梁山慈竹(Dendrocalamus farinosus)体细胞突变体No.30生物量及品质特性分析

本试验对梁山慈竹体细胞突变体No.30的生物量以及品质特性,如纤维素含量、木质素含量和纤维形态等相关指标进行了研究。结果表明,与同龄的实生苗相比,No.30突变体具有生物量大、纤维素含量高、纤维长和长宽比大等特性。尽管No.30突变体中木质素含量也有升高,但木质素组成S/G值并没有明显改变,表明木质素脱除的难易程度并未受影响。基因表达分析表明,在No.30突变体中,纤维素生物合成途径中的Ces A和木质素生物合成途径中的Prx基因明显上调,可能与其纤维素和木质素含量的增加密切相关。     

基于SLAF-seq简化基因组技术的沙冬青SNP位点开发及遗传分析

选取分布在内蒙古、宁夏、甘肃10个地点的沙冬青个体,用SLAF-seq简化基因组技术进行测序。以大豆基因组为参照,通过生物信息学分析进行实验方案的系统设计,筛选特异长度的DNA片断,构建SLAF-seq文库,并通过高通量测序的方式获得海量序列,再通过软件分析比对,获得多态性SLAF标签,最后在多态性SLAF标签上开发大量特异性SNP位点。结果共获得374265个SLAF标签,其中多态性SLAF标签56295个。通过序列分析,共开发得到127278个SNP。对所有的SNP根据完整度>0.5,MAF>0.05过滤,共得到102025个高一致性的群体SNP。基于过滤后的SNP,运用数学统计学方法,对10份沙冬青个体完成系统进化树、群体结构、PCA分析,从基因组水平揭示不同个体之间的遗传分化关系。结果表明,通过群体结构分析发现这10个地点的沙冬青都来源于同一祖先,但由于地理位置的影响使得这10个地点的沙冬青在生长发育过程中发生了遗传分化。通过系统进化树发现分布在内蒙古的沙冬青具有较近的亲缘关系,甘肃和宁夏的沙冬青有较近的亲缘关系。     

基于SLAF-seq技术的木薯SNP标记开发

开发大量木薯(Manihot esculenta)特异分子标记可促进木薯种质资源中优良基因的挖掘和利用。本研究以39份木薯种质资源为材料,利用特异性位点扩增片段测序(specific-locus amplified fragment sequencing,SLAF-seq)技术进行测序,结果显示:本次实验双端比对效率为90.83%,酶切效率为91.18%;通过测序获得304.30 Mb的读长数据,各样本读长范围在2 123 094~15 102 046之间,对照数据量最小,仅395 790个读长;样品测序质量值(Q_(30))在95.62%~96.60%之间,均在95%以上;GC含量在41.27%~44.96%之间,平均GC含量为42.89%,对照的GC含量为42.93%。本研究共开发出725 220个SLAF标签,样本的平均测序深度为20.98倍(×),多态性SLAF标签446 789个,共得到2 504 553个群体SNP标记。可见,SLAF-seq技术适用于木薯SNP位点的开发,其效率较高。     

湿加松亲本间SNP遗传距离与生长性状杂种优势关系研究

通过估算湿加松亲本间SNP位点遗传距离,以有效预测杂交后代的树高、胸径、材积杂种优势,为分子辅助交配设计育种提供参考。利用SLAF-seq技术对131个湿加松亲本种质资源进行测序,获得有效的SNP标记;基于SNP位点信息,利用MEGA5.0软件估算13个湿加松亲本间遗传距离,并进行聚类分析,同时利用SPSS软件分析13个杂交组合生长性状杂种优势与SNP遗传距离的相关性。SLAF-seq共获得53 952个多态性SLAF标签,96 736个有效SNP标记;湿加松亲本间遗传距离介于0.425 1～0.490 6,亲本间SNP遗传距离与树高、胸径、材积生长性状杂种优势相关系数分别为0.680、0.648、0.624,均达到显著正相关水平。SLAF-seq技术可提供海量SNP位点,根据海量SNP位点信息可估算湿加松亲本间遗传距离,以有效预测树高、胸径、材积杂种优势。     

