Introduction

PSEUDOMARKER is linkage analysis software for joint Linkage and LD analysis for qualitative traits. Software package is developed collaboration with Harald H.H. Göring and Alejandro Schäffer (special version of FASTLINK 4.1P).

Pseudomarker can analyze different data structures jointly such as cases and controls, trios, sib pairs, sib ships and extended families. Pseudomarker is family-based association test.

"Don't make your data fit the analysis method, make the analysis method fit your data!"

Tutorial

Introduction

Common confusion is that association analysis is for case-control or trio design and family studies can only be used for linkage analysis, but one can do association analysis in families. It is typically more powerful when done in larger families.

Pseudomarker can analyze different data structures jointly such as cases and controls, trios, sib pairs, sib ships (nuclear families) and extended families. A more powerful and efficient set of statistics can be computed by analyzing all available data jointly. Pseudomarker can handle missing data as well, even when parent's genotypes are missing see 'Tour 2'. Also, see 'Tour 3' for power of having controls in joint analysis.

What's in 1.0.x?

• Minimum input file requirements are (pre-makeped format) pedigree and marker map file
• Family relationship and connectedness check
• Internal Mendelian checking for genotype data (Previously done with UNKNOWN)
• Can handle loops in pedigrees. Consanguinity and marriage loop detection and automatic breaking of loops with algorithms implemented in UNKNOWN (See paper6.ps from FASTLINK 4.1P documentation for more information)
• Allele numbers can be integers (base pairs), characters or strings
• Graphical postscript output

Statistical tests

• Dominant mode of inheritance pseudomarker test
• Recessive mode of inheritance pseudomarker test
• Model-based pseudomarker test

A model-based pseudomarker test requires user to specify a mode of inheritance (disease allele frequency and penetrances) by using a model file.

PSEUDOMARKER uses 'direct search' in ILINK program from the FASTLINK 4.1P package to maximize likelihoods over

• Allele frequencies for the marker loci
• Allele frequencies of marker jointly with linkage to the disease locus
• Conditional allele frequencies of markers allowing for both LD and linkage with disease locus

Then is possible to perform following tests:

Test statistics	Application	Analogous model-free test
	Test of linkage without linkage disequilibrium	Affected sib-pairs tests, affected relative-pair tests
	Test of linkage allowing linkage disequilibrium	Transmission/Disequilibrium test (TDT)
	Test of linkage disequilibrium allowing for linkage	Haplotype Relative Risk (HRR) test, case-controls test
	Joint test of linkage and linkage disequilibrium

Table from review paper: Terwilliger JD and Göring HHH: Gene mapping in the 20th and 21st centuries: statistical methods, data analysis, and experimental design, Hum Biol. 2000 Feb;72(1):63-132.

Pedigree file

The pedigree file contains information about family relationships, gender (=sex) and genetic data (disease and marker phenotypes). The file is a general ASCII-text file, which can be created with your favorite text editor.

The file format described here is so called LINKAGE format, which the most used pedigree file format. The pre-makeped LINKAGE format contains the following columns, separated by space and/or tab characters:

Qualitative phenotypes coding

Example

We have two families, one sib pair (nuclear family) and one multi generational family with two markers loci genotypes (upper is a SNP marker and lower is microsatellite marker). The unique identifier within family for each person is in the pedigree symbol. Note that in Family 1 we don't have genotypes for person ID number 6 and in Family 2 we don't have marker locus genotypes for parents (person ID numbers 1 and 2).

Pedigree symbols

	Affected male		Unaffected male
	Affected female		Unaffected female

Example families

Family 1 coded to (pre-makeped) Linkage format:

In family 1, person IDs 1, 2, 3 and 6 parents are unknown and their ids are set to zero (=0). After disease phenotype column(s) follows marker genotypes. Since the person with ID 6 do not have marker genotype locus information, then alleles are set to zero (=0 0).

