QTL for maize grain yield identified by QTL mapping in six environments and consensus loci for grain weight detected by meta‐analysis

Grain yield is the most important and complicated trait in maize. In this study, a total of 498 recombinant inbred lines (RIL) derived from a biparental cross of two elite inbred lines, 178 and P53, were grown in six different environments. Quantitative trait locus (QTL) mapping was conducted for three grain yield component traits (100 grain weight, ear weight and kernel weight per plant). Subsequently, meta‐analysis was performed after a comprehensive review of the research on QTL mapping for grain weight (100, 300 and 1000) using BioMercator V4.2. In total, 62 QTLs were identified for 100 grain weight, ear weight and kernel weight per plant in six environments. Forty‐three meta‐QTLs (MQTLs) were detected by meta‐analysis. A total of 13 candidate genes homologous to eight functionally characterized rice genes were found, and four candidate genes were located in the two hot spot regions of MQTL1.5 and MQTL2.3. Our results suggest that the combination of literature collection, meta‐analysis and homologous blast searches can offer abundant information for further fine mapping, marker‐assisted selection (MAS) breeding and map‐based cloning for maize.     

Genetic dissection of seed vigour under artificial ageing conditions using two joined maize recombinant inbred line populations

Seed vigour plays an important role in agricultural production, and seeds with high sowing quality are necessary for improving agriculture production. In our study, two connected maize recombinant inbred line (RIL) populations derived from Yu82 × Shen137 and Yu537A × Shen137 crosses were evaluated for the mean germination time (MGT) and other related traits under three artificial ageing treatments. We used meta‐analysis to integrate genetic maps and identify quantitative trait loci (QTLs) across the two populations. In total, 74 QTLs and 20 meta‐QTLs (mQTLs) were identified. Four key mQTLs, mQTL2‐2, mQTL5‐4, mQTL6 and mQTL8, which contained initial QTLs with R² values >10% and included 5–9 initial QTLs, may be hot spots of important QTLs for the associated traits. Twenty‐two key candidate genes associated with four seed vigour‐related traits mapped to 14 mQTLs. In particular, the GRMZM2G163749, GRMZM2G122172/GRMZM2G554885/GRMZM2G122871 and GRMZM2G150367 genes mapped within the important mQTL5–4, mQTL6 and mQTL8 regions, respectively. Fine mapping for the genetic regions of these three mQTLs merits further study and could be utilized for marker‐assisted breeding.     

Mapping quantitative trait loci for oil,starch, and protein concentrations in grain with high-oil maize by SSR markers

The objective of this investigation was to map QTL controlling oil, protein, and starch concentrations in maize grain and to evaluate their genetic effects. The mapping population included 298 F_2:3 family lines containing Beijing high-oil (BHO) maize germplasm. F₂ individuals were genotyped with 183 SSR markers to construct a genetic linkage map, which spanned 1,605.7 cM, with an average interval of 8.77 cM. Oil, protein, and starch concentrations in grain among F_2:3 families were measured by near-infrared (NIR) analyzer. Using QTL Cartographer, we mapped six QTL associated with oil in grain, six associated with protein, and five associated with starch concentrations. The proportion of phenotypic variation explained by single QTL ranged from 4.34 to 13.13% for oil, from 5.19 to 6.66% for protein, and from 4.14 to 7.85% for starch concentrations. QTL for oil, protein, or starch concentrations were often detected in identical intervals and the direction of their effects were consistent with the sign of their phenotypic correlation. They were considered as common QTL for chemical compositions in maize grain. In this study, we identified three QTL for oil in grain, two QTL for protein, and three QTL for starch concentrations, which were on identical or similar chromosomal locations to those previously mapped with Illinois high-oil (IHO) maize germplasm. These suggests that more diverse germplasm should be necessary to detect additional QTL and to discover more favorable alleles for chemical composition of maize grain.     

