This repository includes modules and scripts for loading, manipulating, and simulating with genotype data.
The software works mainly with plink's .bed file format, with the hope that we will extend this to
other genotype data formats in the future.
The features and functionalities that this package supports are:
- Efficient LD matrix construction and storage in Zarr array format.
- Data structures for harmonizing various GWAS data sources.
- Simulating complex traits (continuous and binary) using elaborate genetic architectures.
- Multi-cohort simulation scenarios (beta)
- Simulations incorporating functional annotations in the genetic architecture (beta)
- Interfaces for performing association testing on simulated and real phenotypes.
- Preliminary support for processing and integrating genomic annotations with other data sources.
NOTE: The codebase is still in active development and some of interfaces or data structures will be replaced or modified in future releases. Check the CHANGELOG for the latest updates and modifications.
magenpy is now available on the python package index pypi and
can be minimally installed using the package installer pip:
pip install magenpyTo access the full functionalities of magenpy, however, it is recommended that
you install the full list of dependencies:
pip install magenpy[full]To use magenpy on a shared computing cluster, we recommend installing it in a
python virtual environment. For example:
module load python/3.8
python -m venv magenpy_env
source magenpy_env/bin/activate
pip install --upgrade pip
pip install magenpyFinally, if you wish to install the package from source, you can directly clone it from the GitHub repository and install it locally as follows:
git clone https://github.com/shz9/magenpy.git
cd magenpy
make installmagenpy comes with a sample dataset from the 1000G project that
you can use to experiment and familiarize yourself with its features.
Once the package is installed, you can run a couple of quick tests
to verify that the main features are working properly.
For example, to simulate a quantitative trait, you can invoke
the following commands in a python interpreter:
import magenpy as mgp
g_sim = mgp.GWASimulator(mgp.tgp_eur_data_path(),
h2=0.1)
g_sim.simulate()
g_sim.to_phenotype_table()
# FID IID phenotype
# 0 HG00096 HG00096 0.795651
# 1 HG00097 HG00097 0.550914
# 2 HG00099 HG00099 -0.928486
# 3 HG00100 HG00100 0.893626
# 4 HG00101 HG00101 -0.670106
# .. ... ... ...
# 373 NA20815 NA20815 0.246071
# 374 NA20818 NA20818 1.821426
# 375 NA20819 NA20819 -0.457994
# 376 NA20826 NA20826 0.954208
# 377 NA20828 NA20828 0.088412
#
# [378 rows x 3 columns]This simulates a quantitative trait with heritability set to 0.1,
using genotype data for a subset of 378 individuals of European ancestry
from the 1000G project and approximately 15,000 SNPs on chromosome 22.
By default, the simulator assumes that only 10% of the SNPs are
causal (this is drawn at random from a Bernoulli distribution with p=0.1).
To obtain a list of the causal SNPs in this simulation, you can invoke the
.get_causal_status() method, which returns a boolean vector indicating
whether each SNP is causal or not:
g_sim.get_causal_status()
# {22: array([ True, False, False, ..., False, False, False])}In this case, for example, the first SNP is causal for the simulated phenotype. A note
about the design of data structures in magenpy. Our main data structure is a class known
as GWADataLoader, which is an all-purpose object that brings together different data sources and
harmonizes them together. In GWADataLoader, SNP-related data sources are stored in dictionaries, where
the key is the chromosome number and the value is the data structure associated with that chromosome.
Thus, in the output above, the data is for chromosome 22 and the feature is a boolean
vector indicating whether a given SNP is causal or not.
