遺傳流行病統(tǒng)計分析軟件SAGE的開發(fā)與應(yīng)用
發(fā)布時間：2018-08-12 16:11
【摘要】： 背景與研究目的 遺傳流行病學(xué)(genetic epidemiology)是近年來發(fā)展起來的一門邊緣熱門學(xué)科。主要是研究不同人群中影響疾病分布的遺傳因素和環(huán)境因素，并提出合理預(yù)防措施的學(xué)科。它的理論基礎(chǔ)是群體遺傳學(xué)和流行病學(xué)，主要是應(yīng)用流行病學(xué)群體資料收集和處理的方法，以及分子遺傳學(xué)的實驗手段，借助生物統(tǒng)計學(xué)的有關(guān)原理和方法來研究和探索遺傳因素和環(huán)境因素對疾病的單獨作用以及他們對疾病的聯(lián)合作用。隨著國際人類基因組測序聯(lián)合體對人類基因組DNA完成序列的分析，在人類基因組測序過程中隨著多態(tài)性序列標(biāo)志越來越被人們所發(fā)現(xiàn)，尋找疾病基因的進度日益加快。對多基因疾病的研究已成為當(dāng)前和今后一段相當(dāng)長時間內(nèi)倍受關(guān)注的焦點。 迄今為止，對符合孟德爾遺傳規(guī)律的單基因遺傳病已經(jīng)建立了一套行之有效的研究體系并定位克隆了近千個致病基因。但對于多基因疾病由于其復(fù)雜的表型性狀，這些復(fù)雜的性狀雖然表現(xiàn)出一定的家族聚集傾向性，但并不完全符合孟德爾遺傳規(guī)律，所以在其易感基因的定位和遺傳分析中仍存在很多問題，并成為近年來醫(yī)學(xué)遺傳學(xué)和基因研究的難點和熱點。而在研究人類遺傳相關(guān)疾病的研究過程中，利用家系結(jié)構(gòu)和群體調(diào)查資料進行連鎖分析、關(guān)聯(lián)分析或連鎖不平衡分析已成為基因定位的重要方法。但是由于遺傳學(xué)數(shù)據(jù)龐大，分析繁瑣，結(jié)構(gòu)復(fù)雜，用一般的統(tǒng)計學(xué)方法及軟件往往難以充分利用資料的信息。需要專門的遺傳統(tǒng)計軟件進行分析，目前遺傳流行病學(xué)統(tǒng)計分析軟件雖然較多，但是其綜合分析能力不強。 如對于參數(shù)連鎖分析可供選擇的軟件有FASTLINK，LINKAGE，VITESSE等，對于非參數(shù)連鎖分析可供選擇的軟件有GENEHUNTER，MERLIN，MELINK等。目前國內(nèi)遺傳流行病學(xué)研究正處于發(fā)展階段，在研究中大部分使用的是國外的遺傳統(tǒng)計軟件如LINKAGE，GENEHUNTER等，國內(nèi)已有的遺傳統(tǒng)計軟件為DOS系統(tǒng)的PPAP，，但使用人的不多。由于我國人口龐大，人口學(xué)資料豐富，是一個研究人類遺傳信息很好的資源寶庫。目前國內(nèi)的情況是統(tǒng)計學(xué)與遺傳學(xué)沒有很好的結(jié)合，使得遺傳學(xué)者在信息收集及資料分析時存在不少問題，如對于具體收集哪方面的資料，樣本量大小及使用何種遺傳統(tǒng)計方法等。使得資料信息不能得到充分利用，造成信息巨大的浪費，實在是一件令人遺憾的事。 由于多基因疾病其表型與基因型非嚴(yán)格一一對應(yīng)關(guān)系，因此在分析資料時，需用到多種分析方法，這也使得目前一些專門用于分析某種遺傳分析的軟件越來越暴露其應(yīng)用的局限性，且國外軟件一般為英文軟件，這使得遺傳學(xué)者要浪費大量的人力和物力去學(xué)習(xí)這些軟件，因此急需一個功能強大的綜合性遺傳統(tǒng)計軟件。而遺傳流行病學(xué)統(tǒng)計分析軟件包SAGE(Statistical Analysis for Genetic Epidemiology)恰好滿足我們的需求。它是一個功能強大，能進行各類遺傳統(tǒng)計分析的綜合性軟件，由美國人類遺傳分析資源(Human Genetic Analysis Resource，HGAR)所創(chuàng)編。HGAR成立于美國Cleveland市Case Western ReserveUniversity(CWRU)流行病學(xué)和統(tǒng)計系，由美國公共衛(wèi)生服務(wù)部門、NIH國立研究資源中心資助，該軟件由著名的統(tǒng)計遺傳學(xué)家R．C．Elston及其工作小組于1987研發(fā)而成的，該軟件隨著時間一直不斷更新版本，由剛開始的1.0版本到目前的5.3.0版本，其功能也在不斷增強之中，其在遺傳流行病學(xué)分析中的地位越來越受到重視。 研究方法 通過SAGE軟件自帶的5個example文件作為原始家系數(shù)據(jù)文件，導(dǎo)入各個功能模塊進行詳細分析，該SAGE共有1個自定義模塊及18個功能模塊，共分為18個章節(jié)進行分別講述。其中18個功能模塊分別以以下4項內(nèi)容進行表述： 第1章：SAGE概述。給出了SAGE軟件的基本功能模塊的輸入輸出文件、運行環(huán)境和特點等信息。用戶安裝此軟件時需注意其對系統(tǒng)的要求。 第2章：SAGE數(shù)據(jù)文件的建立、編輯與整理。主要介紹了數(shù)據(jù)文件的3種建立方式，及項目的導(dǎo)入、導(dǎo)出和重命名等內(nèi)容。重點是數(shù)據(jù)文件的建立及導(dǎo)入。 