鸡血糖性状的全基因组关联分析

旨在挖掘影响鸡血糖性状的有效SNP位点及功能基因，为优质肉鸡分子育种工作提供有效的理论支撑。本试验选取407只京星黄母鸡于98日龄屠宰，酚仿法提取血液DNA，进行深度为10×的全基因组重测序；葡萄糖氧化酶法测定血清中血糖水平，基于全基因组重测序和血糖表型数据进行全基因组关联分析（GWAS）。结果，GWAS共筛选到6个血糖相关的SNPs位点（关联阈值P<1.43×10^-6）。基因注释发现，rs734134177在UBE3D基因第8内含子上，其编码蛋白为泛素蛋白连接酶。该位点携带野生型（AA）个体的血糖水平极显著高于突变型（GG）个体（P<0.01）；rs794554022位于ACAD9基因下游D 93.5 kb处。ACAD9蛋白为酰基辅酶A脱氢酶家族的成员之一，是细胞线粒体中脂肪酰基辅酶A进行β-氧化过程中的限速酶。rs794554022位点携带野生型（AA）个体的血糖水平极显著低于携带突变型（CC）个体的（P<0.01）。以上位点可能是调控血糖水平的相关候选SNPs位点，这两个位点所在基因可能参与了肉鸡血糖代谢的调控过程，这些结果将为调控肉鸡血糖代谢进而改善肉品质的育种工作提供候选的分子标记，为肉鸡血糖代谢的调控提供了新的思路。     

Genome-wide pedigree analysis of elite rice Shuhui 527 reveals key regions for breeding

Hybrid rice significantly contributes to the food supply worldwide. Backbone parents play important roles in elite hybrid rice breeding systems. In this study, we performed pedigree-based analysis of the elite backbone parent rice variety, namely, Shuhui 527(SH527, Oryza sativa), to exploit key genome regions during breeding. Twenty-four cultivars(including SH527, its six progenitors and 17 derived cultivars) were collected and analyzed with high-density single nucleotide polymorphism(SNP) array. Scanning all these cultivars with genome-wide SNP data indicated the unique contributions of progenitors to the SH527 genome and identified the key genomic regions of SH527 conserved within all its derivatives. These findings were further supported by known rice yield-related genes or unknown QTLs identified by genome-wide association study. This study reveals several key regions for SH527 and provides insights into hybrid rice breeding.     

Genome-wide Association Study on Abdominal Fat Weight in Jinghai Yellow Chicken

The study was conducted to reveal the genetic basis of abdominal fat weight trait and scan molecular markers which could be used to improve the abdominal fat weight.The abdominal fat weight of female Jinghai Yellow chicken in core group were measured after being slaughtered.Genome-wide association study was carried out using specific-locus amplified fragment sequencing technology to detect SNPs associated with abdominal fat weight.Finally, five SNPs that associated with abdominal fat weight and reached suggestive genome-wide significance (P<1.1×10^-5) were found, and four of them (rs18144674, rs18144680, rs12246236 and rs12257795) reached chromosome significance level.Three SNPs (rs18144674, rs18144680 and rs19745739) located in a 1.6 Mb area of chromosome 2, and two SNPs (rs12246236 and rs12257795) located in a 12 kb area of chromosome 14.Genes in the candidate regions with 0.1 Mb windows were screened.Finally, eleven candidate genes of VIM, TRDMT1, AXIN1, PDIA2, ARHGDIG, gga-mir-1763, gga-mir-1564, CLCN7, ECL1, DNASE1 and SDOS were obtained.The molecular function and biological processes were analyzed using GO database.The results showed that those genes might be candidate genes affecting abdominal fat weight in Jinghai Yellow chicken.     

基于全基因组SNP标记分析中国地方鸡品种的遗传多样性和种群结构

旨在对中国地方鸡品种的遗传多样性与种群结构进行分析。本研究使用Affymetrix Axiom 600K高密度鸡基因分型芯片对来自8个品种的157只地方鸡及233只商品鸡进行基因分型,以品种作为分组来计算各分组的观测杂合度、期望杂合度、次等位基因频率、近交系数及核苷酸多样性分析地方鸡群体的遗传多样性,利用进化树、主成分分析、群体结构、MDS等方法分析鸡群体的群体结构,基于状态同源(IBS)和群体分化系数(Fst)分析种群内部与种群之间的亲缘关系,利用长纯合片段(runs of homozygosity, ROH)估算得到基于ROH的近交系数。结果表明,各群体的观测杂合度均高于期望杂合度,次等位基因频率在0.175～0.236之间,近交系数在0.018～0.205之间,核苷酸多样性在0～6×10^-4之间,进化树与主成分分析表明品种间出现了明显的群体分化,地方鸡群体与商品鸡群的MDS分析发现我国地方鸡与商业肉鸡品种的遗传距离较近;IBS遗传距离在0.092 9～0.319 9之间;各品种成对Fst分析表明,群体间呈现中高分化程度(0.09～0.22);此次分析共得到了...     