Family 2 coded to (pre-makeped) Linkage format:

Then pedigree file should look like:

1  1  0 0 1  1  1 2  2 3
1  2  0 0 2  1  1 1  3 4
1  3  0 0 2  1  1 1  3 4
1  4  1 2 2  2  1 2  3 3
1  5  1 2 2  1  1 2  2 3
1  6  0 0 1  1  0 0  0 0
1  7  4 3 2  2  1 2  3 3
1  8  4 3 2  1  1 1  3 4
1  9  6 5 1  2  1 2  3 3
1 10  6 5 2  2  2 2  3 3
2  1  0 0 1  1  0 0  0 0
2  2  0 0 2  1  0 0  0 0
2  3  1 2 2  2  1 1  3 3
2  4  1 2 2  2  2 2  3 3

Documentation and for more information about the pedigree file, see Handbook of Human Genetic Linkage, Joseph D. Terwilliger and Jurg Ott. Johns Hopkins University Press, Baltimore (1994) or LINKAGE User's Guide.

Singleton file (cases and controls)

Singleton file includes genotypes from cases and controls. Cases and controls must be in separate files.

Format

Columns are: singleton ID, sex and marker genotype(s). First line includes number of markers. Columns are separated by space or tab characters.

Example of singleton file:

3
1000  1     1 2   1 3   2 2
2000  2     2 2   1 3   0 0

To use singleton genotype file, corresponding marker map file must be provided as well. It is also very important to assign cases and controls to correct phenotype with --ccphen option if separate phenotype files is used. Default is 'DISEASE_LOCUS'. See 'Tour 3' for example.

How it's possible to use singletons?

Pseudomarker creates trio families from singleton files. Cases and controls are assigned as unrelated parents (father or mother based on gender) they having 'dummy' kid with no phenotype or genotype information. Let's see example of singletons->trio->pedigree file conversion and use case file above:

Note that singleton IDs are renumbered (1000 -> 1 and 2000 -> 2) in this example.

Locus file (data file)

Data file is not required, but here is the description for compatibility!

Locus file contains information about disease allele frequency, marker allele frequencies, liability classes, penetrances etc. Locus file is general ASCII-text file. File format described here is so called LINKAGE format.

Simple locus files can be created with makedata and more complex with preplink. But usually all locus files can be created with makedata. Locus file structure is:

Line 1: Number of Loci, Risk Locus, Risk Allele, Sex-linked (if 1), Program Code
Line 2: Mutation Locus, Mutation Rate Male, Mutation Rate Female, Haplotype Frequencies (if 1)
Line 3: Locus order

Then disease and marker loci information follows (locus type, number of alleles and allele frequencies). Usually disease loci is before marker loci. In pedigree file howto disease phenotype is before marker phenotypes, this is established practice. Example of fully penetrant dominant disease locus (with 2 alleles):

1 2                     { locus type and number of alleles
0.99 0.01               { gene frequencies (for normal and disease)
1                       { number of liability classes
0.0 1.0 1.0             { penetrances for liab. class 1, P(Aff|++), P(Aff|D+) and P(Aff|DD)

P(Aff|++) is phenocopy rate, P(Aff|D+) is penetrance for one disease allele and P(Aff|DD) is penetrance for two disease alleles.

Then marker locus information is followed by disease locus. Example of marker locus with 4 alleles:

3 4                     { locus type and number of alleles
0.25 0.25 0.30 0.20     { gene frequencies

Locus file can contain any number of markers! And last three lines are:

Third last  : Sex difference, Interference (if 1 or 2)
Second last : Recombination values between markers
Last        : Recombination varied, Increment value, Finishing value

4 0 0 5  << NO. OF LOCI, RISK LOCUS, SEXLINKED (IF 1) PROGRAM
0 0.0 0.0 0 << MUT LOCUS, MUT MALE, MUT FEM, HAP FREQ (IF 1)
1 2 3 4
1 2  << AFFECTION, NO. OF ALLELES
0.99900  0.00100 << GENE FREQUENCIES
1 << NO. OF LIABILITY CLASSES
0.0000  1.0000  1.0000 << PENETRANCES
3 5  << ALLELE NUMBERS, NO. OF ALLELES
0.200000 0.200000 0.200000 0.200000 0.200000 << GENE FREQUENCIES
3 5  << ALLELE NUMBERS, NO. OF ALLELES
0.200000 0.200000 0.200000 0.200000 0.200000 << GENE FREQUENCIES
3 5  << ALLELE NUMBERS, NO. OF ALLELES
0.200000 0.200000 0.200000 0.200000 0.200000 << GENE FREQUENCIES
0 0  << SEX DIFFERENCE, INTERFERENCE (IF 1 OR 2)
0.5000 0.10000 0.10000 << RECOMBINATION VALUES
1 0.10000 0.45000 << REC VARIED, INCREMENT, FINISHING VALUE

Documentation and for more info about Linkage locus file, see Handbook of Human Genetic Linkage, Joseph D. Terwilliger and Jurg Ott. Johns Hopkins University Press, Baltimore (1994) or LINKAGE User's Guide.

