Development of IP and SCAR markers linked to the yellow seed color gene in Brassica juncea L.

Previous studies showed that the yellow seed color gene of a yellow mustard was located on the A09 chromosome. In this study, the sequences of the molecular markers linked to the yellow seed color gene were analyzed, the gene was primarily mapped to an interval of 23.304 to 29.402M. Twenty genes and eight markers’ sequences in this region were selected to design the IP and SCAR primers. These primers were used to screen a BC₈S₁ population consisting of 1256 individuals. As a result, five IP and five SCAR markers were successfully developed. IP4 and Y1 were located on either side of the yellow seed color gene at a distance of 0.1 and 0.3 cM, respectively. IP1, IP2 and IP3 derived from Bra036827, Bra036828, Bra036829 separately, co-segregated with the target gene. BLAST analysis indicated that the sequences of newly developed markers showed good collinearity with those of the A09 chromosome, and that the target gene might exist between 27.079 and 27.616M. In light of annotations of the genes in this region, only Bra036828 is associated with flavonoid biosynthesis. This gene has high similarity with the TRANSPARENT TESTA6 gene, Bra036828 was hence identified as being the gene possibly responsible for yellow seed color, in our research.     

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R²; 0.29–0.69) outperformed both PTF‐1 (R²; 0.11–0.18) and PTF‐2 (R²; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R²; 0.61–0.86) performed comparably as PTF‐1 (R²; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R²; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).     

人工合成小麦紫色胚芽鞘的遗传定位分析

为探明小麦花青素色素沉积的关键位点,以167份重组自交系群体为材料,利用已构建的高密度遗传图谱对来源于硬粒小麦(AABB)和节节麦亚种tauschii(DD)杂交后所得的人工合成小麦SHW-L1中控制紫色胚芽鞘的基因进行遗传定位。同时利用目标性状关联遗传区段对SHW-L1及其亲本进行基因型鉴定。结果表明:目标性状在重组自交系群体中表现为单基因遗传,且被定位于7D染色体,其关联遗传区段在异源六倍化过程中有遗传位点缺失。     

Association analysis for detecting significant single nucleotide polymorphisms for phosphorus-deficiency tolerance at the seedling stage in soybean [Glycine max (L) Merr.]

Tolerance to low-phosphorus soil is a desirable trait in soybean cultivars. Previous quantitative trait locus (QTL) studies for phosphorus-deficiency tolerance were mainly derived from bi-parental segregating populations and few reports from natural population. The objective of this study was to detect QTLs that regulate phosphorus-deficiency tolerance in soybean using association mapping approach. Phosphorus-deficiency tolerance was evaluated according to five traits (plant shoot height, shoot dry weight, phosphorus concentration, phosphorus acquisition efficiency and use efficiency) comprising a conditional phenotype at the seedling stage. Association mapping of the conditional phenotype detected 19 SNPs including 13 SNPs that were significantly associated with the five traits across two years. A novel cluster of SNPs, including three SNPs that consistently showed significant effects over two years, that associated with more than one trait was detected on chromosome 3. All favorable alleles, which were determined based on the mean of conditional phenotypic values of each trait over the two years, could be pyramided into one cultivar through parental cross combination. The best three cross combinations were predicted with the aim of simultaneously improving phosphorus acquisition efficiency and use efficiency. These results will provide a thorough understanding of the genetic basis of phosphorus deficiency tolerance in soybean.     

结球甘蓝抽薹性遗传规律和QTL定位分析

以早抽薹‘S-1’与晚抽薹‘G-1’为亲本，构建F1、BC1、BC2和F2群体，采用植物数量性状主基因 + 多基因混合遗传模型分析法对结球甘蓝抽薹性状进行遗传分析，并采用SLAF-BSA方法对抽薹时间进行QTL定位分析。结果显示，结球甘蓝抽薹性状是由2对加性—显性—上位性主基因 + 加性—显性多基因遗传控制；主基因 + 多基因平均遗传率是93.41%。共检测到2个QTL，分别为2号染色体上的2.31 ~ 3.09 Mb和33.57 ~ 34.40 Mb，总长度为1.61 Mb。     

胡萝卜根中主要类胡萝卜素含量相关QTL的精细定位

以白色的野生胡萝卜‘松滋野生’(Ws)和橘色的栽培胡萝卜品种‘Amsterdam’(Af)为亲本构建的回交重组自交系(BIL)为试材,基于低倍重测序技术开发SNP标记,构建了由1 976个Bin标记组成,包含29 435个SNP标记的遗传图谱。图谱总距离834.28 c M,平均图距0.42 c M。通过对胡萝卜肉质根中类胡萝卜素含量相关QTL分析,在连锁群LG04和LG08中检测到调控α–胡萝卜素、β–胡萝卜素、ζ–胡萝卜素、叶黄素、玉米黄质和总类胡萝卜素含量的主效QTL(M-QTL)2、2、3、2、2和2个,表型贡献率为11.47%~19.18%;另检测到调控α–胡萝卜素、β–胡萝卜素、ζ–胡萝卜素、玉米黄质和总类胡萝卜素含量的上位性QTL(E-QTL)1、1、2、1和1个,表型贡献率为2.50%~3.66%。在M-QTL显著区间内共检索到36个有功能注释的预测基因,其中Dck018297为ζ–胡萝卜素脱氢酶2基因,与调控β–胡萝卜素合成和总类胡萝卜素含量有关;Dck008006为乙烯响应因子2.2的同源基因,与调控α–胡萝卜素、ζ–胡萝卜素合成有关;Dck029898为转录因子b HLH135的同源基因,与调控玉米黄质合成有关。     

Genetic analysis and QTL mapping of growth period traits and plant height traits in soybean recombinant inbred lines from Dongnong 47 × PI 317334-B

High and stable yield is the main goal of soybean genetic improvement. In this study, association analysis was used to detect the quantitative trait loci (QTL) for the plant height, and soybean growth period using 182 SSR markers in the RIL population of 136 F_4:8 lines, which developed from a cross between photoperiod-insensitive cultivar ‘Dongnong 47’ and photoperiod-sensitive variety PI317334–B. The results showed that 33 QTLs related to soybean growth period and plant height traits were detected by compound interval mapping, and were located on 12 linkage groups including N, C1, C2, J, D1a, B2, E, G, A2, O, L, I, with the contribution rate of 7.85–33.84%. These QTL loci and linkage markers related to soybean photoperiod sensitivity, would be helpful to identify key genes that control soybean photoperiod sensitivity, and provide an important basis for the breeding of new photoperiod-insensitive soybean varieties based on molecular design breeding.