玉米叶部性状的QTL定位与候选基因分析

叶片在玉米生长过程中发挥着重要作用,它能够有效地进行光合作用,为玉米提供营养物质,通过影响耐密性等影响产量提升。本研究选用QR273和T32为亲本,构建150份F2、F2∶3家系材料,结合基因型和不同环境中叶部性状的表型评价数据,利用完备区间作图法进行数量性状座位(QTL)定位。结果发现,2个环境下共检测到85个叶部性状相关QTL,其中有12个全株叶片数相关QTL、14个穗上叶片数相关QTL、22个叶长相关QTL、17个叶宽相关QTL、20个叶夹角相关QTL。结合公共数据库和生物信息学分析方法共筛选出7个候选基因。其中Zm00001d013612编码微管蛋白,参与调控细胞骨架结构组成;Zm00001d053543参与油菜素甾醇介导的信号通路;Zm00001d031291编码的蛋白质具有组蛋白乙酰化功能;Zm00001d031292参与富含羟脯氨酸糖蛋白家族基因表达的调控;Zm00001d031296调控钾离子跨膜转运蛋白活性;Zm00001d031300、Zm00001d031303参与碳水化合物代谢过程。蛋白质功能分析结果表明,这7个候选基因均参与细胞分化,与植物的生长发育息息相关。本研究结果将为深度揭示玉米叶部性状变异的遗传基础提供更丰富的理论支持。

糯玉米茎秆穿刺强度QTL分析与基因组选择

茎秆穿刺强度是衡量玉米茎秆机械强度和抗倒伏能力的重要指标之一,本研究以衍生于糯玉米自交系衡白522和通系5的198个重组自交系为试验材料,对茎秆穿刺强度进行数量性状位点(QTL)分析和基因组选择研究。单个环境QTL分析共检测到4个控制糯玉米茎秆穿刺强度的QTL,每个QTL的表型变异贡献率均小于10.00%,且仅在单个环境中被检测到;多个环境QTL分析共检测到8个QTL与环境互作,其加性效应总共可解释24.64%的表型变异,加性效应与环境互作贡献率为17.51%;上位性QTL分析共检测到4对QTL与QTL互作,可解释8.25%的表型变异。基因组选择中,当训练群体占群体总数的80%,随机选择500个标记即可获得较高的预测准确性;但是根据单个环境QTL分析结果,选择机率常用对数值排名前200的标记,即可大幅度提高基因组选择预测准确性。

Validation of qGSIO, a quantitative trait locus for grain size on the long arm of chromosome 10 in rice (Oryza sativa L.)

Grain size is a major determinant of grain weight and a trait having important impact on grain quality in rice. The objective of this study is to detect QTLs for grain size in rice and identify important QTLs that have not been well characterized before. The QTL mapping was first performed using three recombinant inbred line populations derived from indica rice crosses Teqing/IRBB lines, Zhenshan 97/Milyang 46, Xieqingzao/Milyang 46. Fourteen QTLs for grain length and 10 QTLs for grain width were detected, including seven shared by two populations and 17 found in one population. Three of the seven com- mon QTLs were found to coincide in position with those that have been cloned and the four others remained to be clarified. One of them, qGSIO located in the interval RM6100-RM228 on the long arm of chromosome 10, was validated using F2：3 populations and near isogenic lines derived from residual heterozygotes for the interval RM6100-RM228. The QTL was found to have a considerable effect on grain size and grain weight, and a small effect on grain number. This region was also previously detected for quality traits in rice in a number of studies, providing a good candidate for functional analysis and breeding utilization.

