Genetic dissection of the grain-filling rate and related traits through linkage analysis and genome-wide association study in bread wheat

Wheat grain yield is generally sink-limited during grain filling. The grain-filling rate (GFR) plays a vital role but is poorly studied due to the difficulty of phenotype surveys. This study explored the grain-filling traits in a recombinant inbred population and wheat collection using two highly saturated genetic maps for linkage analysis and genome-wide association study (GWAS). Seventeen stable additive quantitative trait loci (QTLs) were identified on chromosomes 1B, 4B, and 5A. The linkage interval between IWB19555 and IWB56078 showed pleiotropic effects on GFR₁, GFR_max, kernel length (KL), kernel width (KW), kernel thickness (KT), and thousand kernel weight (TKW), with the phenotypic variation explained (PVE) ranging from 13.38% (KW) to 33.69% (TKW). 198 significant marker-trait associations (MTAs) were distributed across most chromosomes except for 3D and 4D. The major associated sites for GFR included IWB44469 (11.27%), IWB8156 (12.56%) and IWB24812 (14.46%). Linkage analysis suggested that IWB35850, identified through GWAS, was located in approximately the same region as QGFRmax2B.3-11, where two high-confidence candidate genes were present. Two important grain weight (GW)-related QTLs colocalized with grain-filling QTLs. The findings contribute to understanding the genetic architecture of the GFR and provide a basic approach to predict candidate genes for grain yield trait QTLs.     

Identification and validation of stable and novel quantitative trait loci for pod shattering in soybean [Glycinemax (L.) Merr.]

Pod shattering is an important domesticated trait which can cause great economic loss of crop yield in cultivated soybean. In this study, we utilized two recombinant inbred line populations (RILs, CY, Huachun 2×Wayao; GB, Guizao 1× B13) to identify quantitative trait loci (QTLs) associated with pod shattering in soybean across multiple environments. A total of 14 QTLs for pod shattering were identified in the two RIL populations, which had LOD scores ranging from 2.64 to 44.33 with phenotypic variance explanation (PVE) ranging from 1.33 to 50.85%. One QTL qPS16-1, located on chromosome 16, included a well-known functional gene Poddehiscence1 (Pdh1) that was reported previously. Ten new putative QTLs were validated in two RIL populations, and their LOD scores were between 2.55 and 4.24, explaining 1.33 to 2.60% of the phenotypic variation. Of which four novel QTLs (qPS01-1, qPS03-2, qPS05-1, and qPS07-1) could be detected in two environments where nine genes had specific changes in gene expression. Although the nine genes may have significant effects on pod shattering of soybean, their detailed functions still need to be further explored in the future. The results of this study will facilitate a better understanding of the genetic basis of the pod shattering-resistant trait and benefit soybean molecular breeding for improving pod shattering resistance.     

QTL analysis for plant height and fine mapping of two environmentally stable QTLs with major effects in soybean

Plant height is an important agronomic trait, which is governed by multiple genes with major or minor effects. Of numerous QTLs for plant height reported in soybean, most are in large genomic regions, which results in a still unknown molecular mechanism for plant height. Increasing the density of molecular markers in genetic maps will significantly improve the efficiency and accuracy of QTL mapping. This study constructed a high-density genetic map using 4 011 recombination bin markers developed from whole genome re-sequencing of 241 recombinant inbred lines (RILs) and their bi-parents, Zhonghuang 13 (ZH) and Zhongpin 03-5373 (ZP). The total genetic distance of this bin map was 3 139.15 cM, with an average interval of 0.78 cM between adjacent bin markers. Comparative genomic analysis indicated that this genetic map showed a high collinearity with the soybean reference genome. Based on this bin map, nine QTLs for plant height were detected across six environments, including three novel loci (qPH-b_11, qPH-b_17 and qPH-b_18). Of them, two environmentally stable QTLs qPH-b_13 and qPH-b_19-1 played a major role in plant height, which explained 10.56–32.7% of the phenotypic variance. They were fine-mapped to 440.12 and 237.06 kb region, covering 54 and 28 annotated genes, respectively. Via the function of homologous genes in Arabidopsis and expression analysis, two genes of them were preferentially predicted as candidate genes for further study.     

