Meta-QTL analysis and identification of candidate genes related to root traits in maize

Maize root system architecture determines key functions of uptake of water and nutrients in plants. A large number of quantitative trait loci (QTLs) of root-related traits have been found in different populations of maize. Identification of consistent QTLs across diverse genetic backgrounds could be instrumental on marker-assisted selection of traits and identification of candidate functional genes. In this study, 20 published papers were investigated regarding on reported results of QTLs related to root traits of maize, and in total 428 individual QTLs for 23 root-related traits were used for meta-analysis, resulting in 53 Meta-QTLs (MQTLs) retrieved over ten maize chromosomes. Among these MQTLs regions, in total 45 maize homologs were considered as candidate genes affecting maize root traits by comparing with 7 genes from rice and 36 genes from Arabidopsis. Three maize genes (GRMZM5G813206, GRMZM2G167220 and GRMZM2G467069) identified from MQTL_8-5 could play important roles on lateral root and crown root development of maize. Two of the MQTLs, i.e. MQTL_7-2 and MQTL_9-1, involved in nitrogen (N) and phosphorus (P) stress responses and both of them with small physical distance (less than 3 Mb), could be used for abiotic stress improvement of maize root traits. These MQTLs and candidate genes will be helpful for future gene cloning and marker-assisted selection in maize.     

Identification of quantitative trait locus and prediction of candidate genes for grain mineral concentration in maize across multiple environments

Trace metal elements are essential in daily diets for human health and normal growth. Maize is staple food for people in many countries. However, maize has low mineral concentration which makes it difficult to meet human requirements for micronutrients. The objective of this study was to identify quantitative trait locus (QTL) and predict candidate genes associated with mineral concentration in maize grain. Here, a recombinant inbred line population was used to test phenotype of zinc (Zn), iron (Fe), copper (Cu) and manganese (Mn) concentrations in six environments and then a QTL analysis was conducted using single environment analysis along with multiple environment trial (MET) analysis. These two strategies detected a total of 64 and 67 QTLs for target traits, respectively. Single environment analysis revealed 13 QTL bins distributed on seven chromosomes. We found that five candidate genes associated with mineral concentration were located in the same intervals identified by Comparative mapping of meta-QTLs in our previous study. The genetic and phenotypic correlation coefficients were depended on the nutrient traits and they were significant between Fe and Zn, Fe and Cu, Fe and Mn in all environments. The results of this study illustrated the genetic correlation between maize grain mineral concentrations, and identified some promising genomic regions and candidate genes for further studies on the biofortification of mineral concentration in maize grain.     

Identification of QTLs for fruit quality traits in Japanese apples: QTLs for early ripening are tightly related to preharvest fruit drop

Many important apple (Malus × domestica Borkh.) fruit quality traits are regulated by multiple genes, and more information about quantitative trait loci (QTLs) for these traits is required for marker-assisted selection. In this study, we constructed genetic linkage maps of the Japanese apple cultivars ‘Orin’ and ‘Akane’ using F₁ seedlings derived from a cross between these cultivars. The ‘Orin’ map consisted of 251 loci covering 17 linkage groups (LGs; total length 1095.3 cM), and the ‘Akane’ map consisted of 291 loci covering 18 LGs (total length 1098.2 cM). We performed QTL analysis for 16 important traits, and found that four QTLs related to harvest time explained about 70% of genetic variation, and these will be useful for marker-assisted selection. The QTL for early harvest time in LG15 was located very close to the QTL for preharvest fruit drop. The QTL for skin color depth was located around the position of MYB1 in LG9, which suggested that alleles harbored by ‘Akane’ are regulating red color depth with different degrees of effect. We also analyzed soluble solids and sugar component contents, and found that a QTL for soluble solids content in LG16 could be explained by the amount of sorbitol and fructose.     

Genome-wide association mapping focusing on a rice population derived from rice breeding programs in a region

Plant breeding programs in local regions may generate genetic variations that are desirable to local populations and shape adaptability during the establishment of local populations. To elucidate genetic bases for this process, we proposed a new approach for identifying the genetic bases for the traits improved during rice breeding programs; association mapping focusing on a local population. In the present study, we performed association mapping focusing on a local rice population, consisting of 63 varieties, in Hokkaido, the northernmost region of Japan and one of the northern limits of rice cultivation worldwide. Six and seventeen QTLs were identified for heading date and low temperature germinability, respectively. Of these, 13 were novel QTLs in this population and 10 corresponded to the QTLs previously reported based on QTL mapping. The identification of QTLs for traits in local populations including elite varieties may lead to a better understanding of the genetic bases of elite traits. This is of direct relevance for plant breeding programs in local regions.     