QTL detection for grain oil and starch content and their associations in two connected F<Subscript>2:3</Subscript> populations in high-oil maize

High-oil maize (Zea mays L.) has special value in animal feed and human food. Two hundred and eight-four and 265 F_2:3 families developed from two crosses between one high-oil maize inbred and two normal dent maize inbreds were evaluated for grain oil and starch contents under two environments. Using composite interval mapping, 1–6 QTL for each trait were detected under each environment and in combined analysis in both populations. Only one common QTL across two environments in each population and across two populations were found for starch content. Among the detected QTL, nine digenic interactions with small effects were identified. Comparison of single-trait QTL and the results of multiple-trait QTL mapping showed that oil content might be complicatedly correlated with starch content. Although single-trait QTL with the same parental effects for both traits and oil-starch QTL were all detected at the same genetic bin 6.04 as the cloned high-oil QTL (qHO6) with no unfavorable effects on grain weight, our results did reflect the difficulty to realize simultaneous improvement on grain oil and starch contents. Of course, these results should be validated in further experiments under more environments using RILs, NILs and other permanent populations.     

基于多重相关RIL群体的玉米株高和穗位高QTL定位

株高和穗位高是玉米育种中的重要农艺性状。本研究利用我国玉米育种中骨干亲本黄早四与来自不同杂种优势群的其他11个骨干自交系组配11个RIL群体,开展基于单环境、联合环境的QTL分析,分别检测到269个和176个QTL。通过区段整合,检测到21个株高主效QTL及15个穗位高主效QTL,这些QTL分布在第1、第2、第3、第6、第7、第8、第9、第10染色体上。相对于共同亲本黄早四而言,部分QTL在不同RIL群体中的效应方向一致,来自共同亲本黄早四的等位基因在不同群体中能够稳定地表达。同时,还分别定位到在多环境下稳定表达的5个株高、4个穗位高“环境钝感QTL”。此外,进一步鉴定出5个重要的株高和穗位高QTL富集区段(bin 1.01-1.02,1.08-1.11,3.05,8.03-8.05和9.07),这些区段均包含多个株高和穗位高相关QTL,如bin3.05位点包含7个QTL,bin8.03-8.05位点分别包含9个QTL,且这些QTL至少在3个不同环境中能够被检测到,这些区域对QTL的精细定位和克隆有重要参考价值。     

基于高密度遗传图谱的玉米籽粒性状QTL定位

籽粒大小及百粒重是决定玉米产量的重要因素。为解析籽粒性状遗传基础,本研究以玉米自交系黄早四(HZS)和Mo17为亲本,构建包含130个重组自交系(recombination inbred line,RIL)的RIL群体。基于GBS(genotypingby-sequencing)技术获得的高密度多态性SNP(single nucleotide polymorphism)位点,构建了包含1262个Bin标记的高密度遗传图谱。采用完备区间作图法,对5个环境条件下的粒长、粒宽、百粒重、粒长/粒宽4个性状分别进行QTL(quantitative trait locus)定位,共检测到30个QTL。利用5个环境性状均值,共检测到11个QTL。其中粒长主效QTL qklen1、粒长/粒宽主效QTL qklw1在3个单环境条件下均被检测到,且定位在第1染色体相邻区域,物理位置分别为210~212 Mb、207~208 Mb,表型贡献率分别为22.60%和26.79%,被认为是控制玉米籽粒形状的主效位点。针对第1染色体207~212 Mb区间,采用成组法t检验,对黄早四(受体)和Mo17(供体)构建的BC3F1回交群体进行单标记分析。结果表明,在BC3F1群体中qklen1和qklw1同样具有显著的遗传效应。本研究结果不仅为分子标记辅助选择籽粒性状提供了实用标记,而且为主效基因的进一步精细定位和候选基因挖掘奠定了基础。     

