本文選題：京海黃雞 + 生長性狀　； 參考：《揚(yáng)州大學(xué)》2015年博士論文

【摘要】：生長性狀和屠宰性狀是雞產(chǎn)業(yè)非常重要的兩個(gè)經(jīng)濟(jì)性狀,傳統(tǒng)方法采用選育的方式對(duì)這兩個(gè)經(jīng)濟(jì)性狀進(jìn)行選擇；而雞遺傳方面,目前對(duì)于抗病性狀的研究較少。隨著生物技術(shù)的發(fā)展,標(biāo)記輔助選擇縮短了動(dòng)物的選育過程,節(jié)省了大量的時(shí)間、金錢,成為目前育種工作的重點(diǎn)。遺傳標(biāo)記的選擇方法主要有候選基因法和數(shù)量性狀基因座定位法,但這些方法也有其不足之處。簡化基因組測序法是將雞基因組“簡化”后通過測序得到的整個(gè)基因組范圍內(nèi)的單核昔酸為分子標(biāo)記,以發(fā)現(xiàn)影響復(fù)雜性狀發(fā)生的遺傳標(biāo)記和遺傳標(biāo)記的分布特征為目的,對(duì)復(fù)雜的經(jīng)濟(jì)性狀進(jìn)行直接關(guān)聯(lián)分析的一種方法。該法作為全基因組關(guān)聯(lián)分析的一種,被認(rèn)為是一種確定影響重要性狀(如雞的生長性狀、屠宰性狀和抗病性狀等)分子標(biāo)記的有效方法。全基因組關(guān)聯(lián)分析與之前的方法比較其最大優(yōu)點(diǎn)是不需要構(gòu)建任何不確定的假設(shè)。所以,本研究以京海黃雞為試驗(yàn)動(dòng)物,利用SLAF-seq簡化基因組測序的方法,對(duì)京海黃雞的10個(gè)生長性狀、13個(gè)屠宰性狀和6個(gè)抗病性狀進(jìn)行了基因組水平上的關(guān)聯(lián)分析,旨在尋找影響京海黃雞這些重要性狀的關(guān)鍵遺傳標(biāo)記以及遺傳標(biāo)記的分布規(guī)律,尋找京海黃雞上述重要性狀的候選基因,促進(jìn)京海黃雞重要性狀分子標(biāo)記的研究,為京海黃雞和其他雞種的選育提高提供良好的平臺(tái)。主要研究結(jié)果如下：1.對(duì)樣品DNA進(jìn)行簡化基因組測序,通過adimixture軟件計(jì)算群體結(jié)構(gòu),隨后利用兩種TASSEL模型：一般線性模型(GLM)和混合線性模型(MLM)對(duì)基因型數(shù)據(jù)和京海黃雞生長性狀、屠宰性狀和部分抗病性狀進(jìn)行了全基因組關(guān)聯(lián)分析,同時(shí)采用連鎖不平衡修正的Bonferoni法進(jìn)行多重比較校正,并重點(diǎn)討論了GLM模型基因組水平顯著和MLM模型基因組潛在顯著以上水平的SNPs,并利用COG、GO和KEGG等數(shù)據(jù)庫對(duì)候選基因進(jìn)行功能富集注釋。結(jié)果表明,TASSEL兩種模型均能較好的校正群體分層的影響,并且兩種模型識(shí)別出的SNPs大部分相同,但是相同SNP在MLM中的P值要略大于GLM。兩個(gè)模型中,MLM校正更嚴(yán)格,具有較高的準(zhǔn)確性,能夠較好的避免假陽性結(jié)果,但可能會(huì)由于過于嚴(yán)格的校正導(dǎo)致假陰性結(jié)果,GLM模型具有較好的統(tǒng)計(jì)效力,但可能會(huì)由于較寬松的校正條件造成假陽性結(jié)果,因此本研究采用了GLM和MLM兩種模型互為比較和補(bǔ)充,對(duì)京海黃雞的上述三個(gè)重要性狀進(jìn)行了全基因組關(guān)聯(lián)分析,以期獲得較為準(zhǔn)確和全面的結(jié)果。2.生長性狀的關(guān)聯(lián)分析中,GLM模型發(fā)現(xiàn)了19個(gè)與生長性狀顯著關(guān)聯(lián)的SNPs(P1.87E-06),篩選出相關(guān)基因9個(gè),某些基因與多個(gè)生長性狀同時(shí)顯著關(guān)聯(lián),如LDB2、 QDPR、INTS6、BOD1L1等,發(fā)現(xiàn)了102個(gè)與生長性狀潛在顯著關(guān)聯(lián)的SNPs (1.87E-06 P3.75E-05);MLM模型發(fā)現(xiàn)了16個(gè)達(dá)到潛在顯著以上水平的SNPs(3個(gè)基因組水平顯著),相關(guān)候選基因7個(gè),如QDPR、LDB2、FAM124A、NUK1等,這些基因均十分重要。同時(shí)注意到LDB2、QDPR基因不管在GLM還是MLM模型中均同時(shí)與多個(gè)生長性狀顯著關(guān)聯(lián)。