Fine-Mapping of qTGW1.2a, a Quantitative Trait Locus for 1000-Grain Weight in Rice

Thousand-grain weight (TGW) is a key component of grain yield in rice. This study was conducted to validate and fine-map qTGW1.2a, a quantitative trait locus for grain weight and grain size previously located in a 933.6-kb region on the long arm of rice chromosome 1. Firstly, three residual heterozygotes (RHs) were selected from a BC2F11 population of the indica rice cross Zhenshan 97 (ZS97)///ZS97//ZS97/Milyang 46. The heterozygous segments in these RHs were arranged successively in physical positions, forming one set of sequential residual heterozygotes (SeqRHs). In each of the populations derived, non-recombinant homozygotes were identified to produce near isogenic lines (NILs) comprising the two homozygous genotypes. The NILs were tested for grain weight, grain length and grain width. QTL analyses for the three traits were performed. Then, the updated QTL location was followed for a new run of SeqRHs identification-NIL development-QTL mapping. Altogether, 11 NIL populations derived from four sets of SeqRHs were developed and used. qTGW1.2a was finally delimitated into a 77.5-kb region containing 13 annotated genes. In the six populations segregating this QTL, which were in four generations and were tested across four years, the allelic direction of qTGW1.2a remained consistent and the genetic effects were stable. For TGW, the additive effects ranged from 0.23 to 0.38 g and the proportions of phenotypic variance explained ranged from 26.15% to 41.65%. These results provide a good foundation for the cloning and functional analysis of qTGW1.2a.

大豆遗传图谱构建及粗脂肪含量QTL定位

大豆是我国重要的粮油作物,提高其粗脂肪含量是我国大豆育种的重要目标.以长江春2号与渝蜀鲜2号杂交的F 2代186株单株为定位群体,在实验室前期构建的遗传图谱基础上进行标记加密,利用Joinmap 4.0软件进行遗传连锁分析,构建了包含480个标记的大豆遗传图谱,总长度为2469.3 cM,标记间平均距离为5.14 cM.结合大豆在2021年重庆、2022年重庆、2022年云南和2023年重庆4个环境下的粗脂肪含量表型数据,通过区间作图(Interval Mapping,IM)检测相应性状的数量性状位点(Quantitative Trait Locus,QTL).在4个环境中共检测到21个与粗脂肪含量相关的QTLs,分布在16个连锁群上,其中,有3个QTLs(qOIL01.1,qOIL04.1和qOIL14.2)在两个环境中均能检测到.

高粱×苏丹草后代群体农艺及产量性状QTL初步定位

为进一步探究高粱籽粒和茎秆产量的遗传规律,运用粒用高粱忻粱52和苏丹草TS 185作为亲本进行杂交并得到F_(2)及F_(2)∶3群体,利用115对多态性引物对430份F_(2)群体使用区间作图法构建遗传连锁图谱,以LOD值等于3作为阈值。结果表明,分蘖数、叶片数、茎粗、穗长、株高、茎秆鲜质量、整株鲜质量、着壳率、穗质量、千粒质量、单穗粒质量、单穗粒数12个农艺性状共检测到86个QTL。在1号染色体sam17164—sam15397区间定位到茎秆鲜质量的QTL;在2号染色体Xcup64—Xcup26区间定位到分蘖数的QTL,Xtxp019—sam01138区间定位到叶片的QTL,Xcup26—Xtxp080区间定位到茎秆鲜质量的QTL;在3号染色体sam44791—sam33751区间定位到穗长的QTL;在7号染色体sam39622—sam43980区间定位到着壳率的QTL;在8号染色体sam10491—sam17740区间定位到整株鲜质量的QTL;在10号染色体sam710901b—sam59778区间定位到分蘖数的QTL,以上这些都是新检测到的位点。

基于全基因组关联分析的水稻穗长QTL定位及其候选基因分析

分别于2021年和2022年在湖南长沙种植254份水稻3K种质资源,在成熟期测定各种质的穗长。结合种质基因型进行全基因组关联分析,共检测到3个穗长QTL,分布于水稻第1、5、6号染色体,分别命名为qPL-1、qPL-5和qPL-6,相对贡献率为9.06%~28.27%;结合QTL区间内基因功能注释和基因不同单倍型的穗长差异显著性分析结果,最终获得qPL-1、qPL-5和qPL-6的候选基因,其中qPL-6与sped1-D共定位,Os01g0715600、Os05g0132700为调控穗长的新候选基因;对Os01g0715600、Os05g0132700和Os06g0597500的单倍型进行聚合分析,发现这3个基因存在累加效应。