第3章：用戶自定義功能模塊。主要介紹了如何創(chuàng)建基因組數(shù)據(jù)文件和建立新變量。重點內(nèi)容是建立新的變量。 第4章：SAGE的一般統(tǒng)計分析(PEDINFO)。主要介紹了PEDINFO的功能、原理及如何操作及對結(jié)果的解釋。重點內(nèi)容是對結(jié)果的解釋。以下的14個章節(jié)均是從模塊的功能、原理、操作過程及主要輸出結(jié)果等4個方面進行闡述。 第5章：非孟德爾遺傳統(tǒng)計分析(MARKERINFO)。主要用于檢測家系數(shù)據(jù)中的非孟德爾遺傳信息，幫助用戶對非一致性數(shù)據(jù)的檢測。前提是對孟德爾遺傳定律有所了解。 第6章：親屬對的重新分類(RELTEST)。通過基因組多位點掃描數(shù)據(jù)對原有的親屬對進行重新歸類，主要是基于染色體血緣一致(IBD)等位共享原理。重點是對IBD及IBS有所了解，及對結(jié)果的解釋。 第7章：等位基因頻率估計(FREQ)。估計已知家系結(jié)構(gòu)的個體等位基因頻率及產(chǎn)生標(biāo)記位點描述文件。產(chǎn)生的位點文件可以用于GENIBD，MLOD及其他SAGE程序。該模塊的最主要作用在于輸出位點文件及可輸出近親系數(shù)。 第8章：等位基因關(guān)聯(lián)或者數(shù)據(jù)性狀傳遞不平衡檢驗(ASSOC)。主要用于估計家系數(shù)據(jù)文件中性狀與協(xié)變量，此協(xié)變量可通過標(biāo)記表型轉(zhuǎn)換而來，估計家庭殘差相關(guān)系數(shù)或者遺傳度估計。需要注意的是對數(shù)據(jù)的兩種轉(zhuǎn)化的選擇。 第9章：家庭相關(guān)性分析(FCOR)。主要用于估計家系中所有相關(guān)對的多變量相關(guān)關(guān)系及他們的漸進標(biāo)準(zhǔn)誤。重點是對家庭內(nèi)相關(guān)對相關(guān)關(guān)系的結(jié)果的解釋。 第10章：混合分離分析與復(fù)雜分離分析(SEGREG)。主要用于在所提供的家庭相關(guān)關(guān)系基礎(chǔ)上檢測和選定分離分析模型。其特性可為連續(xù)性，二分類特性或者年齡相關(guān)的二分類特性，產(chǎn)生可用于基于模型的連鎖分析的外顯率文件。重點是對不同特性所適合的模型的選擇的設(shè)定。 第11章：血緣同一等位基因概率產(chǎn)生模塊(GENIBD)。此功能模塊主要用于通過多種算法協(xié)調(diào)計算各種家系數(shù)據(jù)文件中不同相關(guān)對來產(chǎn)生單位點和多位點的血緣一致等位基因分布。重點是不同資料需選用不同的模型。 第12章：年齡相關(guān)發(fā)作分析(AGEON)：適用于同時比較受累相關(guān)對與非受累相關(guān)對的年齡發(fā)作相關(guān)分布資料，允許通過協(xié)變量調(diào)整均值，方差或者偏度分布。需要注意的是如何合并資料。 第13章：單體型分析(DECIPHER)：主要是用于對于人群中常染色體或者X性染色體的單體型頻率的最大似然估計。前提是對單體型有所了解。 第14章：基于模型的單位點連鎖分析(LODLINK)。主要用于計算基于模型的主要特性與各個位點間的兩位點間的LOD值，主要特性可以是任何符合孟德爾傳遞的標(biāo)記或者其他特性。重點是對主要特性及從SEGERG程序所產(chǎn)生的外顯率文件的命名。 第15章：基于模型的多位點連鎖分析(MLOD)。主要用于計算基于模型的小家系或者大家系的多位點間的連鎖分析。重點是基因組數(shù)據(jù)文件的產(chǎn)生及認(rèn)定主要特性。 第16章：患病同胞對連鎖分析方法(SIBPAL)�？梢允菃挝稽c或者多位點的共享血緣一致等位基因信息，并且根據(jù)多位點基因同時使用二分類變量和連續(xù)性變量，同時包括上位交互效應(yīng)和協(xié)變量效應(yīng)。重點是不同特性需進行相應(yīng)的設(shè)定。 第17章：受累同胞對的Lods連鎖分析(LODPAL)。程序進行連鎖分析是基于受累同胞對的Lods記分值，目前執(zhí)行一般條件logistic回歸模型。需注意對效能的設(shè)定。 第18章：傳遞不平衡檢驗(TDT)。程序中的TDT則是基于傳遞不平衡的基礎(chǔ)模型基礎(chǔ)上建立的用于分析前提是已知連鎖不平衡的情況下的標(biāo)記位點與疾病位點的連鎖關(guān)系，其疾病特性為二分類變量。前提是對TDT的原理的掌握。 結(jié)果 通過此論文，使得遺傳學(xué)者可以充分利用其遺傳資料進行遺傳統(tǒng)計分析，節(jié)省人力和物力，學(xué)習(xí)該軟件可以指導(dǎo)遺傳學(xué)者收集遺傳資料，盡可能的利用遺傳資料，從而加快遺傳流行病學(xué)的發(fā)展。
[Abstract]:Background and research objectives
