Logistic regression

Logistic regressions are commonly used to perform genome-wide association studies. It is possible to perform such an analysis using imputation data (dosage format), where each imputed genotypes varies between 0 and 2 (inclusively). A value close to 0 means that a homozygous genotype of the most frequent allele is the most probable. A value close to 2 means that a homozygous genotype of the rare allele is the most probable. Finally, a value close to 1 means that a heterozygous genotype is the most probable.

We suppose that you have followed the main Genome-wide imputation pipeline. The following command will create the working directory for this tutorial.

mkdir -p $HOME/genipe_tutorial/logistic

Input files

Imputed genotypes

After running the genipe pipeline, the imputed genotypes files will have the .impute2 or .impute2.gz extension Those files will be located in the final_impute2 directories of each chromosomes. There should be one impute2 file per chromosome (see the genipe/chrN/final_impute2 directories section in the main Genome-wide imputation pipeline). These files consist of the imputed genotypes required to perform the analysis.

The general structure of the file contains the following columns (which are space delimited): the chromosome, the name of the marker, its position and its two alleles. The subsequent columns correspond to the probabilities of each genotype (hence, there are three columns per sample). The first value correspond to the probability of being homozygous of the first allele. The second value correspond to the probability of being heterozygous. Finally, the third value correspond to the probability of being homozygous of the second allele. The following example shows two lines of the impute2 file.

21 rs376366718:10000302:A:G 10000302 A G 0.986 0.014 0 1 0 0 1 0 0 ...
21 21:10002805:C:T 10002805 C T 0.254 0.736 0.010 0.810 0.188 0.002 0.800 0.195 0.005 ...

Samples file

This file is generated by genipe and has the .sample extension. There should be one sample file per chromosome (see the genipe/chrN/final_impute2 directories section in the main Genome-wide imputation pipeline). These files greatly resembles the Plink fam file. Specifically, it contains the samples that are included in the impute2 file (with the same order). It is needed to correctly interpret the sample described by the impute2 file. The format is as follow:

ID_1 ID_2 missing father mother sex plink_pheno
0 0 0 D D D B
1341 NA06985 0 0 0 2 -9
1341 NA06991 0 NA06993 NA06985 2 -9
1341 NA06993 0 0 0 1 -9
...

The first two rows are part of the format and should be as is.

Warning

The column ID_2 should contain unique sample identification numbers, since the analysis will only consider the ID_2 (which correspond to the sample ID in the Plink file) to correctly match the samples and the imputed genotypes.

Phenotype file

This file describes the phenotype and variables used to perform the logistic regression. The file is tab separated and contains one row per sample, one column per phenotype/variable.

The following is an example of a phenotype file:

SampleID     Pheno2  Age     Var1    Gender
NA06985      1       53      48.01043142060001       2
NA06993      1       47      23.7615117523   1
NA06994      0       48      20.2946857226   1
...

We provide a dummy phenotype file (where values, except for Gender, were randomly generated). The following command should download the phenotype file.

cd $HOME/genipe_tutorial/logistic

wget http://pgxcentre.github.io/genipe/_static/tutorial/phenotypes_logistic.txt.bz2
bunzip2 phenotypes_logistic.txt.bz2

Note

Note that the gender is encoded such that males are 1 and females are 2. Samples with missing gender (encoded as 0) will be excluded only if gender is in the covariable list.

Note

Categorical variables should be specified using the --categorical option.

Warning

The sample identification numbers should match the ones in the sample file (see above). Those numbers should be unique for each sample. Only the samples that are both in the sample and phenotype files will be kept for analysis. The order of the samples in the phenotype file is not important.

Sites to extract (optional)

This file (which is optional) should contain a list of site (one identification number per line) to keep for the analysis. This file might be the .good_sites file automatically generated by genipe (see the genipe/chrN/final_impute2 directories section in the main Genome-wide imputation pipeline).

Executing the analysis

If you followed the Genome-wide imputation pipeline, the following commands should execute the logistic regression analysis.

cd $HOME/genipe_tutorial/logistic

imputed-stats logistic \
    --impute2 ../genipe/chr22/final_impute2/chr22.imputed.impute2.gz \
    --sample ../genipe/chr22/final_impute2/chr22.imputed.sample \
    --pheno phenotypes_logistic.txt \
    --extract-sites ../genipe/chr22/final_impute2/chr22.imputed.good_sites \
    --nb-process 8 \
    --nb-lines 6000 \
    --gender-column Gender \
    --covar Age,Var1,Gender \
    --sample-column SampleID \
    --pheno-name Pheno2

For more information about the arguments and options, see the Usage section. For an approximation of the execution time, refer to the Statistical Analysis Execution Time section.

Output files

There will be two output files: .logistic.dosage will contain the statistics, and .log will contain the execution log.