Quantitative Trait Locus Analysis for Rice Yield Traits under Two Nitrogen Levels

A recombinant inbred line population derived from a super hybrid rice Xieyou 9308（Xieqingzao B/Zhonghui 9308） and its genetic linkage map were used to detect quantitative trait loci（QTLs） for rice yield traits under the low and normal nitrogen（N） levels. A total of 52 QTLs for yield traits distributed in 27 regions on 9 chromosomes were detected, with each QTL explaining 4.93%–26.73% of the phenotypic variation. Eleven QTLs were simultaneously detected under the two levels, and 30 different QTLs were detected under the two N levels, thereby suggesting that the genetic bases controlling rice growth under the low and normal N levels were different. QTLs for number of panicles per plant, number of spikelets per panicle, number of filled grains per panicle, and grain density per panicle under the two N levels were detected in the RM135–RM168 interval on chromosome 3. QTLs for number of spikelets per panicle and number of filled grains per panicle under the two N levels, as well as number of panicles per plant and grain density per panicle, under the low N level, were detected in the RM5556–RM310 interval on chromosome 8. The above described QTLs shared similar regions with previously reported QTLs for rice N recycling.

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

A major quantitative trait locus controlling phosphorus utilization efficiency under different phytate-P conditions at vegetative stage in barley

Organic phosphorus（P） is an important component of the soil P pool, and it has been proven to be a potential source of P for plants. The phosphorus utilization efficiency（PUE） and PUE related traits（tiller number（TN）, shoot dry weight（DW）, and root dry weight） under different phytate-P conditions（low phytate-P, 0.05 mmol L^-1 and normal phytate-P, 0.5 mmol L^-1） were investigated using a population consisting of 128 recombinant inbred lines（RILs） at the vegetative stage in barley. The population was derived from a cross between a P-inefficient genotype（Baudin） and a P-efficient genotype（CN4027, a Hordeum spontaneum accession）. A major locus（designated Qpue.sau-3 H） conferring PUE was detected in shoots and roots from the RIL population. The quantitative trait locus（QTL） was mapped on chromosome 3 H and the allele from CN4027 confers high PUE. This locus explained up to 30.3 and 28.4% of the phenotypic variance in shoots under low and normal phytate-P conditions, respectively. It also explains 28.3 and 30.7% of the phenotypic variation in root under the low and normal phytate-P conditions, respectively. Results from this study also showed that TN was not correlated with PUE, and a QTL controlling TN was detected on chromosome 5 H. However, dry weight（DW） was significantly and positively correlated with PUE, and a QTL controlling DW was detected near the Qpue.sau-3 H locus. Based on a covariance analysis, existing data indicated that, although DW may affect PUE, different genes at this locus are likely involved in controlling these two traits.

深水稻全生育期耐盐性状的QTL定位

盐胁迫是许多沿海地区水稻生产的主要制约因素,尤其是沿海地区的咸淡水交汇区域。耐盐性是一种复杂的性状,可以通过QTL定位来帮助耐盐育种,以培育更高耐盐性的水稻品种。本研究供体亲本为沿海深水稻品种赤禾,受体亲本为美国水稻品种Lemont,杂交获得174份F9代的重组自交系,在芽期、苗期和生殖生长期分别利用浓度为15 g L^(-1)、5 g L^(-1)和5~6 g L^(-1)的NaCl进行胁迫,通过芽期相对发芽率、苗期耐盐性评级和生殖生长期的7个表型性状为基础数据,利用142个SSR分子标记绘制连锁遗传图并进行QTL分析。鉴定结果发现,赤禾在芽期表现敏盐,在苗期和生殖生长期表现耐盐;Lemont相反。3个生长时期分别有70.11%、50.57%和60.34%的品系表现为弱耐盐性,而且耐盐性为弱的负相关。本研究共鉴定出33个LOD值为2.52~10.32的QTL,解释0.06%~13.68%的表型遗传变异,解释最大遗传变异的QTL均由耐盐亲本贡献,其中芽期4个、苗期6个和生殖生长期23个位点,并在生殖生长期发现4个QTL重叠区域。这些QTL可以进一步研究,不仅为提高水稻育种的耐盐性提供了新的遗传资源,还有助于在水稻耐盐育种中,提高水稻品种的耐盐性。

Mapping of qTGW1.1,a Quantitative Trait Locus for 1000-Grain Weight in Rice (Oryza sativa L.)

