Step-by-Step Guide to Basic Genetic Analysis

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1 Step-by-Step Guide to Basic Genetic Analysis Page 1

2 Introduction This document shows you how to clean up your genetic data, assess its statistical properties and perform simple analyses such as case-control association, quantitative trait analysis, linkage disequilibrium and haplotype analysis. A full review of this document will provide you with a basic understanding of a broad number of applications. You can download the data files we will use from the same page where you downloaded this document. The data set we will use is a highly modified subset of GSE34945, originally downloaded from the Gene Expression Omnibus (GEO) web site. The companion document for advanced genetic analysis assumes that you have completed this training, at least through the quantitative trait analysis section. We will be using the same data set, and will assume a working knowledge of how to fill in the standard fields. I. Basic Genetics Data Cleaning Objectives: Understand the relationship between genotype files and annotation files Prepare marker data for analysis Data Set Structure Genetic marker data files in JMP Genomics are arranged in wide format, with samples in rows and genotypes or alleles in columns. Phenotypes and other descriptive variables concerning samples may also be included in the data set, and are usually placed before the columns containing genotype or allele variables. A marker dataset named is provided within the data zip file on the download page with this document. Page 2

3 Note that this marker data file is in genotype format, with each column containing the paired alleles with A or B designations. These genotypes are non-delimited. Descriptions of the characteristics of these SNP markers are contained in a separate optional file, called an annotation file. The corresponding annotation file is provided within the data zip file on the download page with this document. Page 3

4 The annotation file has a row corresponding to each marker. Columns in the annotation file contain any information that is known about the markers: accession numbers, locations, gene assignments, and other characteristics. Data Set Preparation It is critical to understand the relationship between marker files and associated annotation files in JMP Genomics. There is a defined order to the marker data that must be observed in both the marker data file and the annotation file that describes it. The left-to-right order of the columns describing markers in the data file must be identical to the top-to-bottom order of the rows in the annotation file. Because the relationship between marker files and annotation files depends on the order of data columns and rows, it is crucial that whenever marker variables in a marker data file are reordered or subset, the corresponding annotation file must be similarly altered. The Subset and Reorder Genetic Data application process (AP) is particularly useful, because it allows for the simultaneous manipulation of both the data and annotation files. Following initial data formatting and cleaning, the Subset and Reorder Genetic Data AP should always be run before any other processes. Recode Missing Genotypes When there are missing values for genotypes or alleles in a marker data set, they must be converted to valid values prior to analysis in JMP Genomics. The Recode Missing Genotypes process will convert these missing values from other formats to the one required by JMP Genomics. For character variables, a blank cell denotes a missing data point. Missing numeric variables should be coded as. In the example data set, missing genotypes are designated by. If these were allowed to remain in the dataset, JMP Genomics would recognize as a valid genotype, separate from, or. To recode the missing genotypes, complete the following steps: 1) Select Genetics > Genetics Data Utilities > Recode Missing Genotypes from the Genomics starter. 2) Choose the data set. Depending on your directory settings, you may not see the file-type suffix (. ). 3) In the Available Variables window, select the first SNP that starts with, then scroll to the bottom of the window, hold the Shift key and select the final SNP. All SNPs should be selected. Click on the right arrow ( ) to move the selected SNPs to the Marker Variables window. 4) As this data set uses NC to designate missing genotypes, type NC (without quotes), into the Current Value(s) Denoting Missing Genotypes or Alleles. Page 4

5 5) Choose the Output Folder. 6) Type, as the name of the Output Genotype Data Set. Note Because blank spaces are not permissible in SAS data set names, we use _ in place of blanks. The completed dialog should look like the one shown in the figure below: 7) Click Run. When the SAS Message appears, indicating that the process is complete, you may click the Open button to view the output genotype file and verify that missing values are now represented by blanks. Note: If you are working with GWAS data sets, which are typically too large to conveniently open, a subset of the full data table will open, allowing you to check the accuracy of the recoding process. Subset and Reorder Genetic Data The Subset and Reorder Genetic Data AP has multiple functions. It can synchronize the order of associated data and annotation files to ensure that they match. It can sort data files and annotation files simultaneously into a new desired order and it can create subsets of data based on available terms (chromosome number, for example). Here, we show how this AP can be used to perform the initial ordering of SNPs in the data file and the annotation (map) file. Page 5

