京海黄鸡体重性状全基因组关联分析

体重性状是肉鸡重要的经济性状。为了寻找可用于京海黄鸡体重性状遗传改良的分子标记及候选基因,本文以400只京海黄鸡核心群母鸡为基础,测定了0~14周龄体重,利用简化基因组测序技术(Specific-locus amplified fragment sequencing,SLAF-seq)对京海黄鸡体重性状进行全基因组关联研究(Genome-wide association stndy,GWAS),筛选与京海黄鸡体重性状相关的SNPs位点。结果共检测到100个与京海黄鸡体重相关的SNPs位点,其中15个位点效应达到全基因组显著水平(P1.87E-06),85个位点效应达到全基因组潜在显著水平(P3.73E-05)。通过筛选每个显著SNP周围1 Mb区域内的基因,共找到9个可能的候选基因,其中FAM124A(Family with sequence similarity 124A)、QDPR(Quinoid dihydropteridine reductase)、WDR1(WD repeat domain 1)和SLC2A9(Solute carrier family 2(facilitated glucose transporter),member 9)4个基因可能是影响体重性状的重要候选基因。同时还发现,4号染色体75.6~80.7 Mb区域集中了大部分与京海黄鸡中后期体重性状显著相关的SNPs位点,该区域可能是影响京海黄鸡中后期生长体重的重要候选区域。     

基于全基因组重测序技术的‘红叶’杜仲SNP位点开发

为了挖掘与‘红叶’杜仲（Eucommia ulmoides ‘Hongye’）红叶性状紧密联系的SNP位点,进一步揭示红叶性状的遗传基础和分子机理。以‘红叶’杜仲和普通绿叶杜仲‘小叶’杜仲（Eucommia ulmoides ‘Xiaoye’）为研究材料,进行覆盖深度约为10x的全基因组重测序。使用SnpEff软件预测变异位点对蛋白编码的影响,结合花色苷的代谢通路和关键酶基因,筛选与‘红叶’杜仲叶色形成相关的差异位点。利用Sanger测序二代测序筛选的SNP位点,分子标记验证群体是‘红叶’杜仲和‘小叶’杜仲。结果表明,‘红叶’杜仲测序产生Clean data为14.16 Gb,‘小叶’杜仲产生Clean data为14.29 Gb。在‘红叶’杜仲中注释到严重影响蛋白质功能的有1 516个SNP,中度影响的41 328个SNP,在‘小叶’杜仲中存在严重影响蛋白质功能的SNP为1 640个,中度影响功能的SNP为47 192个。测得26 722条基因中有228条基因是与花色苷或类黄酮合成相关的酶基因。经过筛选,确定了12个特异性的SNP位点,均属于外显子区域的错义突变。利用一代测序验证,根据SNP位置设计了7对引物,SNP准确率达到100%。     

中国人群肝功能检测指标全基因组关联分析研究

肝功能检测(liver function test,LFTs)指标是受遗传和环境影响的复杂性状,具有个体差异性。为系统性研究中国人群全基因组范围内单核苷酸多态性(single nucleotide polymorphism,SNP)与肝功能指标之间的联系,本研究利用英国生物银行(UK Biobank)中1653名中国人的基因分型数据和表型数据为研究对象,利用PLINK软件进行全关联分析研究(genome-wide association study,GWAS),发现229个SNP与中国人群血液中的总胆红素(total bilirubin,TB)相关,27个SNP与中国人群血液中碱性磷酸酶(alkaline phosphatase,ALP)相关,36个SNP与中国人群血液中的γ-谷氨酰转肽酶(γ-glutamyl transpeptidase,GGT)相关,1个SNP与中国人群血液中的门冬氨酸氨基转移酶(aspartate transaminase,AST)相关,最显著的位点中有11个位点是新的LFTs关联位点。通过功能基因组分析,发现这些位点的临床意义(如吉尔伯特综合征),确定了候选基因(UGT1A,ABO,GGT1),为从遗传角度理解中国人群LFTs的个体差异性和肝功能指标临床精准检测提供了前期研究基础。     