Map file

Map file contains information about markers: chromosome, location, order and name of the marker. File format described here is used by Mega2 program. Map file is general ASCII-text file (created with notepad or some other text editor) and it contains header line and three columns.

Header line

Chromosome   Haldane   Name

Second column in header line specifies map function (haldane or kosambi) used in conversion from cM to recombination fraction (needed for multipoint). Columns after header line:

Column 1: Chromosome number
Column 2: Location              (continuous cM-map)
Column 3: Name of the marker

Example of chromosome 7 with 6 markers:

Chromosome   Haldane    Name
7            0          D7S2233
7            2          ATA100
7            6          D7S9339
7            11         WATR567
7            11.9       D7S1122
7            15         D7S5566

Markers distances are in cM (centi-morgans) and map is continuous. Map does not have to start from 0 cM. Markers have to be in same order in pedigree file. More info http://watson.hgen.pitt.edu/docs/mega2_html/mega2.html

Model file

Model file contains information about disease allele frequency and penetrances. This is information is needed for model-based linkage analysis.

Line 1:  Autosomal/X-Linked                  { 0=Autosomal, 1=X-linked
Line 2:  Disease Allele Frequency
Line 3:  Number of liability classes
Line 4+: Penetrances for each liability class

Penetrances in this order (Autosomal/Females), D = disease allele, + = healthy allele:

Pen(Affected | ++)  Pen(Affected | D+)  Pen(Affected | DD)

Penetrances in this order for males (X-linked):

Pen(Affected | +)  Pen(Affected | D)

Example of autosomal dominant mode of inheritance with one liability class:

0
0.01
1
0.01  0.9  0.9

Example of autosomal mode of inheritance with 2 liability classes:

0
0.01
2
0.01  0.9  0.9
0.01  0.5  0.5

Example of X-linked mode of inheritance with 1 liability class:

1
0.01
1
0.01  0.8  0.8     <== Females
0.01  0.8          <== Males

Example of X-linked mode of inheritance with 2 liability class:

1
0.01
2
0.01  0.8  0.8     <== Females liability class 1
0.01  0.4          <== Males   liability class 1
0.01  0.7  0.7     <== Females liability class 2
0.01  0.3          <== Males   liability class 2

If liability class is used, phenotype file must include liability column after the trait!

Phenotype file

Phenotype file contains information about additional individual qualitative phenotype values. Separate phenotype file enables easy analysis of multiple phenotypes, there is no need to change disease phenotype column in pedigree file, one can use phenotype file instead. Missing value is label is x.

Format

Columns are: pedigree ID, person ID, Phenotype(s). Pedigree ID and Person ID must correspond to IDs in pedigree file. First line includes number of phenotypes and phenotype names. Columns are separated by space or tab characters.

One qualitative phenotype:

    1     DISEASE
    1  1  1
    1  3  1
    2  2  2
    2  3  0

Multiple qualitative phenotypes:

    2     DISEASE1	DISEASE2 
    1  1  2		0
    1  3  2		0  
    2  2  2		2  
    2  3  2		2

Command line based Pseudomarker

Input / Output options

Data analysis options

Visual Pseudomarker

Installing Visual Pseudomarker:

NOTE: Installation requires administrator level priviledges, since Pseudomarker installation path have to be added to system path!

Choose if you want to install example files and start menu shortcuts.

Choose where to install your Visual Pseudomarker copy.

Running Visual Pseudomarker:

From here you can choose how many processes you want to run at the same time.

From here you change your pseudomarker binary.

From here you can choose input files for your analysis. Note that both pedigree file and map file are compulsory.

From here you can browse your input files.