1000-grain weight （ TGW） is one ot the three component traits ot the grain yiela in rice （Oryza sativa L）. This study was conducted to validate and fine-map qTGWl. 1, a minor QTL for TGW which was previously located in a 3.7-Mb region on the long arm of rice chromosome 1. Five sets of near isogenic lines （NILs） were developed from two BC2F4 populations of the indica rice cross Zhenshan 973/Milyang 46 The NIL sets consisted of two homozygous genotypic groups differing in the regions RM11448-RM11522, RM11448-RM11549, RM1232-RM11615, RM11543-RM11554 and RM11569-RM11621, respectively. Four traits, including TGW, grain length, grain width and heading date, were measured. Phenotypic difference between the two genotypic groups in each NIL population was analyzed using SAS procedure GLM. Significant QTL effects were detected on TGW with the Zhenshan 97 allele increasing grain weight by 0.12 g to 0.14 g and explaining 8.30% to 15.19% of the phenotypic variance. Significant effects were also observed for grain length and width, whereas no significant effect was found for heading date. Based on comparison among the five NILs on the segregating regions and the results of QTL analysis, qTGWl. 1 was delimited to a 376.9-kb region flanked by DNA markers Wn28382 and RMl1554. Our results indicate that the effects of minor QTLs could be steadily detected in a highly isogenic background and suggest that such QTLs could be utilized in the breeding of high-yielding rice varieties.

Dissection of Genetic Effects of Quantitative Trait Loci(QTL) in Transgenic Cotton

When alien DNA inserts into cotton genome in multi-copy manner,several QTL in cotton genome are disrupted,which are called dQTL in this study.Transgenic mutant line is near-isogenic to its recipient which is divergent for the dQTL from remaining QTL.So,a set of data from a

Identification of Quantitative Trait Loci for Anthesis-Silking Interval and Yield Components Under Drought Stress in Maize

A genetic linkage map with 89 SSR marker loci was constructed based on a maize (Zea mays L.) population consisting of 184 F-2 individuals from the cross, Huangzao 4 X Ye 107. The 184 F-3 families were evaluated in the field under well-watered and drought-stressed regimes in Shanxi Province of China. The objectives of the study were to identify genetic segments responsible for the expression of anthesis-silking interval (ASI), ear setting and grain yield, and to examine if the quantitative trait loci (QTLs) for ASI or yield components can be used in marker-assisted selection (MAS) to improve grain yield under drought conditions. Results showed that under well-watered and drought-stressed regimes, three and two QTLs involved in the expression of ASI were detected on chromosomes 1, 2 and 3, and 2 and 5, respectively. Under well-watered regime, two QTLs for ear setting were detected on chromosomes 3 and 6, explaining about 19.9% of the phenotypic variance, and displayed additive and partial dominant effects, respectively. Under drought-stressed condition, four QTLs for ear setting were detected on chromosomes 3, 7 and 10, which were responsible for interpreting 60.4% of the phenotypic variance, and showed dominant or partial dominant effects. Under well-watered condition, four QTLs controlling grain yield were identified on chromosomes 3, 6 and 7, while five QTLs were identified under drought stress on chromosomes 1, 2, 4 and 8. The gene action was of additive or partial dominant effects, and each QTL could explain 7.3% to 22.0% of the phenotypic variance, respectively. Under drought conditions, ASI and ear setting percentage were highly correlated with grain yield, which can be used as secondary traits for grain yield selection. Based on linked markers detected and gene action analyzed, an MAS strategy for yield improvement under drought condition could be established, which consists of QTLs contributing to decreased ASI and to increased ear setting and grain yield, respectively.