You can also get the full information
about the genetic architecture by invoking the method .to_true_beta_table(),
which returns a pandas dataframe with the effect size, expected heritability contribution,
and causal status of each variant in the simulation:
g_sim.to_true_beta_table()
# CHR SNP A1 MixtureComponent Heritability BETA Causal
# 0 22 rs131538 A 1 0.000063 -0.008013 True
# 1 22 rs9605903 C 0 0.000000 0.000000 False
# 2 22 rs5746647 G 0 0.000000 0.000000 False
# 3 22 rs16980739 T 0 0.000000 0.000000 False
# 4 22 rs9605923 A 0 0.000000 0.000000 False
# ... ... ... .. ... ... ... ...
# 15933 22 rs8137951 A 0 0.000000 0.000000 False
# 15934 22 rs2301584 A 0 0.000000 0.000000 False
# 15935 22 rs3810648 G 0 0.000000 0.000000 False
# 15936 22 rs2285395 A 0 0.000000 0.000000 False
# 15937 22 rs28729663 A 0 0.000000 0.000000 False
#
# [15938 rows x 7 columns]We can also simulate a more complex genetic architecture by, e.g. simulating effect sizes from 4 Gaussian mixture components, instead of the standard spike-and-slab density used by default:
g_sim = mgp.GWASimulator(mgp.tgp_eur_data_path(),
pi=[.9, .03, .03, .04],
d=[0., .01, .1, 1.],
h2=0.1)
g_sim.simulate()
g_sim.to_phenotype_table()
# FID IID phenotype
# 0 HG00096 HG00096 0.435024
# 1 HG00097 HG00097 1.030874
# 2 HG00099 HG00099 0.042322
# 3 HG00100 HG00100 1.392733
# 4 HG00101 HG00101 0.722763
# .. ... ... ...
# 373 NA20815 NA20815 -0.402506
# 374 NA20818 NA20818 -0.321429
# 375 NA20819 NA20819 -0.845630
# 376 NA20826 NA20826 -0.690078
# 377 NA20828 NA20828 0.256937
#
# [378 rows x 3 columns]The parameter pi specifies the mixing proportions for the Gaussian mixture
distribution and the d is a multiplier on the variance (see references below). In this case, 90% of the variants
are not causal, and the remaining 10% are divided between 3 mixture components that contribute
differentially to the heritability. The last component, which constitutes 4% of all SNPs, contributes 100
times and 10 times to the heritability than components 2 an 3, respectively.
magenpy may be used for complex trait simulation employing a variety of different
genetic architectures and phenotype likelihoods. For example, to simulate a quantitative
trait with heritability set to 0.25 and where a random subset of 15% of the variants are causal,
you may invoke the following command:
import magenpy as mgp
g_sim = mgp.GWASimulator(mgp.tgp_eur_data_path(),
pi=[.85, .15],
h2=0.25)
g_sim.simulate()Then, you can export the simulated phenotype to a pandas dataframe as follows:
g_sim.to_phenotype_table()
# FID IID phenotype
# 0 HG00096 HG00096 -2.185944
# 1 HG00097 HG00097 -1.664984
# 2 HG00099 HG00099 -0.208703
# 3 HG00100 HG00100 0.257040
# 4 HG00101 HG00101 -0.068826
# .. ... ... ...
# 373 NA20815 NA20815 -1.770358
# 374 NA20818 NA20818 1.823890
# 375 NA20819 NA20819 0.835763
# 376 NA20826 NA20826 -0.029256
# 377 NA20828 NA20828 -0.088353
#
# [378 rows x 3 columns]To simulate a binary, or case-control, trait, the interface is very similar. First,
you need to specify that the likelihood for the phenotype is binomial (phenotype_likelihood='binomial'), and then
specify the prevalence of the positive cases in the population. For example,
to simulate a case-control trait with heritability of 0.3 and prevalence of 8%, we can invoke the following
command:
import magenpy as mgp
g_sim = mgp.GWASimulator(mgp.tgp_eur_data_path(),
phenotype_likelihood='binomial',
prevalence=.08,
h2=0.3)
g_sim.simulate()
g_sim.to_phenotype_table()
# FID IID phenotype
# 0 HG00096 HG00096 0
# 1 HG00097 HG00097 0
# 2 HG00099 HG00099 0
# 3 HG00100 HG00100 0
# 4 HG00101 HG00101 0
# .. ... ... ...
# 373 NA20815 NA20815 0
# 374 NA20818 NA20818 0
# 375 NA20819 NA20819 1
# 376 NA20826 NA20826 0
# 377 NA20828 NA20828 0
#
# [378 rows x 3 columns]magenpy is not a GWAS tool. However, we do support preliminary association
testing functionalities either via closed-form formulas for quantitative traits, or
by providing a python interface to third-party association testing tools, such as plink.