QTL analysis of kernel shape and weight using recombinant inbred lines in wheat

Quantitative trait locus (QTL) analysis of kernel shape and weight in common wheat was conducted using a set of 131 recombinant inbred lines (RIL) derived from ‘Chuan 35050’ × ‘Shannong 483’. The RIL and their two parental genotypes were evaluated for kernel length (KL), kernel width (KW), thousand-kernel weight (TKW), and test weight (TW) in four different environments. Twenty QTL were located on 12 chromosomes, 1A, 1B, 1D, 2A, 2B, 3B, 4A, 4B, 5D, 6A, 6B, and 7B, with single QTL in different environments explaining 5.9–26.4% of the phenotypic variation. Six, three, four, and seven QTL were detected for KL, KW, TKW, and TW, respectively. The additive effects for 17 QTL were positive with Chuan 35050 increasing the QTL effects, whereas the remaining three QTL were negative with Shannong 483 increasing the effects. Eight QTL (40%) were detected in two or more environments. Two QTL clusters relating to KW, TKW, and TW were located on chromosomes 2A and 5D, and the co-located QTL on chromosome 6A involved a QTL for KW found in two environments and a QTL for TKW detected in four environments.     

Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize

Grain yield is one of the most important and complex quantitative traits in maize breeding. In the present study, a total of 11 connected RIL populations, derived from crosses between elite inbreed “Huangzaosi” as the common parent and 11 elite inbreeds, were evaluated for five yield components and kernel-related traits under six environments. Quantitative trait loci (QTL) were detected for the traits under each environment and in joint analysis across all environments for each population. A total of 146 major QTL with R² > 10 % in at least one environment and also detected based on joint analysis across all environments were identified in the 11 populations. Lqkwei4 conferring kernel weight and Lqklen4-1 conferring kernel length both located in the adjacent marker intervals in bin 4.05 were stably expressed in four environments and in joint analysis across six environments, with the largest R² over 27 and 24 % in a single environment, respectively. Moreover, all major QTL detected in the 11 populations were aligned on the IBM2 2008 neighbors reference map. Totally 16 common QTL (CQTL) were detected. Seven important CQTL (CQTL1-2, CQTL1-3, CQTL4-1, CQTL4-2, CQTL4-3, CQTL4-4, and CQTL6-1) were located in bin 1.07, 1.10, 4.03, 4.05, 4.08, 4.09 and 6.01–6.02, respectively. These chromosomal regions could be targets for fine mapping and marker-assisted selection.     

QTL mapping of nutrient components in maize kernels under low nitrogen conditions

A set of 213 F_2:3 families were used to investigate the effects of nitrogen (N) on grain yield and the concentrations of three nutrient components in maize (Zea mays L.) kernels. A genetic linkage map was constructed using 189 SSR (simple sequence repeat) markers, spanning a total of 2003 cM, including 11 linkages, and the families were evaluated under high N and low N conditions at two farm locations. The results indicate that low N conditions may induce an increase in starch concentration, but a decrease in protein levels. Twenty‐six quantitative trait loci (QTL) were detected for four measured traits in the two N treatments at both locations, including eight QTL for grain yield, seven QTL for oil content, six QTL for protein content and five QTL for starch content. The total number of QTL detected for the four measured traits under high N levels was greater than the QTL detected under low N conditions, and several QTL were identified that specifically expressed under different N conditions. These particular QTL could help provide greater understanding of the genetic basis of N‐usage efficiency.     

Quantitative trait loci mapping of forage stover quality traits in six mapping populations derived from European elite maize germplasm

Four forage maize stover quality traits were analysed including in vitro digestibility of organic matter (IVDOM), neutral detergent fibre (NDF), water‐soluble carbohydrates (WSC) and digestibility of NDF (DNDF). We mapped quantitative trait loci (QTL) in three DH (doubled haploid) populations (totally 250–720 DH lines): one RIL population (358 lines) and two testcross (TC) populations, based on field phenotyping at multiple locations and years for each. High phenotypic and genotypic correlations were found for all traits and significant (p < .01) at two locations, and NDF was negatively correlated with the other traits. QTL analyses were conducted by composite interval mapping. A total of 33, 23, 32 and 25 QTL were identified for IVDOM, NDF, WSC and DNDF, respectively, with three, four, five and two major QTL for each. Few consistent QTL for IVDOM, WSC and DNDF were detected in more than two populations. This study contributed to the identification of key QTL associated with forage maize digestibility traits and is beneficial for marker‐assisted breeding and fine mapping of candidate genes associated with forage maize quality.     

