ld and haplotype analysis tutorial - golden helix documentation

LD and Haplotype Analysis TutorialRelease 8.1

Golden Helix, Inc.

Feb 12, 2019

Contents

1. Generating LD Plots 2A. Open the Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B. Generate a –log10 P-value plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2C. Add LD Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2. Computing Haplotype Blocks 6A. Automatically Computing Haplotype Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6B. Manually Manipulating Haplotype Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3. Comparing Multiple LD Plots 9

4. Haplotype Frequency Tables 11A. Generating Frequency Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

5. Haplotype Association Tests 13A. Performing Per Haplotype Association Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13B. Performing Per Block Haplotype Association Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

6. Large-Scale Haplotype Association Testing 16A. Calculating Haplotype Blocks for Chromosome 22 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16B. Haplotype Association Tests Using Block Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 16C. Plotting Haplotype and Single Marker Association Results Together . . . . . . . . . . . . . . . . . . . . 18

7. Haplotype Trend Regression 21A. Full Model Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21B. Full vs Reduced Model Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

i

LD and Haplotype Analysis Tutorial, Release 8.1

Updated: December 14, 2018

Level: Intermediate

Packages: SNP Analysis, Power Seat

This tutorial leads you through various LD and haplotype analyses in SVS 8. For demonstration purposes, a simulateddataset is used consisting of actual Affymetrix 500K genotypes from the four HapMap populations (Phase II) mappedto the hg19 Human reference build GRCh_37, a simulated case/control status, and a simulated quantitative phenotype.

Caution: This tutorial does not cover quality assurance, and therefore no quality assurance steps have beenperformed on the data in this tutorial. As it may be appropriate to filter markers based on Hardy-Weinberg Equi-librium or filter out markers with low call rates and minor allele frequencies, it is recommended that you performsuch measures with your own data prior to performing LD and haplotype analysis.

Requirements

To follow along you will need to download and unzip the following file, which includes several datasets:

Download

LD_and_Haplotype_Tutorial.zip

We hope you enjoy the experience and look forward to your feedback.

Contents 1

http://doc.goldenhelix.com/SVS/tutorials/ld_haplotype_analysis/LD_and_Haplotype_Analysis.zip

1. Generating LD Plots

The general workflow outlined in this tutorial is intended to emulate a study wherein one first does a whole genomescan on individual markers, then hones in on significant regions for a more in-depth investigation of LD and haplotypes.

A. Open the Project

• Launch Golden Helix SVS and choose File > Open Project.

• Navigate to the LD and Haplotypes Analysis.ghp file downloaded previously and click Open.

You’ll notice a couple of datasets already created in the project, including a joined spreadsheet of phenotype andgenotype data for the HapMap samples (Phenotype Dataset + 500K Genotypes), as well as an association test resultsspreadsheet (Association Tests (Genotypic Tests)).

B. Generate a –log10 P-value plot

• Open the Association Tests (Genotypic Tests) spreadsheet.

• Right-click on the Chi-Squared –log10 P column (2) and select Plot Variable in GenomeBrowse.

A p-value plot is created. Notice, there are two regions of significance, one on chr14 and the other on chr22. In thispart of the tutorial we will focus on the chr22 region.

• Before you move on, go back to the Project Navigator and rename the plot node just created to -log10 P + LD.(To do this, right-click on the node and select Rename Node.)

• Now, from the –log10 P + LD plot, copy and paste 22:37,284,796-37,342,082 into the Region text box (the textbox that now says “1: 1 - MT: 16,569”) located at the top of the plot window. Press Enter on your keyboard tocomplete the “zoom”.

You should now be zoomed into a region on 22q12.3. (Figure 1a).

C. Add LD Plot

You can add an LD plot to an existing graph from any spreadsheet that contains column marker mapped genotype data.In this case you want to generate an LD plot from the same genotype spreadsheet used to produce the association testresults.

• From the -log10 P + LD plot, select File > Plot... and click the Project button (if it is not already selected).Select the Phenotype Dataset + 500K Genotypes - Sheet 1 spreadsheet and choose LD.

2


Figure 1a. P-value plot initial zoom

C. Add LD Plot 3


• Make sure the screen looks like Figure 1b and then click Plot & Close.

Figure 1b. Add LD Plot to Graph

An LD plot will now appear above the p-value plot and an LD node will appear in the Plot Tree (Figure 1c).