From here you can see witch input files are used for your analysis.

Here you can choose the prefix for your output file. Note that it's optional and default is set to "pseudomarker" witch means that output files are named pseudomarker.out, pseudomarker_dominant.ps, pseudomarker_recessive.ps and pseudomarker_model-based.ps

From here you can choose your pedigree file format.

From here you can choose witch analysis you want to run.

From here you can choose witch marker, phenotype and case control phenotype you want to analyse.

From here you can select to analyse x-chromosomal data.

From here you can give extra command line parameters.

Here you can set your pseudomarker process to pause before exit. Useful when you want to see the output for your analysis attempt or you have errors in inputfiles.

Here you can start and stop your current analysis process.

Here you can see the output and results of your analysis process.

Here you can see the status of your analysis process.

This is the actual pseudomarker process witch is running when you start your analysis process.

From here you can reload the results into below.

From here you can open different results in postscript file which are shown below (requires postscript viewer, like Ghostview).

Output files

Pseudomarker text output file contains for each analyzed phenotype:

• Linkage LOD SCORE
• Linkage and/or association p-values for:
  • Linkage
  • Linkage given LD
  • LD given no Linkage
  • Linkage given LD
  • Joint test of Linkage and LD
• -2ln(likelihood) differences
• Maximized likelihoods
• Maximized recombination fractions, allele frequencies and conditional allele frequencies on disease locus
• Detail maximization information from each hypothesis and maximization step

Note that phenotype in pedigree file is named as 'DISEASE_LOCUS' in pseudomarker output files.

Example of pseudomarker.out.

Pseudomarker graphical postscript multi-page output files contains Linkage LOD SCORE histogram(s) and -log₁₀(p-value) histogram(s) for all tests. Graphical output is only useful if one has more multiple markers in same analysis, since histogram width depends of number of markers. Examples here (opens in separate window):

Linkage: LOD SCORE	Linkage: -log10(p-value)	LD given Linkage: -log10(p-value)
LD given No Linkage: -log10(p-value)	Linkage given LD: -log10(p-value)	Joint test of LD and Linkage: -log10(p-value)

Example of files:

If separate phenotype file is used, then postscript output files are named based on phenotype name. For example if phenotype name is FCHL, then dominant output file name is pseudomarker_fchl_dominant.ps.

Tip: If you are using Linux system, it's easy to convert PS files to PDF format with ps2pdf command.

Extra

Visual Pseudomarker has capability to draw 2D -2ln(Likelihood) surfaces, where x-axis is recombination fraction, θ, and y-axis is D-prime. This option is available for diallelic markers. Command line based Pseudomarker outputs -2ln(Likelihood) matrix file with option --lnlikematrix. Calculation of surface is performed with special version of MLINK and values found in surface scan are used as starting values for maximization routines.

Usage

After you have run dominant, recessive or model-based Pseudomarker analysis which included some SNP marker data, you can open 2D -2ln(Likelihood) window from extra file menu.

From here you can change minimum and maximum theta values (x-axis).

From here you can change minimum and maximum D-prime values (y-axis).

From here you can change the colors scale.

From here you can change the maximum -2ln(Likelihood) to show on surface.

You must press draw button to draw your surface by current settings

Here you can see the result -2ln(Likelihood) surface in 2D mode. You can also see the hypotesis dots around the -2ln(Likelihood) surface.

Here you can see the value ranges for the colors.

Here is the loading status due to the minor computing latency when you press draw button.

Here you can select the phentotype and the marker you chooce to draw.

Here are the specific information for minimum -2ln(Likelihood) H0 dot (theta = 0.5, D' = 0.0) on surface. Note that you can see it when you press your second mouse button down over the H0 dot.

Here are the specific information for minimum -2ln(Likelihood) H1 dot (theta < 0.5, D' = 0.0). Note that you can see it when you press your second mouse button down over the H1 dot.

Here are the specific information for minimum -2ln(Likelihood) H2 dot (theta = 0.5, D' <> 0.0). Note that you can see it when you press your second mouse button down over the H2 dot.

Here is the specific information for minimum -2ln(Likelihood) H3 dot (theta < 0.5, D' <> 0.0). Note that you can see it when you press your second mouse button down over the H3 dot.