.logistic.dosage file

This file contains the results from the logistic regression. It shows the following information:

  • chr: the chromosome.

  • pos: the position on the chromosome.

  • snp: the name of the marker.

  • major: the major allele.

  • minor: the minor allele.

  • maf: the frequency of the minor allele.

  • n: the number of samples that were used for this marker.

  • coef: the coefficient.

  • se: the standard error.

  • lower: the lower value of the 95% confidence interval.

  • upper: the upper value of the 95% confidence interval.

  • z: the z-statistic.

  • p: the p-value.

Note

By default, the statistics are computed only for markers with a minor allele frequency of 1% and higher. Markers with lower MAF will have NA values. To modify this behavior, use the --maf option.

Usage

The following command will display the documentation for the logistic regression analysis in the console:

$ imputed-stats logistic --help
usage: imputed-stats logistic [-h] [-v] [--debug] --impute2 FILE --sample FILE
                              --pheno FILE [--extract-sites FILE] [--out FILE]
                              [--nb-process INT] [--nb-lines INT] [--chrx]
                              [--gender-column NAME] [--scale INT]
                              [--prob FLOAT] [--maf FLOAT] [--covar NAME]
                              [--categorical NAME] [--missing-value NAME]
                              [--sample-column NAME] [--interaction NAME]
                              --pheno-name NAME

Performs a logistic regression on imputed data using a GLM with a binomial
distribution. This script is part of the 'genipe' package, version 1.4.2.

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit
  --debug               set the logging level to debug

Input Files:
  --impute2 FILE        The output from IMPUTE2.
  --sample FILE         The sample file (the order should be the same as in
                        the IMPUTE2 files).
  --pheno FILE          The file containing phenotypes and co variables.
  --extract-sites FILE  A list of sites to extract for analysis (optional).

Output Options:
  --out FILE            The prefix for the output files. [imputed_stats]

General Options:
  --nb-process INT      The number of process to use. [1]
  --nb-lines INT        The number of line to read at a time. [1000]
  --chrx                The analysis is performed for the non pseudo-autosomal
                        region of the chromosome X (male dosage will be
                        divided by 2 to get values [0, 0.5] instead of [0, 1])
                        (males are coded as 1 and option '--gender-column'
                        should be used).
  --gender-column NAME  The name of the gender column (use to exclude samples
                        with unknown gender (i.e. not 1, male, or 2, female).
                        If gender not available, use 'None'. [Gender]

Dosage Options:
  --scale INT           Scale dosage so that values are in [0, n] (possible
                        values are 1 (no scaling) or 2). [2]
  --prob FLOAT          The minimal probability for which a genotype should be
                        considered. [>=0.9]
  --maf FLOAT           Minor allele frequency threshold for which marker will
                        be skipped. [<0.01]

Phenotype Options:
  --covar NAME          The co variable names (in the phenotype file),
                        separated by coma.
  --categorical NAME    The name of the variables that are categorical (note
                        that the gender is always categorical). The variables
                        are separated by coma.
  --missing-value NAME  The missing value in the phenotype file.
  --sample-column NAME  The name of the sample ID column (in the phenotype
                        file). [sample_id]
  --interaction NAME    Add an interaction between the genotype and this
                        variable.

Logistic Regression Options:
  --pheno-name NAME     The phenotype.

Results comparison

The logistic regression results from genipe and Plink were compared for validity. The following figure shows the comparison for, from left to right, the coefficients, the standard errors and the p-values. The x axis shows the results from genipe, and the y axis shows the results for Plink. This comparison includes 58,871 “good” imputed markers with a MAF higher or equal to 10%, analyzed for 60 samples (i.e results from this tutorial). Note that for this comparison, the probability threshold (--prob) was changed from 0.9 to 0 to imitate Plink analysis (see note below for more information).

Note

Only markers with minor allele frequency (MAF) higher or equal to 10% were compared, since markers with lower MAF might have convergence issues (e.g. all exposed samples are all cases or all controls). In that case, the coefficient is large, and the odds ratio (\(e^{coef}\)) gets too large.

Logistic regression comparison between genipe and Plink (prob. of 0)

Note

The sign of the coefficients might be different when comparing genipe to Plink, since genipe computes the statistics on the rare allele, while Plink computes them on the second (alternative) allele. The alternative allele might not always be the rarest.

Note

By default, genipe excludes samples with a maximum probability lower than 0.9 (the --prob option), while Plink keeps all the samples for the analysis. In order to get the same results as Plink, the analysis must be done with a probability threshold of 0 (i.e. --prob 0, keeping all imputed genotypes including those with poor quality). This is what was done for the previous figure.

The following figure shows the comparison between Plink and genipe for the same analysis, but using the default probability threshold of 0.9 (excluding imputed genotypes with poor quality). Hence, 58,769 markers were compared.

Logistic regression comparison between genipe and Plink