Notice the apparent block of LD (red) in the middle of the plot interrupted by a single SNP that is uncorrelated (blue)with the other markers.

4 1. Generating LD Plots


Figure 1c. P-value and LD plot.

C. Add LD Plot 5

2. Computing Haplotype Blocks

In SVS 8 you can compute haplotype blocks manually via the LD interface or automatically using the Gabriel, et al.method. This tutorial will lead you through a combination of both.

A. Automatically Computing Haplotype Blocks

• From the -log10 P + LD plot select the LD item in the Plot Tree, and under the Marker Blocks tab in theControls box, select Visible Blocks from the Compute options.

Note: Selecting Blocks would compute haplotype blocks across the entire 500K dataset.

Notice that the top of the Haplotype Block Detection window displays for how many markers on how many chromo-somes haplotype blocks will be computed. In this case blocks will be computed for 22 markers active in 1 chromosome.

• Use the default parameters and click Run.

The algorithm produces two haplotype blocks which appear as black outlined pentagons at the top of the LD plot(Figure 2a).

One could argue there should only be one block instead of two. For this reason, SVS 8 makes it easy to manuallymanipulate blocks when needed and then save the block definitions for subsequent analyses.

B. Manually Manipulating Haplotype Blocks

In this step you will manually define a single block from two separate blocks.

• Click inside the larger block. This will change the outline to green, and details for this block will appear in theConsole window.

• While your mouse cursor is over the left edge, press and hold down your left mouse button. Then drag the cursorto the left, expanding the larger block over the smaller block. Release the mouse button and a new block will becreated. (See Figure 2b.)

Note: You can generate Haplotype frequencies for the selected block by clicking the option to Compute HaplotypeTables under the Marker Blocks Tab of the Controls dialog.

6


Figure 2a. LD plot with haplotype blocks.

B. Manually Manipulating Haplotype Blocks 7


Figure 2b. Haplotype frequencies in Data Console.

8 2. Computing Haplotype Blocks

3. Comparing Multiple LD Plots

This step is covered as it may be useful in your own study to compare multiple LD plots to understand how thecorrelation structure in one dataset compares to that of a similar dataset, e.g. comparing a random set of Caucasians inyour study with CEU samples of HapMap. Another useful example is comparing a less dense array (e.g. Affymetrix500K) with a denser array (e.g. Affymetrix 6.0).

Though not a standard practice, for demonstration purposes this tutorial compares the overall LD structure of allHapMap populations with that of only Yorubans.

• From the -log10 P + LD plot viewer, right-click on the LD (either the LD item in the plot tree or on the titleitself in the upper-left-hand corner of the LD plot) and select Edit Title.... Enter LD - All Populations.

• Open the Phenotype Dataset + 500K Genotypes - Sheet 1 spreadsheet, right-click on the Ethnicity (column4) and select Activate by Category. Highlight YRI and click OK. This will inactivate all samples of a differentethnicity from YRI and create Phenotype Dataset + 500K Genotypes - Sheet 2.

• Then, in the -log10 P + LD plot viewer, go to File > Plot..., click the Project button (if it is not already selected),select the Phenotype Dataset + 500K Genotypes - Sheet 2 spreadsheet, then select the LD option. Click Plot& Close.

• A second LD plot should appear on top, pushing the original LD plot and the other plots farther down. Right-click on the second LD plot’s title and select Edit Title.... Set the new title to “LD - YRI Population”.

• Zoom into the region around the block defined in the LD - All Populations plot (Figure 3a).

In this instance there is a slight difference in LD structure displayed in the two plots. If you observed this in your owndata, you would want to investigate why such a difference exists.

• Go ahead and delete the LD - YRI Population LD plot by right-clicking its associated node in the Plot Treeand selecting Delete.

9


Figure 3a. Comparing LD between all HapMap and Yorubans.

10 3. Comparing Multiple LD Plots

4. Haplotype Frequency Tables

Once you define a given haplotype block you can investigate haplotype and diplotype frequency estimates for both theentire population – broken down by cases and controls if applicable – and each individual sample in the dataset.

A. Generating Frequency Tables

• In the –log10 P + LD plot, select the LD - All Populations item in the Plot Tree and click the ComputeHaplotype Tables button on the Marker Blocks tab.

