醫(yī)學(xué)遺傳資料統(tǒng)計(jì)分析方法的研究與SAS實(shí)現(xiàn)
發(fā)布時(shí)間：2018-09-14 07:01
【摘要】： 數(shù)理統(tǒng)計(jì)分析方法在醫(yī)學(xué)遺傳學(xué)的發(fā)展過(guò)程中發(fā)揮了不可替代的作用,隨著基礎(chǔ)醫(yī)學(xué)的發(fā)展、遺傳學(xué)實(shí)驗(yàn)技術(shù)的不斷更新,許多遺傳統(tǒng)計(jì)分析技術(shù)已經(jīng)成熟,應(yīng)用越來(lái)越普及,同時(shí)新的分析方法不斷地涌現(xiàn)出來(lái)。針對(duì)新的、更為復(fù)雜的方法如何運(yùn)用,成熟、普及方法如何快速實(shí)現(xiàn)計(jì)算是當(dāng)今醫(yī)學(xué)遺傳科研人員面臨的問(wèn)題。本研究針對(duì)醫(yī)學(xué)遺傳資料統(tǒng)計(jì)分析方法進(jìn)行了比較細(xì)致的研究,特別是遺傳結(jié)果多重比較的校正、多個(gè)位點(diǎn)與疾病的關(guān)聯(lián)研究、連鎖分析等問(wèn)題,通過(guò)反復(fù)測(cè)算,提出了自己的見(jiàn)解,并將全部方法運(yùn)用世界權(quán)威的統(tǒng)計(jì)分析軟件—SAS軟件,通過(guò)調(diào)用過(guò)程步、編程實(shí)現(xiàn)了計(jì)算。 針對(duì)目前醫(yī)學(xué)遺傳學(xué)中主要的統(tǒng)計(jì)分析方法,本研究側(cè)重進(jìn)行了以下幾部分工作: 第一部分:測(cè)算基因頻率、基因型頻率以及驗(yàn)證Hardy-Weinberg平衡定律哈代-溫伯格平衡定律在遺傳學(xué)的研究中起著非常重要的作用。在對(duì)遺傳基因型數(shù)據(jù)進(jìn)行分析前,最好能夠先檢驗(yàn)數(shù)據(jù)是否符合哈代-溫伯格平衡定律。本章介紹了哈代-溫伯格平衡定律的基礎(chǔ)理論,并利用軟件計(jì)算基因、基因型頻率、驗(yàn)證哈代-溫伯格平衡定律、利用蒙特卡洛模擬校正概率。 第二部分:運(yùn)用病例對(duì)照方法尋找疾病的關(guān)聯(lián)位點(diǎn) 病例-對(duì)照研究是分析流行病學(xué)研究方法中最基本、最重要的研究類型之一,是檢驗(yàn)病因假說(shuō)的重要工具。在遺傳流行病學(xué)中,利用病例-對(duì)照研究可以找到復(fù)雜疾病的關(guān)聯(lián)基因�？刹捎靡话悝�2檢驗(yàn)與Armitage趨勢(shì)檢驗(yàn)。采用一般χ2檢驗(yàn)來(lái)求得疾病與某個(gè)位點(diǎn)的相關(guān)性,要求檢驗(yàn)的群體滿足哈代-溫伯格平衡定律。研究表明,如果哈代-溫伯格平衡定律不成立,χ2檢驗(yàn)的第一類錯(cuò)誤會(huì)增加,因此應(yīng)根據(jù)基因型數(shù)據(jù)用Armitage趨勢(shì)檢驗(yàn)來(lái)作統(tǒng)計(jì)分析。 第三部分:遺傳分析結(jié)果的校正 在病例-對(duì)照遺傳流行病數(shù)據(jù)分析過(guò)程中,隨著生物技術(shù)的迅速發(fā)展,實(shí)驗(yàn)室快速檢測(cè)大量位點(diǎn)已經(jīng)成為常規(guī)手段。對(duì)于每一個(gè)位點(diǎn)都需要進(jìn)行統(tǒng)計(jì)學(xué)檢驗(yàn),如果位點(diǎn)過(guò)多,多重比較會(huì)導(dǎo)致假陽(yáng)性率無(wú)限增大,從而使得結(jié)論不可信,因此需要對(duì)多重比較進(jìn)行校正,本章運(yùn)用3種平滑修正的方法,以及校正方法Bonferroni法、Sidak法等。 第四部分:家系數(shù)據(jù)的關(guān)聯(lián)分析 利用家庭成員作為對(duì)照是按祖先起源匹配的最好辦法,以遺傳背景一致的家庭成員作為對(duì)照,可以很好地解決人群分層問(wèn)題。根據(jù)家庭成員不同,分析方法也不盡相同。本章對(duì)家系病例對(duì)照數(shù)據(jù)進(jìn)行了TDT、s-TDT、SDT檢驗(yàn)。 第五部分:連鎖不平衡與單體型分析 連鎖不平衡分析、單體型分析是一類對(duì)疾病相關(guān)聯(lián)基因進(jìn)行精確定位的高效的方法,在檢測(cè)復(fù)雜疾病基因時(shí)起到了巨大的作用。在數(shù)據(jù)收集方面,它不需要收集家系數(shù)據(jù),這是與家系數(shù)據(jù)疾病關(guān)聯(lián)分析的一個(gè)區(qū)別——它的應(yīng)用條件比較寬泛。