爆裂玉米膨化倍数QTL分析及其环境稳定性

膨化倍数是爆裂玉米最重要的品质指标。以普通玉米自交系丹232和爆裂玉米自交系N04杂交构建的259个F2：3家系为定位群体,采用完全随机区组设计在郑州春播和夏播条件下测定了膨化倍数。利用覆盖玉米10条染色体的183对多态性分子标记构建连锁图,采用复合区间作图法（CIM）进行QTL定位分析,采用多区间作图法（MIM）分析定位QTL间的互作效应。共检测出22个QTLs,单个QTL的贡献率为3．07％～12．84％,累计贡献率为66．46％和51．90％。其中5个QTLs在两种环境条件下均检测到,3个QTLs（qPF-6-1、qPF-8-1和qPF-1-3）的贡献率大于10％。大多数QTLs的加性效应值大于显性效应,表现为加性、部分显性、显性和超显性基因作用方式的QTLs数目在两种环境下分别为4、5、0、2和2、5、2、2。仅6对（占2．60％）QTLs或标记区间存在显著互作效应,表现为AA、DA或DD互作方式。     

利用三倍体胚乳遗传模型定位玉米籽粒淀粉含量QTL

在两种环境条件下种植以普通玉米自交系丹232和爆裂玉米自交系N04为亲本构建的259个F2:3家系群体, 采用SSR标记构建了包含183个标记的玉米遗传连锁图谱, 覆盖玉米基因组1 762.2 cM, 标记间平均距离为9.6 cM。利用三倍体胚乳遗传模型和区间作图方法对籽粒淀粉含量进行了QTL定位和遗传效应分析, 春、夏播条件下共检测到10个QTL, 春播条件下检测到的QTL在夏播均被检测到, 分别位于第1、3、4、5、7染色体上,可解释淀粉的表型总变异分别为36.84%和72.65%, 单个QTL解释表型变异介于4.74%~11.26%。在检测到的 QTL中, 有2个QTL的遗传作用方式在春播均表现为超显性, 而夏播分别为加性和部分显性; 其他2个为加性, 1个为部分显性, 5个为超显性。3个QTL的增效基因来自丹232, 其余QTL的增效基因均来自N04。     

Genetic dissection of developmental behavior of grain weight in wheat under diverse temperature and water regimes

As a quantitatively inherited trait related to high yield potential, grain weight (GW) development in wheat is constrained by abiotic stresses such as limited water supply and high temperature. Data from a doubled haploid population, derived from a cross of (Hanxuan 10?×?Lumai 14), grown in four environments were used to explore the genetic basis of GW developmental behavior in unconditional and conditional quantitative trait locus (QTL) analyses using a mixed linear model. Thirty additive QTLs and 41 pairs of epistatic QTLs were detected, and were more frequently observed on chromosomes 1B, 2A, 2D, 4A, 4B and 7B. No single QTL was continually active during all stages or periods of grain growth. The QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the flowering to the seventh day after) formed a foundation for GW development. GW development at these stages can be used as an index for screening superior genotypes under diverse abiotic stresses in a wheat breeding program. One QTL, i.e. Qgw.cgb-6A.2, showed high adaptability for water-limited and heat-stress environments. Many A-QTLs interacted with more than one other QTL in the two genetic models, such as Qgw.cgb-4B.2 interacted with five QTLs, showing that the genetic architecture underlying GW development involves a collective expression of genes with additive and epistatic effects.     

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %. Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive main-effect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.     

QTL Analysis of Head Splitting Resistance in Cabbage (Brassica oleracea L. var. capitata) Using SSR and InDel Makers Based on Whole-Genome Re-Sequencing

