本文選題：全基因組關(guān)聯(lián)分析 + 基因組選擇�。� 參考：《中國農(nóng)業(yè)科學(xué)院》2015年碩士論文

【摘要】：全基因組關(guān)聯(lián)分析和基因組選擇是近年來畜禽育種的研究熱點(diǎn)。本研究利用BovineHD芯片,對西門塔爾牛的部分胴體性狀和肉質(zhì)性狀進(jìn)行全基因組關(guān)聯(lián)分析,初步探索低密度芯片對脂肪酸含量性狀的基因組預(yù)測準(zhǔn)確性。1.使用混合壓縮線性模型(CMLM)和線性模型(LM)對霖肉、后腱子和骨重三個(gè)胴體性狀進(jìn)行全基因組關(guān)聯(lián)分析,共檢驗(yàn)出186個(gè)顯著關(guān)聯(lián)的位點(diǎn)(P10-5),其中有55個(gè)位點(diǎn)在兩種模型中均顯著,多數(shù)標(biāo)記落于6號和14號染色體的LAP3、LCORL、FAM184B、PLAG1等基因上,顯著SNP重疊于相關(guān)胴體重和骨重的數(shù)量性狀基因座位(QTL)。2.使用CMLM和LM對大理石花紋、脂肪顏色、總脂肪酸含量(TFA)、飽和脂肪酸含量(SFA)、單不飽和脂肪酸含量(MUFA)和多不飽和脂肪酸含量(PUFA)六個(gè)肉質(zhì)性狀的分析,檢測出91個(gè)顯著位點(diǎn)(P10-5),其中44個(gè)位點(diǎn)在兩模型中均顯著,與三個(gè)脂肪酸含量性狀均顯著相關(guān)的標(biāo)記落在14號染色體的MYC基因附近,多數(shù)顯著位點(diǎn)落在相關(guān)于大理石花紋、第十二肋的背膘厚和脂肪酸含量的QTL上。3.本研究構(gòu)建不同標(biāo)記數(shù)目的低密度芯片,包括均勻分布低密度芯片,基于Bayes A、Bayes B估計(jì)標(biāo)記效應(yīng)的絕對值及顯著性的篩選標(biāo)記低密度芯片,使用低密度芯片對四個(gè)脂肪酸含量性狀進(jìn)行基因組預(yù)測,通過五倍交叉驗(yàn)證衡量準(zhǔn)確性。均勻分布低密度芯片整合BovineHD和已有低密度芯片位點(diǎn),其標(biāo)記數(shù)目分別為3K,7K,9K,20K和40K,準(zhǔn)確性在9K時(shí)較高,低于篩選標(biāo)記低密度芯片,與其他模擬數(shù)據(jù)中低密度芯片基因組預(yù)測的結(jié)果一致。依據(jù)標(biāo)記效應(yīng)及顯著性的篩選標(biāo)記低密度芯片,其標(biāo)記數(shù)目分別為0.3 K,0.5 K,0.7 K,1 K,3 K,5 K,7 K,9 K,11 K,13 K,15 K和30K,在標(biāo)記數(shù)目達(dá)到7K時(shí)準(zhǔn)確性基本穩(wěn)定,基于Bayes B估計(jì)標(biāo)記效應(yīng)篩選標(biāo)記準(zhǔn)確性最高,基于Bayes A估計(jì)標(biāo)記效應(yīng)篩選標(biāo)記準(zhǔn)確性略高于基于標(biāo)記顯著性篩選標(biāo)記。交叉驗(yàn)證的對比試驗(yàn)中,基于同態(tài)一致性(IBS)距離矩陣分組的準(zhǔn)確性略高于隨機(jī)分組。
[Abstract]:The whole genome association analysis and genome selection are the focus of animal breeding in recent years. The whole genome association analysis of carcass traits and meat quality traits of Simmental cattle was carried out by using BovineHD chip, and the accuracy of genome prediction of fatty acid content traits by low density microarray was preliminarily explored. Using mixed compression linear model (CMLM) and linear model (LM) to analyze the whole genome association of three carcass traits, Lin-meat, posterior tendon and bone weight, a total of 186 significantly correlated loci (P10-5) were detected, 55 of which were significant in both models. Most of the markers were found on chromosome 6 and chromosome 14, such as LAP3FCL, FAM18B, PLAG1 and so on. Significant SNP overlapped with QTL1 gene locus of quantitative traits related to carcass weight and bone weight. CMLM and LM were used to analyze six fleshy characters, such as marbling, fat color, total fatty acid content, saturated fatty acid content, monounsaturated fatty acid content and polyunsaturated fatty acid content. 91 significant loci (P10-5) were detected, 44 of which were significant in both models. The markers associated with the three fatty acid content traits fell near the MYC gene on chromosome 14, and most of the significant loci were found in marbling patterns. The back fat thickness and fatty acid content of the twelfth rib were on QTL. 3. In this study, we constructed low density chips with different number of markers, including uniformly distributed low density chips, and estimated the absolute value of labeling effect and significant screening of low density chips based on Bayes Agnes Bayes B. The genome of four fatty acid content traits was predicted by low density microarray, and the accuracy was verified by five times cross validation. The uniform distribution of low density chip integrated with BovineHD and the number of low density chip sites were 3K ~ 7K ~ 9K ~ (-1) 20 K and 40 K, respectively. The accuracy of the labeled low density chip was higher at 9K than that of screening labeled low density chip. The results are consistent with those predicted by low density microarray in other simulated data. According to the labeling effect and the significance of screening low density microarray, the labeling number of the low density microarray was 0.3 KG 0.5 KX 0.7 KG 1 KN 3 KN 5 KN 7 KN 9 KN 11 KN 13 KG 15 K and 30 K, and the accuracy was stable when the labeling number reached 7 K. The accuracy of marker screening based on Bayes B estimation was the highest, and the accuracy of marker screening based on Bayes A was slightly higher than that based on marker significance. The accuracy of distance matrix grouping based on homomorphic consistency is slightly higher than that of random grouping.
【學(xué)位授予單位】：中國農(nóng)業(yè)科學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S823

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相關(guān)期刊論文 前1條

1 王重龍;丁向東;劉劍鋒;殷宗俊;張勤;;基因組育種值估計(jì)的貝葉斯方法[J];遺傳;2014年02期

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本文編號：1921187