基于SLAF-seq技术对京海黄鸡生长、屠宰及部分抗病性状的全基因组关联分析

发布时间：2018-06-04 09:31

本文选题：京海黄鸡 + 生长性状　；参考：《扬州大学》2015年博士论文

【摘要】：生长性状和屠宰性状是鸡产业非常重要的两个经济性状,传统方法采用选育的方式对这两个经济性状进行选择；而鸡遗传方面,目前对于抗病性状的研究较少。随着生物技术的发展,标记辅助选择缩短了动物的选育过程,节省了大量的时间、金钱,成为目前育种工作的重点。遗传标记的选择方法主要有候选基因法和数量性状基因座定位法,但这些方法也有其不足之处。简化基因组测序法是将鸡基因组“简化”后通过测序得到的整个基因组范围内的单核昔酸为分子标记,以发现影响复杂性状发生的遗传标记和遗传标记的分布特征为目的,对复杂的经济性状进行直接关联分析的一种方法。该法作为全基因组关联分析的一种,被认为是一种确定影响重要性状(如鸡的生长性状、屠宰性状和抗病性状等)分子标记的有效方法。全基因组关联分析与之前的方法比较其最大优点是不需要构建任何不确定的假设。所以,本研究以京海黄鸡为试验动物,利用SLAF-seq简化基因组测序的方法,对京海黄鸡的10个生长性状、13个屠宰性状和6个抗病性状进行了基因组水平上的关联分析,旨在寻找影响京海黄鸡这些重要性状的关键遗传标记以及遗传标记的分布规律,寻找京海黄鸡上述重要性状的候选基因,促进京海黄鸡重要性状分子标记的研究,为京海黄鸡和其他鸡种的选育提高提供良好的平台。主要研究结果如下：1.对样品DNA进行简化基因组测序,通过adimixture软件计算群体结构,随后利用两种TASSEL模型：一般线性模型(GLM)和混合线性模型(MLM)对基因型数据和京海黄鸡生长性状、屠宰性状和部分抗病性状进行了全基因组关联分析,同时采用连锁不平衡修正的Bonferoni法进行多重比较校正,并重点讨论了GLM模型基因组水平显著和MLM模型基因组潜在显著以上水平的SNPs,并利用COG、GO和KEGG等数据库对候选基因进行功能富集注释。结果表明,TASSEL两种模型均能较好的校正群体分层的影响,并且两种模型识别出的SNPs大部分相同,但是相同SNP在MLM中的P值要略大于GLM。两个模型中,MLM校正更严格,具有较高的准确性,能够较好的避免假阳性结果,但可能会由于过于严格的校正导致假阴性结果,GLM模型具有较好的统计效力,但可能会由于较宽松的校正条件造成假阳性结果,因此本研究采用了GLM和MLM两种模型互为比较和补充,对京海黄鸡的上述三个重要性状进行了全基因组关联分析,以期获得较为准确和全面的结果。2.生长性状的关联分析中,GLM模型发现了19个与生长性状显著关联的SNPs(P1.87E-06),筛选出相关基因9个,某些基因与多个生长性状同时显著关联,如LDB2、 QDPR、INTS6、BOD1L1等,发现了102个与生长性状潜在显著关联的SNPs (1.87E-06 P3.75E-05);MLM模型发现了16个达到潜在显著以上水平的SNPs(3个基因组水平显著),相关候选基因7个,如QDPR、LDB2、FAM124A、NUK1等,这些基因均十分重要。同时注意到LDB2、QDPR基因不管在GLM还是MLM模型中均同时与多个生长性状显著关联。其中部分基因有一些报道,如LDB2、QDPR等,而CHST1、GPR78、VISIG4、 HS3ST1、FHIT等9个基因为本研究首次发现的可能影响京海黄鸡生长性状的候选基因。发现4号染色体上75.6-80.7Mb区域为影响京海黄鸡生长性状的主要功能区域。根据KEGG注释到了2个可能对京海黄鸡生长性状有重要作用的信号通路：叶酸合成通路和粘多糖合成通路(KEGG:00790和KEGG:00533)。最终根据关联分析结果和已有的文献报道,初步确定了一些影响京海黄鸡生长性状的SNPs和候选基因,如LDB2基因附近的rs313973972、QDPR附近的rs14491071、BOD1LI附近的rs14492338、INTS6附近的rs14917720和GPR78附近的rs317168946等。3.屠宰性状的全基因组关联分析中,GLM模型共发现与屠宰性状基因组水平显著的SNPs共16个(P1.87E-06),其中4号染色体的75.50-76.14Mb区域内的7个SNPs与屠体重、脚重、翅重均关联显著,同时筛选出相关功能基因12个,如FAM184B、QDPR,LAP3和ECL1等,发现了81个与屠宰性状潜在显著关联的SNPs(1.87E-06P3.75E-05)。MLM模型共识别出12个潜在显著以上水平的SNPs(8个达到基因组显著水平),相关基因8个,且这些位点也均在GLM模型中被检测到。所有的SNPs中,部分SNPs与多个性状显著关联或潜在显著关联。根据KEGG注释到四个可能影响京海黄鸡屠宰性状的通路：脂肪酸代谢通路、叶酸合成通路、基底转录因子通路和精氨酸代谢通路(ko00330、KEGG:00790、KEGG:03022和ko00330)。其中有些基因有一些报道,如FAM184B、 QDPR、LAP3、FGFBP2、LDB2等,同时发现了ECL1、ZNF302、GTF2H5等七个新的候选基因。根据文章得到的结果结合相关文献,初步确定了京海黄鸡屠宰性状的关键SNPs和基因,如FAM184B内的rs14710787、rs16023603对脚重、SKIDA1附近的rs14710787、 rs13755802对腹脂重以及GTF2H5附近的rs14359385、rs315486571和TMEM181附近的rs318008335对全净膛重、FGFBP2内的rs15148082对屠体重、PG02内的rs317080707对翅重等。4.部分抗病性状的全基因组关联分析中,GLM模型共发现4个基因组水平显著SNPs,其中1个与禽流感抗病性状、2个与新城疫抗病性状、1个与γ干扰素抗病性状,相关候选基因3个；MLM模型共识别出8个达到潜在显著以上水平的SNPs,相关基因5个,其中SETBP1基因也被GLM模型检测到。由于鸡遗传方面抗病性状研究较少,因此这些基因在鸡上均未见报道,但结合文献发现有些基因的突变会导致一些重要信号通路的中断,影响多种疾病进程。因此推测这些基因的突变也可能对京海黄鸡的抗病性状有重要的影响,为下一步工作指明了方向,值得进一步研究,如Plexin B1基因内部的rs316966201、rs312624692和PDGFC内的rs317837423对新城疫抗病性状,NSUN7基因内部的rs15613786对禽流感抗病性状和USP7附近的rs313017675对传染性支气管炎抗病性状等。
[Abstract]:Growth traits and slaughter traits are two important economic traits in the chicken industry. The traditional methods choose the two economic characters by selection, while the chicken genetics, the study of the resistance traits is less. With the development of biotechnology, marker assisted selection shortens the breeding process of animals and saves a lot. Time and money have become the focus of current breeding work. The selection methods of genetic markers mainly include candidate gene method and quantitative trait loci location method, but these methods also have its shortcomings. A method of direct correlation analysis for complex economic traits, as one of the whole genome association analysis, which