Genetic epidemiology is a frontier hot subject developed in recent years. It mainly studies the genetic and environmental factors that affect the distribution of diseases in different populations and puts forward reasonable preventive measures. Its theoretical basis is population genetics and epidemiology, mainly the application of epidemiological population data. Methods of collection and processing, as well as experimental methods of molecular genetics, with the help of relevant principles and methods of biostatistics, to study and explore the individual effects of genetic and environmental factors on diseases and their combined effects on diseases. With the discovery of polymorphic sequence markers in the process of human genome sequencing, the search for disease genes is accelerating. The study of polygenic diseases has become the focus of attention for a long time.
Up to now, a set of effective research systems have been established for single-gene genetic diseases which conform to Mendelian inheritance and nearly one thousand pathogenic genes have been cloned. However, for polygenic diseases, these complex traits show a certain tendency of family clustering, but they do not fully conform to Mendelian. Delphi inheritance law, therefore, still has many problems in the mapping and genetic analysis of susceptible genes, and has become a difficult and hot spot in medical genetics and gene research in recent years. Balance analysis has become an important method for gene mapping. However, due to the huge genetic data, complicated analysis and complex structure, it is difficult to make full use of the information of the data with general statistical methods and software. Analytical ability is not strong.
For example, FASTLINK, LINKAGE, VITESSE and GENEHUNTER, MERLIN, MELINK are available for parametric linkage analysis, while GENEHUNTER, MERLIN and MELINK are available for non-parametric linkage analysis. Because of the huge population and abundant demographic data, our country is a good resource repository for studying human genetic information. At present, there is no good combination of statistics and genetics, which makes geneticists in information collection and data analysis. There are a lot of problems, such as what kind of data to collect, sample size and what kind of genetic statistics method to use. It is a pity that the information can not be fully utilized, resulting in a huge waste of information.