本章細(xì)致地研究了連鎖不平衡檢測(cè)方法、單體型與疾病關(guān)聯(lián)分析。 第六部分:近交系數(shù)與親緣系數(shù)的計(jì)算 近親婚配為非隨機(jī)婚配,這類婚配嚴(yán)重影響著群體中的基因平衡法則,導(dǎo)致群體中純合子和雜合子的比率發(fā)生變化。哈代-溫伯格法則僅僅適用于隨機(jī)婚配的群體而不適用于這類群體。本章將對(duì)近親婚配中近交系數(shù)和親緣系數(shù)進(jìn)行計(jì)算。 第七部分:連鎖分析 個(gè)體形成性細(xì)胞過(guò)程中,減數(shù)分裂時(shí)同源染色體間發(fā)生交換的頻率稱為重組率。重組率的大小與同一條染色體上兩個(gè)基因座位距離有關(guān),一般說(shuō)距離遠(yuǎn)時(shí)發(fā)生交換的機(jī)會(huì)多,重組率高,若重組率超過(guò)0.50,表明這兩個(gè)基因座位不在同一條染色體上。重組率比較低,說(shuō)明兩個(gè)基因座位位置比較近,這兩個(gè)基因座位上的等位基因傳遞到下一代是不獨(dú)立的,這種現(xiàn)象在遺傳學(xué)中稱為連鎖。本章主要介紹貝葉斯方法和蒙特卡洛模擬法估計(jì)重組率。 文中采用SAS9.1.3、SAS9.2分析軟件genetics模塊、stat模塊中多個(gè)過(guò)程步以及編程方法對(duì)醫(yī)學(xué)遺傳學(xué)資料和數(shù)據(jù)進(jìn)行了統(tǒng)計(jì)運(yùn)算。本文運(yùn)用了統(tǒng)計(jì)模型理論與實(shí)例分析相結(jié)合,理論研究與軟件實(shí)現(xiàn)結(jié)合,數(shù)學(xué)方法與遺傳實(shí)驗(yàn)技術(shù)結(jié)合的總體思路,按著由簡(jiǎn)到繁的過(guò)程系統(tǒng)地介紹了各種遺傳統(tǒng)計(jì)分析方法,以及統(tǒng)計(jì)分析模型及計(jì)算原理,尤其對(duì)于遺傳結(jié)果校正、多個(gè)位點(diǎn)與疾病關(guān)聯(lián)、連鎖分析等方法進(jìn)行了詳細(xì)的闡述,提出了新觀點(diǎn)。文中突出了統(tǒng)計(jì)分析方法的應(yīng)用技巧和便捷實(shí)現(xiàn),不但為醫(yī)學(xué)遺傳學(xué)提供了統(tǒng)計(jì)方法學(xué),更為該分支學(xué)科的數(shù)據(jù)運(yùn)算提供了新平臺(tái)。
[Abstract]:Mathematical statistics analysis method plays an irreplaceable role in the development of medical genetics. With the development of basic medicine and the renewal of genetic experiment technology, many genetic statistics analysis techniques have been mature and applied more and more widely. At the same time, new analysis methods have emerged constantly. How to use, mature, and popularize the method of computing is a problem facing medical genetic researchers. This study focuses on the statistical analysis of medical genetic data, especially the correction of multiple comparisons of genetic results, the association between multiple loci and disease, linkage analysis and other issues. Through Repeated calculation, put forward their own views, and all the methods used in the world's authoritative statistical analysis software-SAS software, through the call process step, programming to achieve the calculation.