is considered to be a method for determining important traits (such as chicken growth traits, slaughter traits and disease resistance traits, etc.), in order to detect the distribution characteristics of genetic markers and genetic markers that affect complex traits. An effective method of molecular markers. The greatest advantage of the whole genome association analysis and the previous method is that there is no need to construct any uncertain hypothesis. Therefore, this study took Beijing sea chicken as a test animal, using SLAF-seq simplified genome sequencing method, 10 growth traits, 13 slaughter traits and 6 disease resistance traits of Jinghai yellow chicken. In order to find the key genetic markers and the distribution rules of the important traits of Jinghai yellow chicken, to find the candidate genes of the important traits of Jinghai yellow chicken, and to promote the study on the molecular markers of the important characters of the Beijing yellow chicken, and improve the breeding of the yellow chicken and other chicken breeds in Beijing. The main research results were as follows: 1. simplified genome sequencing of sample DNA, calculated population structure by adimixture software, and then used two TASSEL models: general linear model (GLM) and mixed linear model (MLM) for genotypic data and Jing Haihuang chicken growth traits, slaughter traits and partial resistance traits. Complete genome association analysis and multiple comparison correction using Bonferoni method of linkage disequilibrium correction, and focus on the significant GLM model genome level and the potential significant level of MLM model genome, and use COG, GO, KEGG and other databases to enrich the candidate base for functional enrichment. The results show that TASSEL is two species. The model can well correct the influence of group stratification, and the two models identified most of the same SNPs, but the P value of the same SNP in MLM is slightly larger than that of the two GLM. models. The MLM correction is more strict and has a higher accuracy, which can better avoid false positive results, but it may be due to too strict correction to lead to false negative negative. As a result, the GLM model has a good statistical effect, but it may result in false positive results due to the looser correction conditions. Therefore, two models of GLM and MLM are compared and supplemented in this study. The whole genome association of the three important characters of Beijing yellow chicken is analyzed in order to obtain a more accurate and comprehensive result of.2. birth. In the association analysis of long traits, the GLM model found 19 SNPs (P1.87E-06) which was significantly associated with growth traits, and screened 9 related genes. Some genes were associated with multiple growth traits, such as LDB2, QDPR, INTS6, BOD1L1 and so on. 102 SNPs (1.87E-06 P3.75E-05), which was potentially associated with the growth traits, was found, and the MLM model was found. 