Due to the non-strict one-to-one correspondence between the phenotype and genotype of polygenic diseases, it is necessary to use a variety of analytical methods in the analysis of data. This also makes some special software for genetic analysis more and more expose the limitations of its application, and foreign software is generally English software, which makes geneticists waste a lot of money. The amount of manpower and material resources to learn these software, so the urgent need for a powerful comprehensive genetic statistics software. And genetic epidemiology statistical analysis software package SAGE (Statistical Analysis for Genetic Epidemiology) just meets our needs. HGAR, created by Human Genetic Analysis Resource (HGAR), was founded in the Department of Epidemiology and Statistics of Case Western Reserve University (CWRU) in Cleveland, USA. It was funded by the US Public Health Service and the NIH National Research Resource Center. The software was developed by R.C. Elston, a famous statistical geneticist. Developed in 1987 by its team, the software has been continuously updated over time, from the initial version 1.0 to the current version 5.3.0, and its functions are also increasing, and its position in genetic epidemiological analysis is getting more and more attention.
research method
Through the introduction of five examples files from SAGE software as original data files, each function module is analyzed in detail. The SAGE has one custom module and 18 function modules, which are divided into 18 chapters.
Chapter 1: Overview of SAGE. The input and output files, running environment and characteristics of the basic functional modules of SAGE software are given. Users should pay attention to the system requirements when installing the software.
Chapter 2: Establishment, editing and sorting of SAGE data files. It mainly introduces three methods of establishing data files, the import, export and renaming of projects, etc.
Chapter 3: User-defined functional modules. It mainly introduces how to create genomic data files and create new variables. The emphasis is to create new variables.
Chapter 4: General Statistical Analysis of SAGE (PEDINFO). It mainly introduces the function, principle and operation of PEDINFO, and explains the results. The emphasis is on the explanation of the results. The following 14 chapters are from the function, principle, operation process and main output results of the module.
Chapter 5: Non-Mendelian Genetic Statistical Analysis (MARKERINFO). Mainly used to detect non-Mendelian genetic information in the family coefficient data, to help users detect inconsistent data. The premise is to understand Mendelian genetic law.
Chapter 6: Reclassification of Relative Pairs (RELTEST). The original relatives are reclassified by genomic multilocus scanning data, mainly based on the principle of chromosomal consanguinity (IBD) allele sharing. The emphasis is on understanding IBD and IBS, and explaining the results.
Chapter 7: Allele Frequency Estimation (FREQ). Estimation of individual allele frequencies of known family structures and generation of marker site descriptors. The resulting site files can be used in GENIBD, MLOD and other SAGE programs. The main functions of this module are to output site files and output intimacy coefficients.
Chapter 8: Allelic Association or Data Trait Transfer Disequilibrium Test (ASSOC). It is mainly used to estimate the family coefficient. The covariate can be transformed from the marker phenotype to estimate the family residual correlation coefficient or heritability.
Chapter 9: Family Correlation Analysis (FCOR). It is mainly used to estimate the multivariate correlations of all related pairs in a family and their asymptotic standard errors.
Chapter 10: Mixed Separation Analysis and Complex Separation Analysis (SEGREG). Mainly used to detect and select separation analysis models on the basis of family-related relationships provided. Its characteristics can be continuous, binary or age-related binary classification characteristics, producing an explicit rate file for model-based linkage analysis. Selection of suitable models for different characteristics.
Chapter 11: GENIBD. This function module is mainly used to coordinate the calculation of various family coefficients through a variety of algorithms to produce a uniform allele distribution of units and multiple loci. The emphasis is on different models for different data.
Chapter 12: Age-related seizure analysis (AGEON): Applies to the simultaneous comparison of age-related distribution data between affected and non-involved pairs, allowing for covariate adjustment of mean, variance, or skewness distributions.
Chapter 13: Haplotype Analysis (DECIPHER): Mainly used to estimate the maximum likelihood of haplotype frequencies of autosomal or X-sex chromosomes in a population.
Chapter 14: Model-based Unit Point Linkage Analysis (LODLINK). Mainly used to calculate the LOD values between the main model-based features and the two points between the loci. The main characteristics may be any marker or other characteristics that conform to Mendelian transmission. The emphasis is on the naming of the main features and the explicit file generated from the SEGERG program.