In view of the main statistical analysis methods in medical genetics, this study focuses on the following parts:
Part one: Estimating gene frequency, genotype frequency and verifying Hardy-Weinberg equilibrium law Hardy-Weinberg equilibrium law play a very important role in genetics research. It is better to check whether the data conform to Hardy-Weinberg equilibrium law before analyzing the genotype data. The basic theory of Weinberg's equilibrium law, and the use of software to calculate the gene, genotype frequency, verify Hardy-Weinberg equilibrium law, using Monte Carlo simulation correction probability.
The second part: using case control method to find the related sites of disease.
Case-control study is one of the most basic and important types of epidemiological research methods and an important tool to test the hypothesis of etiology. In genetic epidemiology, case-control study can be used to find the genes associated with complex diseases. Studies have shown that if Hardy-Weinberg equilibrium law does not hold, the first type of errors in the_2 test will increase, so the Armitage trend test should be used for statistical analysis based on genotype data.
The third part: correction of genetic analysis results.
With the rapid development of biotechnology, rapid detection of large numbers of loci has become a routine method in case-control genetic epidemiological data analysis. Statistical tests are required for each locus. If too many loci are present, multiple comparisons will lead to an infinite increase in false positive rates, making the conclusions unreliable. To correct multiple comparisons, three smoothing correction methods, Bonferroni method and Sidak method are used in this chapter.
The fourth part: family data correlation analysis.
Using family members as controls is the best way to match according to ancestral origin. Using family members with identical genetic background as controls can solve the problem of population stratification.
The fifth part: linkage disequilibrium and haplotype analysis.
Linkage disequilibrium analysis, haplotype analysis, is a class of highly efficient methods for precise mapping of disease-related genes, which plays a huge role in detecting complex disease genes. In this chapter, the linkage disequilibrium detection methods, haplotype and disease association are studied in detail.
The sixth part: Calculation of inbreeding coefficient and kin coefficient.
Inbreeding is a kind of non-random mating, which seriously affects the law of gene balance in the population, resulting in changes in the ratio of homozygotes and heterozygotes in the population.
The seventh part: linkage analysis.
In the process of individual morphogenetic cells, the frequency of exchange between homologous chromosomes during meiosis is called recombination rate. The size of recombination rate is related to the distance between two loci on the same chromosome. Generally speaking, there are more chances of exchange and higher recombination rate when the distance is long. If the recombination rate exceeds 0.50, the two loci are not the same. The low recombination rate on chromosome indicates that the two loci are close together, and the allele transfer from the two loci to the next generation is not independent. This phenomenon is called linkage in genetics. This chapter mainly introduces Bayesian method and Monte Carlo simulation to estimate recombination rate.
In this paper, we use SAS 9.1.3, SAS 9.2 analysis software genetics module, stat module in many process steps and programming methods for statistical calculation of medical genetic data and data. According to the process from simplicity to complexity, this paper systematically introduces various methods of genetic statistical analysis, as well as statistical analysis models and calculation principles, especially elaborates on the methods of genetic result correction, multiple loci associated with disease, linkage analysis and so on, and puts forward new viewpoints. Simple implementation not only provides statistical methodology for medical genetics, but also provides a new platform for data processing in this branch of science.