Here you can print the -2ln(Likelihood) surface

Examples

2D -2ln(Likelihood) surfaces in this example are from mixed-dataset which is in 'Tour 1'.

SNP1

2D -2ln(Likelihood) surfaces under dominant:

recessive:

and model-based:

SNP2

2D -2ln(Likelihood) surfaces under dominant:

recessive:

and model-based:

SNP3

2D -2ln(Likelihood) surfaces under dominant:

recessive:

and model-based:

Download

Pseudomarker binaries are available for following platforms:

Source code distribution: pseudomarker-1.0.6b.tar.gz

ChangeLog.txt

Register your version of Pseudomarker to receive notifications of the new versions.

Pseudomarker license agreement PSEUDOMARKER_LICENSE.txt

Example data download (detail description of the data in Tutorial):

• Tar/Gzip file: pseudomarker-sampledata.tar.gz
• Windows ZIP file: pseudomarker-sampledata.zip

Example data is included in VisualPseudomarker installer.

Unix/Linux install

Uncompress tar.gz file with command (requires GNU zip):

gunzip pseudomarker-1.0.6b-linux-AMD64.tar.gz
tar xvf pseudomarker-1.0.6b-linux-AMD64.tar

Or with command (requires GNU tar):

tar zxvf pseudomarker-1.0.6b-linux-AMD64.tar.gz

Copy uncompressed binaries under your system path, like /usr/local/bin for example.

Windows install

See Tutorial->Usage for windows installation.

References

When you publish your work and pseudomarker method was used you should use cite all following papers. Thanks!

Pseudomarker references

Linkage Analysis in the Presence of Errors IV: Joint Pseudomarker Analysis of Linkage and/or Linkage Disequilibrium on a Mixture of Pedigrees and Singletons When the Mode of Inheritance Cannot Be Accurately Specified. Harald H. H. Göring and Joseph D. Terwilliger (American Journal of Human Genetics 66:1310-1327, 2000). (Link)

PSEUDOMARKER: A Powerful Program for Joint Linkage and/or Linkage Disequilibrium Analysis on Mixtures of Singletons and Related Individuals. T. Hiekkalinna, A. A. Schäffer, B. W. Lambert, P. Norrgrann, H.H.H. Göring, J.D. Terwilliger. Hum Hered 2011;71:256-266 (link).

Comparison of family-based association methods

On the statistical properties of family-based association tests in datasets containing both pedigrees and unrelated case-control samples. T. Hiekkalinna, H.H.H. Göring, B. W. Lambert, K. M. Weiss, P. Norrgrann, A. A. Schäffer, J.D. Terwilliger. European Journal of Human Genetics advance online publication 21 September 2011; doi: 10.1038/ejhg.2011.173 (link).

FASTLINK 4.1P references

R. W. Cottingham Jr., R. M. Idury, and A. A. Schaffer, Faster Sequential Genetic Linkage Computations, American Journal of Human Genetics, 53(1993), pp. 252-263.

A. A. Schaffer, S. K. Gupta, K. Shriram, and R. W. Cottingham, Jr., Avoiding Recomputation in Linkage Analysis, Human Heredity, 44(1994), pp. 225-237.

G. M. Lathrop, J.-M. Lalouel, C. Julier, and J. Ott, Strategies for Multilocus Analysis in Humans, PNAS 81(1984), pp. 3443-3446.

G. M. Lathrop and J.-M. Lalouel, Easy Calculations of LOD Scores and Genetic Risks on Small Computers, American Journal of Human Genetics, 36(1984), pp. 460-465.

G. M. Lathrop, J.-M. Lalouel, and R. L. White, Construction of Human Genetic Linkage Maps: Likelihood Calculations for Multilocus Analysis, Genetic Epidemiology 3(1986), pp. 39-52.

FAQ

Feature related

Q: Can Pseudomarker analyze quantitative trait locus (QTL)?
A: Not at the moment.

Q: Can Pseudomarker do multipoint analysis?
A: Not at the moment.