基于元分析和生物信息学分析的玉米抗旱相关性状QTL一致性区间定位

定位玉米基因组中一致的抗旱性区段是玉米抗旱分子育种的重要基础。本研究对至今发表的在干旱条件下定位的相关性状QTL信息搜集整理,以IBM2 2008 Neighbors为参考图谱,利用overview分析和元分析方法进行Meta-QTL (MQTL)检测,共发掘79个MQTL,生物信息学分析结果显示,有43个区间内包含抗旱相关基因信息,占检出MQTL总数的54.43%。基于MaizeGDB网站的Genome Browser中的遗传图谱与物理图谱的整合信息,进行MQTL物理距离的估算,根据maizesequence网站的玉米基因组序列信息,进行初步的抗旱基因预测表明,这些区段中包含丰富的MYB、bZIP以及DREB转录因子序列信息以及大量的LEA基因家族成员。     

Identification of QTL for stalk sugar-related traits in a population of recombinant inbred lines of maize

Increasing sugar content in silage maize stalk improves its forage quality and palatability. The genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement, yet little information on QTL for stalk sugar content in maize has been reported. To this end, we investigated QTLs associated with stalk sugar traits including Brix, plant height (PHT), three ear leaves area (TELA), and days to silking (DTS) in two environments using a population of 202 recombinant inbred lines from a cross between YXD053, which has a high stalk sugar content, and Y6-1, which has a low stalk sugar content. A genetic map with 180 SSR and 10 AFLP markers was constructed, which spanned 1,648.6 cM of the maize genome with an average marker distance of 8.68 cM, and QTLs were detected using composite interval mapping. Seven QTLs controlling Brix were mapped on chromosomes 1, 2, 6 and 9 in the combined environments. These QTLs could explain 2.69–13.08 % of the phenotypic variance. One major QTL for Brix on chromosome 2 located between the markers bnlg1909 and umc1635 explained 13.08 % of the phenotypic variance. Y6-1 also contributed QTL allele for increased Brix on chromosome 6. One major QTLs controlling PHT on chromosome 1 and TELA on chromosome 4 were also identified and accounted for 13.68 and 12.49 % of the phenotypic variance, respectively. QTL alleles for increased DTS were located on chromosomes 1 and 5 of YXD053. Significant epistatic effects were identified in four traits, but no significant QTL × environment interactions were observed. The information presented here may be valuable for stalk sugar content improvement via marker-assisted selection in silage maize breeding programs.     

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。     

小麦籽粒产量及穗部相关性状的QTL定位

由小麦品种花培3号和豫麦57杂交获得DH群体168个株系,种植于3个环境中,利用305个SSR标记对籽粒产量和穗部相关性状(穗长、穗粒数、总小穗数、可育小穗数、小穗着生密度、千粒重和粒径)进行了QTL定位。利用基于混合线性模型的QTLNetwork 2.0软件,共检测到27个加性效应和13对上位效应位点,其中 8个加性效应位点具有环境互作效应。相关性高的性状间有一些共同的QTL位点,表现出一因多效或紧密连锁效应。5D染色体区段Xwmc215–Xgdm63,检测到控制籽粒产量、穗粒数、总小穗数、可育小穗数和小穗着生密度5个性状的QTL位点,各位点的遗传贡献率较大且遗传效应方向相同,增效等位基因均来源于豫麦57,适用于分子标记辅助育种和聚合育种。控制千粒重与穗粒数的QTL位于染色体不同区段,有利于实现穗粒数与粒重的遗传重组。     

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.     

Evaluation of the effects of five QTL regions on Fusarium head blight resistance and agronomic traits in spring wheat (Triticum aestivum L.)

