, QTss-1A, and QScn-7A.1) were identified in at least three environments and the QTkw-1D.1 was significant in DS across the 2 years. By unconditional and conditional QTL analysis, spike number per plant and kernel number per spike were more important than thousand-kernel weight for grain yield (GY) at the given genetic background. Meta-analysis identified 67 meta-QTLs that contained QTLs for at least two traits. High frequency co-location of QTLs was found among either the spike-related traits or the six physiological traits. Four photosynthesis traits (CHL, LWUE, P _N, and C _i) were co-located with GY and/or yield components on various MQTLs. The results provided QTLs that warrant further study for drought tolerance breeding and are helpful for understanding the genetic basis of drought tolerance and the genetic contribution of yield components to GY at individual QTL level in wheat.