Genome-Wide Selection of MAPKKK Gene Family in Gossypium raimondii and the Expression of Orthologs in Gossypium hirsutum

[Objective] The MAPKKK gene family plays an important regulating role in response to multiple abiotic stresses and the development of plant. This study aims to identify MAPKKK genes of Gossypium raimondii and analyze their functions. [Method] In this study, based on G. raimondii genome database and bioinformatics method, G. raimondii MAPKKK family genes were identified and analyzed. Using the MEGA5, GSDS and Mapchart program, the phylogenetic tree, gene structure and chromosomes location analyses were accomplished. Based on the existing microarray data in cotton and comparative profiles of these MAPKKK genes, different expression of them in multiple abiotic stresses and the expression at different cotton fiber developmental stages were analyzed. [Result] A sum of 114 MAPKKK genes were identified systematically in G. raimondii and classified into 3 subfamilies (Raf, ZIK and MEKK) according to the gene stucture and phylogenetic tree analyses. They were distributed on all the 13 chromosomes of G. raimondii, and segmental duplication and tandem duplication events may have occurred. Compared with the recently released 78 genes of G. raimondii MAPKKK family genes, 47 sequences are exactly the same ones. [Conclusion] The results are helpful to understand the evolution and function of MAPKKK gene family. Our results provide a foundation for future functional characterizations of MAPKKK genes in cotton and probably other Gossypium plants.     

Genome-wide association study reveals loci associated with seed longevity in common wheat (Triticum aestivum L.)

Seed longevity could significantly determine seed regeneration cycle and greatly affect wheat production. With the 90 K chip assays, a genome-wide association study was performed to identify seed longevity-related markers and loci in common wheat. Seed germination ratios (GR) under artificially ageing of 166 wheat accessions across three environments were evaluated to assess seed longevity. Totally, 23 longevity-related loci were identified in the study, explaining 6.7%–11.4% of the phenotypic variations. Of these, QlgGR.cas-1A and QlgGR.cas-2B.2 were deemed as stable loci associated with wheat seed longevity. Fifteen loci were found overlapped with known quantitative trait loci or genes. Besides, QlgGR.cas-1A, QlgGR.cas-2B.2, QlgGR.cas-3D.1, QlgGR.cas-3D.2, QlgGR.cas-4A.2, QlgGR.cas-5A.1, QlgGR.cas-5A.2 and QlgGR.cas-6A.1 were colocated with seed dormancy-related loci. Significant additive effects were obtained for seed longevity by pyramiding favourable alleles. Several candidate genes were found involved in signal transduction and stress resistance pathways by sequencing analysis of significantly longevity-related molecular markers. These results might provide new sights into the genetic architecture of seed longevity.     