• Keep the default values, except make sure that Per sample EM, Per sample diplotype, and Overall haplotypefrequencies are selected. Click Run.

This will create three tables, one for each feature selected in the Haplotype Tables dialog.

The Block #2 - Haplotype Table contains overall haplotype frequencies for the entire sample set. Notice that only thefirst marker is listed in the row label column along with the various alleles represented in the haplotype.

• To see all the SNPs in the haplotype block go to the Project Navigator and select the Block #2 - HaplotypeTable node. All SNPs are listed in the Node Change Log in addition to other summary statistics (Figure 4a).

The Block #2 - EM Frequencies Table displays the various genotypes for each sample and their respective frequencyestimates for each haplotype calculated with the EM algorithm.

The Block #2 - Diplotype Table displays each sample’s haplotype pair, combined as diplotypes, and each diplotype’srespective frequency estimates.

11


Figure 4a. List of SNPs in Node Change Log.

12 4. Haplotype Frequency Tables

5. Haplotype Association Tests

Golden Helix SVS provides two overall methods for association testing on haplotypes–per haplotype tests and perblock tests.

A. Performing Per Haplotype Association Tests

• Open the –log10 P + LD plot and select the defined block by clicking inside the block on the LD plot. Theblock boundary will change green to indicate it has been selected.

• Click the Selected Block button for Subset options on the Marker Blocks tab.

This creates a subset spreadsheet (Phenotype Dataset + 500K Genotypes – Marker Block Subset) of only thosemarkers in the block. The phenotype data has been lost, so we will need to rejoin before proceeding to Associationtesting.

• Open the Phenotype Dataset + 500K Genotypes - Sheet 1 spreadsheet, go to Select > Column > InactivateAll Columns, then reactivate the first 4 columns by left-clicking once on each column header.

• Go to File > Join or Merge Spreadsheets and select Phenotype Dataset + 500K Genotypes - Marker BlockSubset and click OK.

• On the join dialog change the New dataset name: to Phenotype + Marker Block Subset, leave all other optionsas their defaults and click OK.

• In Phenotype + Marker Block Subset - Sheet 1, set the C/C phenotype as dependent by clicking once on thecolumn header, turning it magenta. Then, select Genotype > Haplotype Association Tests.

The Haplotype Association Tests window appears with a number of parameter settings. Set the parameters as follows:

• In this case we are treating all markers in the subset spreadsheet as a single block. Thus, under Haplotype BlockDefinition, select Use all markers as single block. Also check Show marker names in output at the bottomof this box.

• Under Haplotype Association Tests, select Calculate per haplotype.

• Under Tests select Chi-squared test and Odds ratio with 95% CI.

• Under Multiple Testing Correction check only Bonferroni adjustment (on N covariates).

• Under Additional Outputs, check Haplotype frequencies and Output data for P-P/Q-Q plots.

• Click Run to finish.

A single spreadsheet (Haplotype Association Tests (Per Haplotype)) is produced with a row for each haplotype anda column for each test statistic selected. Notice again that only the first marker of the block is represented in the rowlabel column. However, in this case, because we selected Show marker names in output, the entire list of markersused is also output (in column 2).

13


Figure 5a. Per-haplotype association test results.

B. Performing Per Block Haplotype Association Tests

Another, perhaps more informative test of association is a per block test where a 2 X N chi-square table is used withN being the number of haplotypes represented.

• Again, open the Phenotype + Marker Block Subset - Sheet 1 spreadsheet and select Genotype > HaplotypeAssociation Tests.

• Leave all the parameters the same except this time select Calculate per block under Haplotype AssociationTests. Also uncheck Show marker names in output under Haplotype Block Definition.

• Click Run to finish.

A new spreadsheet is created (Haplotype Association Tests (Per Block)) with a single row of data representing perblock association results.

14 5. Haplotype Association Tests


Figure 5b. Per-block association test results.

B. Performing Per Block Haplotype Association Tests 15

6. Large-Scale Haplotype AssociationTesting

Now that you know how haplotype association testing works on a single haplotype, perhaps you want to investigatehaplotypes on a larger, multi-haplotype scale. For the sake of computation time, this tutorial will lead you throughhaplotype association on chromosome 22 only. This workflow, however, can be applied directly to the entire genome.