Q: Pseudomarker is quite slow on my big pedigrees, why?
A: It's summary of the parts;

• Total number of families
• Size of the families
• Number of the alleles in a marker
• Proportion of genotyped members in the family

Pseudomarker uses FASTLINK for likelihood maximizations, which uses Elston-Stewart--algorithm and therefore uses all family relationship information correctly. So: ['If ain't tough to get,it ain't worth having', Hatfield FC, Power: A Scientific Approach, Contemporary Books, 1989] :) Eric Sobel's SimWalk2 documentation web pages has really nice table about General-Pedigree Linkage Analysis Packages and Algorithms.

Running Pseudomarker

Q: Why do I get following error message when I run VisualPseudomarker?

'makepedpseudo'is not recognized as an internal or external command, operable 
program or batch file. Reading pedigree file: 
C:\Program Files\VisualPseudomarker\temp_files\Process1\temp.ped.linkage ...failed. 
Unable to open pedigree file 
C:\Program Files\VisualPseudomarker\temp_files\Process1\temp.ped.linkage for reading. 
aborting program due to errors! 

A: When installing VisualPseudomarker you need administrator level system rights, since Pseudomarker install path needs to be added to system path. Reinstall VisualPseudomarker with system administrator priviledges.

Q: Why do I get following 'ilinkpseudo' error message when running Pseudomarker?

******************************************************************
Error in executing ILINKPSEUDO: ilinkpseudo -s 
 
Something is wrong....
******************************************************************

A: This should not happen. For some reason input files for ilinkpseudo have been corrupted. Run ilinkpseudo manually with the options after ilinkpseudo command. Then you should see correct error message. If possible email me pedfile.dat and datafile.dat, so I can solve problem right away.

Q: Why do I get following 'unknownpseudo' error message when running Pseudomarker?

******************************************************************
Error in executing UNKNOWNPSEUDO: unknownpseudo 
 
Something is wrong....
******************************************************************

A: Reason is UNKNOWN! :D But seriously, this should not happen. For some reason input files for unknownpseudo have been corrupted. Run unknownpseudo manually with the options after unknownpseudo command. Then you should see correct error message. If possible email me pedfile.dat and datafile.dat, so I can solve problem right away.

Other

Q: Is that possible to get Pseudomarker for Mac OS X?

A: I don't have access to Mac OS X, but when I do, I will make Mac version of it.

Tour 1

Data used in this example is available on download section (mixed.ped, mixed.dat and mixed.map).

Example family data consist 50 controls, 50 trios, 50 sib pairs, 50 sib trios and 30 extended families (Here are the pedigree drawings by CraneFoot software).

Each person has genotypes from 3 SNP and 3 microsatellite markers. Disease model was highly penetrant dominant mode of inheritance with rare disease allele frequency. Markers were simulated under null hypothesis, under linkage and under linkage and linkage disequilibrium.

Let's run dominant pseudomarker analysis on marker SNP1:

pseudomarker -p mixed.ped -m mixed.map --dom --marker SNP1

LOD SCORE results:

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         SNP1     0.041615

SNP1 does not show significant linkage, but how about association? P-values:

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         SNP1     0.330786     0.782981     0.726543     0.703190     0.739917

Nothing! Make sense, since our material is mostly families and therefore association cannot exist without linkage.

Next, how about SNP2?:

pseudomarker -p mixped.ped -m mixed.map --dom --marker SNP2

LOD SCORE results:

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         SNP2     5.316922

SNP2 shows significant linkage! How about association? P-values:

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         SNP2 3.812674e-07     0.216318     0.625973 3.908910e-07     0.000001

Since SNP2 shows significant linkage it's very useful to do LD given Linkage test, which treats linkage as nuisance parameter, but test does not show any significant evidence of association. (p-value = 0.216318).

Linkage given LD (equivalent to TDT type test) show significant results since signal is only coming from linkage. The joint test of Linkage and LD is significant for same reasons. No association found!

Next, how about SNP3?:

pseudomarker -p mixped.ped -m mixed.map --dom --marker SNP3

LOD SCORE results:

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         SNP3     5.739484

SNP3 shows significant linkage! How about association? P-values:

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         SNP3 1.392988e-07 2.877274e-09     0.006054 1.877506e-13 2.154081e-14

SNP3 shows significant association (LD given Linkage = 2.877274e-09), when linkage was nuisance parameter. Joint test of Linkage and LD is most significant results since both exists in this SNP3 marker.

