本文選題：全基因組關聯(lián)分析 + 中國荷斯坦牛��； 參考：《中國農(nóng)業(yè)大學》2017年博士論文

【摘要】：全基因組關聯(lián)分析(GWAS)近年來廣泛用于人類疾病和動植物重要經(jīng)濟性狀的候選基因挖掘工作中,多年來在各物種中都取得了一定的成功。但是,由于群體結構、分析模型等因素的限制,很多與性狀相關的基因尚未被完全發(fā)掘。同時,大量的研究也報道了很多假陽性結果,降低了后續(xù)的工作效率。因此,找到合適的關聯(lián)分析方法,降低假陽性比例的同時提高檢驗功效,對該領域的研究具有重要意義。本研究使用了一種優(yōu)化求解的方法(FarmCPU)對中國荷斯坦牛的生長性狀進行GWAS,并通過模擬對該方法在閾性狀GWAS中的應用進行了評價。首先,本研究利用FarmCPU方法對3325頭中國荷斯坦牛的6、12、18和24月齡體高和胸圍進行了 GWAS分析,發(fā)現(xiàn)了 27個SNP位點存在與生長性狀在全基因組水平的顯著相關,并通過候選基因分析找到66個候選基因。在對候選基因信息的進一步挖掘過程中,找到ATP1A1、DYRK1A、JUN、CEP135、CYP26B1、MYC、SOX6和FGFRLI1等8個候選基因在人類和小鼠的骨骼和肌肉發(fā)育中發(fā)揮重要作用,但是這些候選基因在奶牛的生長性狀研究中均為首次報道。這一結果證明FarmCPU方法可用于奶牛生長性狀的GWAS研究。之后,本研究基于logistic回歸模型(1ogistic regression model)理論提出了新的閾性狀模擬方法并開發(fā)了相應的閾性狀模擬程序。新方法有別于傳統(tǒng)的基于閾模型理論的閾值模擬法,在模擬過程中引入"偏好性"概念,為閾性狀的表型增加了隨機性。通過對比不同參數(shù)組合下兩種模擬方法模擬的表型在GWAS分析中的表現(xiàn),發(fā)現(xiàn)現(xiàn)有的閾值模擬法進行模擬的閾性狀表型進行GWAS檢測時的檢測效果要好于與新的模擬方法,這可能是導致對閾性狀的GWAS方法類研究中模擬效果優(yōu)于實際數(shù)據(jù)的原因。用兩種模擬方法均可以證明,閾性狀GWAS分析時,logistic回歸模型在檢測閾性狀時的效果與一般線性模型沒有顯著差異。奶牛的繁殖性狀中閾性狀較多,且遺傳力較低,GWAS分析的檢測效果不理想。通過對比不同方法在模擬閾性狀中的表現(xiàn),本研究證明FarmCPU方法在閾性狀GWAS分析中的表現(xiàn)優(yōu)于其他方法。因此,本研究通過對FarmCPU方法在中國荷斯坦牛生長性狀和使用該群體部分數(shù)據(jù)模擬的閾性狀進行GWAS分析,發(fā)現(xiàn)FarmCPU在對中國荷斯坦牛重要經(jīng)濟性狀的GWAS分析中具有優(yōu)勢。
[Abstract]:Genome-wide Association Analysis (Gwas) has been widely used in candidate gene mining for human diseases and important economic traits of animals and plants in recent years, and has been successful in various species for many years. However, due to the limitation of population structure and analysis model, many genes related to traits have not been fully explored. At the same time, a large number of studies also reported a lot of false positive results, reducing the efficiency of subsequent work. Therefore, it is of great significance for the research in this field to find a suitable association analysis method to reduce the false positive rate and improve the test efficacy. In this study, an optimal solution method (FarmCPU) was used to evaluate the growth traits of Chinese Holstein cattle by GWASS, and the application of the method in threshold traits was evaluated by simulation. Firstly, the body height and chest circumference of 3325 Chinese Holstein cattle at the age of 18 and 24 months were analyzed by using FarmCPU method. It was found that 27 SNP loci were significantly correlated with growth traits at the whole genome level. 66 candidate genes were found by candidate gene analysis. In the process of further mining candidate gene information, we found that the eight candidate genes, such as ATP-1A1DYRK1ANJUNP135, CYP26B1, MYCNSOX6 and FGFRLI1, play an important role in human and mouse skeletal and muscle development. However, these candidate genes are reported for the first time in the study of growth traits in dairy cattle. The results show that FarmCPU method can be used to study the growth traits of dairy cattle. Then, based on the logistic regression model (1ogistic regression model) theory), a new method of threshold trait simulation is proposed and a corresponding program is developed. The new method is different from the traditional threshold simulation method based on threshold model theory. The concept of "preference" is introduced in the simulation process, which increases randomness for the phenotypes of threshold traits. By comparing the phenotypic performance of the two simulation methods in Gwas analysis with different parameter combinations, it is found that the existing threshold simulation method is better than the new simulation method in detecting the phenotypes of threshold traits. This may be the reason that the simulation effect of Gwas method for threshold traits is better than the actual data. It was proved by two simulation methods that there was no significant difference in the effectiveness of the logistic regression model between the general linear model and the general linear model in the detection of threshold traits. There were more threshold traits in breeding traits of dairy cows, and the detection effect of Gwas analysis with lower heritability was not satisfactory. By comparing the performance of different methods in simulated threshold traits, this study proves that FarmCPU method is superior to other methods in threshold trait Gwas analysis. Therefore, this study analyzed the growth traits of Chinese Holstein cattle using FarmCPU method and the threshold traits simulated by part of the population data. It was found that FarmCPU had advantages in the analysis of important economic traits of Chinese Holstein cattle.
【學位授予單位】：中國農(nóng)業(yè)大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：S823

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本文編號：2090958