Arlequin的使用说明.doc

Arlequin3.1的使用说明Arlequin功能的概述 Molecular diversity分子多态性 Mismatch distribution错配分布 Haplotype frequency estimation单倍型频率估计 Linkage disequilibrium连锁不平衡：检测不同位点上等位基因的非随机关联 Hardy-Weinberg equilibrium哈温伯格平衡 Tajimas neutrality testTajima中性检测 Fu s neutrality testFu中性检测 Ewens-watterson neutrality testEwens-watterson中性检测以上三个中性检测都是基于无限位点模型，适用于DNA sequence和RFLP单倍型。 Chakraboetys amalgamation testChakraboetys融合检测，检测人群的均一性和同质性，和中性选择等。 Minimu Spanning Network(MSN，最小扩张树或称之为最小支撑树，给予分子差异。 AMOVA分子差异度分析，用以评测人群的遗传结构 Parwise genetic distances遗传距离的估计 Exact test of population differentiation检测随机交配群体单倍型的非随机分布 Assignment test of genotype通过估计等位基因平率将单个基因型分被盗特定的人群中。Arlequin3.1分析的数据文件的格式必须是以*arq为扩增名方法：用Clustalx比对后保存一个PHY格式的文件，在DNAsp中打开该文件，点击Generate中的Haplotype date file ，设置considered，其余的参数不变，在其中的Generate中选其中的Arlequin Haplotype List 即可保存下用Arlequin3.1分析的文件的格式（在import date中可以进行文件格式的转换，Arlequin的数据转化为Arlequin、Genepop、Biosys、phylip、Mega、WinAmova）首先打开Arlequin3.1的界面如下：点击open project，出现如下的界面：选择你要分析数据的文件，一般扩增名为*arp下面以例子中的文件加以说明:点击打开，Project title 所分析数据的名称Genotypic date 输入数据是单倍型还是双倍性Gametic phase：确定输入数据中配子片段是否已知Recessive date：确定输入数据是否为阴性Date type：输入数据的类型Missing date：缺失数据用什么字符表示对上面的例子进行数据设置点击Setting 出现如下的界面点击General setting情况如下：点击Haplotype inference（单倍型频率），出现如下界面选中右侧的设置情况，如果输入的数据的形式属于配子片段已知的单倍型数据或基因型数据，则操作界面如下继续设置，点击Molecular diversity indices （在分子水平上计算遗传分歧度的几个参数）设置后，出现如下图的结果Standard diversity indices:计算几种常见的分歧度参数，如等位基因的数目、分离位点的数目、杂合的水平等.Molecular diversity indices:隔离位点的数目(s)、单倍型的数目（nh）、单倍型的多样性（Hd）Compute minmum spanning network among haplotype:利用每个居群的单倍型数据计算最小支撑树和最小支撑扩张网络图。Molecular distance：选择遗传距离，包括配对差异距离和核苷酸差异数的百分比。Theata（Hom）:通过估计观测到的纯质性H而得到一个参数Theata（s）: 通过估计观测到的隔离位点S的个数而得到的一个参数Theata（k）: 通过估计观测到的等位基因K的个数Theata（）通过平均配对差异数而得到的一个参数。点击Arlequin configuration 出现如下的结果点击Browse ,选择要分析的数据，如下图点击打开如下图点击project wizard 进行设置，这里面数据的设置，可以通过view project 打开的文本格式来作为参考，进行设置点击view project 出现的文本格式中包含了所要设置的数据的情况设置后，出现如下的数据：点击Start ,就会进行分析，将会得到一系列的结果。出现的结果在一个与原来的文件名相同的文件夹，运行的结果是Html文件。AMOVA,是指对分子差异性的分析。它通过对所研究居群进行不同层次的归类和划分，可界定不同的遗传结构并进行统计学检验，从而估计出群体间、群体内以及个体间不同层次所表现出的差异占总变异的多少，这种方法可以讨论不同海拔高度、不同语系、以及地理群体间是否存在相应的遗传变异。Locus by locus AMOVA 每个基因单独进行分子差异性的分析Include in individual level for genotypic data 包括个体间金银分歧度协方差组成和相关的固定指数。它计算出的是观察到的基因间的差异。Number of permutations 用来检测方差组成和固定指数的置换数的值，如果数值是0则不会有任何检测结果。Compute minimum spanning network among haplotype 利用分子差异计算并绘制单倍型之间的系统树。Genetic strcture中的population comparision选项，会出现如下的界面Population comparation 计算人群之间不相似指数（遗传距离）的大小，如Fst（短片段的基因的遗传距离）和Neis（人群之间和人群内部的平均背对差异）。Computation of Fst：计算所有配对人群的Fst值。Renyoldss distance：计算Renyoldss等线性化的Fst，这适合于分歧时间较短的样本。Slatkins distance：计算源于配对间的Fst的Slatkins遗传距离。Pairwise difference：计算Nei人群内部和人群之间的平均配对差异数。Compute relative populations：计算所有背对人群之间的相对人群大小，也可以计算人群之间的分析时间。出现的几个界面的设置ConfigurationProject wizard Importexport Structure editorArlequin settingArlequin configurationELB setting AMOVA settingLD settingNeutrality testOutputInput文件的建立：Profile Title=An example of DNA sequence date NbSamples=29 GenotypicData=0 MissingData=? DataType=DNA LocusSeparator=NONEData Samples SampleName= DC1 SampleSize= 5 SampleData=Hap7 1 G-TGAAC-TCGAT-AC-G-T-TG-A-Hap10 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap20 3 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= LH1 SampleSize= 5 SampleData=Hap1 1 A-GTGAACA-TCA-T-AC-G-T-TGA-Hap2 1 A-GTGAAC-TCA-T-AC-G-T-TGA-Hap8 1 G-TGAAC-TC-AT-AC-G-T-TGG-A- Hap17 1 GT-GAAC-TC-AT-AC-G-G-T-TG-A- Hap20 1 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= LH2 SampleSize= 5 SampleData=Hap6 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap12 2 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap35 2 A-GTGA-AC-TC-AT-ACC-G-T-TGGA- SampleName= DF1 SampleSize= 5 SampleData=Hap6 2 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap10 