Genetic dissection of wheat uppermost-internode diameter and its association with agronomic traits in five recombinant inbred line populations at various field environments

Uppermost-internode diameter (UID) is a key morphological trait associated with spike development and yield potential in wheat. Our understanding of its genetic basis remains largely unknown. Here, quantitative trait loci (QTLs) for UID with high-density genetic maps were identified in five wheat recombinant inbred line (RIL) populations. In total, 25 QTLs for UID were detected in five RIL populations, and they were located on chromosomes 1A, 1D (3 QTL), 2B (2), 2D (3), 3B, 3D, 4A, 4B (3), 4D, 5A (5), 5B (2), 6B, and 7D. Of them, five major and stable QTLs (QUid.sau-2CN-1D.1, QUid.sau-2SY-1D, QUid.sau-QZ-2D, QUid.sau-SC-3D, and QUid.sau-AS-4B) were identified from each of the five RIL populations in multiple environments. QUid.sau-2CN-1D.1, QUid.sau-2SY-1D and QUid.sau-SC-3D are novel QTLs. Kompetitive Allele Specific PCR (KASP) markers tightly linked to them were further investigated for developing near-isogenic lines (NILs) carrying the major loci. Furthermore, candidate genes at these intervals harboring major and stable QTLs were predicted, and they were associated with plant development and water transportation in most cases. Comparison of physical locations of the identified QTL on the ‘Chinese Spring’ reference genome showed that several QTLs including two major ones, QUid.sau-2CN-1D.1 and QUid.sau-2SY-1D, are likely allelic confirming their validity and effectiveness. The significant relationships detected between UID and other agronomic traits and a proper UID were discussed. Collectively, our results dissected the underlying genetic basis for UID in wheat and laid a foundation for further fine mapping and map-based cloning of these QTLs.     

Analyses and identifications of quantitative trait loci and candidate genes controlling mesocotyl elongation in rice

Rice direct seeding has the significant potential to save labor and water, conserve environmental resources, and reduce greenhouse gas emissions tremendously. Therefore, rice direct seeding is becoming the major cultivation technology applied to rice production in many countries. Identifying and utilizing genes controlling mesocotyl elongation is an effective approach to accelerate breeding procedures and meet the requirements for direct-seeded rice(DSR) production.This study used a permanent ma...     

Construction of a genetic linkage map and QTL analysis for some leaf traits in pear (Pyrus L.)

The major incompatibility barriers to specific inbred lines and the long generation duration in Pyrus L. may hinder the Pyrus breeding process. A genetic linkage map provides the foundation for quantitative trait loci (QTL) mapping and molecular marker-assisted breeding. In this study, we constructed a genetic map with 145 F₁ populations from a cross of two cultivars, Yali and Jingbaili, using AFLP and SSR markers. The map consisted of 18 linkage groups which included 402 genetic markers and covered 1395.9 cM, with an average genetic distance of 3.8 cM. The interval mapping was used to identify quantitative trait loci associated with four leaf agronomic traits in the F₁ population. The results indicated that four QTLs were associated with leaf length, two QTLs with leaf width, two with leaf length/leaf width, and three with petiole length. The eleven QTLs were associated with 9.9%–48.5% of the phenotypic variation in different traits. It is considered that the map covers almost the whole genome, and molecular markers will be greatly helpful to the related breeding.     