Head splitting resistance (HSR) in cabbage is an important trait closely related to both quality and yield of head. However, the genetic control of this trait remains unclear. In this study, a doubled haploid (DH) population derived from an intra-cross between head splitting-susceptible inbred cabbage line 79–156 and resistant line 96–100 was obtained and used to analyze inheritance and detect quantitative trait loci (QTLs) for HSR using a mixed major gene/polygene inheritance analysis and QTL mapping. HSR can be attributed to additive-epistatic effects of three major gene pairs combined with those of polygenes. Negative and significant correlations were also detected between head Hsr and head vertical diameter (Hvd), head transverse diameter (Htd) and head weight (Hw). Using the DH population, a genetic map was constructed with simple sequence repeat (SSR) and insertion–deletion (InDel) markers, with a total length of 1065.9 cM and average interval length of 4.4 cM between adjacent markers. Nine QTLs for HSR were located on chromosomes C3, C4, C7, and C9 based on 2 years of phenotypic data using both multiple-QTL mapping and inclusive composite interval mapping. The identified QTLs collectively explained 39.4 to 59.1% of phenotypic variation. Three major QTLs (Hsr 3.2, 4.2, 9.2) showing a relatively larger effect were robustly detected in different years or with different mapping methods. The HSR trait was shown to have complex genetic mechanisms. Results from QTL mapping and classical genetic analysis were consistent. The QTLs obtained in this study should be useful for molecular marker-assisted selection in cabbage breeding and provide a foundation for further research on HSR genetic regulation.     

Novel pleiotropic loci controlling panicle architecture across environments in japonica rice （Oryza sativa L.）

To identify quantitative trait loci (QTLs) controlling panicle architecture in japonica rice, a genetic map was constructed based on simple sequence repeat (SSR) markers and 254 recombinant inbred lines (RILs) derived from a cross between cultivars Xiushui 79 and C Bao. Seven panicle traits were investigated under three environments. Single marker analysis indicated that a total of 27 SSR markers were highly associated with panicle traits in all the three environments. Percentage of phenotypic variation explained by single locus varied from 2% to 35%. Based on the mixed linear model, a total of 40 additive QTLs for seven panicle traits were detected by composite interval mapping, explaining 1.2%-35% of phenotypic variation. Among the 9 QTLs with more than 10% of explained phenotypic variation, two QTLs were for the number of primary branches per panicle (NPB), two for panicle length (PL), two for spikelet density (SD), one for the number of secondary branches per panicle (NSB), one for secondary branch distribution density (SBD), and one for the number of spikelets per panicle (NS), respectively. qPLSD-9-1 and qPLSD-9-2 were novel pleiotropic loci, showing effects on PL and SD simultaneously. qPLSD-9-1 explained 34.7% of the phenotypic variation for PL and 25.4% of the phenotypic variation for SD, respec- tively. qPLSD-9-2 explained 34.9% and 24.4% of the phenotypic variation for PL and SD, respectively. The C Bao alleles at the both QTLs showed positive effects on PL, and the Xiushui 79 alleles at the both QTLs showed positive effects on SD. Genetic variation of panicle traits are mainly attributed to additive effects. QTL × environment interactions were not significant for additive QTLs and additive × additive QTL pairs.     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

不同遗传背景及环境中水稻（Oryza sativa L.）穗长的QTLs和上位性分析

以粳稻Ａｚｕｃｅｎａ为父本与灿稻ＩＲ６４杂交发展的一双单倍体（ＤＨ） 本,与灿稻ＩＲ１５５２杂交发展的一重组自交系（ＲＩ）群体为材料,应用分子标记图说对２个群体在大田答舅栽２个环境下的穗长进行ＱＴＬｓ及上位性效应分析,ＤＨ群体中共检测６个穗长ＱＴＬｓ,位于第１、４长染色体上的３个ＱＴＬｓ,,在２个环境中稳定表达,未检测一闰性效应,加性效应为穗长遗传主效应,ＲＩ群体中,共检测到３个穗长ＱＴＬｓ及６对     