RTM-GWAS方法应用于大豆RIL群体百粒重QTL检测的功效

【目的】为全面解析大豆重组自交系群体中调控百粒重性状的QTL体系,将限制性两阶段多位点全基因组关联分析方法（RTM-GWAS）和不同定位方法进行比较、优选,为后续候选基因体系探索及分子标记辅助育种设计提供依据。【方法】利用以科丰1号和南农1138-2为亲本衍生的重组自交系群体NJRIKY的427个家系,通过由全基因组39 353个SNP构建的3 683个SNPLDB标记及3个环境下的百粒重表型数据,选用复合区间作图法（CIM）、基于混合线性模型的全基因组关联分析方法（MLM-GWAS）和RTM-GWAS3种方法检测百粒重QTL,通过QTL数目和总的表型变异解释率比较检测功效,挑选最佳定位结果进行NJRIKY群体中的百粒重遗传体系解析。通过候选基因体系的功能注释,挖掘调控大豆百粒重的生物学途径。【结果】科丰1号与南农1138-2的百粒重差异较大,多环境平均数分别为9.0和17.9 g,遗传变异系数为12.4%,遗传率为85.4%,适用于百粒重性状的遗传解析。比较3种方法定位结果表明RTM-GWAS方法表现最佳,检测QTL数目最多（57个）,解释表型变异最多（70.78%）。而CIM仅检测到14个QTL,解释了56.47%的表型变异,MLM-GWAS仅定位到6个QTL,解释了18.47%的表型变异。RTM-GWAS共检测到57个QTL,分布在19条染色体上,表型变异解释率为0.03%—7.57%,其中41个QTL覆盖了已报道的来自30个双亲群体的81个百粒重QTL,16个QTL为新发现位点,包含一个表型变异解释率大于3%的大效应位点Sw-09-2。此外,检测的57个QTL中有20个位点与环境存在互作效应。这57个QTL构成了影响NJRIKY群体百粒重性状的遗传体系。通过SNPLDB标记与预测基因内的SNP进行χ²检验,共筛选到36个候选基因,其中4个候选基因来自大效应QTL,剩余32个候选基因来自小效应QTL。通过GO注释发现这些候选基因功能注释丰富,其中13个候选基因与籽粒发育直接相关,剩余的候选基因功能丰富,包含转运、转录调节因子等,表明不同生物学途径的基因共同调控NJRIKY群体中百粒重性状的表达。【结论】3种定位方法中,高效的RTM-GWAS方法检测到较为全面的NJRIKY群体的百粒重QTL,更适用于双亲RIL群体的QTL定位。不同功能的候选基因共同调控了复杂的百粒重性状的表达。     

粳稻分蘖数全基因组关联分析及候选基因的挖掘

【目的】利用全基因组关联分析（genome-wide association study,GWAS）检测与粳稻分蘖数显著相关的SNP位点,筛选影响分蘖数的候选基因,为分子辅助育种提高产量提供理论基础。【方法】利用295份来自世界各地的粳稻品种,于2018和2019年分蘖盛期调查水稻分蘖数,结合高通量重测序获得的788 396个高质量多态性SNP,利用TASSEL 5.0软件的MLM模型进行全基因组关联分析,对GEC软件计算的有效独立SNP数目进行阈值确定,判定SNP标记与目标性状关联的显著性。根据2年检测到的峰值SNP和水稻每条染色体LD衰减距离,确定2年共定位分蘖数主效QTL,并进一步提取QTL区间内所有基因外显子区非同义突变SNP和启动子区SNP进行单倍型分析,结合基因注释筛选影响粳稻分蘖数候选基因。【结果】295份粳稻品种的分蘖数在2018和2019年总趋势基本一致,并且均具有较大的表型分布。通过全基因组关联分析,在P<5.46×10^-6阈值条件下,从第8、9和10染色体上共鉴定3个与粳稻分蘖数相关的QTL（qTiller8、qTiller9和qTiller10）,其中,在2年中均检测到qTiller9,在2018和2019年的表型贡献率分别为11.86%和10.61%,而qTiller8和qTiller10仅在2018年被检测到,表型贡献率分别为9.36%和9.10%。对qTiller9区间内的全部15个基因进行单倍型分析,结果表明,在qTiller9区间内共有6个基因（LOC_Os09g25090、LOC_Os09g25100、LOC_Os09g25150、LOC_Os09g25190、LOC_Os09g25200和LOC_Os09g25220）的不同单倍型分蘖数之间存在极显著差异,LOC_Os09g25090被启动子区SNP分为2种单倍型,hap2（TAA）分蘖数极显著大于hap1（AGG）;LOC_Os09g25100被非同义突变SNP分为2种单倍型,hap2（GAGA）分蘖数极显著大于hap1（AGCC）;LOC_Os09g25150被非同义突变SNP分为2种单倍型,hap2（ATG）分蘖数极显著大于hap1（GCC）;LOC_Os09g25190被启动子区SNP分为2种单倍型,hap2（GCATCGCATCGACGCCGA）分蘖数极显著大于hap1（ATGCTGATGAAGTCATCC）;LOC_Os09g25200被非同义突变SNP分为2种单倍型,hap2（TAG）分蘖数极显著大于hap1（AGA）;LOC_Os09g25220被非同义突变SNP分为2种单倍型,hap1（GG）分蘖数极其显著大于hap2（AA）。结合基因注释发现,LOC_Os09g25090和LOC_Os09g25100均预测编码钙调蛋白依赖性蛋白激酶,是脱落酸（ABA）表达所必需Ca²⁺的传感器,推测LOC_Os09g25090和LOC_Os09g25100为影响粳稻分蘖数的候选基因。【结论】筛选出LOC_Os09g25090和LOC_Os09g25100为影响粳稻分蘖数的候选基因。     