四川2种丛生竹理化特性及纤维形态研究

以四川不同地区两种丛生竹—慈竹和梁山慈竹为研究对象,通过测定纤维素和木质素含量以及纤维形态,对其变异规律进行研究。结果表明,同一地区不同竹种间纤维素和木质素含量差异显著,同一竹种不同地区间纤维素和木质素含量、纤维长度、宽度和纤维长宽比因地域差异而异,且与竹龄有关。从纸浆用竹和纤维原料综合考虑,竹海地区的慈竹（纤维素含量在45%左右,木质素含量低于28%）、梁山慈竹（纤维素含量在50%以上,木质素含量低于20%）比较适合竹浆造纸。     

一个籼稻脆性突变体的生物学特性及基因定位研究

水稻脆性突变体是研究细胞壁组分结构形成机制的重要材料。通过离子束诱变籼稻9311获得1个茎秆、叶片均脆的突变体,命名为bc9311-1。bc9311-1突变体与野生型9311相比,分蘖数减少,结实率显著降低,其他农艺性状无明显差异。叶片和茎秆的细胞壁成分分析表明,与野生型相比,bc9311-1突变体茎秆中的纤维素和木质素含量明显降低,半纤维素和SiO2含量显著增加;叶片中的纤维素含量降低,半纤维素和木质素含量增加,SiO2含量无明显差异。遗传分析表明,该脆性突变体脆性性状受单隐性基因控制。以bc9311-1突变体与02428杂交的F2群体为基因定位群体,利用SSR标记将bc9311-1突变位点定位在水稻第1染色体上,位于SSR分子标记的RM1095和RM3632之间,遗传距离分别为0.6cM和3.4cM,与其中的标记RM1183表现共分离。这些结果为进一步克隆突变基因,揭示脆性性状的分子机制奠定坚实基础。     

利用改进的MutMap方法克隆水稻雄性不育基因

通过对籼稻黄华占EMS(甲磺酸乙酯)诱变, 筛选得到一隐性核不育的水稻雄性不育突变体osms55, 遗传分析表明该突变体为单基因控制的隐性核不育, 采用高通量的Illumina Infinium iSelect SNP(50 K)芯片检测技术鉴定该突变体的遗传背景, 确认该突变体的遗传背景与黄华占一致。文章利用改进的MutMap方法成功克隆该雄性不育基因, 突变位点与突变表型的共分离分析表明LOC_Os02g40450(MER3)是控制osms55突变体雄性不育的基因, 该基因的剪切识别位点发生变异后导致剪切异常, 造成第5外显子缺失15个碱基, 从而产生雄性不育。改进的MutMap方法无需精确组装的野生型基因组序列作对照, 而是通过将定位群体中有突变表型植株的DNA pool和野生型植株DNA的重测序结果分别与日本晴参考基因组进行比对, 然后再比较突变体和野生型的差异SNP来确定候选基因, 该方法大大降低了野生型基因组测序和组装成本, 进一步扩大了MutMap方法的应用范围。     

中华蜜蜂的单核苷酸多态性和插入缺失突变位点鉴定

【目的】本研究拟利用已获得的中华蜜蜂Apis cerana cerana幼虫肠道的转录组数据对单核苷酸多态性（Single nucleotide polymorphism,SNP）和插入缺失（Insertion-Deletion,InDel）突变位点进行挖掘和分析,旨在丰富中华蜜蜂的SNP和InDel信息,并为新型分子标记的开发提供基础。【方法】根据有效读段与东方蜜蜂Apis cerana参考基因组的比对情况,采用GATK软件识别单碱基错配和碱基的插入缺失情况,再利用ANNOVAR软件对SNP位点和InDel位点进行分析。通过相关生物信息学软件将SNP和InDel位点所在基因分别比对GO和KEGG数据库,以获得相应的功能和通路注释。【结果】共鉴定到中华蜜蜂的58 919个SNP位点,包括24 548个纯合位点和34 371个杂合位点;发生转换和颠换的SNP位点分别有49102和9817个;数量最多和最少的突变类型分别是C/T和T/G;分布在外显子区的SNP位点数量最多,达到22 649个;此外,发生同义突变的SNP位点数量最多,其次是非同义突变;SNP位点所在基因可注释到46个GO条目和121条KEGG通路。共鉴定到6 551个InDel位点,包括3 270个插入突变和3 281个缺失突变;分布在内含子区InDel位点最多,共计2 793个;发生移码插入的InDel位点最多;进一步分析结果显示InDel位点所在基因可注释到27个GO条目和28条KEGG通路。【结论】本研究鉴定到中华蜜蜂的大量SNP位点和InDel位点,解析了SNP和InDel位点的突变类型、基因组功能元件分布和密码子突变类型,并揭示SNP和InDel位点对中华蜜蜂的重要生物学过程具有潜在影响。     