水稻粒长QTL定位与主效基因的遗传分析

该研究利用短粒普通野生稻矮杆突变体和长粒栽培稻品种KJ01组配杂交组合F_1,构建分离群体F_2;并对该群体粒长进行性状遗传分析,利用平均分布于水稻的12条染色体上的132对多态分子标记对该群体进行QTL定位及主效QTLs遗传分析,为进一步克隆新的主效粒长基因奠定基础,并为水稻粒形育种提供理论依据。结果表明:(1)所构建的水稻杂交组合分离群体F_2的粒长性状为多基因控制的数量性状。(2)对543株F_2分离群体进行QTL连锁分析,构建了控制水稻粒长的连锁遗传图谱,总长为1 713.94 cM,共检测出24个QTLs,只有3个表现为加性遗传效应,其余位点均表现为遗传负效应。(3)检测到的3个主效QTLs分别位于3号染色体的分子标记PSM379～RID24455、RID24455～RM15689和RM571～RM16238之间,且三者对表型的贡献率分别为54.85%、31.02%和7.62%。(4)在标记PSM379～RID24455之间已克隆到的粒长基因为该研究新发现的主效QTL位点。     

Quantitative trait loci for growing degree days to flowering and photoperiod response in Sunflower (Helianthus annuus L.)

The number of days from seedling emergence to flowering (DTF) is a major consideration in sunflower breeding programs. This is a complex trait determined by the genotype, environmental conditions and interactions. Photoperiod and temperature have major effects on DTF and could be important sources of genotype× environment interaction. The objectives of this study were to locate quantitative trait loci (QTLs) associated with growing degree days (GDD) to flowering and photoperiod (PP) response in an elite sunflower population. Two hundred and thirty five F₂-generation plants and their F_2:3 and F_2:4 progenies of a single-cross population of two divergent inbred lines were evaluated in six environments (locations, years and sowing dates) with photoperiods known to elicit a PP response between the inbred lines. Detection of QTLs was facilitated with a genetic linkage map of 205 RFLP loci and composite interval mapping. The 205 restriction fragment length polymorphism (RFLP) loci covered 1380 cM and were arranged in 17 linkage groups, which is the haploid number of chromosomes in this species. The average interval size was 5.9 cM. Six QTLs in linkage groups A, B, F, I, J and L were associated with GDD to flowering and accounted for 76% of the genotypic variation in the mean environment. QTLs in linkage groups A and B accounted for 72% of the genetic variation. QTL×environment (QTL×E) interactions were highly significant for linkage groups A, B, F and J (P<0.01). QTLs in linkage groups A and B were highly dependent on PP. Also, QTL mapping of the ratio of the GDD required by a progeny to flower at a PP of 12.1 and 15.0 h, defined as the photoperiod response (PPR), suggested that alleles at QTLs in linkage groups A and B were responsive to PP. QTLs in linkage groups F and J showed QTL×E interaction but the LOD values were not associated with PP. QTL×E interactions for additive effects were highly significant (P<0.01) for linkage groups A, B and F. QTL×E interactions for QTLs with dominant effects were significant (P<0.01) for linkage groups A, B and J. The dominant effect of QTLs in linkage group B increased in environments with a longer PP. The knowledge of how these QTLs influence the GDD for flowering and how they interact with the environment will facilitate marker- assisted selection and backcross conversion of photoperiod-sensitive germplasm. Received: 7 February 2000 / Accepted: 13 June 2000     

The effect of epistasis between linked genes on quantitative trait locus analysis

The effect of epistasis between linked genes on quantitative trait locus (QTL) analysis was studied as a function of their contribution to the phenotypic variance and their genetic distance by simulation of F2 (at least 200 individuals) and recombinant inbred line (RIL) populations. Data sets were replicated 100 times. For F2 populations, the presence of epistasis improves the detection of QTLs having effects in opposite directions. Epistasis between linked QTLs (26.5 cM) was poorly detected even when its contribution was relatively high compared to the main effects, and was null for heritabilities lower than 0.10. The detection of false-positive main effects is strongly affected by the distance between epistatic QTLs. The closer they are (≤11.5 cM), the higher the probability of detecting false-positive main-effect QTLs and the lower the probability of detecting epistatic effects. In this case, the presence of main-effect QTLs is due to the deviation of the heterozygote from the homozygotes at each linked interacting QTL and is algebraically explained by the joint effect of the linkage and the additive-by-additive interaction, resulting in a heterosis at a single genomic region in the absence of simulated dominant genetic effects. The number of false-positive main effects only reached nominal levels at about 100 cM. For RIL populations, the number of false positives or the detection of existing epistasis does not depend on the distance, and the power to detect epistatic QTLs is much higher even with small sample sizes and low contributions to the trait. RIL populations are highly recommended to detect epistatic QTLs and to better infer the genetic architecture of a quantitative trait.     

Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations

Key message

A novel high-density consensus wheat genetic map was obtained based on three related RIL populations, and the important chromosomal regions affecting yield and related traits were specified.

Abstract

A prerequisite for mapping quantitative trait locus (QTL) is to build a genetic linkage map. In this study, three recombinant inbred line populations (represented by WL, WY, and WJ) sharing one common parental line were used for map construction and subsequently for QTL detection of yield-related traits. PCR-based and diversity arrays technology markers were screened in the three populations. The integrated genetic map contains 1,127 marker loci, which span 2,976.75 cM for the whole genome, 985.93 cM for the A genome, 922.16 cM for the B genome, and 1,068.65 cM for the D genome. Phenotypic values were evaluated in four environments for populations WY and WJ, but three environments for population WL. Individual and combined phenotypic values across environments were used for QTL detection. A total of 165 putative additive QTL were identified, 22 of which showed significant additive-by-environment interaction effects. A total of 65 QTL (51.5 %) were stable across environments, and 23 of these (35.4 %) were common stable QTL that were identified in at least two populations. Notably, QTkw-5B.1, QTkw-6A.2, and QTkw-7B.1 were common major stable QTL in at least two populations, exhibiting 11.28–16.06, 5.64–18.69, and 6.76–21.16 % of the phenotypic variance, respectively. Genetic relationships between kernel dimensions and kernel weight and between yield components and yield were evaluated. Moreover, QTL or regions that commonly interact across genetic backgrounds were discussed by comparing the results of the present study with those of previous similar studies. The present study provides useful information for marker-assisted selection in breeding wheat varieties with high yield.     

Detection of grain protein content QTLs across environments in tetraploid wheats

Grain protein content (GPC) is an important quality factor in both durum and bread wheats. GPC is considered to be a polygenic trait influenced by environmental factors and management practice. The objectives of this study were both to compare the quantitative trait loci (QTL) for GPC in a population of 65 recombinant inbred lines of tetraploid wheats evaluated in three locations for several years (eight data sets), and to investigate the genetic relationship among GPC and grain yield. QTLs were determined based on the Messapia × dicoccoides linkage map which covers 217 linked loci on the 14 chromosomes with 42 additional loci as yet unassigned to linkage groups. The map extends to 1352 cM; the average distance between adjacent markers was 6.3 cM. Seven QTLs for GPC, located on the chromosome arms 4BS, 5AL, 6AS (two loci), 6BS, 7AS and 7BS, were detected that were significant in at least one environment at P<0.001 or in at least two environments at P<0.01. One QTL was significant in all but one environment, two were significant in four or five environments, and four were significant in two out of eight environments. Six out of seven protein content QTLs had pleiotropic effects or were associated to QTLs for grain yield and explained the negative correlation among GPC and yield components. The present results support the concept that studies conducted in a single environment are likely to underestimate the number of QTLs that can influence a trait and that the phenotypic data for a quantitative trait should be collected over a range of locations to identify putative QTLs and determine their phenotypic effects.     

基于CSSL的水稻抽穗期QTL定位及遗传分析

抽穗期是水稻（Oryza sativa）品种的重要农艺性状之一,适宜的抽穗期是获得理想产量的前提。鉴定和定位水稻抽穗期基因/QTL,分析其遗传效应对改良水稻抽穗期至关重要。以籼稻品种9311（Oryzasativa ssp.indica‘Yangdao 6’）为受体,粳稻品种日本晴（Oryza sativa ssp.japonica‘Nipponbare’）为供体构建的94个染色体片段置换系群体为材料,以P≤0.01为阈值,对置换片段上的抽穗期QTL进行了鉴定。采用代换作图法共定位了4个控制水稻抽穗期的QTL,分别位于第3、第4、第5和第8染色体;QTL的加性效应值变化范围为–6.4––2.7,加性效应百分率变化范围为–6.4%––2.7%;qHD-3和qHD-8加性效应值较大,表现主效基因特征。为了进一步定位qHD-3和qHD-8,在目标区域加密16对SSR引物,qHD-3和qHD-8分别被界定在第3染色体RM3166–RM16206之间及第8染色体RM4085–RM8271之间,其遗传距离分别为13.9cM和6.4cM。研究结果为利用分子标记辅助选择改良水稻抽穗期奠定了基础。     