A. Calculating Haplotype Blocks for Chromosome 22

• Open the Phenotype Dataset + 500K Genotypes - Sheet 1 spreadsheet and select Select > Activate by Chro-mosomes.

• Click Uncheck All and then check 22 and click OK.

This will create a new spreadsheet (Phenotype Dataset + 500K Genotypes - Sheet 4) where only genotypes inchromosome 22 are active along with the phenotype data.

• Rename this spreadsheet (using the Project Navigator) to Chr22 Genotypes.

• From Chr22 Genotypes select Genotype > Haplotype Block Detection.

• Keep the defaults and click Run.

A new block definition spreadsheet will be created (Haplotype blocks, 1362 markers in 542 groups) with a singlecolumn representing various markers and the blocks to which they belong (Figure 6a).

B. Haplotype Association Tests Using Block Definitions

• Open Chr22 Genotypes and select Genotype > Haplotype Association Tests.

• Under Haplotype Block Definition select Use precomputed blocks. Also check Show marker names inoutput.

• Click Select Sheet. Select the Haplotype blocks, 1362 markers in 542 groups block definition spreadsheetand click OK.

• Keep the rest of the parameters the same as before (make sure Calculate per block is selected) and click Run.

A new p-value spreadsheet will be created, Haplotype Association Tests (Per Block), this time with results for eachhaplotype block defined across chromosome 22.

• In the Project Navigator, rename this spreadsheet to Haplotype Association Tests (Per Block) - Chr22.

16


Figure 6a. Block definitions spreadsheet.

B. Haplotype Association Tests Using Block Definitions 17


C. Plotting Haplotype and Single Marker Association Results To-gether

To see if haplotypes provide additional power in association testing, you can compare haplotype association resultsside-by-side with single marker association results.

• Open the -log 10 P + LD plot and select the first Chi-Squared -log10 P node in the Plot Tree. Under the Addtab select Add Plot Item(s).

• Click the Project Button (if it is not already selected), select the Haplotype Association Tests (per Block) -Chr22 spreadsheet, then select Chi-Squared -log10 P and click Plot & Close.

• In the Plot Tree, rename the first Chi-Squared –log10 P graph item (by right-clicking it, then selecting EditTitle) to Haplotype –log10 P. Rename the second Chi-Squared –log10 P graph item to Single Marker –log10P.

You can change the attributes of the Haplotype –log10 P graph item to differentiate it more from the Single Marker–log10 P graph item.

• Select the Haplotype –log10 P graph item and under the Display tab change the Connector from None to DropLine. Increase the weight (the number in the box to the right of Drop Line) to 3.

• Under the Style tab change the color to green and the symbol size (the number in the box to the right) to 5.

• Zoom into the region surrounding the peak on chromosome 22 by copying and pasting 22:36,372,272-37,995,325 into the location bar at the top of the plot window. The result is shown in Figure 6b.

You can add the generated block set to the LD plot.

• From the Plot Tree, select the LD - All Populations plot, and under the Marker Blocks tab click Load underthe Blocks options.

• Select the Haplotype blocks, 1362 markers in 542 groups spreadsheet and click OK.

You now have a p-value plot with single marker and haplotype association results along with an LD plot of chromo-some 22 with automatically defined haplotype blocks.

You can zoom in to any region by left-click-and-dragging in either graph.

• Press the left mouse button on the p-value plot’s x-axis on one side of the significant peak in chromosome 22and, holding the mouse button down, drag to the other side of the significant peak (Figure 6c).

18 6. Large-Scale Haplotype Association Testing


Figure 6b. Single marker association vs. haplotype association results.

C. Plotting Haplotype and Single Marker Association Results Together 19


Figure 6c. Haplotype vs. single marker association zoomed in.

20 6. Large-Scale Haplotype Association Testing

7. Haplotype Trend Regression

New to SVS 8 is the ability to perform haplotype regression analysis using a quantitative phenotype.

Haplotype Trend Regression (HTR) takes one or more block(s) of genotypic markers and for each block of markers,estimates the haplotypes for these markers, then regresses their by-sample haplotype probabilities against a dependentvariable.

Please see the SVS manual for full details on all of the options available for this new tool.

A. Full Model Regression

• Open the Phenotype Dataset + 500K Genotypes - Sheet 1 spreadsheet and left-click once on the C/C pheno-type to inactivate the column. Now set the quantitative variable Pheno as dependent.