梨CDPK基因家族全基因组序列鉴定分析

CDPK是植物特有的一类丝氨酸/苏氨酸型蛋白激酶,在介导植物生育信号和逆境信号转导中具有重要作用。为揭示CDPK基因在梨生长发育与抗逆防御机制中的作用,本研究通过生物信息学手段,结合梨基因组注释信息,鉴定梨基因组中CDPK家族基因成员,利用MEGA6.0程序进行多序列比对、分类并构建系统进化树;利用per1程序以及GSDS工具进行基因结构与保守性分析,为植物CDPK基因功能分析与利用提供依据。结果表明,本研究成功鉴定出26个CDPK家族成员,根据系统发育树分析,所有Pb CDPK被分为4类;基因结构预测结果表明,大多数Pb CDPK均含有6~7个内含子;且预测的Pb CDPK氨基酸序列绝大多数含4个EF-hand功能域;为了深入分析梨与其他物种的同源进化关系,构建了梨与拟南芥、水稻的CDPK基因系统进化树,进化树聚类分析结果将所有的CDPK蛋白分为四类。综上所述,目前已初步获得26个梨CDPK基因,为在基因组范围内研究CDPK基因在梨生长发育与逆境响应中的功能奠定了基础。     

整合数字基因表达谱与全基因组关联分析鉴定猪血液性状候选基因

【目的】整合数字基因表达谱与全基因组关联分析鉴定白色杜洛克×二花脸F₂资源群体的血液性状候选基因。【方法】白色杜洛克×二花脸F₂资源群体在(240±3)d屠宰,收集血液于抗凝管中进行血常规检测。利用Illumina 60K SNP芯片对1 020头F₂资源群体进行基因分型。剔除基因型检出率< 90%和孟德尔错误检出率> 5%的个体。检出率< 95%、次等位基因频率< 5%、哈代-温伯格检验(HWE) P < 5×10^-6、与性染色体连锁疑似常染色体的SNP被筛除。利用Illumina GA II 测序仪测序对502头F₂资源群体的肝脏进行数字基因表达谱测序。测序得到的原始数据经过滤获得清洁标签后与参考标签数据库比对,将能唯一比对到参考基因序列的清洁标签数量进行标准化处理以获得标准化的基因表达量。每个转录本的表达水平进一步转化为lg2值。在少于20%的个体中表达的转录本被滤去。表型性状和基因表达性状使用R程序包中GenABEL内polygentic功能进行性别、批次和亲缘关系的校正。其残差使用R程序包中斯皮尔曼系数评估基因表达水平与表型数据的关联性,设定保守阈值P < 5×10^-4时调整多重检验。将检测到的表达数量性状位点(eQTL)及其对应基因根据其位置相对照的关系进行绘图。搜寻前期GWAS最高点5.0 Mb区域内eQTL结合GWAS结果进行综合分析。Gene Ontology & KEGG pathway富集分析使用在线工具DAVID。基因共表达网络使用在线工具GeneMANIA进行构建。【结果】白色杜洛克×二花脸F₂资源群体中502个个体的20 108个肝脏转录本通过了质检。当P < 5×10^-4时鉴别到与血红蛋白(HGB)、红细胞数目(RBC)、红细胞压积(HCT)、平均红细胞体积(MCV)、平均红细胞血红蛋白含量(MCH)和白细胞数目(WBC)关联的转录本共259个。有34个转录本与一个以上表型关联。用上述血细胞性状关联的转录本进行eQTL定位,当阈值P < 10^-5时得到304个eQTL,每个转录本映射到1-6个eQTL,其中有35个顺式eQTL,120个反式eQTL。MCH和MCV的顺式eQTL位置重叠位于8号染色体。7号染色体存有数量最多的eQTL,其中多数为反式eQTL。通过eQTL定位确定了KIT为候选基因。Gene Ontology & KEGG pathway富集分析鉴别到与红细胞性状相关的基因KIT、PSEN2和TFRC,与白细胞性状相关的基因THBS1和CYR61。通过整合前期GWAS数据及eQTL定位并建立基因共表达网络,鉴定到与白细胞性状相关的基因RPS10。【结论】利用基于白色杜洛克×二花脸F₂资源群体的eQTL定位并结合前期GWAS数据,鉴别到与红细胞性状相关的基因KIT、PSEN2和TFRC,与白细胞性状相关的基因THBS1、CYR61和RPS10。