Identification of genetic loci for grain yield-related traits in the wheat population Zhongmai 578/Jimai 22

The identification of stable quantitative trait locus (QTL) for yield-related traits and tightly linked molecular markers is important for improving wheat grain yield. In the present study, six yield-related traits in a recombinant inbred line (RIL) population derived from the Zhongmai 578/Jimai 22 cross were phenotyped in five environments. The parents and 262 RILs were genotyped using the wheat 50K single nucleotide polymorphism (SNP) array. A high-density genetic map was constructed with 1 501 non-redundant bin markers, spanning 2 384.95 cM. Fifty-three QTLs for six yield-related traits were mapped on chromosomes 1D (2), 2A (9), 2B (6), 2D, 3A (2), 3B (2), 4A (5), 4D, 5B (8), 5D (2), 7A (7), 7B (3) and 7D (5), which explained 2.7–25.5% of the phenotypic variances. Among the 53 QTLs, 23 were detected in at least three environments, including seven for thousand-kernel weight (TKW), four for kernel length (KL), four for kernel width (KW), three for average grain filling rate (GFR), one for kernel number per spike (KNS) and four for plant height (PH). The stable QTLs QKl.caas-2A.1, QKl.caas-7D, QKw.caas-7D, QGfr.caas-2B.1, QGfr.caas-4A, QGfr.caas-7A and QPh.caas-2A.1 are likely to be new loci. Six QTL-rich regions on 2A, 2B, 4A, 5B, 7A and 7D, showed pleiotropic effects on various yield traits. TaSus2-2B and WAPO-A1 are potential candidate genes for the pleiotropic regions on 2B and 7A, respectively. The pleiotropic QTL on 7D for TKW, KL, KW and PH was verified in a natural population. The results of this study enrich our knowledge of the genetic basis underlying yield-related traits and provide molecular markers for high-yield wheat breeding.     

Identification of QTLs for biomass production in maize (Zea mays L.) under different phosphorus levels at two sites

The biomass production (BP), the leaf age (LA), and the plant height (PH) as well as the quantitative trait loci (QTLs) associated with these traits were determined for F_2:3 population derived from the cross of two contrasting maize (Zea mays L.) genotypes: 082 and Ye107. By using composite interval mapping, a total of 12 and 12 distinct QTLs were identified at Kaixian and Southwest University under deficient phosphorus. Another 9 and 8 distinct QTLs were identified at two sites under normal phosphorus, respectively. Seven coincident QTLs for two traits (BP and LA) were detected in the interval bnlg1832-P2M8/j (bin 1.05) on Chromosome 1, and four consistent QTLs for one trait (PH) were coincident in the interval umc1102-P1M7/d (bin 3.05) on Chromosome 3. These coincident QTLs in two important genomic regions were identified under different phosphorus levels and two different environments. Therefore, the above two segments one (bnlg1832-P2M8/j) identified in Chromosome 1 and the other (umc1102-P1M7/d) identified in Chromosome 3 may be used in future for marker-assisted selection and high-resolution mapping leading to map-based cloning of QTLs for agronomically important traits under phosphorus deficiency.     

Identification of quantitative trait loci and candidate genes controlling seed pigments of rapeseed

Rapeseed (Brassica napus L.) is an important source of edible vegetable oil and feed protein; however, seed pigments affect the quality of rapeseed oil and the feed value of the residue from oil pressing. Here, we used a population of rapeseed recombinant inbred lines (RILs) derived from the black-seeded male parent cultivar Zhongyou 821 and the yellow-seeded female parent line GH06 to map candidate genes controlling seed pigments in embryos and the seed coat. We detected 94 quantitative trait loci (QTLs) for seed pigments (44 for embryos and 50 for seed coat), distributed over 15 of the 19 rapeseed chromosomes. These included 28 QTLs for anthocyanidin content, explaining 2.41–44.66% of phenotypic variation; 24 QTLs for flavonoid content, explaining 2.41–20.26% of phenotypic variation; 16 QTLs for total phenol content, accounting for 2.74–23.68% of phenotypic variation; and 26 QTLs for melanin content, accounting for 2.37–24.82% of phenotypic variation, indicating that these traits are under multigenic control. Consensus regions on chromosomes A06, A09 and C08 were associated with multiple seed pigment traits, including 15, 19 and 10 QTLs, respectively, most of which were major QTLs explaining >10% of the phenotypic variation. Based on the annotation of the B. napus “Darmor-bzh” reference genome, 67 candidate genes were predicted from these consensus QTLs regions, and 12 candidate genes were identified as potentially involved in pigment accumulation by RNA-seq and qRT-PCR analysis. These preliminary results provide insight into the genetic architecture of pigment biosynthesis and lay a foundation for exploring the molecular mechanisms underlying seed coat color in B. napus.     