Detection of Quantitative Trait Loci Associated with Several Internal Organ Traits and Teat Number Trait in a Pig Population

Quantitative trait loci （QTL） were detected for 8 internal organ traits, 3 carcass length traits, and teat number trait in 214 pigs in a resource population that included 180 F2 individuals. A total of 39 microsatellite markers were examined on SSC4, SSC6, SSC7, SSC8, and SSC13. The genetic traits included heart weight （HW）, lung weight （LW）, liver and gallbladder weight （LGW）, spleen weight （SPW）, stomach weight （STW）, small intestine weight （S1W）, large intestine weight （LIW）, kidney weight （KW）, carcass length to the first cervical vertebra （CL1）, carcass length to the first thoracic vertebra （CL2）, rib numbers （RNS）, and teat numbers （TNS）. Results indicated that, 3 highly significant QTL （P≤0.01 at chromosome-wise level） for HW （at 30 cM on SSC6）, RNS （at 115 cM on SSC7）, TNS （at 110 cM on SSC7）, and 6 significant QTL （P≤0.05 at chromosome-wise level） for LW （at 119 cM on SSC13）, LGW （at 94 cM on SSC6）, SPW （at 106 cM on SSC8）, SIW （0 cM on SSC4）, LIW （170 cM on SSC 4）, and TNS （at 95 cM on SSC6） were detected. The phenotypic variances for which these QTL were accounted ranged from 0.04 % to 14.06 %. Most of these QTL had not been previously reported.

Mapping QTLs for Panicle Traits Based on Rice RIL Population Derived from TD70 and Kasalath

Two hundred and forty recombinant inbred lines （RIL） derived from a cross TD70/Kasalath and its linkage map including 141 SSR markers were used to map QTLs controlling panicle length （PL）, total seeds per panicle （TSP） and grain density （GD） in 2010 and 2011. The results showed that a total of 23 QTLs controlling three panicle traits were detected on chromosomes 2, 3, 4, 6, 7, 8 and 10, respec- tively, including 5 QTLs controlling PL, 8 QTLs controlling TSP, 10 QTLs controlling GD, with the LOD value ranging between 2.5-9.3, and the QTLs explained the ob- served phenotypic by 4.0%-20.8%. The marker interval RM5699-RM424 on chro- mosome 2, RM489-RM1278 on chromosome 3, RM3367-RM1018 on chromosome 4, RM3343-RM412 on chromosome 6 were common marker intervals for TSP and GD; six QTLs （qPL3, qTSP4, qTSP6-2, qTSP7, qGD3-2 and qGDT） were detected in two years. Among these QTLs, the qPL3, qTSP6-2, qGD3-2 and qGD7 were major QTLs. All QTLs for PL mapped in the present study had been mapped QTLs previously by other research groups, 16 QTLs controlling TSP and GD were new ones which contributed the observed phenotypic variance range by 4%-9.5%. These results laid a founda^ion for further fine positioning or cloning these QTLs.

利用双向导入系定位再生稻外观品质的QTL

再生稻具有较好的外观品质。为解析再生稻外观品质的遗传基础,本研究利用籼稻明恢63和粳稻02428构建的双向导入系为材料,连续两年在湖北荆州考察了双向导入系头季和再生季的外观品质。结果表明,明恢63头季和再生季的外观品质均显著优于02428,双向导入系的所有性状均表现为连续分布。同一性状在头季和再生季间表现为显著正相关,同一季节内不同性状间也表现出显著的相关性,其中粒宽对外观品质的影响最大。结合双向导入系已有的4568个Bin的高密度基因型数据,共定位到57个影响再生稻外观品质的数量性状点位(QTL),位于全部12条染色体上。其中25个QTL在两年间稳定表达,17个QTL在两个遗传背景下被重复鉴定到。此外,第3号染色体16.28~17.33 Mb、第5号染色体3.35~4.28 Mb、第7号染色体24.68~25.46 Mb和第11号染色体6.19~6.97 Mb这4个区间同时影响4个以上性状,来自明恢63的等位基因在这4个区间均可提高外观品质。最后,利用分离群体验证了第11号染色体6.19~6.97 Mb具有真实性。本研究结果为分子育种改良再生稻的外观品质提供了遗传基础。