基于多个相关群体的玉米雄穗相关性状QTL分析

雄穗相关性状对玉米生产至关重要。为了解析玉米雄穗相关性状的遗传机制,利用以黄早四为共同亲本组配的11个重组自交系群体,对玉米雄穗一级分枝数、雄穗主轴长和雄穗干重3个性状进行QTL分析。经过对11个群体及亲本两年三点的田间鉴定,单环境和联合环境下的玉米雄穗相关性状QTL定位,及基因型与环境互作和上位性互作分析,检测到15个在多环境下稳定表达(5个环境以上)的“环境钝感”主效QTL,其中,在染色体bin3.04区域,齐319群体和旅28群体中都定位到1个主效雄穗一级分枝数相关QTL,其平均贡献率分别为17.4%和14.4%,并且2个群体的QTL标记区间高度重叠,在IBM2008 Neighbors图谱上的重叠区间为226.0~230.1。对比不同群体结果发现,在2个群体以上都能检测到的一致性区间21个,其中在第2、第3、第6、第8染色体上的5个一致性区间在3个群体中可稳定表达。这些多环境和多个遗传背景下稳定表达的位点可作为玉米雄穗性状分子标记辅助选择、精细定位及基因克隆的候选位点。     

QTL for flag leaf size and their influence on yield-related traits in wheat

Flag leaf-related traits (FLRTs) are determinant traits affecting plant architecture and yield potential in wheat (Triticum aestivum L.). In this study, three related recombinant inbred line (RIL) populations with a common female parent were developed to identify quantitative trait loci (QTL) for flag leaf width (FLW), length (FLL), and area (FLA) in four environments. A total of 31 QTL were detected in four environments. Two QTL for FLL on chromosomes 3B and 4A (QFll-3B and QFll-4A) and one for FLW on chromosome 2A (QFlw-2A) were major stable QTL. Ten QTL clusters (C1–C10) simultaneously controlling FLRTs and yield-related traits (YRTs) were identified. To investigate the genetic relationship between FLRTs and YRTs, correlation analysis was conducted. FLRTs were found to be positively correlated with YRTs especially with kernel weight per spike and kernel number per spike in all the three RIL populations and negatively correlated with spike number per plant. Appropriate flag leaf size could benefit the formation of high yield potential. This study laid a genetic foundation for improving yield potential in wheat molecular breeding programs.     

Identification of QTL for zinc and iron concentration in maize kernel and cob

To provide theoretical and applied references for biofortification of maize by increasing Zn and Fe concentration, the correlation and quantitative trait loci (QTL) of four mineral-related traits, i.e. zinc concentration of kernel (ZnK), zinc concentration of cob (ZnC), iron concentration of kernel (FeK) and iron concentration of cob (FeC) were studied for two sets of F_2:3 populations derived from the crosses Mu6 × SDM (MuS) and Mo17 × SDM (MoS) under two different environments (CQ and YN). The parental lines were very different in Zn and Fe concentration of kernels and cobs. A large genetic variation and transgressive segregation of two F_2:3 populations were observed for the four traits. The heritability of FeK was relatively lower (<0.6) than other three traits (>0.7). Analysis for each environment and joint analysis across two environments were used to identify QTL for each population. 16 and 15 QTL were identified in CQ and YN respectively via single environment analysis, some of which were identical in different environments and were also detected in joint analysis. The common regions for same trait at different environments were 3 and 5 in MuS and MoS respectively. Compared with the IBM2 2008 Neighbors Frame6, the distribution and effect of some QTL in two populations were highly consistent and many QTL on chromosome 2, 7 and 9 were detected in both populations. Moreover, several mineral QTL co-localized with each other for both populations such as the QTL for ZnK, ZnC, FeK and FeC on chromosome 2, QTL for Znk, FeK and FeC on chromosome 9 and QTL for ZnK and ZnC on chromosome 7, which probably were closely linked to each other, or were the same pleiotropic QTL.     