6 1) Select Genetics > Genetics Utilities > Subset and Reorder Genetic Data from the Genomics starter menu. 2) Choose the data set you just created. 3) Type seq: rs: (without quotes) into the List-Style Specification of Marker Variables field. This shorthand notation must be used when there are more than 5000 markers in the data set. o There are a number of ways to list groups of marker variables. All the SNP variables may have a common prefix, for example,,,, etc. If so, then type SNP: in the box to represent all variables starting with the characters SNP. If necessary, multiple prefixes may be specified. o If the markers do not share a common prefix, then a list of all variables found positionally in the dataset between and may be specified using the double-dash notation: VarX--VarY. o See the question mark help button next to the List-Style Specification of Marker Variables for more information. 4) For the Format of Marker Variables, select the Nondelimited Genotypes radio button. 5) If Delimited Genotypes is selected, then any valid delimiter may be indicated in the Genotype Delimiter field. 6) Set the Minimum Proportion of Nonmissing Genotypes to 0. 7) Specify as the Name of the Output Genotype Data Set. 8) Choose the Output Folder. The completed General tab should look like the figure below: Page 6

7 9) Click on the Annotation tab at the top of the AP window. 10) Choose the file as the Annotation File. 11) Select as the Variable Containing Names of Marker Variables. 12) Select and as the Reorder Variables. Note: If you are working with a large data set (>5000 markers) that has multiple prefixes for marker IDs, then you will always be using the List- Style Specification function. o The markers in the data set and in subsequent analyses must be in alphanumeric order for this to function properly. Adding the marker ID as the first Reorder Variable ensures that this will be the case. 13) Check the Remove Markers option. Note: Checking this box will remove markers from the genotype data set that are not in the annotation (map) file. This can be a very efficient way of reducing a large genotype data set to a subset of desired candidate genes, for example. All you need to run this process is the wide genotype data set, and a tall SAS data set containing annotation information for your desired markers. 14) Specify as the Output Annotation Data Set. Page 7

8 The completed Annotation tab should look like the figure below: 15) Click Run to start the process. When the process is complete, Open the resulting data sets. The order of the SNPs in the data set from left to right should be identical to the order of the SNPs in the annotation file from top to bottom. Marker Properties The Marker Properties process calculates standard summaries and statistics from marker data, including allele and genotype frequencies, polymorphism information content, minor allele frequency, diversity index and the Hardy-Weinberg equilibrium test. This AP is frequently used as a quality control screen to identify uninformative or unwanted markers that should be removed prior to further analysis. 1) Select Genetics > Genetic Marker Statistics > Marker Properties from the Genomics Starter menu. 2) Choose the data set. 3) Select the marker variables, and the output folder where files from the analysis will be saved as done previously. Page 8

9 There are a few available options that are not used in this example: o To restrict analysis of the X-chromosome for X-linked markers, select the gender variable from the data set as the Sex Variable. o To stratify the analysis for different populations, place a variable that differentiates the populations into the By Variables box. For example, to calculate marker statistics separately for males and females, you could specify here. o The Filter to Include Observations field allows you to specify criteria that will be used to remove individual rows (individuals) in the marker dataset either from the whole analysis. Boolean statements can be used to construct filters on multiple criteria. Individuals for whom the statements are true will be included in the analysis. The filters use the syntax of a SAS where statement. See the question mark help ( ) next to the filter fields for further details. For exhaustive documentation on where statement options, see the SAS 9.3 Language Reference in the Base SAS documentation. 4) The Filter to Include Observations for HWE Test option is used to calculate the test on control subjects only, excluding affected case subjects. Type RESP = N into this field. Note Because we are specifying a character instead of a numeric value, we must enclose the N in quotation marks. This filter restricts the HWE results to only the group(s) specified here. All other analyses, such as PIC, HET, etc., are done for the full population. 5) Select the Annotation tab. Choose as the Annotation SAS Data Set. 6) Choose as the Annotation Label Variable and the Marker Names Variable (for checking order only) Note: This field should be filled in for at least the first analysis of a data set. 7) Select as the Annotation Group Variable, and as the Annotation Location Variable. The remainder of fields may be left blank. If a Sex Variable was selected on the General tab, then a value should be entered into the X-Linked Marker Claus. 8) Select the Options tab. 9) Choose as the Format. 10) Choose as the Test for HWE. Keep the remainder of the options unchanged. Page 9

10 11) Select the HWE P-Value Plots tab. 12) From the Multiple Correction Menu, select from the options. 13) Select the Output tab. 14) Uncheck the Create Data Set with Numerically Coded Genotypes option. Checking this option will create a data set with numerically additive coding. If you will be creating relationship matrices using Identity by State or Descent calculations, or would like a numerically coded dataset for other purposes, select this option. We will be using the Recode Genotypes AP in the second part of the course. 15) Click Run to start the process. The results are shown below: Page 10