花生蔗糖含量与蛋白质和含油量的相关性分析及蔗糖含量QTL定位

随着食用型花生市场规模的日益扩大,食用花生的品质特性备受关注。甜味是影响食用花生风味和口感的重要指标,而花生甜味主要来源于蔗糖。提高蔗糖含量是培育食用型花生的关键,定位蔗糖含量相关的QTL,将为花生高糖分子育种奠定基础。但提高蔗糖可能对蛋白质和含油量有影响。本研究以徐花13和中花6号为亲本构建的重组自交系群体(RIL)为材料,分析了蔗糖与蛋白质和含油量的关系,并对蔗糖含量进行了QTL定位。结果表明,蔗糖含量与蛋白质含量无显著相关性,而与含油量呈极显著负相关;RIL群体中蔗糖含量变异广泛,变异范围为14.33~61.42mg g^(-1);筛选出2份高蛋白低脂肪中等含糖量的材料。通过该群体的遗传连锁图,在3个环境下共检测到10个QTL,分布在6条染色体上,贡献率为4.56%~12.25%。其中,qSUCA07能在3个环境下被重复检测到。本研究为蔗糖含量精细定位和培育性状优良的食用型花生品种提供了重要信息。

基于高密度遗传图谱的芝麻籽粒品质相关性状QTL定位

芝麻是我国重要的优质油料作物,对芝麻籽粒品质性状进行数量性状位点(QTL)定位对定向培育高品质芝麻品种具有重要意义。以豫芝4号为母本、孟加拉小籽为父本,分别构建F2、F2:3、BC1和BC1F2群体,结合特异位点扩增片段(SLAF)标记和简单重复序列(SSR)标记构建F2和BC1遗传图谱,以F2:3、BC1和BC1F23个群体的表型数据为基础,进行脂肪、蛋白质、芝麻素、芝麻林素含量等4个品质性状的QTL作图分析。结果表明,在F2:3群体中共检测到16个QTL,解释表型变异的5.08%~27.12%,其中仅有1个主效QTL qOC_10-1在2个环境中被重复检测到,分别解释表型变异的9.62%和27.12%。在BC1和BC1F2群体中共检测到35个QTL,其中3个主效QTL qOC_4-1、qOC_10-2和qSmin_7-2在3个环境中被重复检测到,分别解释表型变异的8.08%~12.42%、11.95%~12.60%和4.24%~10.56%;3个主效QTL qSmin_8、qSmol_5-2和qSmol_7-2在2个环境被重复检测到,分别解释表型变异的13.36%~26.75%、11.44%~14.33%和5.77%~12.38%。经过对2个图谱进行整合和比对分析,共发现10个QTL簇,其中QTL簇loci4、loci7、loci8和loci10均与多个性状相关联,并且均至少包含1个主效QTL,其对应的最大表型变异解释率分别为12.42%、12.38%、26.75%和27.12%。

作物杂种优势相关基因挖掘及QTL定位研究进展

杂种优势利用是提升作物产量、抗逆性和品质的重要手段之一,目前杂种优势已被广泛应用于杂交育种研究中。随着分子生物学、基因工程、高通量测序技术等的高速发展,研究人员在不同层面不断探索作物杂种优势的遗传基础,杂种优势数量性状基因座(Quantitative trait locus,QTL)定位与相关基因挖掘研究是其中的重要方面,对解析杂种优势分子机理具有重要的理论意义。本文对玉米、水稻、大豆等主要农作物中已定位的株型、粒质量及产量等杂种优势相关QTL或克隆基因的类型、功能及分子机理进行阐述和总结,以期通过结合现代分子生物学技术与高通量组学数据分析技术,深度解析作物杂种优势遗传基础,为推动杂种优势高效利用提供参考。