Mapping and genetic validation of a grain size QTL qGS7.1 in rice(Oryza sativa L.)

Grain size is a major determinant of grain weight, which is one of the components of rice yield. The objective o this study was to identify novel, and important quantitative trait loci(QTLs) for grain size and weight in rice. QTLs were mapped using a BC_4F_4 population including 192 backcross inbred lines(BILs) derived from a backcross between Xiaolijing(XLJ) and recombinant inbred lines(RILs). The mapping population was planted in both Lingshui(Hainan, 2015) and Fuyang(Zhejiang, 2016), with the short-and long-day conditions, respectively. A total of 10 QTLs for grain length, four for grain width, four for the ratio of grain length to width, and 11 for grain weight were detected in at least one environment and were distributed across 11 chromosomes. The phenotypic variance explained ranged from 6.76–25.68%, 14.30–34.03%, 5.28–26.50%, and 3.01–22.87% for grain length, grain width, the ratio of grain length to width, and thousand grain weight, respectively. Using the sequential residual heterozygotes(SeqRHs) method, qGS7.1, a QTL for grain size and weight, was mapped in a 3.2-Mb interval on chromosome 7. No QTLs about grain size and weight were reported in previous studies in this region, providing a good candidate for functional analysis and breeding utilization.     

Comparative mapping of QTLs for H+ secretion of root in maize (Zea mays L.) and cross phosphorus levels on two growth stages

H⁺ is a root secretion that affects P acquisition and P-use efficiency (PUE) under deficient phosphorus in maize. The secretion of H⁺, difference value of H⁺ between deficient and normal phosphorus (DH), and relative H⁺ (RH) as well as the quantitative trait loci (QTLs) associated with these traits were determined for a F_2:3 population derived from the cross of two contrasting maize (Zea mays L.) genotypes, 082 and Ye107. By using composite interval mapping (CIM), a total of 14, 8, and 9 distinct QTLs were identified for H⁺, DH, and RH, respectively. Most loci of QTLs for traits H⁺, DH, and RH had different cross environments. It showed that H⁺ secretion possessed an environment-sensitive and multi-gene nature. The gene × environment interaction was actually reflected by H⁺ secretion. One region for QTL of trait H⁺ was detected at the interval of bnlg2228-bnlg100 (bin 1.08) on chromosome 1. Coincident QTLs in the important genomic region reflected the cross phosphorus levels, different cross growth stages, and two different cross environments. The QTL explained 10% to 14% total phenotypic variance of H⁺. Therefore, the above segment (bnlg2228-bnlg100) (bin 1.08) identified on chromosome 1 may be used in the future for MAS to improve the phosphorus efficiency in maize.     

Identification of the genetic locus associated with the crinkled leaf phenotype in a soybean (Glycine max L.) mutant by BSA-Seq technology