11 1) The results are contained in a dashboard. Under the Tabs outline in the upper left are pull-down menus for each chromosome. In the middle of the top are tabs for graphs of different results. On the lower left are action buttons where further actions on the data-set results can be performed. 2) The Number of Specific Markers by Chr graphic shows that there are three markers that are significant in the data set, on chromosomes 8, 9, and 19. 3) Select the Manhattan Plot tab. The markers in this graph are small, which is desirable for a GWAS result. To make them larger, right-click over the figure, select Marker Size from the options and XXXL from the list. The graph should look like the figure below: The markers on each chromosome are displayed in a different color and each SNP is represented by a point. The y-axis displays the -log 10 (pvalue) for the HWE test and the dotted line at approximately 1.3 is equivalent to a p-value of From the graphic, it is apparent that three SNPs in the data set are out of equilibrium. 4) Select the All Distributions tab. The plots in this tab show the distributions of the Minor Allele Frequency (MAF) and Missing Genotype Proportion. Scroll though the plots and note which chromosomes contain SNPs with MAF of 0. 5) Select the Individual Missing Genotype Proportion tab. This plot shows the distribution of the proportion of missing genotypes by the individuals in the data. 6) Return to the Action Buttons on the lower left of the dashboard. We can filter and reorder the data directly from this dashboard, based on a number of different criteria. In the Filter for Markers, set the cut-off for MAF to Leave the other three values at the defaults and then click OK. The Subset and Reorder Genetic Data AP automatically runs. 7) From the results from Subset and Reorder Genetic Data, click the Reopen Dialog button in the lower left. This will reopen the AP dialog that created this result. All result dashboards in JMP Genomics have this option. 8) Click on the Annotation tab of the dialog, and observe the Filter to Include Markers field. It should resemble the figure shown below: Page 11

12 The syntax allowable in this field can be found by clicking on the? next to the Clear button. gt is equivalent to >, le to <= and so on. This field can contain any terms in the available variables window. II. Basic Genetic Statistics Objectives: Understand basic association tests available for genetic data Understand linkage disequilibrium and haplotype analysis Using the data set we just prepared, we will now consider the standard association tests in JMP Genomics. We will begin with case-control analysis and then move into more advanced testing methods, such as model-based binary trait analysis and continuous trait analysis. We will also examine haplotype and linkage disequilibrium analyses on the data set. Page 12

13 Association Tests We will begin with the simplest option, the Case-Control test. This process allows for testing of a binary trait with no additional covariates. Three tests can be run simultaneously: Genotype, Trend and Allele. 1) Select Genetics > GWAS Testing > Case-Control Association from the Genomics starter. 2) Choose created from the Subset and Reorder Genetic Data process that was run following Marker Properties analysis. 3) Select RESP as the Trait Variable. 4) Type rs: seq: in the List Style Specification of Marker Variables field. 5) Choose the Output Folder. The completed window should look like the figure below: 6) Select the Annotation tab, Choose the data set, and complete the remainder of the dialog as you did for the Marker Properties process. 7) Select and complete the Options tab as shown below: Page 13

14 Checking the All markers are biallelic option will result in faster run times. The Allele, Genotype and Trend tests can be run at the same time. The Dominant, Recessive and MAX tests require special coding of the data. Both the Asymptotic and Fisher Exact tests will be run in this analysis. VIF (Variance Inflation Factor) can be applied to control for population stratification. The Calculate allele odds ratios option is available only when the Allele test is selected. 9) Keep the default options set on the P-Value tab. 10) Click Run to start the process. The results are shown below: Page 14

15 The buttons under the Tabs section at the upper left allow you to View individual results by chromosome. The Action Buttons at the middle left are used for performing additional functions. In this instance, SNPs can be selected and a subset can be created that contains only those SNPs. The Output Data outline can be expanded to show the output data sets. Below that, the Reopen Dialog will open the Case-Control AP with the settings that created this result. Create Report will produce an. file containing the figures. Close All will close the graphics and all hidden tables. 11) The Summary Chart indicates how many SNPs pass the significance p-value, colored by test, and separated by chromosome. For example, we can see that there are significant SNPs on chromosomes 8 and 9, but not 7 nor ) Select the Manhattan Plot tab. Place your mouse over the top plot, hold down the Ctrl key, right mouse click and select Marker Size > XXXL from the pop-up menu. The result is similar to what we saw previously with Marker Properties. The -log 10 (p-value) for each test is shown on the y-axis and chromosomes are on the x-axis, with each point representing an individual SNP. Page 15