If we analyze SNP1, SNP2 and SNP3 under recessive pseudomarker analysis model, results are:

LOD SCORE statistics
====================

Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         SNP1     0.223044
         SNP2     1.210150
         SNP3     5.099877

p-value statistics
==================

Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         SNP1     0.155407     0.927194     0.944336     0.309997     0.452358
         SNP2     0.009131     0.053836     0.026676     0.036423     0.005958
         SNP3 6.401566e-07     0.000002     0.007135 5.051727e-10 5.861033e-11

Recessive pseudomarker analysis is not as significant as dominant pseudomarker, since true (simulation) model was dominant.

Bonus: Pedigree file (mixed.ped) also contains three microsatellite markers: STR1, STR2 and STR3. and dominant and recessive pseudomarker analysis results are:

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         STR1     0.077618
         STR2     6.956333
         STR3     9.747656
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         STR1     0.052639
         STR2     3.510388
         STR3     5.590726
Model-based (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         STR1     0.076828
         STR2     7.724878
         STR3    10.939468

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         STR1     0.274975     0.719843     0.666758     0.651347     0.699834
         STR2 7.762360e-09     0.314906     0.827796 5.753830e-09 1.011943e-07
         STR3 1.079931e-11 1.616351e-19     0.020290 1.095678e-28 1.477013e-28
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         STR1     0.311246     0.844112     0.791397     0.736268     0.824227
         STR2     0.000029     0.614732     0.975988     0.000036     0.000426
         STR3 1.985011e-07 8.109951e-20     0.000021 8.620222e-22 9.975074e-25
Model-based (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         STR1     0.275994     0.822344     0.781951     0.614943     0.775791
         STR2 1.262375e-09     0.322808     0.946570 8.387814e-10 1.836306e-08
         STR3 6.595516e-13 2.289524e-31 2.316665e-09 0.000000e+00 5.879940e-29

STR1 show no evidence of linkage (and/or association), STR2 show only evidence of linkage and STR3 shows evidence of linkage and association (Dominant LD given Linkage p-value = 1.616351e-19).

Tour 2

Data used in this example is available on download section (noparents.ped, noparents.dat and noparents.map).

Example family data consists 100 controls, 100 trios (parents not genotyped), 100 sib pairs (parents not genotyped) and 100 sib trios (parents not genotyped) with two microsatellite markers. Simulation model was recessive with common disease allele frequency with low penetrance. Note: No parents genotyped!

Let's run dominant and recessive pseudomarker analyses:

pseudomarker -p noparents.ped -m noparents.map --dom --rec

LOD SCORE results:

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         STR4     4.003723
         STR5     6.768000
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
         STR4     4.541576
         STR5     8.024795

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         STR4     0.000009     0.405264     0.414863     0.000017     0.000179
         STR5 1.212352e-08     0.007265     0.535167 1.556535e-10 5.809028e-09
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
         STR4     0.000002     0.177490     0.184411     0.000005     0.000022
         STR5 6.221613e-10     0.000001     0.323051 1.805690e-15 6.270684e-14

Both STR4 and STR5 shows evidence of linkage, but STR5 shows evidence of association (LD given Linkage p-value = 0.000001) as well. Recessive pseudomarker analysis was more powerful since true (simulation) model was recessive.

Tour 3

Data used in this example is available on download section (100sibs.ped, 100sibs.dat, 100sibs.map, controls.dat and cases.dat).

Example family data consists 100 sib pairs (parents not genotyped) and 200 cases and 200 controls with one microsatellite marker. Simulation model was dominant with common disease allele frequency with low penetrance.