The leaf is the main photosynthetic organ of plants, and it plays a significant role in the yield of crop species. Identifying the causal mutations and candidate genes that underlie leaf phenotypic variation is an important breeding target in soybean grain yield improvement. An ethyl methyl sulfonate (EMS)-induced soybean mutant DWARFCRINKLEDLEAF1 (DCL1) with an aberrant crinkled leaf phenotype was identified in the background of the soybean cultivar Zhongpin 661 (Zp661). We constructed an F₂ segregating population from a cross between Zp661 and DCL1 in order to investigate the genomic locus associated with the crinkled leaf trait. Using bulk segregant analysis (BSA) combined with the whole-genome resequencing method, the Euclidean distance (ED) correlation algorithm detected 12 candidate genomic regions with a total length of 20.32 Mb that were linked to the target trait. Following a comparative analysis of the sequence data for the wild-type and mutant pools, only one single nucleotide mutation (C:G>T:A) located on the first exon of Glyma.19G207100 was found to be associated with the trait. Candidate gene validation based on a CAPS marker derived from the detected single-nucleotide polymorphism (SNP) indicated a nucleotide polymorphism between the two parents. Therefore, our findings reveal that Glyma.19G207100, which is renamed as GLYCINE MAX DWARF CRINKLED LEAF 1 (GmDCL1), is a promising candidate gene involved in the morphogenesis of the crinkled leaf trait of the soybean mutant DCL1. This study provides a basis for the functional validation of this gene, with prospects for soybean breeding targeting grain yield enhancement.     

QTL Mapping of Chlorophyll Contents in Rice

The objectives of this study were to investigate the genetic factors controlling the chlorophyll content of rice leaf using QTL analysis. A linkage map consisting of 207 DNA markers was constructed by using 247 recombinant inbred lines (RILs) derived from an indica-indica rice cross of Zhenshan97B×Milyang 46. In 2002 and 2003, the contents of chlorophyll a and b of the parents and the 247 RILs were measured on the top first leaf, top second leaf, and top third leaf, respectively. The software QTLMapper 1.6 was used to detect quantitative trait loci (QTLs), additive by environment (AE) interactions, and epistatic by environment (AAE) interactions. A total of eight QTLs in four intervals were detected to have significant additive effects on chlorophyll a and b contents at different leaf positions, with 1.96-9.77% of phenotypic variation explained by a single QTL, and two QTLs with significant AE interactions were detected. Epistasis analysis detected nine significant additive-by-additive interactions on chlorophyll a and b contents, and one pair of QTLs with significant AAE interactions was detected. On comparison with QTLs for yield traits detected in the same population, it was found in many cases that the QTLs for chlorophyll a and b contents and those for yield traits were located in the same chromosome intervals.     

RTM-GWAS方法应用于大豆RIL群体百粒重QTL检测的功效

【目的】为全面解析大豆重组自交系群体中调控百粒重性状的QTL体系,将限制性两阶段多位点全基因组关联分析方法（RTM-GWAS）和不同定位方法进行比较、优选,为后续候选基因体系探索及分子标记辅助育种设计提供依据。【方法】利用以科丰1号和南农1138-2为亲本衍生的重组自交系群体NJRIKY的427个家系,通过由全基因组39 353个SNP构建的3 683个SNPLDB标记及3个环境下的百粒重表型数据,选用复合区间作图法（CIM）、基于混合线性模型的全基因组关联分析方法（MLM-GWAS）和RTM-GWAS3种方法检测百粒重QTL,通过QTL数目和总的表型变异解释率比较检测功效,挑选最佳定位结果进行NJRIKY群体中的百粒重遗传体系解析。通过候选基因体系的功能注释,挖掘调控大豆百粒重的生物学途径。【结果】科丰1号与南农1138-2的百粒重差异较大,多环境平均数分别为9.0和17.9 g,遗传变异系数为12.4%,遗传率为85.4%,适用于百粒重性状的遗传解析。比较3种方法定位结果表明RTM-GWAS方法表现最佳,检测QTL数目最多（57个）,解释表型变异最多（70.78%）。而CIM仅检测到14个QTL,解释了56.47%的表型变异,MLM-GWAS仅定位到6个QTL,解释了18.47%的表型变异。RTM-GWAS共检测到57个QTL,分布在19条染色体上,表型变异解释率为0.03%—7.57%,其中41个QTL覆盖了已报道的来自30个双亲群体的81个百粒重QTL,16个QTL为新发现位点,包含一个表型变异解释率大于3%的大效应位点Sw-09-2。此外,检测的57个QTL中有20个位点与环境存在互作效应。这57个QTL构成了影响NJRIKY群体百粒重性状的遗传体系。通过SNPLDB标记与预测基因内的SNP进行χ²检验,共筛选到36个候选基因,其中4个候选基因来自大效应QTL,剩余32个候选基因来自小效应QTL。通过GO注释发现这些候选基因功能注释丰富,其中13个候选基因与籽粒发育直接相关,剩余的候选基因功能丰富,包含转运、转录调节因子等,表明不同生物学途径的基因共同调控NJRIKY群体中百粒重性状的表达。【结论】3种定位方法中,高效的RTM-GWAS方法检测到较为全面的NJRIKY群体的百粒重QTL,更适用于双亲RIL群体的QTL定位。不同功能的候选基因共同调控了复杂的百粒重性状的表达。     