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap12 1 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap25 1 G-TGA-AC-TCGAT-AC-G-T-TG-A- SampleName= DF2 SampleSize= 5 SampleData=Hap6 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap20 4 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= BY1 SampleSize= 5 SampleData=Hap1 1 A-GTGAACA-TCA-T-AC-G-T-TGA-Hap9 1 G-TGAAC-TC-AT-AC-AGG-ATCATGG-A-Hap20 4 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= BY2 SampleSize= 5 SampleData=Hap6 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap10 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap18 1 G-TGAAC-TC-AT-AC-G-T-TGGA-Hap20 1 G-TGA-AC-TC-AT-AC-G-T-TG-A-Hap26 1 G-TGA-AC-TC-AT-TAC-G-T-TG-A- SampleName= BY3 SampleSize= 5 SampleData=Hap2 1 A-GTGAAC-TCA-T-AC-G-T-TGA-Hap10 2 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap20 1 G-TGA-AC-TC-AT-AC-G-T-TG-A-Hap30 1 G-TGAA-C-TC-AT-AC-G-T-TG-A- SampleName= GZ1 SampleSize= 5 SampleData=Hap10 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap12 3 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap20 1 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= LT1 SampleSize= 5 SampleData=Hap10 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap12 2 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap20 1 G-TGA-AC-TC-AT-AC-G-T-TG-A-Hap29 1 GA-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= LT2 SampleSize= 5 SampleData=Hap20 5 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= ZD1 SampleSize= 5 SampleData=Hap20 2 G-TGA-AC-TC-AT-AC-G-T-TG-A-Hap22 1 G-TGGAGAC-TC-AT-AC-G-T-TG-A-Hap27 1 G-TGA-AC-CC-AT-AC-G-T-TG-A-Hap32 1 AAGTGA-AC-TC-AT-AC-G-T-TG-A- SampleName= ZD2 SampleSize= 5 SampleData=Hap10 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap20 4 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= DZ SampleSize= 5 SampleData=Hap14 1 GTTGCAATCA-TC-AT-AC-G-T-TG-A-Hap20 4 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= AD SampleSize= 5 SampleData=Hap16 1 GT-GAAC-TC-AT-AC-G-T-TG-AGHap20 3 G-TGA-AC-TC-AT-AC-G-T-TG-A-Hap34 1 A-GTGA-AC-TC-AT-AC-G-T-TG-A- SampleName= HY1 SampleSize= 5 SampleData=Hap3 1 A-GTGAAC-TCA-T-AC-GT-T-TGAA-Hap20 4 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= GB SampleSize= 5 SampleData=Hap20 5 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= DX1 SampleSize= 5 SampleData=Hap12 1 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap20 4 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= HYS SampleSize= 5 SampleData=Hap12 1 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap20 4 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= LS1 SampleSize= 5 SampleData=Hap11 1 GA-TGAAC-TC-AT-AG-G-T-TG-A-Hap12 1 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap20 2 G-TGA-AC-TC-AT-AC-G-T-TG-A-Hap29 1 GA-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= MZ SampleSize= 5 SampleData=Hap4 1 AAGTGAAC-TCAAT-AC-G-T-TGA-Hap10 1 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap12 1 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap13 1 GT-GAAC-TC-AT-AC-AGAG-G-T-TG-A-Hap19 1 GA-TGAAC-TC-AT-AC-G-T-TG-A- SampleName= GC SampleSize= 5 SampleData=Hap4 1 AAGTGAAC-TCAAT-AC-G-T-TGA-Hap12 3 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap20 1 G-TGA-AC-TC-AT-AC-G-T-TG-A- SampleName= LS2 SampleSize= 5 SampleData=Hap4 1 AAGTGAAC-TCAAT-AC-G-T-TGA-Hap10 3 G-TGAAC-TC-AT-AC-G-T-TG-A-Hap12 1 GT-GAAC-TC-AT-AC-G-T-TG-A- SampleName= HY2 SampleSize= 5 SampleData=Hap12 3 GT-GAAC-TC-AT-AC-G-T-TG-A-Hap15 1 GT-GAAC-TC-ATA-AC-G-T-TG-A-Hap16 1 GT-GAAC-TC-AT-AC-G-T-TG-AG SampleName= LK1 SampleSize= 5 SampleData=Hap20 1 G-TGA-AC-TC-AT-AC-G-T-TG-A-Hap21 1 G-TGA-AC-TC-AT-AC-G-C-TG-A-Hap23 1 G-TGA-AC-TC-AT-AC-A-G-C-TGGA-Hap24 1 G-TGA-ACCTG-AA-TC-G-T-TG-A-Hap27 1 G-TGA-AC-CC-AT-AC-G-T-TG-A- SampleName= DX2 SampleSize= 5 Samp