Let's run dominant and recessive pseudomarker analyses using only sibs:

pseudomarker -p 100sibs.ped -m 100sibs.map --dom --rec

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
        STR10     0.902818
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
        STR10     0.940528

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
        STR10     0.020733     0.037442     0.734660     0.001481     0.008991
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
        STR10     0.018719     0.323968     0.999934     0.010301     0.061760

No significant linkage or association found, but under dominant model LD given Linkage shows 'suggestive' association. Let's do joint analysis of sib pairs and cases and run dominant and recessive pseudomarker analysis. Note that we use same map file for singletons as for sib pair pedigrees, since all files has only one and same microsatellite marker:

pseudomarker -p 100sibs.ped -m 100sibs.map --casegt cases.dat --casemap 100sibs.map --dom --rec

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
        STR10     0.975430
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
        STR10     1.007823

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
        STR10     0.017037     0.045367     0.270097     0.004539     0.009204
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
        STR10     0.015617     0.772410     0.485852     0.053973     0.118246

No significant linkage or association. Let's do joint analysis of sibpairs, cases and controls:

pseudomarker -p 100sibs.ped -m 100sibs.map --controlgt controls.dat 
--controlmap 100sibs.map --casegt cases.dat --casemap 100sibs.map --dom --rec

LOD SCORE statistics
====================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
        STR10     1.071018
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage
        STR10     1.080179

p-value statistics
==================

Dominant (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
        STR10     0.013191 1.691206e-09     0.000002     0.000014 4.661603e-10
Recessive (Phenotype: DISEASE_LOCUS)
       Marker      Linkage   LD|Linkage LD|NoLinkage   Linkage|LD   LD+Linkage
        STR10     0.012873 5.905058e-08     0.000004     0.000249 1.488900e-08

Significant association (dominant LD given Linkage p-value = 1.691206e-09) after adding controls!

Two stage analyses

In general, we recommend running PSEUDOMARKER only for a subset of markers in a genome-wide analysis (GWA). First, one should run the computationally simple and rapid lod score and HHRR analyses for all markers in the study. Then from these analysis results, one can select a subset of the markers to perform the much more computationally-intensive (and slow) Pseudomarker analysis. Users have the option of selecting all markers with p-values in either or both simple tests exceeding some pre-defined critical value, or of rank-ordering the results by statistical significance and selecting the best N markers for full PSEUDOMARKER analysis. A script (written in Python) and additional accessory programs which can automatically perform such two-stage analyses are distributed together with PSEUDOMARKER and use the same file formats.

Usage

Stage 1

In stage 1, 2-point linkage analysis is performed by using the FASTLINK 4.1P package and the HHRR method is implemented in a Python script. Input file formats are the same as for PSEUDOMARKER. The required files include pedigree and map files. By default linkage analysis is done with the recessive pseudomarker model² unless model file is used which will override default model.

Let's run stage 1 analyses:

twostage.py -p gwa.ped -m gwa.map --stage1

Output files are:

• Linkage results (under homogeneity & heterogeneity): analyze_stage1_summary.out
• HHRR results: hhrr.out
• Combined linkage (LodHom) & HHRR results: combined_pvalues.out

Stage 2

In stage 2, the output files from stage 1 are parsed, markers are selected for follow-up analysis, and new input files are created, either based on whether or not the marker exceeds the selected critical values for either or both tests, or by rank-ordering the markers on each test and selecting the top N for follow-up. In the follow-up analysis PSEUDOMARKER analysis is performed with the pseudomarker recessive model and any user-defined models from the modelfile. In heterogeneity analysis, only families which have shown prior evidence of linkage, are selected for the Pseudomarker analysis and note that, only test of linkage disequilibrium conditional on linkage (LD|Linkage) is meaningful.

Let's run stage 2 analyses and use homogeneity p-value threshold of 0.0001:

twostage.py -p gwa.ped -m gwa.map --stage2 --pvalue-hom 0.0001

Output files are named as pseudomarker_[ld_hom/ld_het].out

Let's run stage 2 analyses for the best 10 markers in homogeneity analysis:

twostage.py -p gwa.ped -m gwa.map --stage2 --best-hom 10

Output file is named as pseudomarker_best_[ld_hom/ld_het].out

More detailed info can be found from the log-file: twostage.log

References:

1. Ott J. Linkage anaysis and family classification under genetic heterogeneity. Annals of Human Genetics, 47:311-320, 1983.

2. Harald H. H. Göring and Joseph D. Terwilliger. Linkage Analysis in the Presence of Errors IV: Joint Pseudomarker Analysis of Linkage and/or Linkage Disequilibrium on a Mixture of Pedigrees and Singletons When the Mode of Inheritance Cannot Be Accurately Specified. AJHG 66:1310-1327, 2000.

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