小麦耐低磷相关性状的全基因组关联分析

通过对小麦耐低磷相关性状进行全基因组关联分析(GWAS,genome-wide association study),挖掘与小麦耐低磷性显著相关的单核苷酸多态性标记(SNP,single nucleotide polymorphism)位点及候选基因,为小麦耐低磷性状的遗传基础和分子机制研究提供理论参考。本试验以198份黄淮麦区小麦品种(系)为试验材料,设置低磷和正常磷营养液水培试验,利用小麦35K芯片对分布于小麦全基因组的11896个SNP,采用Q+K关联模型对小麦耐低磷性相关性状进行关联分析。结果表明,小麦耐低磷性状表现出广泛的表型变异,变异系数为15.65%~26.59%,多态性信息含量(PIC,polymorphic information content)为0.095~0.500。群体结构分析表明,试验所用自然群体可分为2个亚群,GWAS共检测到67个与小麦耐低磷相关性状显著关联的SNP位点(P≤0.001),这些位点分布在除3A、3B和3D以外的18条染色体上,单个SNP位点可解释5.826%~9.552%的表型变异。在这些显著位点中有4个SNP位点同时关联到了2个不同的耐低磷性状。对67个SNP位点进行发掘,筛选到7个可能与小麦耐低磷性有关的候选基因。TraesCS6A02G001000和TraesCS6A02G001100在锌指合成中有重要作用;TraesCS6A02G118100可能为低磷胁迫诱导基因;TraesCS5D02G536400、TraesCS1B02G154200和TraesCS5D02G536500与低磷胁迫相关酶类基因家族有关;TraesCS1D02G231200与植物DUF 538结构域蛋白有关,是植物胁迫相关调控蛋白候选基因。     

小麦根部耐盐性状全基因组关联分析

为了解小麦耐盐相关性状的遗传机理,挖掘与小麦耐盐性显著相关的SNP位点及候选基因,本研究利用浓度200 mmol/L的NaCl溶液和正常营养液对全国300份小麦品种(系)进行耐盐性试验,并利用小麦90 K芯片对分布于小麦全基因组的16650个SNP,采用Q+K关联混合模型对小麦最长根长、根干重、根鲜重、根平均直径、根尖数、根表面积、根体积和总根长等8个根部耐盐性相关性状进行全基因组关联分析(GWAS,genome-wide association study)。研究结果表明,小麦根部性状表现出广泛的表型变异,变异系数为24.3%~50.0%,多态性信息含量(PIC,polymorphic information content)为0.170~0.562,全基因组LD衰减距离为6 Mb;群体结构分析表明,试验所用300份小麦品种(系)可分为3个亚群,亚群1包含143个(47.67%)试验材料,主要来自河南、陕西和四川;亚群2包含74个(24.67%)试验材料,主要来自北京;亚群3包含83个(27.67%)试验材料,主要来自河南。GWAS共检测到77个与小麦耐盐相关性状显著关联的SNP位点(P≤0.001),这些位点分布在小麦除6D外的20条染色体上,单个SNP位点可解释3.70%~19.45%的表型变异,其中位于1A、3A、4A、7A、3D和5D染色体上的RAC875_c13169_459等6个位点同时关联到2个或2个以上性状,贡献率为3.78%~19.45%;对77个SNP位点进行发掘,筛选到17个可能与小麦耐盐性有关的候选基因。TraesCS5B01G031800(阳离子反转运蛋白)在Na+等阳离子转运中起重要作用,TraesCS5A01G329000(防御素)可以在阻断Na+等阳离子进入过程中起作用,TraesCS2A01G079000(重复富脯氨酸细胞壁蛋白)在细胞壁的形成中起重要作用,这些候选基因可作为耐盐性重要基因。     

