Comparative Map-based Cloning for Genes Conferring Resistance to Soybean Cyst Nematode

Soybean cyst nematode （SCN, Heterodera glycines Ichinohe） is the most dangerous disease to cause significant yield losses. Most recent estimates for SCN indicate losses of nearly 9 million metric tons worldwide in 1998 and 3.5 million metric tons in the China in 2004. Soybean cyst nematode causes yield reductions by feeding on plant nutrients, retarding root growth, and inhibiting Bradyrhizobium nodulation.     

Chickpea molecular breeding: New tools and concepts

Summary Chickpea is a cool season grain legume of exceptionally high nutritive value and most versatile food use. It is mostly grown under rain fed conditions in arid and semi-arid areas around the world. Despite growing demand and high yield potential, chickpea yield is unstable and productivity is stagnant at unacceptably low levels. Major yield increases could be achieved by development and use of cultivars that resist/tolerate abiotic and biotic stresses. In recent years the wide use of early maturing cultivars that escape drought stress led to significant increases in chickpea productivity. In the Mediterranean region, yield could be increased by shifting the sowing date from spring to winter. However, this is hampered by the sensitivity of the crop to low temperatures and the fungal pathogen Ascochyta rabiei. Drought, pod borer (Helicoverpa spp.) and the fungus Fusarium oxysporum additionally reduce harvests there and in other parts of the world. Tolerance to rising salinity will be a future advantage in many regions. Therefore, chickpea breeding focuses on increasing yield by pyramiding genes for resistance/tolerance to the fungi, to pod borer, salinity, cold and drought into elite germplasm. Progress in breeding necessitates a better understanding of the genetics underlying these traits. Marker-assisted selection (MAS) would allow a better targeting of the desired genes. Genetic mapping in chickpea, for a long time hampered by the little variability in chickpea’s genome, is today facilitated by highly polymorphic, co-dominant microsatellite-based markers. Their application for the genetic mapping of traits led to inter-laboratory comparable maps. This paper reviews the current situation of chickpea genome mapping, tagging of genes for ascochyta blight, fusarium wilt resistance and other traits, and requirements for MAS. Conventional breeding strategies to tolerate/avoid drought and chilling effects at flowering time, essential for changing from spring to winter sowing, are described. Recent approaches and future prospects for functional genomics of chickpea are discussed.     

籼稻9311辐射突变体的分离与鉴定

利用350Gy的60Co-γ对籼稻9311进行辐射处理,以诱导产生大量的农艺与发育性状突变体,为水稻遗传研究和功能基因组学研究提供基础材料。M1代单本栽插并实行单株收获;M2代种植5000个家系,根据各个生育阶段形态性状表现进行初步鉴定,共有1665个家系的4136个单株发生形态变异,并筛选出各类形态突变体2001份;M3代按系谱种植进行重复鉴定获得包括叶、茎、穗、育性和熟期变化等各类突变体1996份。形态性状的突变频率按M2代种植家系中突变家系的百分比计算为33.3%,表明9311对γ射线是十分敏感的。本研究还发现辐射突变表现为单基因突变和多基因突变同时发生。同时,还筛选到一些优良农艺性状的突变体,可以直接应用于育种及生产当中。     

梨Trihelix转录因子家族成员鉴定及表达分析

Trihelix转录因子可以和光应答相关的GT元件结合,因此又称GT转录因子。本研究从梨基因组中鉴定出16个Trihelix家族基因,依次命名为PbGT1~PbGT16,从同属于蔷薇科的草莓和桃基因组中分别鉴定出11和16个Trihelix家族基因。染色体定位与基因复制事件分析表明,梨、草莓和桃Trihelix家族成员分别分布在12、6和8条染色体上,且梨Trihelix家族存在片段复制事件。种间系统进化树分析表明,梨、草莓和桃Trihelix家族成员分为6个亚族,梨家族成员(PbGT)属于GT-2、Subfamily O和SIP1亚族。结合进化关系及qRT-PCR验证,筛选出PbGT15可能参与调控梨果实石细胞团木质化。     