16 potential significant levels of SNPs (3 genomic levels are significant), and 7 related candidate genes, such as QDPR, LDB2, FAM124A, NUK1, are all very important. At the same time, it is noted that LDB2, QDPR genes are associated with multiple growth traits in both GLM and MLM models. Some of these genes have some reports, such as LDB2, Q. DPR et al, and CHST1, GPR78, VISIG4, HS3ST1, FHIT and other 9 genes that may affect the growth traits of the Beijing yellow chicken for the first time. It is found that the 75.6-80.7Mb region on chromosome 4 is the main function area affecting the growth traits of Beijing yellow chicken. According to the KEGG annotation, 2 may be important for the growth traits of the Beijing yellow chicken. The signal pathways used: folic acid synthesis pathway and mucopolysaccharide synthesis pathway (KEGG:00790 and KEGG:00533). Finally, according to the results of association analysis and the existing literature, some SNPs and candidate genes affecting the growth traits of Beijing yellow chicken, such as rs313973972 near the LDB2 gene, rs14491071 near QDPR, and rs144923 near BOD1LI, are determined. 38, in the whole genome association analysis of.3. slaughter traits near rs14917720 and GPR78 near INTS6, the GLM model found a total of 16 (P1.87E-06) of SNPs in the genome level of slaughtering traits, of which 7 SNPs in the 75.50-76.14Mb region of chromosome 4 were significantly associated with slaughter weight, foot weight and wing weight, and were screened at the same time. 12 related functional genes, such as FAM184B, QDPR, LAP3 and ECL1, have found that 81 SNPs (1.87E-06P3.75E-05).MLM models, which are potentially significantly associated with slaughter traits, identify 12 potentially significant levels of SNPs (8 significant levels of genome), and 8 related genes, and all these loci are detected in the GLM model. All SNPs are also detected. Part SNPs is significantly associated with or potentially significant associations with multiple traits. According to the KEGG annotation, there are four pathways that may affect the slaughter traits of the Beijing yellow chicken: the fatty acid pathway, the folate synthesis pathway, the basal transcription factor pathway and the arginine metabolic pathway (ko00330, KEGG:00790, KEGG:03022 and ko00330). Some of these genes have some reports. Seven new candidate genes, such as FAM184B, QDPR, LAP3, FGFBP2, LDB2 and so on, have been found, such as ECL1, ZNF302, GTF2H5 and so on. According to the results obtained in this article, the key SNPs and genes are identified, such as rs14710787 in FAM184B. In the whole genome association analysis of the total net weight, rs15148082 in FGFBP2, rs15148082 in FGFBP2, rs317080707 to wing weight and other.4. part resistance traits in PG02 near rs14359385, rs315486571 and TMEM181 near GTF2H5, the GLM model found that 4 genes were significant SNPs, of which 1 were resistant to avian influenza. 2 against NDV disease resistance traits, 1 with interferon gamma resistance traits and 3 related candidate genes; MLM model identified 8 potential significant levels and 5 related genes, of which the SETBP1 gene was also detected by the GLM model. The genes were not reported in chicken genetic aspects, so these genes were not reported on chickens. But it is found that mutations in some genes may lead to interruptions of some important signaling pathways and affect a variety of disease processes. Therefore, it is presumed that the mutation of these genes may also have an important impact on the disease resistance of the yellow chicken in Beijing, which is a direction for further work, such as rs316966201 within the Plexin B1 gene. The resistance traits of rs317837423 in rs312624692 and PDGFC to Newcastle disease, the rs15613786 against avian influenza in the NSUN7 gene and the resistance to infectious bronchitis by rs313017675 near USP7, and so on.
【学位授予单位】：扬州大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：S831

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