基于CSSL的水稻抽穗期QTL定位及遗传分析

抽穗期是水稻(Oryza sativa)品种的重要农艺性状之一, 适宜的抽穗期是获得理想产量的前提。鉴定和定位水稻抽穗期基因/QTL, 分析其遗传效应对改良水稻抽穗期至关重要。以籼稻品种9311(Oryza sativa ssp. indica ‘Yangdao 6’)为受体,粳稻品种日本晴(Oryza sativa ssp. japonica ‘Nipponbare’)为供体构建的94个染色体片段置换系群体为材料, 以P≤0.01为阈值, 对置换片段上的抽穗期QTL进行了鉴定。采用代换作图法共定位了4个控制水稻抽穗期的QTL, 分别位于第3、第4、第5和第8染色体; QTL的加性效应值变化范围为–6.4 – –2.7, 加性效应百分率变化范围为–6.4%– –2.7%; qHD-3和qHD-8加性效应值较大, 表现主效基因特征。为了进一步定位qHD-3和qHD-8, 在目标区域加密16对SSR引物, qHD-3和qHD-8分别被界定在第3染色体RM3166–RM16206之间及第8染色体RM4085-RM8271之间, 其遗传距离分别为13.9 cM和6.4 cM。研究结果为利用分子标记辅助选择改良水稻抽穗期奠定了基础。     

Quantitative trait loci associated with blood pressure of metabolic syndrome in the progeny of NZO/HILtJ × C3H/HeJ intercrosses

In a previous study in 15 inbred mouse strains, we found highest and lowest systolic blood pressures in NZO/HILtJ mice (metabolic syndrome) and C3H/HeJ mice (common lean strain), respectively. To identify the loci involved in hypertension in metabolic syndrome, we performed quantitative trait locus (QTL) analysis for blood pressure with direction of cross as a covariate in segregating F₂ males derived from NZO/HILtJ and C3H/HeJ mice. We detected three suggestive main-effect QTLs affecting systolic and diastolic blood pressures (SBP and DBP). We analyzed the first principle component (PC1) generated from SBP and DBP to investigate blood pressure. In addition to all the suggestive QTLs (Chrs 1, 3, and 8) in SBP and DBP, one suggestive QTL on Chr 4 was found in PC1 in the main scan. Simultaneous search identified two significant epistatic locus pairs (Chrs 1 and 4, Chrs 4 and 8) for PC1. Multiple regression analysis revealed three blood pressure QTLs (Bpq10, 100 cM on Chr 1; Bpq11, 6 cM on Chr 4; Bpq12, 29 cM on Chr 8) accounting for 29.4% of blood pressure variance. These were epistatic interaction QTLs constructing a small network centered on Chr 4, suggesting the importance of genetic interaction for development of hypertension. The blood pressure QTLs on Chrs 1, 4, and 8 were detected repeatedly in multiple studies using common inbred nonobese mouse strains, implying substantial QTL independent of development of obesity and insulin resistance. These results enhance our understanding of complicated genetic factors of hypertension in metabolic diseases. Eri Nishihara, Shirng-Wern Tsaih, Chieko Tsukahara and Sarah Langley contributed equally to this work.     