Fusarium head blight (FHB) is an important disease of wheat (Triticum aestivum L.). The aim of this study was to determine the effects of quantitative trait locus (QTL) regions for resistance to FHB and estimate their effects on reducing FHB damage to wheat in Hokkaido, northern Japan. We examined 233 F₁-derived doubled-haploid (DH) lines from a cross between ‘Kukeiharu 14’ and ‘Sumai 3’ to determine their reaction to FHB during two seasons under field conditions. The DH lines were genotyped at five known FHB-resistance QTL regions (on chromosomes 3BS, 5AS, 6BS, 2DL and 4BS) by using SSR markers. ‘Sumai 3’ alleles at the QTLs at 3BS and 5AS effectively reduced FHB damage in the environment of Hokkaido, indicating that these QTLs will be useful for breeding spring wheat cultivars suitable for Hokkaido. Some of the QTL regions influenced agronomic traits: ‘Sumai 3’ alleles at the 4BS and 5AS QTLs significantly increased stem length and spike length, that at the 2DL QTL significantly decreased grain weight, and that at the 6BS QTL significantly delayed heading, indicating pleiotropic or linkage effects between these agronomic traits and FHB resistance.     

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。     

Fine mapping of major QTLs for alkaline tolerance at the seedling stage in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.) through genetic linkage analysis combined with high-throughput DNA sequencing

Exploiting genes and quantitative trait loci (QTLs) related to maize (Zea mays L.) alkaline tolerance is helpful for improving alkaline resistance. To explore the inheritance of maize alkaline tolerance at the seedling stage, a mapping population comprising 151 F_2:3 lines derived from the maize cross between Zheng58, tolerant to alkaline, and Chang7-2, sensitive to alkaline, was used to establish a genetic linkage map with 200 SSR loci across the 10 maize linkage groups, with an average interval of 6.5 cM between adjacent markers. QTLs for alkaline resistant traits of alkaline tolerance rating (ATR), germination rate (GR), relative conductivity (RC), weight per plant (WPP) and proline content (PC) were detected. The obtained results were as follows: Five QTLs on chromosomes 2, 5 and 6 (GR and WPP: chr. 2; PC and ATR: chr. 5; and RC: chr. 6) were mapped. For precise mapping of the QTLs related to alkaline resistance, two bulked deoxyribonucleic acid (DNA) pools were constructed using individual DNAs from the most tolerant 30 F₂ individuals and the most sensitive 30 F₂ individuals according to the ATR and used to establish a high density map of SLAF markers strongly associated with the ATR by specific locus amplified fragment sequencing (SLAF-Seq) combined with super bulked segregant analysis (superBSA). One marker-intensive region involved three SLAFs at 296,000–6,203,000 bp on chromosome 5 that were closely related to the ATR. Combined with preliminary QTL mapping with superBSA, two major QTLs on chromosome 5 associated with alkaline tolerance at the maize seedling stage were mapped to marker intervals of dCap-SLAF31521 and dCap-SLAF31535 and phi024 and dCap-SLAF31521, respectively. These QTL regions involved 9 and 75 annotated genes, respectively. These results will be helpful for improving maize alkaline tolerance at the seedling stage by marker-assisted selection programs and will be useful for fine mapping QTLs for maize breeding.     

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance–covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities.     

Development and evaluation of chromosome segment substitution lines (CSSLs) carrying chromosome segments derived from Oryza rufipogon in the genetic background of Oryza sativa L.

The wild relatives of rice (Oryza sativa L.) are useful sources of alleles that have evolved to adapt in diverse environments around the world. Oryza rufipogon, the known progenitor of the cultivated rice, harbors genes that have been lost in cultivated varieties through domestication or evolution. This makes O. rufipogon an ideal source of value-added traits that can be utilized to improve the existing rice cultivars. To explore the potential of the rice progenitor as a genetic resource for improving O. sativa, 33 chromosome segment substitution lines (CSSLs) of O. rufipogon (W0106) in the background of the elite japonica cultivar Koshihikari were developed and evaluated for several agronomic traits. Over 90% of the entire genome was introgressed from the donor parent into the CSSLs. A total of 99 putative QTLs were detected, of which 15 were identified as major effective QTLs that have significantly large effects on the traits examined. Among the 15 major effective QTLs, a QTL on chromosome 10 showed a remarkable positive effect on the number of grains per panicle. Comparison of the putative QTLs identified in this study and previous studies indicated a wide genetic diversity between O. rufipogon accessions.     

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.     