基于全基因组SNP分子标记分析青海云杉遗传结构

以张掖龙渠青海云杉（Picea crassifolia）无性系种子园中的106个青海云杉亲本无性系为研究材料,采用改良CTAB法,提取的青海云杉基因组DNA,构建SLAF文库并进行高通量测序,之后分析SLAF测序数据和筛选SNP位点,基于邻接法分析得到样品的聚类情况。研究得出：将测序的水稻日本晴reads与其参考基因组进行比对,显示本试验双端比对效率为95.33%,说明SLAF建库成功。本研究中所测序列的Q30较高,碱基测 序错误率低,测序质量高;本研究共开发4 058 883个SLAF标签,标签的平均测序深度为21.21×。共开发 12 275 765个青海云杉SNP标记,各青海云杉样本的SNP数量为1 890 934~4 487 841。利用已开发的高质量青海云杉SNP标记,构建了106个青海云杉的系统发育树,发现来自不同种源的青海云杉在各组中分布比较均匀,不同种源的青海云杉多聚为一类。通过SNP标记和主成分分析,这些无性系来源于同一个祖先的可能性较大,表明无性系间亲缘关系相近。为今后遗传多样性的分析、遗传图谱的构建等提供了基础数据,也为青海云杉初级种子园去劣疏伐提供依据,为高世代种子园的营建奠定基础。     

基于简化基因组测序的大黄鱼耐高温性状全基因组关联分析

利用Illumina HiSeqTM 2500测序平台, 对通过高温胁迫实验筛选得到的20尾耐高温和20尾不耐高温的大黄鱼(Larimichthys crocea)进行了简化基因组测序(SLAF-seq), 每个样本的平均测序深度达到10.26×, 共获得419211个高质量的群体单核苷酸多态性(SNP)位点 。利用TASSEL软件的混合线性模型(MLM)进行全基因组关联分析(GWAS), 共筛选到38个与大黄鱼耐高温性状显著相关的SNP位点(P<2.39E–08)。利用BLAST程序定位每个SNP位点在大黄鱼基因组中的位置, 并分析其周围的功能基因。结果在38个SNPs附近共找到26个已知的功能基因, 这些基因主要与细胞转录、代谢、免疫等功能相关。研究结果可为下一步大黄鱼耐高温分子机制解析及耐高温品种的选育提供参考。     

Mapping the Flavor Contributing Traits on "Fengwei Melon" (Cucumis melo L.) Chromosomes Using Parent Resequencing and Super Bulked-Segregant Analysis

We used a next-generation high-throughput sequencing platform to resequence the Xinguowei and Shouxing melon cultivars, the parents of Fengwei melon. We found 84% of the reads (under a coverage rate of “13×”) placed on the reference genome DHL92. There were 2,550,000 single-nucleotide polymorphisms and 140,000 structural variations in the two genomes. We also identified 1,290 polymorphic genes between Xinguowei and Shouxing. We combined specific length amplified fragment sequencing (SLAF-seq) and bulked-segregant analysis (super-BSA) to analyze the two parents and the F₂ extreme phenotypes. This combined method yielded 12,438,270 reads, 46,087 SLAF tags, and 4,480 polymorphic markers (average depth of 161.81×). There were six sweet trait-related regions containing 13 differential SLAF markers, and 23 sour trait-related regions containing 48 differential SLAF markers. We further fine-mapped the sweet trait to the genomic regions on chromosomes 6, 10, 11, and 12. Correspondingly, we mapped the sour trait-related genomic regions to chromosomes 2, 3, 4, 5, 9, and 12. Finally, we positioned nine of the 61 differential markers in the sweet and sour trait candidate regions on the parental genome. These markers corresponded to one sweet and eight sour trait-related genes. Our study provides a basis for marker-assisted breeding of desirable sweet and sour traits in Fengwei melons.     