三倍体香蕉优良品种‘Grand Nain’全基因组变异挖掘

为全面揭示香蕉(Musa spp.)优良品种的全基因组变异类型,本研究采用高通量重测序技术,对当前香蕉主栽品种‘Grand Nain’(AAA group,Cavendish subgroup)开展全基因组重测序,测序深度53.79 X。与野生香蕉‘DH-Pahang’参考基因组比对,共检测到4 598 633个单核苷酸多态位点(SNP),484 752个小片段插入缺失位点(Indel),57 047个结构变异(SV),共导致36 277个基因变异;代谢通路分析(KEGG)发现,植物激素信号转导途径相关基因的变异最多,存在442个基因变异,其中乙烯合成和信号转导途径中1-氨基环丙烷-1-羧酸氧化酶基因(ACO)、1-氨基环丙烷-1-羧酸合成酶基因(ACS)、EIN3/EILs、ERS、RAN、EBF、EIN2等基因都存在变异。特别是序列分析发现‘Grand Nain’中的Ma ACO1基因与参考基因组相比存在2个变异位点并导致氨基酸的突变,且在A和B基因组中MaACO1基因存在2个相邻的变异位点。本研究为香蕉贮藏保鲜等相关分子标记开发、基因功能研究,以及基于基因组编辑技术的香蕉遗传改良提供依据。     

普通菜豆镰孢菌枯萎病抗性种质资源筛选及全基因组关联分析

普通菜豆镰孢菌枯萎病是严重制约菜豆(Phaseolus vulgaris)产量的主要病害之一。采用下胚轴双孔注射法对601份普通菜豆种质资源进行枯萎病抗性鉴定, 共筛选出4份高抗材料。在此基础上, 基于分布在全基因组上的3 765 456个单核苷酸多态性(SNP)标记, 进行全基因组关联分析, 以P<1×10^-5为阈值。结果检测到57个显著关联的SNP位点, 分布于1、2、6、8和11号染色体上; 共获得8个显著关联区域, 其中位于1号染色体上的区域1包含SNP最多(48个), 最显著SNP P值为2.18E-07。在8个显著关联区域中, 共检测到186个基因, 其中157个基因有注释信息, 编码过氧化物酶、抗病蛋白、转录因子和蛋白激酶等。结合KEGG富集分析和序列同源性比对, 鉴定出9个候选基因可能与抗性相关。     

10株铜绿假单胞菌的全基因组序列分析

目的 了解不同分离来源铜绿假单胞菌的全基因组基本特征,以此分析基因组多态性及其遗传进化关系。方法 选择10株医源性和食源性来源的铜绿假单胞菌代表性菌株,应用Solexa高通量测序技术对其进行全基因组测序,以此进行多位点序列分型(multilocus sequence typing, MLST),比较各菌株基因组中携带的耐药基因、毒力基因及插入序列(insertion sequence, IS)元件,并通过比较基因组学分析方法拟合泛基因组和核心基因组积累曲线,筛选核心基因SNP构建系统发育分子进化树。结果 10株菌的基因组从6.3～7.0 Mbp大小不一,包含5 868～6 598个基因,平均G+C含量为67.1%;发现10个菌株各具不同的ST型。在这10个菌株的基因组中,共检测到75种耐药基因,包括抗β-内酰胺酶类、抗氨基糖苷类、抗氟喹诺酮类等;共发现188种毒力基因,不同来源菌株间无明显差异;各菌株之间IS元件种类和数量差异较大。分析发现,铜绿假单胞菌具有开放型泛基因组和稳定型核心基因组;10株菌可分为3个进化分支,且不同分离时间和来源无明显相关性。结论 本研究获得10株不同分离来源的铜绿假单胞菌的全基因组序列,初步证实食品及患者分离来源菌株基因组数据无明显相关性,为后续铜绿假单胞菌的分子流行病学和致病性机制研究提供数据参考。     