Chapter 15: Model-based multilocus linkage analysis (MLOD). It is mainly used to calculate the multilocus linkage analysis between small or large model-based families. The emphasis is on the generation and identification of major characteristics of genomic data files.
Chapter 16: Siblin-to-Siblin Linkage Analysis (SIBPAL). It can be a shared consanguineous allele information at a single point or multiple loci. Bivariate and contiguous variables are used simultaneously according to the multilocus genes, including epistatic interactions and covariate effects. The emphasis is on different characteristics that need to be set accordingly.
Chapter 17: Lods linkage analysis of affected siblings (LODPAL). The program is based on Lods scores of affected siblings. Currently, the general conditional logistic regression model is implemented. Attention should be paid to the setting of effectiveness.
Chapter 18: Transfer Disequilibrium Test (TDT). The TDT in the program is based on the basic model of transfer disequilibrium. It is used to analyze the linkage between marker sites and disease sites under the condition of known linkage disequilibrium. The disease characteristics are binary variables. The premise is to master the principle of TDT.
Result
Through this paper, geneticists can make full use of their genetic data for genetic statistical analysis, saving manpower and material resources, learning this software can guide geneticists to collect genetic data, as far as possible use of genetic data, thus speeding up the development of genetic epidemiology.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2007
【分類號】：TP311.52;R181.3
本文編號：2179580
[Abstract]:Background and research objectives
Genetic epidemiology is a frontier hot subject developed in recent years. It mainly studies the genetic and environmental factors that affect the distribution of diseases in different populations and puts forward reasonable preventive measures. Its theoretical basis is population genetics and epidemiology, mainly the application of epidemiological population data. Methods of collection and processing, as well as experimental methods of molecular genetics, with the help of relevant principles and methods of biostatistics, to study and explore the individual effects of genetic and environmental factors on diseases and their combined effects on diseases. With the discovery of polymorphic sequence markers in the process of human genome sequencing, the search for disease genes is accelerating. The study of polygenic diseases has become the focus of attention for a long time.
Up to now, a set of effective research systems have been established for single-gene genetic diseases which conform to Mendelian inheritance and nearly one thousand pathogenic genes have been cloned. However, for polygenic diseases, these complex traits show a certain tendency of family clustering, but they do not fully conform to Mendelian. Delphi inheritance law, therefore, still has many problems in the mapping and genetic analysis of susceptible genes, and has become a difficult and hot spot in medical genetics and gene research in recent years. Balance analysis has become an important method for gene mapping. However, due to the huge genetic data, complicated analysis and complex structure, it is difficult to make full use of the information of the data with general statistical methods and software. Analytical ability is not strong.
For example, FASTLINK, LINKAGE, VITESSE and GENEHUNTER, MERLIN, MELINK are available for parametric linkage analysis, while GENEHUNTER, MERLIN and MELINK are available for non-parametric linkage analysis. Because of the huge population and abundant demographic data, our country is a good resource repository for studying human genetic information. At present, there is no good combination of statistics and genetics, which makes geneticists in information collection and data analysis. There are a lot of problems, such as what kind of data to collect, sample size and what kind of genetic statistics method to use. It is a pity that the information can not be fully utilized, resulting in a huge waste of information.
Due to the non-strict one-to-one correspondence between the phenotype and genotype of polygenic diseases, it is necessary to use a variety of analytical methods in the analysis of data. This also makes some special software for genetic analysis more and more expose the limitations of its application, and foreign software is generally English software, which makes geneticists waste a lot of money. The amount of manpower and material resources to learn these software, so the urgent need for a powerful comprehensive genetic statistics software. And genetic epidemiology statistical analysis software package SAGE (Statistical Analysis for Genetic Epidemiology) just meets our needs. HGAR, created by Human Genetic Analysis Resource (HGAR), was founded in the Department of Epidemiology and Statistics of Case Western Reserve University (CWRU) in Cleveland, USA. It was funded by the US Public Health Service and the NIH National Research Resource Center. The software was developed by R.C. Elston, a famous statistical geneticist. Developed in 1987 by its team, the software has been continuously updated over time, from the initial version 1.0 to the current version 5.3.0, and its functions are also increasing, and its position in genetic epidemiological analysis is getting more and more attention.
research method
Through the introduction of five examples files from SAGE software as original data files, each function module is analyzed in detail. The SAGE has one custom module and 18 function modules, which are divided into 18 chapters.