16 13) Hover over one of the points. The marker ID will appear. 14) Select a few points with your mouse. These selected markers appear in bold in the other plots, while the rows representing these markers in the underlying data table are selected. 15) To see the select point, choose Tables > Subset from the JMP menu. A table containing the values for only the selected points is created. What values are in the table? 16) Click Close All, to close the graphics and data tables. Quantitative Trait and Model-Based Analysis Genetic data sets often have a continuous response that we would like to correlate with markers and/or have covariate and other effects that need to be included in the model. In these cases, the SNP-Trait AP is most appropriate. This process can work with binary, nominal, ordinal, continuous or survival traits. 1) Select Genetics > GWAS Testing > SNP-Trait Association from the Genomics Starter window. 2) Choose as the Input SAS Data Set. Specify the List-Style Specification of SNP Variables as previously done. 3) In the General tab, select the PCT_CHANGE_APOC3 as the trait variable. 4) Select the Model Variables tab. 5) Select Continuous as the Type of Trait and DIABETES as a Class Variable. 6) Right-mouse click on Diabetes and copy it, then paste it into the Fixed Effects field. The completed window should look like the one below: Page 16

17 Continuous covariates can be added to the Fixed Effects box along with interactions between fixed effects. The Interaction Effects field is used to determine interactions at the SNP level only. Random effects or a blocking variable can be added to the Random Effects field. This will activate the option in the Advanced Random Effect Model Options. Additional random effect options and alternative degrees of freedom options can be set here. 7) Choose as the Annotation SAS Data Set. The Annotation tab should be completed similarly to the one the Case- Control test AP. Note: There is a field for the Major Allele Variable, which can be filled in with the major allele variable from our marker properties results. This can be especially helpful in speeding up the process when working with large, non-numeric genotype data sets. 8) The Options tab should be filled out as shown below: Page 17

18 The genotypes are converted to numeric values as part of the process. In this case, 2 will be the value for the homozygous minor allele. We will also create the LS means and difference values in the results. 9) Leave the default parameter values on the P-Value Plots tab. 10) Click Run to start the process. 11) The results are very similar in format to the Case-Control results. 12) Expand the Output Data by clicking on the triangle to the left of the outline topic. There are four output files. o Genotype LSmeans o Genotype parameter estimates o Trend parameter estimates o P-values 13) Scroll to the bottom of the listings under Tabs. Click on the Volcano Plots pulldown menu, and select View Tab. Page 18

19 Volcano plots depict a difference value on the x-axis and the -log 10 p-value on the y-axis. The dotted red line denotes the significance threshold. The estimate of the minor allele is in the top graph. In this case, most of the significant minor allele effects are positive. The LS means comparison differences with the homozygous major allele are shown in the bottom two figures. We can see that the homozygous minor allele is responsible for most of the significant differences. To the right of the plots is a data filter, which allows you to view values associated with a subset of the total chromosomes. 14) Click Close All to close the results. Linkage Disequilibrium Next, we will run the Linkage Disequilibrium AP. Linkage disequilibrium analysis can be used to find regions in a section of the genome where markers are correlated, indicating that there is difference between the observed and expected combinations of genotypes and alleles across markers. 1) Select Genetics > Genetic Marker Statistics > Linkage Disequilibrium from the Genomics Starter window. 2) Choose for the Input SAS Data Set and for the Annotation SAS Data Set. 3) Select the Marker Variables and the Output Folder as done previously. Page 19

20 4) On the Annotation tab, enter Chr= 08 in the Filter to Include Markers where field. The quotes are needed as the value is a character. Numeric values do not require quotes. 5) Select and complete the Options tab as shown below: The Perform LD Calculations for All Pairs option is selected. Performing all pairwise calculations will work for a relatively small (<300 or so) number of markers. However, when there are more markers, do not check this option. Instead, select a Distance Unit and a Maximum Distance to test between markers. 6) Select the Output tab. Select Dprime for both the LD Contour Plot and the LD Decay Plot. 7) Enter 9 in the Number of Contours field. 8) Click Run to start the process. The results are shown below: Page 20

