Instructions

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MFQLS beta as of December 4, 2014
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◎ Copyright(c) Sungyoung Lee and Suyeon Park, All rights reserved.
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The current version of MFQLS release is beta version. A complete version of the software will be appearing soon.
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1. Input file

     1.1 Genotype data
            1.1.1 PED FILE
            1.1.2 MAP FILE
     1.2 Pheotype data
     1.3 Set file(gene-marker set)

2. Create kinship matrix

     2.1 Theoretical matrix
     2.2 Empirical matrix

3. Run FQLS1/FQLS2

     3.1 FQLS1
     3.2 FQLS2

4. Run MFQLS

     4.1 Adjusting Phenotype
     4.2 Run MFQLS
          4.2.1 MFQLS with single SNP
          
4.2.2 MFQLS with gene set

5. Output file

     5.1 FQLS
     5.2 MFQLS

6. Options

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1.Input file

1.1 Genotype data

: FQLS1/FQLS2 support PLINK’s ped and binary format.

1.1.1 PED FILE

: The PED file is a white-space(tab or space) delimited file : the first six columns are mandatory:

Family ID

Individal ID

Paternal ID (missing=0)

Maternal ID (missing=0)

Sex(male=1, female=2, missing=0)

Phenotype( missing=-9(default) ) 

1.1.2 MAP FILE

: Each line of the MAP file describes a single marker and must contain 4 columns :

Chromosome(1~22,23=X,24=Y)

MarkerID or rs name

Genetic distance(Morgans)

Base-pair position(bp) 

1.2 Phenotype data

: Sample lines of Phenotype, covariate and proband information file :


 FID

 IID  Age  Sex pheno1 pheno2 pheno3 PROBAND

1

11

56

1

0

1

12

50

2

0

1

13

24

1

1

2

21

45

1

0

1.3 Set file(gene-marker set)

: The name of the marker set file which defines Gene sets. The first column of the file must be Gene ID, and the second column must be Marker ID

  geneA    22:1000  
  geneA    22:1001
  geneB    22:2000
  geneC    22:2100
  geneD    22:2110

 2. Create kinship matrix

2.1 Theoretical matrix

Ex1) fqls –bed file.bed –makecor –pddt –out corr1 (will generate corr1.theo.cor)
Ex2) fqls –ped file.ped –makecor –pddt –out corr1 (will generate corr1.theo.cor)

2.2 Empirical matrix

Ex1) fqls –bed file.bed  –makecor –out corr2 (will generate corr2.empi.cor)
Ex2) fqls –ped file.ped  –makecor –out corr2 (will generate corr2.empi.cor)

NOTE : MAF returns to default 0.05

3. Run FQLS1/FQLS2

: (1Correlation matrix )  2Run FQLS

3.1 FQLS1 (test.txt : proband information file)

Ex1) fqls –ped test.ped –fqls –mqls –sampvar test.txt –pddt –thread 4 –prevalence 0.05 –heri 0.8 –out test1(will generate test1.family.qls.res)
Ex2) fqls –ped test.ped –fqls –mqls –sampvar test.txt (–cor) –thread 4 –prevalence 0.05 –heri 0.8 –out test2(will generate test2.family.qls.res)

3.2 FQLS2

Ex1) fqls –ped test.ped –fqls –mqls –pddt –thread 4 –prevalence 0.05 –heri 0.8 –out test3(will generate test3.family.qls.res)
Ex2) fqls –ped test.ped –fqls –mqls (–cor) –thread 4 –prevalence 0.05 –heri 0.8 –out test4(will generate test4.family.qls.res)

4. Run MFQLS

 : (1Correlation matrix )  2Adjusting phenotype → 3Run MFQLS

4.1 Adjusting Phenotype

: If genotype frequencies of affected and unaffected samples are compared to detect the genetic association, it has been shown that the statistical efficiency can be improved by modifying the phenotype ( MS McPeek et al, 2004; BL Browning et al, 2009; T Thornton et al,2012 )