Mapping quantitative trait loci for lint yield and fiber quality across environments in a Gossypium hirsutum × Gossypium barbadense backcross inbred line population

Identification of stable quantitative trait loci (QTLs) across different environments and mapping populations is a prerequisite for marker-assisted selection (MAS) for cotton yield and fiber quality. To construct a genetic linkage map and to identify QTLs for fiber quality and yield traits, a backcross inbred line (BIL) population of 146 lines was developed from a cross between Upland cotton (Gossypium hirsutum) and Egyptian cotton (Gossypium barbadense) through two generations of backcrossing using Upland cotton as the recurrent parent followed by four generations of self pollination. The BIL population together with its two parents was tested in five environments representing three major cotton production regions in China. The genetic map spanned a total genetic distance of 2,895 cM and contained 392 polymorphic SSR loci with an average genetic distance of 7.4 cM per marker. A total of 67 QTLs including 28 for fiber quality and 39 for yield and its components were detected on 23 chromosomes, each of which explained 6.65–25.27 % of the phenotypic variation. Twenty-nine QTLs were located on the At subgenome originated from a cultivated diploid cotton, while 38 were on the Dt subgenome from an ancestor that does not produce spinnable fibers. Of the eight common QTLs (12 %) detected in more than two environments, two were for fiber quality traits including one for fiber strength and one for uniformity, and six for yield and its components including three for lint yield, one for seedcotton yield, one for lint percentage and one for boll weight. QTL clusters for the same traits or different traits were also identified. This research represents one of the first reports using a permanent advanced backcross inbred population of an interspecific hybrid population to identify QTLs for fiber quality and yield traits in cotton across diverse environments. It provides useful information for transferring desirable genes from G. barbadense to G. hirsutum using MAS.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

GBS-SNP and SSR based genetic mapping and QTL analysis for drought tolerance in upland cotton

A recombinant inbred line mapping population of intra-species upland cotton was generated from a cross between the drought-tolerant female parent (AS2) and the susceptible male parent (MCU13). A linkage map was constructed deploying 1,116 GBS-based SNPs and public domain-based 782 SSRs spanning a total genetic distance of 28,083.03 cM with an average chromosomal span length of 1,080.12 cM with inter-marker distance of 10.19 cM.A total of 19 quantitative trait loci (QTLs) were identified in nine chromosomes for field drought tolerance traits. Chromosomes 3 and 8 harbored important drought tolerant QTLs for chlorophyll stability index trait while for relative water content trait, three QTLs on chromosome 8 and one QTL each on chromosome 4, 12 were identified. One QTL on each chromosome 8, 5, and 7, and two QTLs on chromosome 15 linking to proline content were identified. For the nitrate reductase activity trait, two QTLs were identified on chromosome 3 and one on each chromosome 8, 13, and 26. To complement our QTL study, a meta-analysis was conducted along with the public domain database and resulted in a consensus map for chromosome 8. Under field drought stress, chromosome 8 harbored a drought tolerance QTL hotspot with two in-house QTLs for chlorophyll stability index (qCSI01, qCSI02) and three public domain QTLs (qLP.FDT_1, qLP.FDT_2, qCC.ST_3). Identified QTL hotspot on chromosome 8 could play a crucial role in exploring abiotic stress-associated genes/alleles for drought trait improvement.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12298-021-01041-y.     

Mapping of quantitative trait loci for oil content in cottonseed kernel

Oil content in cottonseed is a major quality trait which when improved through breeding could enhance the competitiveness of cottonseed oil among other vegetable oils. Cottonseed oil content is a quantitative trait controlled by genes in the tetraploid embryo and tetraploid maternal plant genomes, and the knowledge of quantitative trait loci (QTLs) and the genetic effects related to oil content in both genomes could facilitate the improvement in its quality and quantity. However, till date, QTL mapping and genetic analysis related to this trait in cotton have only been conducted in the tetraploid embryo genome. In the current experiment, an IF₂ population of cottonseed kernels from the random crossing of 188 intraspecific recombinant inbred lines which were derived from the hybrid of two parents, HS46 and MARCABUCAG8US-1-88, were used to simultaneously locate QTLs for oil content in the embryo and maternal plant genomes. The four QTLs found to be associated with oil content in cottonseed were: qOC-18-1 on chromosome 18; qOC-LG-11 on linkage group 11; qOC-18-2 on chromosome 18; and qOC-22 on chromosome 22. At a high selection threshold of 0.05, there was strong evidence linking the QTLs above the oil content in cottonseed. Embryo additive and dominant effects from the tetraploid embryo genome, as well as maternal additive effects from the tetraploid maternal plant genome were found to be significant contributors to genetic variation in cottonseed oil content.