其中部分基因有一些報(bào)道,如LDB2、QDPR等,而CHST1、GPR78、VISIG4、 HS3ST1、FHIT等9個(gè)基因?yàn)楸狙芯渴状伟l(fā)現(xiàn)的可能影響京海黃雞生長性狀的候選基因。發(fā)現(xiàn)4號(hào)染色體上75.6-80.7Mb區(qū)域?yàn)橛绊懢┖｜S雞生長性狀的主要功能區(qū)域。根據(jù)KEGG注釋到了2個(gè)可能對(duì)京海黃雞生長性狀有重要作用的信號(hào)通路：葉酸合成通路和粘多糖合成通路(KEGG:00790和KEGG:00533)。最終根據(jù)關(guān)聯(lián)分析結(jié)果和已有的文獻(xiàn)報(bào)道,初步確定了一些影響京海黃雞生長性狀的SNPs和候選基因,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.屠宰性狀的全基因組關(guān)聯(lián)分析中,GLM模型共發(fā)現(xiàn)與屠宰性狀基因組水平顯著的SNPs共16個(gè)(P1.87E-06),其中4號(hào)染色體的75.50-76.14Mb區(qū)域內(nèi)的7個(gè)SNPs與屠體重、腳重、翅重均關(guān)聯(lián)顯著,同時(shí)篩選出相關(guān)功能基因12個(gè),如FAM184B、QDPR,LAP3和ECL1等,發(fā)現(xiàn)了81個(gè)與屠宰性狀潛在顯著關(guān)聯(lián)的SNPs(1.87E-06P3.75E-05)。MLM模型共識(shí)別出12個(gè)潛在顯著以上水平的SNPs(8個(gè)達(dá)到基因組顯著水平),相關(guān)基因8個(gè),且這些位點(diǎn)也均在GLM模型中被檢測到。所有的SNPs中,部分SNPs與多個(gè)性狀顯著關(guān)聯(lián)或潛在顯著關(guān)聯(lián)。根據(jù)KEGG注釋到四個(gè)可能影響京海黃雞屠宰性狀的通路：脂肪酸代謝通路、葉酸合成通路、基底轉(zhuǎn)錄因子通路和精氨酸代謝通路(ko00330、KEGG:00790、KEGG:03022和ko00330)。其中有些基因有一些報(bào)道,如FAM184B、 QDPR、LAP3、FGFBP2、LDB2等,同時(shí)發(fā)現(xiàn)了ECL1、ZNF302、GTF2H5等七個(gè)新的候選基因。根據(jù)文章得到的結(jié)果結(jié)合相關(guān)文獻(xiàn),初步確定了京海黃雞屠宰性狀的關(guān)鍵SNPs和基因,如FAM184B內(nèi)的rs14710787、rs16023603對(duì)腳重、SKIDA1附近的rs14710787、 rs13755802對(duì)腹脂重以及GTF2H5附近的rs14359385、rs315486571和TMEM181附近的rs318008335對(duì)全凈膛重、FGFBP2內(nèi)的rs15148082對(duì)屠體重、PG02內(nèi)的rs317080707對(duì)翅重等。4.部分抗病性狀的全基因組關(guān)聯(lián)分析中,GLM模型共發(fā)現(xiàn)4個(gè)基因組水平顯著SNPs,其中1個(gè)與禽流感抗病性狀、2個(gè)與新城疫抗病性狀、1個(gè)與γ干擾素抗病性狀,相關(guān)候選基因3個(gè)；MLM模型共識(shí)別出8個(gè)達(dá)到潛在顯著以上水平的SNPs,相關(guān)基因5個(gè),其中SETBP1基因也被GLM模型檢測到。由于雞遺傳方面抗病性狀研究較少,因此這些基因在雞上均未見報(bào)道,但結(jié)合文獻(xiàn)發(fā)現(xiàn)有些基因的突變會(huì)導(dǎo)致一些重要信號(hào)通路的中斷,影響多種疾病進(jìn)程。因此推測這些基因的突變也可能對(duì)京海黃雞的抗病性狀有重要的影響,為下一步工作指明了方向,值得進(jìn)一步研究,如Plexin B1基因內(nèi)部的rs316966201、rs312624692和PDGFC內(nèi)的rs317837423對(duì)新城疫抗病性狀,NSUN7基因內(nèi)部的rs15613786對(duì)禽流感抗病性狀和USP7附近的rs313017675對(duì)傳染性支氣管炎抗病性狀等。
[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.
【學(xué)位授予單位】：揚(yáng)州大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：S831

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