QTL analysis for eating quality-related traits in an F2:3 population derived from waxy corn × sweet corn cross

In order to identify quantitative trait loci (QTL) for the eating quality of waxy corn and sweet corn (Zea mays L.), QTL analysis was conducted on an F₂ population derived from a cross between a waxy corn inbred line and a sweet corn inbred line. Ten QTLs for pericarp thickness (PER), amylose content (AMY), dextrose content (DEX) and sucrose content (SUC) were found in the 158 F₂ families. Among them, four QTLs, qAMY4 (10.43%), qAMY9 (19.33%), qDEX4 (21.31%) and qSUC4 (30.71%), may be considered as major QTLs. Three of these, qAMY4, qDEX4 and qSUC4, were found to be located within a region flanked by two adjacent SSR markers on chromosome 4 (umc1088 and bnlg1265), making this SSR marker pair a useful selection tool for screening the eating quality traits of AMY, DEX and SUC. The QTL for amylose content was found to be located between markers phi027 and umc1634, raising the possibility of its identity being the Wx1 gene, which encodes a granule-bound amylose synthase. The new QTLs identified by the present study could serve as useful molecular markers for selecting important eating quality traits in subsequent waxy corn breeding studies.     

QTL mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (Zea mays L.)

Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought‐stressed and unstressed conditions, which explained 0.71%–20.62% of phenotypic variation in single watering condition. Forty‐five of the 100 QTLs were identified under water‐stressed conditions, and 29 stable QTLs (sQTLs) were identified under water‐stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via QTL pyramiding. We further integrated 27 independent QTL studies in a meta‐analysis to identify 21 meta‐QTLs (mQTLs). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding mQTLs. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker‐assisted selection to generate high‐yielding maize varieties.     

玉米产量及产量相关性状QTL的图谱整合

利用生物信息学方法,借助高密度分子标记遗传图谱IBM2 2008 neighbors,利用图谱映射和元分析的方法,对不同试验中定位的400个玉米产量及产量相关性状QTL进行了图谱整合,构建了玉米产量及产量相关性状QTL的综合图谱和一致性图谱。结果表明,玉米产量及产量相关性状QTL在10条染色体上呈非均匀分布,第1染色体上最多,第10染色体上最少;发掘出96个玉米产量及产量相关性状的“一致性”QTL;关联性较强的产量性状的QTL常集中在相同或相近的座位上。     

不同水分环境下玉米产量构成因子及籽粒相关性状的QTL分析

干旱胁迫对玉米产量及其相关性状有重要影响。本研究以我国玉米育种骨干亲本齐319和掖478分别和黄早四组配构建的两个F_2:3群体为材料,应用逐步联合分析的QTL定位方法,剖析新疆不同水分环境下(包含水区和旱区)玉米产量构成因子及籽粒相关性状的遗传基础。结果表明,在相同水分处理不同年份间产量构成因子和籽粒相关性状超过70%的QTL可稳定表达,旱区QTL的稳定性明显低于水区,当全部环境联合分析时,各性状QTL稳定性呈现一定程度的降低,但超过60%的QTL仍然稳定表达。两群体中共检测到11个环境钝感的主效QTL(在2个以上环境中检测到,且至少在一个环境下的贡献率大于10%),分布在bin1.10、2.00、4.09、7.02、9.02、10.04和10.07共7个基因组区段上,除bin10.04外所有环境钝感的主效QTL在全部环境下稳定表达。因此,玉米产量构成因子和籽粒相关性状的QTL在新疆相同水分处理不同年份间,甚至不同水分条件下大部分均可稳定表达,这些主效QTL位点可为抗旱分子育种和进一步精细定位提供参考。     

Variation in cooking and eating quality traits in Japanese rice germplasm accessions

The eating quality of cooked rice is important and determines its market price and consumer acceptance. To comprehensively describe the variation of eating quality in 183 rice germplasm accessions, we evaluated 33 eating-quality traits including amylose and protein contents, pasting properties of rice flour, and texture of cooked rice grains. All eating-quality traits varied widely in the germplasm accessions. Principal-components analysis (PCA) revealed that allelic differences in the Wx gene explained the largest proportion of phenotypic variation of the eating-quality traits. In 146 accessions of non-glutinous temperate japonica rice, PCA revealed that protein content and surface texture of the cooked rice grains significantly explained phenotypic variations of the eating-quality traits. An allelic difference based on simple sequence repeats, which was located near a quantitative trait locus (QTL) on the short arm of chromosome 3, was associated with differences in the eating quality of non-glutinous temperate japonica rice. These results suggest that eating quality is controlled by genetic factors, including the Wx gene and the QTL on chromosome 3, in Japanese rice accessions. These genetic factors have been consciously selected for eating quality during rice breeding programs in Japan.