深度学习在植物基因组学与作物育种中的应用现状与展望

[目的/意义]随着单细胞测序、高通量技术的突破,植物基因组学也取得了巨大进步,可以低成本获取多维全基因组分子表型的海量数据。深度学习技术可以作为强大的数据挖掘工具对获取的分子表型进行进一步预测和解释。当前研究表明,深度学习在植物基因组学与作物育种研究任务中取得显著效果。但目前尚缺乏对于深度学习在植物基因组学中应用的完整综述。[方法/过程]本文首先概述了深度学习方法背景,包括最新的图神经网络;随后着重从基因特性、蛋白质特性方面综述了基因组学和深度学习交叉领域的两个突出问题：1）如何对从植物基因组DNA序列到分子表型的信息流进行建模?2）如何使用深度学习模型识别自然种群中的功能变异?[结果/结论]本文总结了当前研究中如何应用传统深度学习算法、图深度学习、生成对抗网络以及可解释性AI等方法解决上述两个问题。最后分析了深度学习在未来植物基因组学研究和作物遗传改良中的发展前景。     

昆虫病原真菌基因组学及多组学研究进展

昆虫病原真菌是重要的生物防治资源,利用昆虫病原真菌防治农业害虫具有不易产生抗性、易造成昆虫病害流行、生态安全环保等优势,推广应用昆虫病原真菌控制害虫是今后生态农业的发展趋势。现代测序技术的飞速发展促进了真菌基因组学的研究,在全基因组测序、系统发育基因组学、比较基因组学以及多组学(转录组学、蛋白组学、代谢组学)联合分析等研究领域取得显著成果。文章就昆虫病原真菌白僵菌、绿僵菌、冬虫夏草菌、蛹虫草菌等基因组学的最新研究进展,结合本团队的研究工作进行综述,涉及利用基因组学及多组学联合揭示昆虫病原真菌的种内或物种间的进化、寄主范围改变、侵染过程效应蛋白鉴定、真菌代谢产物合成,以及真菌侵染机制、昆虫免疫机制、真菌-昆虫互作机制等方面,并探讨昆虫病原真菌基因组学研究的未来发展方向,为今后农业害虫生物控制的应用提供科学依据。     

Assessing single-nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a <3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.     

南瓜矮生基因Bu的比较定位

 以中国南瓜矮生突变体为供体亲本,以印度蔓生南瓜为轮回亲本,构建了BC6F2分离群体。利用黄瓜基因组序列,将南瓜矮生基因Bu比较定位至黄瓜5号染色体,并开发了一个新的PCR标记IF3629,该标记与矮生基因Bu连锁遗传距离为1.0 cM。该标记不仅可以用于分子标记辅助选择育种,而且为Bu基因的克隆奠定了基础。     

基于选择消除分析挖掘豌豆耐冻相关基因

近年来，冬季的低温对豌豆生产造成严重损失。本试验对16 份极端耐冻豌豆和14 份极端冷敏感豌豆的全基因组重测序数据与参考基因组进行比对获得SNP，再基于过滤后的SNP 数据对所有材料进行系统发育树构建、主成分分析、连锁不平衡分析以及选择消除分析等，并依据选择消除分析的群体多样性（θπ）、杂合率（Hp）和群体分化（Fst）三者交集的前5%候选区域，最终筛选获得34 个与豌豆耐冻相关的基因。研究结果为豌豆耐冻品种选育和“冬豆北移”栽培模式提供理论支撑。     

生防菌株Bacillus velezensis Z全基因组测序分析

生防菌株Bacillus velezensis Z对胡椒瘟病等多种植物病害具有良好的生防效果;全基因组测序能够为其分子机理研究和开发应用提供信息基础。本研究开展该菌株全基因组测序,并进行比较基因组学和抑菌次生代谢产物合成基因簇预测研究。结果表明：B. velezensis Z菌株的基因组中含有1条4 054 780 bp大小的环形染色体DNA和1个17 122 bp大小的环形质粒,总基因组的GC含量为46.24%,共编码基因4173个;包含27个rRNA,86个tRNA基因,34个sRNA;含有串联重复序列179个,其中13个微卫星DNA,138个小卫星DNA;通过比较基因组学分析,结果发现该菌株与贝莱斯芽孢杆菌模式菌株FZB42同源性极高,与枯草芽孢杆菌模式菌株168之间具有一定的遗传距离;同时发现B. velezensis Z基因组中共编码次生代谢产物合成基因簇13个,其中8个与表面活性素（surfactin）、泛革素（fengycin）、溶杆菌素（bacilysin）、macrolactin H、bacillaene、difficidin、plantazolicin、amylocyclicin等已知基因簇完全相似或高度相似,其他5个基因簇皆功能未知。总之,本研究揭示了B. velezensis Z的全基因组遗传信息,明确其与贝莱斯芽孢杆菌和枯草芽孢杆菌的比较基因组学关系,预测了抑菌产物合成编码基因簇,为该生防菌株及其抑菌产物的机理研究和开发应用奠定基础。