Prevalence             :   –prevalence

BLUP by covariate  :   –makeblup           

 Ex1) mqls –bed mfqls_pop_data.bed –sampvar mfqls_input_file.txt –pname PHENO1,PHENO2,PHENO3 –cname SEX,AGE –makeblup –out blup_output
        (will generate  blup_output.AI.blup)

4.2 Run MFQLS

4.2.1 MFQLS with single SNP

Ex1) mfqls –bed mfqls_pop_data.bed –mqls –sampvar mfqls_input_file.txt –pname PHENO1,PHENO2,PHENO3 –blup blup_output.AI.blup –out mfqls_output
        (will generate  mfqls_output.extended.qls.res)

4.2.2 MFQLS with geneSet

 Ex1) mfqls –bed mfqls_pop_data.bed –mqls –sampvar mfqls_input_file.txt –pname PHENO1,PHENO2,PHENO3 –blup blup_output.AI.blup –set gene_set_data –out mfqls_gene_set_output
         (will generate mfqls_gene_set_output.extended.qls.res)

5. Output file

:This will generate the following files :

5.1 FQLS ( [test].family.qls.res )

 Colunm Name         Description 
 CHR  
 SNP  
 POS  
 NMISSING  Number of the missing genotype for the each marker    
 MqlsStat  Test statistic of the MQLS from the Family QLS test
 Mqlspval  p-value of the MQLS from the Family QLS test
 FQLS1Stat  Test statistic of the with FQLS1 from the Family QLS test
 FQLS1pval  p-value of the FQLS1 from the Family QLS test
 FQLS2Stat  Test statistic of the FQLS2 from the Family QLS test
 FQLS2pval  p-value of the FQLS2 from the Family QLS test

5.2 MFQLS 

5.2.1 [  ].AI.blup

Colunm name Description
 FID  Family ID
 IID  Individual ID
 PHENO1.blup  Blup of PHENO1
 PHENO1.predicted  Predicted value of PHENO1
 PHENO2.blup  Blup of PHENO2
 PHENO2.predicted  Predicted value of PHENO2
 PHENO3.blup  Blup of PHENO3
 PHENO3.predicted  Predicted value of PHENO3

5.2.2 [  ].extended.qls.res

 Colunm name  Description 
 CHR  
 SNP  
 ALT  
 STAT Test statistic of the MFQLS 
 PVAL P-values of the MFQLS 

5.2.3 [  ].miss.pheno.lst (individual who has the missing values)

 Colunm name Description 
FID Family ID 
IID  Individual ID   

5.2.4 [  ].stat.sample.lst

Colunm name  Description 
 IDX  
 FILEIDX  
 FID  Family ID
 IID  Individual ID
PHENO   Phenotype value 

6. Options

FQLS options

I/O related 

 

 –bed/–ped

 Assigns input with one of  PED/BED format

 –prevalence

 prevalence of  target phenotype

 –heri

 heritability of target phenotype

 

 

 –mispheno

 -9(default) or NA

 –misgeno

 -9(default) or NA

 –out

 Assigns output prefix, all outputs from FQLS will be

 generated with this prefix

Analysis

related

 

 –mqls

 Perform MQLS test

 –fqls

 Perform FQLS test (FQLS1/FQLS2)

 –sampvar

 Assigns PROBAND information file : If you want to run

 the FQLS2 test, you MUST use this option.

 –fqlsnopddt

 Do not utilize PDDT (theoretical correlation structure)

 for FQLS to compute offset (optional). In this case, an

 assigned correlation structure will be used.

Sample 

structure 

related

 –makecor

 Make samples correlation matrix

 –cor(default)

 Load/compute empirical correlation matrix

 –pddt

 Load/compute theoretical correlation matrix

Others

 –samprem

 Remove these individuals

 –thread

 Set the number of threads will be used in the analysis

 

 

 MFQLS options

–sampvar   assign file which includes phenotype and covarate informations   
 –pname  assign phenotype name
 –cname  assign covariate name
 –makeblup      make blup
 –blup  assign blup file for mfqls
 –set  assign gene set file

 

 


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