大田环境下玉米抗旱相关性状QTL定位

干旱是世界范围内导致玉米产量损失的主要因素。为了阐明玉米抗旱性的遗传基础并定位相关的数量性状位点,利用抗旱自交系临1和敏感的湘97-7组配160个F2:3家系定位群体,于2011年在湖南省作物研究所和长沙县高桥镇,分别在大田干旱胁迫和正常水分条件下进行表型鉴定。所考察性状包括抽雄至吐丝间隔、株高、千粒重和产量,用抗旱系数来衡量抗旱性。结果表明,110个SSR标记构建连锁图,图谱总长1246.1 cM,标记间平均距离11.33 cM。抗旱相关性状定位的QTL介于8～14个,共检测到43个QTL。单个QTL解释的表型变异为6.27%～18.27%。不同水分条件下定位到的QTL大多数不相同,表明对干旱胁迫的适应存在不同机制。抗旱性相关性状定位到的QTL,除第2和10染色体外,在其它染色体上都有分布,主要集中在第1染色体1.02-03区域和1.06-07区域,以及第3染色体3.04-05区域。第1染色体标记umc2224和bnlg176区间同时检测到与株高、千粒重和产量有关的QTL簇;标记bnlg1556和umc1128区间检测到与抽雄至吐丝间隔和产量有关的QTL簇。第3染色体标记umc1773和umc1311区间同时检测到与株高、千粒重和产量有关的QTL簇。这些QTL簇可能有助于通过分子标记辅助选择的方法提高干旱地区玉米的抗旱性。     

Genetic loci linking improved heat tolerance in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) to lower leaf and spike temperatures under controlled conditions

Heat stress adversely affects wheat production in many regions of the world and is particularly detrimental during reproductive development. The objective of this study was to identify novel quantitative trait loci (QTL) associated with improved heat tolerance in wheat (Triticum aestivum L.) and to confirm previous QTL results. To accomplish this, a recombinant inbred line (RIL) population was subjected to a three-day 38°C daytime heat stress treatment during early grain-filling. At maturity, a heat susceptibility index (HSI) was calculated from the reduction of three main spike yield components; kernel number, total kernel weight, and single kernel weight. The HSI, as well as temperature depression (TD) of the main spike and main flag leaf during heat stress were used as phenotypic measures of heat tolerance. QTL analysis identified 14 QTL for HSI, with individual QTL explaining from 4.5 to 19.3% of the phenotypic variance. Seven of these QTL co-localized for both TD and HSI. At all seven loci, the allele for a cooler flag leaf or spike temperature (up to 0.81°C) was associated with greater heat tolerance, indicated by a lower HSI. In a comparison to previous QTL results in a RIL population utilizing the same source of heat tolerance, seven genome regions for heat tolerance were consistently detected across populations. The genetic effect of combining three of these QTL, located on chromosomes 1B, 5A, and 6D, demonstrate the potential benefit of selecting for multiple heat tolerance alleles simultaneously. The genome regions identified in this study serve as potential target regions for fine-mapping and development of molecular markers for more rapid development of heat tolerant germplasm.     

玉米出籽率、籽粒深度和百粒重的QTL分析

为研究玉米出籽率、籽粒深度、百粒重的遗传机制,以豫82×沈137组配的229个F_2:3家系为试验材料,采用复合区间作图法进行QTL定位分析。在3个环境下共检测到10个QTL。其中,控制出籽率、籽粒深度、百粒重相关QTL分别为3个、3个和4个,它们的联合贡献率分别为35.5%、28.1%和39.0%。位于第1染色体上介于标记umc1335与umc2236之间控制出籽率的QTL qKR1b和位于第9染色体上介于标记bnlg1209–umc2095之间控制百粒重QTL q100-KW9b,分别解释18.9%和11.7%的表型变异,且作用方式为加性效应,分析表明这些区域可能包含调控玉米籽粒性状关键基因,对剖析玉米产量形成机制具有重要的参考价值。     

Confirmation of a major QTL on chromosome 10 for maize kernel row number in different environments