【學(xué)位授予單位】：中國(guó)人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2010
【分類號(hào)】：R311
本文編號(hào)：2241915
[Abstract]:Mathematical statistics analysis method plays an irreplaceable role in the development of medical genetics. With the development of basic medicine and the renewal of genetic experiment technology, many genetic statistics analysis techniques have been mature and applied more and more widely. At the same time, new analysis methods have emerged constantly. How to use, mature, and popularize the method of computing is a problem facing medical genetic researchers. This study focuses on the statistical analysis of medical genetic data, especially the correction of multiple comparisons of genetic results, the association between multiple loci and disease, linkage analysis and other issues. Through Repeated calculation, put forward their own views, and all the methods used in the world's authoritative statistical analysis software-SAS software, through the call process step, programming to achieve the calculation.
In view of the main statistical analysis methods in medical genetics, this study focuses on the following parts:
Part one: Estimating gene frequency, genotype frequency and verifying Hardy-Weinberg equilibrium law Hardy-Weinberg equilibrium law play a very important role in genetics research. It is better to check whether the data conform to Hardy-Weinberg equilibrium law before analyzing the genotype data. The basic theory of Weinberg's equilibrium law, and the use of software to calculate the gene, genotype frequency, verify Hardy-Weinberg equilibrium law, using Monte Carlo simulation correction probability.
The second part: using case control method to find the related sites of disease.
Case-control study is one of the most basic and important types of epidemiological research methods and an important tool to test the hypothesis of etiology. In genetic epidemiology, case-control study can be used to find the genes associated with complex diseases. Studies have shown that if Hardy-Weinberg equilibrium law does not hold, the first type of errors in the_2 test will increase, so the Armitage trend test should be used for statistical analysis based on genotype data.
The third part: correction of genetic analysis results.
With the rapid development of biotechnology, rapid detection of large numbers of loci has become a routine method in case-control genetic epidemiological data analysis. Statistical tests are required for each locus. If too many loci are present, multiple comparisons will lead to an infinite increase in false positive rates, making the conclusions unreliable. To correct multiple comparisons, three smoothing correction methods, Bonferroni method and Sidak method are used in this chapter.
The fourth part: family data correlation analysis.
Using family members as controls is the best way to match according to ancestral origin. Using family members with identical genetic background as controls can solve the problem of population stratification.
The fifth part: linkage disequilibrium and haplotype analysis.
Linkage disequilibrium analysis, haplotype analysis, is a class of highly efficient methods for precise mapping of disease-related genes, which plays a huge role in detecting complex disease genes. In this chapter, the linkage disequilibrium detection methods, haplotype and disease association are studied in detail.
The sixth part: Calculation of inbreeding coefficient and kin coefficient.
Inbreeding is a kind of non-random mating, which seriously affects the law of gene balance in the population, resulting in changes in the ratio of homozygotes and heterozygotes in the population.
The seventh part: linkage analysis.
In the process of individual morphogenetic cells, the frequency of exchange between homologous chromosomes during meiosis is called recombination rate. The size of recombination rate is related to the distance between two loci on the same chromosome. Generally speaking, there are more chances of exchange and higher recombination rate when the distance is long. If the recombination rate exceeds 0.50, the two loci are not the same. The low recombination rate on chromosome indicates that the two loci are close together, and the allele transfer from the two loci to the next generation is not independent. This phenomenon is called linkage in genetics. This chapter mainly introduces Bayesian method and Monte Carlo simulation to estimate recombination rate.
In this paper, we use SAS 9.1.3, SAS 9.2 analysis software genetics module, stat module in many process steps and programming methods for statistical calculation of medical genetic data and data. According to the process from simplicity to complexity, this paper systematically introduces various methods of genetic statistical analysis, as well as statistical analysis models and calculation principles, especially elaborates on the methods of genetic result correction, multiple loci associated with disease, linkage analysis and so on, and puts forward new viewpoints. Simple implementation not only provides statistical methodology for medical genetics, but also provides a new platform for data processing in this branch of science.
【學(xué)位授予單位】：中國(guó)人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2010
【分類號(hào)】：R311
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 易洪剛;陳峰;于浩;趙楊;婁東華;;病例同胞對(duì)照設(shè)計(jì)[J];中華流行病學(xué)雜志;2006年02期
2 湯在祥;王學(xué)楓;吳雯雯;徐辰武;;基于貝葉斯統(tǒng)計(jì)的遺傳連鎖分析方法[J];遺傳;2006年09期
，本文編號(hào)：2241915
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