Chapter 1: Overview of SAGE. The input and output files, running environment and characteristics of the basic functional modules of SAGE software are given. Users should pay attention to the system requirements when installing the software.
Chapter 2: Establishment, editing and sorting of SAGE data files. It mainly introduces three methods of establishing data files, the import, export and renaming of projects, etc.
Chapter 3: User-defined functional modules. It mainly introduces how to create genomic data files and create new variables. The emphasis is to create new variables.
Chapter 4: General Statistical Analysis of SAGE (PEDINFO). It mainly introduces the function, principle and operation of PEDINFO, and explains the results. The emphasis is on the explanation of the results. The following 14 chapters are from the function, principle, operation process and main output results of the module.
Chapter 5: Non-Mendelian Genetic Statistical Analysis (MARKERINFO). Mainly used to detect non-Mendelian genetic information in the family coefficient data, to help users detect inconsistent data. The premise is to understand Mendelian genetic law.
Chapter 6: Reclassification of Relative Pairs (RELTEST). The original relatives are reclassified by genomic multilocus scanning data, mainly based on the principle of chromosomal consanguinity (IBD) allele sharing. The emphasis is on understanding IBD and IBS, and explaining the results.
Chapter 7: Allele Frequency Estimation (FREQ). Estimation of individual allele frequencies of known family structures and generation of marker site descriptors. The resulting site files can be used in GENIBD, MLOD and other SAGE programs. The main functions of this module are to output site files and output intimacy coefficients.
Chapter 8: Allelic Association or Data Trait Transfer Disequilibrium Test (ASSOC). It is mainly used to estimate the family coefficient. The covariate can be transformed from the marker phenotype to estimate the family residual correlation coefficient or heritability.
Chapter 9: Family Correlation Analysis (FCOR). It is mainly used to estimate the multivariate correlations of all related pairs in a family and their asymptotic standard errors.
Chapter 10: Mixed Separation Analysis and Complex Separation Analysis (SEGREG). Mainly used to detect and select separation analysis models on the basis of family-related relationships provided. Its characteristics can be continuous, binary or age-related binary classification characteristics, producing an explicit rate file for model-based linkage analysis. Selection of suitable models for different characteristics.
Chapter 11: GENIBD. This function module is mainly used to coordinate the calculation of various family coefficients through a variety of algorithms to produce a uniform allele distribution of units and multiple loci. The emphasis is on different models for different data.
Chapter 12: Age-related seizure analysis (AGEON): Applies to the simultaneous comparison of age-related distribution data between affected and non-involved pairs, allowing for covariate adjustment of mean, variance, or skewness distributions.
Chapter 13: Haplotype Analysis (DECIPHER): Mainly used to estimate the maximum likelihood of haplotype frequencies of autosomal or X-sex chromosomes in a population.
Chapter 14: Model-based Unit Point Linkage Analysis (LODLINK). Mainly used to calculate the LOD values between the main model-based features and the two points between the loci. The main characteristics may be any marker or other characteristics that conform to Mendelian transmission. The emphasis is on the naming of the main features and the explicit file generated from the SEGERG program.
Chapter 15: Model-based multilocus linkage analysis (MLOD). It is mainly used to calculate the multilocus linkage analysis between small or large model-based families. The emphasis is on the generation and identification of major characteristics of genomic data files.
Chapter 16: Siblin-to-Siblin Linkage Analysis (SIBPAL). It can be a shared consanguineous allele information at a single point or multiple loci. Bivariate and contiguous variables are used simultaneously according to the multilocus genes, including epistatic interactions and covariate effects. The emphasis is on different characteristics that need to be set accordingly.
Chapter 17: Lods linkage analysis of affected siblings (LODPAL). The program is based on Lods scores of affected siblings. Currently, the general conditional logistic regression model is implemented. Attention should be paid to the setting of effectiveness.
Chapter 18: Transfer Disequilibrium Test (TDT). The TDT in the program is based on the basic model of transfer disequilibrium. It is used to analyze the linkage between marker sites and disease sites under the condition of known linkage disequilibrium. The disease characteristics are binary variables. The premise is to master the principle of TDT.
Result
Through this paper, geneticists can make full use of their genetic data for genetic statistical analysis, saving manpower and material resources, learning this software can guide geneticists to collect genetic data, as far as possible use of genetic data, thus speeding up the development of genetic epidemiology.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2007
【分類號】：TP311.52;R181.3
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