Maize kernel row number (KRN) is an important agronomic trait. In this study, 13 quantitative trait loci (QTL) for maize KRN were identified in different environments using F_2:3 and F_2:4 populations developed from two inbred lines. These QTL are distributed on chromosomes 2,3,5,8 and 10, and the genic effects are additive or partially dominant. Using the BC₃F_2:3 populations developed from the same parental lines, QTL of KRN located on chromosomes 5 and 10 were also identified in two environments. Three BC₅F_2:3 populations were used to confirm the major QTL for KRN between ssr1430 and umc1077 on chromosome 10(qKRN10). This result will facilitate the fine mapping and map‐based cloning of this major QTL in the future.     

Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level

In wheat, strong genetic correlations have been found between grain yield (GY) and tiller number per plant (TN), fertile spikelet number per spike (FSN), kernel number per spike (KN) and thousand‐kernel weight (TKW). To investigate their genetic relationships at the individual quantitative trait locus (QTL) level, we performed both normal and multivariate conditional QTL analysis based on two recombinant inbred lines (RILs) populations. A total of 79 and 48 normal QTLs were identified in the International Triticeae Mapping Initiative (ITMI)/SHW‐L1 × Chuanmai 32 (SC) populations, respectively, as well as 55 and 35 conditional QTLs. Thirty‐two QTL clusters in the ITMI population and 18 QTL clusters in the SC population explained 0.9%–46.2% of phenotypic variance for two to eight traits. A comparison between the normal and conditional QTL mapping analyses indicated that FSN made the smallest contribution to GY among the four GY components that were considered at the QTL level. The effects of TN, KN and TKW on GY were stronger at the QTL level.     

Dent corn genetic background influences QTL detection for grain yield and yield components in high-oil maize

Despite the well-recognized importance of grain yield in high-oil maize (Zea mays L.) breeding and production, few studies have reported the application of QTL mapping of such traits. An inbred line of high-oil maize designated ‘GY220’ was crossed with two dent maize inbred lines to generate two connected F_2:3 populations with 284 and 265 F_2:3 families. Our main objective was to evaluate the influence of genetic background on QTL detection of grain yield traits through comparisons between the F_2:3 populations. The field experiments were conducted during the spring in Luoyang and summer in Xuchang, Henan, China. Two genetic linkage maps were constructed with a genetic distance of 2111.7 and 2298.5 cM using 185 and 173 polymorphic SSR markers, respectively. In total, 18 and 15 QTL were detected for six grain yield traits in the two populations. Only one common QTL marker was shared between the two populations. A QTL cluster associated with five traits was identified at bin 1.05–1.06, including the shared QTL for 100GW, which demonstrated the largest effect (16.7%). Among the detected QTL, 12 digenic interactions were identified. Our results reflect the substantial influence of dent maize genetic background on QTL detection of grain yield traits.     

Quantitative trait loci mapping of maize (Zea mays) ear traits under low-phosphorus stress

We constructed an F_2:3 population of 180 individuals from a cross between the maize genotypes 082 (high phosphorus use efficiency) and Ye107 (low phosphorus use efficiency). We used the population to perform quantitative trait loci (QTL) mapping for eight ear traits (ear diameter [ED], ear length [EL], ear weight [EW], ear kernel weight [EKW], row number per ear [ERN], bald tip length [BTL], kernel number per row [RKN] and number of seeds per plant [NSP]) in three low-phosphorus environments. A total of 36 QTL for ear traits were detected in at least one of three single environments and were located on all maize chromosomes except for chromosome 10; the explained phenotypic variation ranged from 5.91% to 12.80%. The QTL JAqEKW5-1, JAqEL3-1, JAqEW1-1, JAqBTL1-1 and JAqNSP5-2 were identified and stably expressed in single-environment and joint-environment analyses. Some novel QTL that have not been reported previously were also detected. Most of the identified QTL had stable effects in all low-phosphorus environments, suggesting that they may be useful for molecular breeding to develop low-phosphorus tolerant maize varieties.