21 This view shows the absolute value of D versus the distance between markers for all combinations. Where D is high, the markers are more likely to be in disequilibrium. Note that there is a trend for D to decrease as the distance becomes greater. The All Marker Plots tab shows the p-values by locus position and the D values in a contour plot by locus position. The p-value plot in particular may be difficult to interpret as points may be superimposed. 9) Click the All Markers button under the Action Buttons to launch the LD Plots process. Note: If there is more than one chromosome, you will see a separate button for each one. Page 21

22 This plot shows the R 2 for all combinations with high values in red. You can see that there are regions of LD in this locus. Click on a colored square to select a region, and then click on the Zoom on Selected Block below the figure to create a subset of the initial figure. Haplotype Analysis The goal of haplotype analysis is to find sets of alleles that are from the same chromosome and then use these multilocus units for testing linkage disequilibrium, association testing and to infer parental haplotypes. This is a multi-step process in JMP Genomics. The first step is to estimate haplotype frequencies and perform tests for multilocus LD and association with a binary trait, as well as generate phase and frequency tables. For continuous traits, or traits with covariates, an additional trend regression can be performed. Finally, we identify sets of haplotype tagging SNPs, based on the occurrence of those markers within a haplotype, which explain the majority of observed diversity. 1) Select Genetics > Haplotype Analysis > Haplotype Estimation from the Genomics Starter window. 2) Choose as the Input SAS Data Set. 3) Specify the marker variables and format as described above. Choose an output folder. 4) Choose as the Annotation SAS Data Set. 5) Specify the annotation variables as described above. 6) Type Chr="08" in the Filter to Include Markers where field. 7) Complete the Options tab as shown below: Page 22

23 The value 5 in the Sliding Window Size, combined with the Window Size Unit of Markers, indicates that we will create haplotypes of 5 markers, with a Window Overlap of 4. The Estimation Method, Frequency Initialization and Standard Error Estimate are pull-down menus that contain the various options. Please click on the ( ) buttons for detailed explanations of these options. 8) Complete the Association Tests tab as shown below: Page 23

24 We will test for Allelic Association across the alleles within each haplotype and furthermore, we will perform a case-control association with our binary trait, RESP, at both the group level of all potential haplotypes within a window, and the individual haplotypes within a window. 7) Complete the Output tab as shown below: Page 24

25 Data sets with the Phase Assignment and Frequency Estimates will be created. Any variables that may be covariates in the Trend Regression test should be selected and added to the Phase Assignment ID Variables field. Values for Frequency Cutoff and Phase Assignment Probability Cutoff can be included if desired. In this instance, only one haplotype pair per individual will be created. 8) Click Run to start the process. The results are shown below: Page 25

26 The Overlay Plot shows the -log 10 (p-values) by position for the haplotype LD in blue and the haplotype association in red. These p-values are global calculations for all possible haplotypes in the window. The values on the y- axis are censored at 32. By hovering over a point on the figure, you can see the boundary markers for the group as well at the window to which the group belongs. Expanding the Output Data outline reveals three data sets: phase assignment, frequency data, and haplotype trait-association, which contains the individual haplotype association tests. We will skip the trend regression in this exercise. The AP can be populated with the phase and frequency tables by clicking on the Haplotype Trend Regression button under the Launch Follow-Up Process outline. 9) Click on htsnp Selection to load the AP. 10) Complete the General tab as below: Page 26

27 The Window = 6 in the Filter to Include Observations where refers to the haplotype Window values to be tested. This window had the highest association value in the Haplotype Estimation results. Different criteria for evaluation and searching for tagged SNPs are available. Refer to the SAS on-line documentation for details. 10) Complete the Options tab as shown below: Page 27

28 We are looking for two SNPs, specified in the Subset Size field, within each region of 5 which contain the majority of the diversity explained, calculated with the PDE (Proportion of Diversity Explained) metric. Three different combinations, specified in the Number of Selections to Display field, will be shown in the graph and contained in the data table. The annotation table will have a column added, designating the two tagged SNPs (value =1) with the highest PDE. This value will be created only for those SNPs tested, i.e., those in Window 6. 11) Click Run. The results are shown below: Page 28

29 The PDE value is on the y-axis and the position of each marker is on the x-axis. There are two markers from Window 6 in combination that have a PDE > 0.91, two markers with a PDE of about 0.90 and two markers with a PDE < The Output Data Set is the annotation file with the tag SNP information added. This concludes the basics of genetic analysis in JMP Genomics. The advanced analysis document will start with the same data sets ( and ) and will cover: Recoding Genotypes Identity by Descent/State Eigenstrat Analysis Multimarker Association Tests Pleiotropic Association Rare-Variant Analysis Page 29

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