Note: For this simulated phenotype, performing a Corr/Trend Association test using an Additive model would showgenome-wide significance for several markers in chromosome 14, so for the purpose of saving time we will only lookat markers in chromosome 14.

• Go to Select > Activate by Chromosome, click Uncheck All, then check 14 and click OK.

• We will first compute our haplotype blocks for chromosome 14 to use in the analysis by selecting Genotype >Haplotype Block Detection. Leave the defaults and click Run.

• Then, from the Phenotype Dataset + 500K Genotypes - Sheet 5 spreadsheet, go to Genotype > HaplotypeTrend Regression.

• Under Haplotype Block Definition select Use precomputed blocks and select the Haplotype blocks, 4105markers in 1537 groups block definition spreadsheet. Make sure Show marker names in spreadsheet outputis checked and leave the rest of the settings at their default values (Figure 7a). Click Run.

The resulting spreadsheet Haplotype Trend Regression Results is produced. The rows of this spreadsheet correspondto the haplotype blocks used. The row labels will correspond to the first marker in the block. The names of the markerscomprising each block are shown in Column 2.

• Plot the results by right-clicking on the -log10 Full-Model P column and selecting Plot Variable in Genome-Browse. Double-click on Chromosome 14 to zoom in on it.

• Zoom into the area around the most significant block (first marker SNP_A-1859412) and add the LD Plot. (Addthe LD plot by selecting File > Plot..., clicking the Project button (if it is not already selected), choosing thePhenotype Dataset + 500K Genotypes - Sheet 5 spreadsheet, checking LD and clicking Plot & Close.)

• Add in the computed marker blocks by selecting the LD node in the Plot Tree, then under the Marker Blockstab select Load under the Blocks options. Select the Haplotype blocks, 4105 markers in 1537 groups blockdefinition spreadsheet. The plot window should look similar to Figure 7c.

21

http://doc.goldenhelix.com/SVS/latest/svsmanual/haplotype_trend_regression.html


Figure 7a. Full Model Haplotype Trend Regression Options

Figure 7b. Haplotype Trend Regression Results

22 7. Haplotype Trend Regression


Figure 7c. Full Model Haplotype Regression and LD Plot

A. Full Model Regression 23


Note that in the two haplotype blocks having SNP_A-1859412 and SNP_A-4204238 as their first markers, respec-tively, there is high linkage disequilibrium among the markers within each of these haplotype blocks. Since theseblocks correspond to the two most significant p-values for this haplotype regression analysis, we conclude that thehaplotypes within these blocks are clearly defined and have a very significant effect on the phenotype being analyzed.

B. Full vs Reduced Model Regression

Haplotype Trend Regression can also be used while correcting for any covariates. In this tutorial dataset we have somepotential covariates included in the data–namely, Ethnicity and Gender.

• Open Phenotype Dataset + 500K Genotypes - Sheet 5 and once again select Genotype > Haplotype TrendRegression.

• Under Haplotype Block Definition select Use precomputed blocks and choose the Haplotype blocks, 4105markers in 1537 groups spreadsheet.

• Under Fixed Covariates, select Add Covariate and choose the Ethnicity column. Click Add then Close.

• Choose the Use as the reduced model for a full-vs.-reduced regression option under Fixed Covariate Op-tions.

• Make sure Show marker names in spreadsheet output is checked and leave the rest of the settings at theirdefaults (Figure 7d). Click Run.

Figure 7d. Full vs. Reduced Model Options

24 7. Haplotype Trend Regression


Now we will add these results to the previous plot to compare.

• Open the Plot of Column -log10 Full-Model P from Haplotype Trend Regression Results and select the first-log10 Full-Model P node in the Plot Tree. Then on the Add tab click Add Plot Item(s). Select the secondHaplotype Trend Regression Results spreadsheet and check -log10 FvR Model P to be added to the plot.Click Plot & Close.

• Change the color of the new points by selecting the -log10 FvR Model P node in the Plot Tree and under theStyle tab clicking the blue square and changing it to green.

You will see that the same two blocks are still significant after correcting for ethnicity, but not quite as significant asbefore (Figure 7e).

Figure 7e. Addition of Regression Results Corrected for Ethnicity

B. Full vs Reduced Model Regression 25

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