甘蓝型油菜开花期的QTL定位及候选基因鉴定

为了解析油菜开花期性状的遗传机制，利用KN DH群体在冬性、半冬性和春性环境的开花期表型和KN 高密度遗传连锁图谱，通过Wincart 2.5软件的符合区间作图法对油菜开花期性状进行QTL定位及候选基因鉴定。结果显示，共鉴定到119个开花期QTL，单个QTL解释表型变异最大是qFT-13DL16-4 （25.96%），最小的是qFT-13ZY2-1（2.48%）。利用元分析的方法将初步鉴定的QTL整合为consensus QTL，共获得26个环境稳定表达QTL，包括7个开花期主效QTL。如cqFT-A2-3、cqFT-A2-4在春性环境稳定表达，cqFT-C6-4、cqFT-C6-7、cqFT-C6-12、cqFT-C6-13在冬性和半冬性环境稳定表达, cqFT-C6-14在冬性环境稳定表达QTL。主效QTL置信区间共鉴定到15个与成花诱导相关的候选基因，如 BnaA02g12260D（RGA1）、 BnaA02g15390D（AGL12）、 BnaA02g16710D（LKP2）和BnaC06g19930D（NUA）等，这些候选基因主要涉及赤霉素、光周期、生物钟、春化作用响应和花发育等功能。可见，油菜开花期主效QTL及其候选基因的鉴定为开花期基因的精细定位和图位克隆奠定基础，也为培育早熟、高产油菜品种提供指导。     

黄瓜单性结实性状遗传与QTL定位

【目的】单性结实性是影响设施黄瓜产量和品质的重要性状。深入解析黄瓜单性结实性状遗传规律并对其进行QTL定位,有助于提高设施专用黄瓜品种育种效率。【方法】以强单性结实自交系‘6457’和弱单性结实自交系‘6426’构建的重组自交系F_2:8为材料,基于3年表型数据,采用黄瓜基因组测序SSR分子标记构建黄瓜遗传连锁图谱,结合QTL-Seq分析,对黄瓜单性结实性进行QTL定位。【结果】黄瓜单性结实性状符合数量遗传特征。利用SSR标记构建了1张包含11个连锁群的遗传图谱,覆盖基因组555.0 cM,平均图距为6.8 cM。2016—2018年春季在3号染色体上均检测到1个与黄瓜单性结实性相关的QTL位点,位于标记SSR19430和SSR15419之间（3.33—5.57 Mb）,遗传距离6.6 cM,贡献率分别为11%、12.5%和6.3%。进一步进行QTL-Seq分析,发现4个与黄瓜单性结实性相关的QTL,分别位于1号（4.38—11.00 Mb）、3号（2.24—10.66 Mb）、6号（15.67—17.93 Mb,26.33—27.49 Mb）染色体上。其中在3号染色体上检测到的QTL与Map QTL所得的QTL区间重叠。推测Csa3G047740、Csa3G073810、Csa3G043910和Csa6G362930为与黄瓜单性结实性状相关的候选基因。【结论】分别在1、3、6号染色体上检测到4个与黄瓜单性结实性相关的QTL位点,其中3号染色体上的QTL年度间稳定,贡献率较高。