喜温嗜酸硫杆菌SM-1在不同温度下基因组的可塑性

【目的】驯化得到喜温嗜酸硫杆菌(Acidithiobacillus caldus)SM-1在低于最适生长温度下具有较高生长活力的突变菌株,并认知喜温嗜酸硫杆菌在不同温度下的基因组可塑性。【方法】利用实验室长期进化实验对菌株进行3个温度的驯化:37、40、45°C。运用454测序技术对驯化获得的菌株进行基因组重测序,通过比较基因组分析驯化株基因组单核苷酸位点变化(SNPs),对包含位点变化的基因从功能上进行分类,在此基础上,分析可能与温度适应性相关的基因。【结果】通过不同温度下的长期驯化,得到了在低于最适生长温度下具有较高活力的菌株;重测序结果发现,SM-1基因组具有较好的可塑性,不同温度(37、40、45°C)生长的菌株中,基因组中分别有418、384和347个核苷酸位点发生累计变化,其中3个温度下有20个相同的非同义突变位点,分别分布于编码重金属和毒性抗性系统、DNA甲基化和蛋白乙酰化酶、核酸代谢相关酶类等相关基因上;相比而言,在低于最适生长温度(37、40°C)下生长菌株特有的位点变化涉及能量代谢、信号转导以及DNA/RNA稳定性相关基因;其中,2个低温菌株共同发生位点变化的基因有3个,其中两个编码转座相关的蛋白Atc_1031与Atc_1623,另一个编码假想蛋白Atc_1130,该蛋白分别与外膜蛋白组装因子B和二硫键形成蛋白具有23%和35%的相似性。另外,不同生长温度下相关蛋白中氨基酸的组成也发生变化。【结论】喜温嗜酸硫杆菌SM-1基因组具有较好的可塑性,对于其基因组变化的研究结果为认识微生物温度适应性提供了组学数据。本研究揭示喜温嗜酸硫杆菌(At.caldus)SM-1可能通过多种途径适应向低温过渡生长,既包括微生物通用的环境适应机制,也存在菌株特有的温度适应途径。     

高原肺水肿易感基因多态性的全基因组关联分析

目的：高原肺水肿严重影响高原人群的健康。筛选高原肺水肿易感基因以用于高原肺水肿易感者的评估及防护。方法：利用Affymetrix SNP Array6．0芯片对23例高原肺水肿患者和17个健康对照进行全基因组SNP分型,利用PLINK软件进行了全基因组关联分析,利用Go和Pathway软件进行分析及作图。结果：全基因组关联分析获得39个相对显著的SNPs位点（P〈10^-4）。通过对这些SNP位点附近27个基因的c0和Pathway富集分析,发现这些基因主要参与细胞增殖调控过程、氮代谢过程和G蛋白耦联受体蛋白信号转导通路等。结论：本文发现的多态性位点及相关基因可能与高原肺水肿易感性相关。     

Comparison of the power between microsatellite and single-nucleotide polymorphism markers for linkage and linkage disequilibrium mapping of an electrophysiological phenotype

We performed linkage and linkage disequilibrium (LD) mapping analyses to compare the power between microsatellite and single nucleotide polymorphism (SNP) markers. Chromosome-wide analyses were performed for a quantitative electrophysiological phenotype, ttth1, on chromosome 7. Multipoint analysis of microsatellite markers using the variance component (VC) method showed the highest LOD score of 4.20 at 162 cM, near D7S509 (163.7 cM). Two-point analysis of SNPs using the VC method yielded the highest LOD score of 3.98 in the Illumina SNP data and 3.45 in the Affymetrix SNP data around 152-153 cM. In family-based single SNP and SNP haplotype LD analysis, we identified seven SNPs associated with ttth1. We searched for any potential candidate genes in the location of the seven SNPs. The SNPs rs1476640 and rs768055 are located in the FLJ40852 gene (a hypothetical protein), and SNP rs1859646 is located in the TAS2R5 gene (a taste receptor). The other four SNPs are not located in any known or annotated genes. We found the high density SNP scan to be superior to microsatellites because it is effective in downstream fine mapping due to a better defined linkage region. Our study proves the utility of high density SNP in genome-wide mapping studies.     

A comparison in association and linkage genome-wide scans for alcoholism susceptibility genes using single-nucleotide polymorphisms

We conducted genome-wide linkage scans using both microsatellite and single-nucleotide polymorphism (SNP) markers. Regions showing the strongest evidence of linkage to alcoholism susceptibility genes were identified. Haplotype analyses using a sliding-window approach for SNPs in these regions were performed. In addition, we performed a genome-wide association scan using SNP data. SNPs in these regions with evidence of association (P 相似文献   

Putative SNP discovery in interspecific hybrids of catfish by comparative EST analysis

In this study, we identified putative SNP markers within genes by comparative analysis of expressed sequence tags (ESTs). Comparison of 849 ESTs from blue catfish (Ictalurus furcatus) with >11,000 ESTs from channel catfish (I. punctatus) deposited in GenBank resulted in the identification of 1020 putative SNPs within 161 genes, of which 145 were nuclear genes of known function. The observed frequency of SNPs within ESTs of the two closely related catfish species was 1.32 SNP per 100 bp. The majority of identified SNPs differed between the two species and, therefore, these SNPs are useful for mapping genes in channel catfish x blue catfish interspecific resource families. The SNPs that differed within species were also observed; these can be applied to genome scans in channel catfish resource families.     