基于SLAF-seq技术的苗药八爪金龙遗传分析

为探究苗药八爪金龙群体遗传进化和亲缘关系的远近,该研究利用简化基因组测序技术(SLAFseq)对42份八爪金龙样品进行测序,获得多态性SLAF标签。同时采用GATK和SAMtools软件在多态性SLAF中检测单核苷酸多态性(SNP)分子标记,并利用SNP分子标记分析八爪金龙样品间的遗传分化关系。结果表明:(1) 42份八爪金龙共获得246.35 Mb reads,测序质量值Q30的平均值为95.66%,GC含量的平均值为41.14%。(2)通过生物信息学的分析,获得1 769 265个SLAF标签,其中379 829个多态性SLAF标签,共开发2 299 640个SNPs分子标记。(3)利用开发的SNPs数据构建八爪金龙的系统发育树,42份八爪金龙分成两个大的类群。第一类群为细柄百两和原变种百两金;第二类群由贵州朱砂根、红凉伞、湖北朱砂根和江西朱砂根组成。江西朱砂根与其余群体关系较远,有明显的分群现象。该研究从基因组水平揭示不同地区八爪金龙资源之间的遗传关系,为八爪金龙种质资源的鉴定和遗传多样性分析提供了理论基础,所开发的SNP位点可进一步用于挖掘与品质、抗逆性等相关的基因。     

基于SNP标记的短须裂腹鱼自然群体遗传多样性分析

研究应用SLAF-seq技术开发了短须裂腹鱼(Schizothoraxwangchiachii)的SNP标记, 并使用开发的SNP标记分析了野生种群的遗传多样性。结果显示实验中RsaⅠ-HaeⅢ的酶切效率为86.93%, 共得到80.08 M reads。通过生物信息学分析, 共获得709758个SLAF标签, 其中多态性的SLAF标签共有243304个, 共得到777082个群体SNP, 测序平均Q30为94.79%, 平均GC含量为40.16%, 测得观测等位基因数为2.005, 期望等位基因数为1.5272, 观测杂合度为0.2007, 期望杂合度为0.3160, 平均遗传距离为0.1523, 遗传多样性指数为0.3386, 香浓指数为0.4827, 多态性信息含量(PIC)为0.2562, 次要基因型频率(MAF)为0.2313, 结果表明目前金沙江阿海至龙开口段短须裂腹鱼野生种群遗传多样性处于中等水平, 这和人为干扰有关。近年来短须裂腹鱼数量逐年减少, 研究短须裂腹鱼的遗传多样性对保护其种质资源起着重要的作用。     

Construction of a high-density genetic map using specific length amplified fragment markers and identification of a quantitative trait locus for anthracnose resistance in walnut (Juglans regia L.)

Background

Walnut (Juglans regia, 2n = 32, approximately 606 Mb per 1C genome) is an economically important tree crop. Resistance to anthracnose, caused by Colletotrichum gloeosporioides, is a major objective of walnut genetic improvement in China. The recently developed specific length amplified fragment sequencing (SLAF-seq) is an efficient strategy that can obtain large numbers of markers with sufficient sequence information to construct high-density genetic maps and permits detection of quantitative trait loci (QTLs) for molecular breeding.

Results

SLAF-seq generated 161.64 M paired-end reads. 153,820 SLAF markers were obtained, of which 49,174 were polymorphic. 13,635 polymorphic markers were sorted into five segregation types and 2,577 markers of them were used to construct genetic linkage maps: 2,395 of these fell into 16 linkage groups (LGs) for the female map, 448 markers for the male map, and 2,577 markers for the integrated map. Taking into account the size of all LGs, the marker coverage was 2,664.36 cM for the female map, 1,305.58 cM for the male map, and 2,457.82 cM for the integrated map. The average intervals between two adjacent mapped markers were 1.11 cM, 2.91 cM and 0.95 cM for three maps, respectively. ‘SNP_only’ markers accounted for 89.25 % of the markers on the integrated map. Mapping markers contained 5,043 single nucleotide polymorphisms (SNPs) loci, which corresponded to two SNP loci per SLAF marker. According to the integrated map, we used interval mapping (Logarithm of odds, LOD > 3.0) to detect our quantitative trait. One QTL was detected for anthracnose resistance. The interval of this QTL ranged from 165.51 cM to 176.33 cM on LG14, and ten markers in this interval that were above the threshold value were considered to be linked markers to the anthracnose resistance trait. The phenotypic variance explained by each marker ranged from 16.2 to 19.9 %, and their LOD scores varied from 3.22 to 4.04.

Conclusions

High-density genetic maps for walnut containing 16 LGs were constructed using the SLAF-seq method with an F1 population. One QTL for walnut anthracnose resistance was identified based on the map. The results will aid molecular marker-assisted breeding and walnut resistance genes identification.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1822-8) contains supplementary material, which is available to authorized users.     

High resolution genetic and physical mapping of the mouse asebia locus: A key gene locus for sebaceous gland differentiation

The asebia (ab) mutation in the mouse is an autosomal recessive trait with hypoplastic sebaceous glands. As a first step toward cloning the ab gene, we report here the genetic mapping of the ab locus with respect to Chromosome 19 microsatellite markers. 644 backcross progeny were generated by mating (CAST/EiJ × DBA/1LacJ-ab^2J/ab^2J) F₁ heterozygous females and parental ab^2J/ab^2J mutant males. Our results located the ab gene to an interval of 1.6 cM on mouse Chromosome 19 defined by flanking markers D19Mit11 and D19Mit53/D19Mit27, and identified a tightly linked polymorphic marker, D19Mit67, that co-segregates with the mutation in the backcross progeny examined. This places ab locus 4 cM distal to its present position as indicated in Mouse Genome Database at The Jackson Laboratory. We have also mapped a yeast artificial chromosome (YAC) contig in this locus interval which suggests the ab interval to be less than one megabase of DNA.     

InDel标记的研究和应用进展

InDel是指在近缘种或同一物种不同个体之间基因组同一位点的序列发生不同大小核苷酸片段的插入或缺失(insertion-deletion), 是同源序列比对产生空位(gap)的现象。InDel在基因组中分布广泛、密度大、数目众多。InDel多态性分子标记是基于插入/缺失位点两侧的序列设计特异引物进行PCR扩增的标记, 其本质仍属于长度多态性标记, 可利用便捷的电泳平台进行分型。InDel标记准确性高、稳定性好, 避免了由于特异性和复杂性导致的后续分析模糊。此外, InDel标记能扩增混合DNA样品和高度降解的微量DNA样品, 并进行有效分型。InDel标记目前已开始应用于动植物群体遗传分析、分子辅助育种以及人类法医遗传学、医学诊断等领域。随着位于功能基因上InDel标记的开发, 结合染色体步移和基因精细定位, 可将这些标记应用于相关物种经济性状的功能基因的筛选, 有利于优良基因的进一步开发和利用。     

Rapid Identification of Candidate Genes for Seed Weight Using the SLAF-Seq Method in Brassica napus

Seed weight is a critical and direct trait for oilseed crop seed yield. Understanding its genetic mechanism is of great importance for yield improvement in Brassica napus breeding. Two hundred and fifty doubled haploid lines derived by microspore culture were developed from a cross between a large-seed line G-42 and a small-seed line 7–9. According to the 1000-seed weight (TSW) data, the individual DNA of the heaviest 46 lines and the lightest 47 lines were respectively selected to establish two bulked DNA pools. A new high-throughput sequencing technology, Specific Locus Amplified Fragment Sequencing (SLAF-seq), was used to identify candidate genes of TSW in association analysis combined with bulked segregant analysis (BSA). A total of 1,933 high quality polymorphic SLAF markers were developed and 4 associated markers of TSW were procured. A hot region of ~0.58 Mb at nucleotides 25,401,885–25,985,931 on ChrA09 containing 91 candidate genes was identified as tightly associated with the TSW trait. From annotation information, four genes (GSBRNA2T00037136001, GSBRNA2T00037157001, GSBRNA2T00037129001 and GSBRNA2T00069389001) might be interesting candidate genes that are highly related to seed weight.