A pipeline for high throughput detection and mapping of SNPs from EST databases

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation.     

SNPing away at complex diseases: analysis of single-nucleotide polymorphisms around APOE in Alzheimer disease

There has been great interest in the prospects of using single-nucleotide polymorphisms (SNPs) in the search for complex disease genes, and several initiatives devoted to the identification and mapping of SNPs throughout the human genome are currently underway. However, actual data investigating the use of SNPs for identification of complex disease genes are scarce. To begin to look at issues surrounding the use of SNPs in complex disease studies, we have initiated a collaborative SNP mapping study around APOE, the well-established susceptibility gene for late-onset Alzheimer disease (AD). Sixty SNPs in a 1.5-Mb region surrounding APOE were genotyped in samples of unrelated cases of AD, in controls, and in families with AD. Standard tests were conducted to look for association of SNP alleles with AD, in cases and controls. We also used family-based association analyses, including recently developed methods to look for haplotype association. Evidence of association (P相似文献   

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.     

Genome-wide association study of shank length and diameter at different developmental stages in chicken F2 resource population

In order to find SNPs and genes affecting shank traits, we performed a GWAS in a chicken F2 population of eight half-sib families from five hatches derived from reciprocal crosses between an Arian fast-growing line and an Urmia indigenous slow-growing chicken. A total of 308 birds were genotyped using a 60K chicken SNP chip. Shank traits including shank length and diameter were measured weekly from birth to 12 weeks of age. A generalized linear model and a compressed mixed linear model (CMLM) were applied to achieve the significant regions. The value of the average genomic inflation factor (λ statistic) of the CMLM model (0.99) indicated that the CMLM was more effective than the generalized linear model in controlling the population structure. The genes surrounding significant SNPs and their biological functions were identified from NCBI, Ensembl and UniProt databases. The results indicated that 12 SNPs at 12 different ages passed the LD-adjusted 5% Bonferroni significant threshold. Two SNPs were significant for shank length and nine SNPs were significant for shank diameter. The significant SNPs were located near to or inside 11 candidate genes. The results showed that a number of significant SNPs in the middle ages were higher than the rest. The MXRA8 gene was related to the significant SNP at week 1 that promotes proliferation of growth plate chondrocytes. A unique SNP of Gga_rs16689511 located on chicken Z chromosome within the LOC101747628 gene was related to shank length at three different ages of birds (weeks 8, 9 and 11). The significant SNPs for shank diameter were found at weeks 4 and 7 (four and five SNPs respectively). The identifications of SNPs and genes here could contribute to a better understanding of the genetic control of shank traits in chicken.     

Association studies in candidate genes: strategies to select SNPs to be tested

OBJECTIVE: When numerous single nucleotide polymorphisms (SNPs) have been identified in a candidate gene, a relevant and still unanswered question is to determine how many and which of these SNPs should be optimally tested to detect an association with the disease. Testing them all is expensive and often unnecessary. Alleles at different SNPs may be associated in the population because of the existence of linkage disequilibrium, so that knowing the alleles carried at one SNP could provide exact or partial knowledge of alleles carried at a second SNP. We present here a method to select the most appropriate subset of SNPs in a candidate gene based on the pairwise linkage disequilibrium between the different SNPs. METHOD: The best subset is identified through power computations performed under different genetic models, assuming that one of the SNPs identified is the disease susceptibility variant. RESULTS: We applied the method on two data sets, an empirical study of the APOE gene region and a simulated study concerning one of the major genes (MG1) from the Genetic Analysis Workshop 12. For these two genes, the sets of SNPs selected were compared to the ones obtained using two other methods that need the reconstruction of multilocus haplotypes in order to identify haplotype-tag SNPs (htSNPs). We showed that with both data sets, our method performed better than the other selection methods.