If you are conducting simple tests based on simulated data, an easy way to perform
association testing is to tell the simulator that you'd like to perform GWAS on the
simulated trait, with the perform_gwas=True flag:
import magenpy as mgp
g_sim = mgp.GWASimulator(mgp.tgp_eur_data_path(),
pi=[.85, .15],
h2=0.25)
g_sim.simulate(perform_gwas=True)Alternatively, you can conduct association testing on real or
simulated phenotypes using the .perform_gwas() method and exporting the
summary statistics to a pandas dataframe with .to_summary_statistics_table():
g_sim.perform_gwas()
g_sim.to_summary_statistics_table()
# CHR SNP POS A1 A2 ... N BETA Z SE PVAL
# 0 22 rs131538 16871137 A G ... 378 -0.046662 -0.900937 0.051793 0.367622
# 1 22 rs9605903 17054720 C T ... 378 0.063977 1.235253 0.051793 0.216736
# 2 22 rs5746647 17057138 G T ... 378 0.057151 1.103454 0.051793 0.269830
# 3 22 rs16980739 17058616 T C ... 378 -0.091312 -1.763029 0.051793 0.077896
# 4 22 rs9605923 17065079 A T ... 378 0.069368 1.339338 0.051793 0.180461
# ... ... ... ... .. .. ... ... ... ... ... ...
# 15933 22 rs8137951 51165664 A G ... 378 0.078817 1.521782 0.051793 0.128064
# 15934 22 rs2301584 51171497 A G ... 378 0.076377 1.474658 0.051793 0.140304
# 15935 22 rs3810648 51175626 G A ... 378 -0.001448 -0.027952 0.051793 0.977701
# 15936 22 rs2285395 51178090 A G ... 378 -0.019057 -0.367949 0.051793 0.712911
# 15937 22 rs28729663 51219006 A G ... 378 0.029667 0.572805 0.051793 0.566777
#
# [15938 rows x 11 columns]If you wish to use plink2 for association testing (highly recommended), ensure that
you tell GWASimulator (or any GWADataLoader-derived object) to use plink by explicitly
specifying the backend software that you wish to use:
import magenpy as mgp
g_sim = mgp.GWASimulator(mgp.tgp_eur_data_path(),
backend='plink',
pi=[.85, .15],
h2=0.25)
g_sim.simulate(perform_gwas=True)When using plink, we sometimes create temporary intermediate files to pass to the software. To clean up
the temporary directories and files, you can invoke the .cleanup() command:
g_sim.cleanup()One of the main features of the magenpy package is an efficient interface for computing
and storing Linkage Disequilibrium (LD) matrices. LD matrices record the pairwise SNP-by-SNP
Pearson correlation coefficient. In general, LD matrices are computed for each chromosome separately
or may also be computed within LD blocks from, e.g. LDetect. For large autosomal chromosomes,
LD matrices can be huge and may require extra care from the user.
In magenpy, LD matrices can be computed using either xarray or plink, depending on the
backend that the user specifies (see Section 5 below). In general, at this moment, we do not recommend using
xarray as a backend for large genotype matrices, as it is less efficient than plink. When using the default
xarray as a backend, we compute the full X'X (X-transpose-X) matrix first, store it on-disk in chunked
Zarr arrays and then perform all sparsification procedures afterwards. When using plink as a
backend, on the other hand, we only compute LD between variants that are generally in close proximity
along the chromosome, so it is generally more efficient. In the end, both will be transformed such that
the LD matrix is stored in sparse Zarr arrays.
A note on dependencies: If you wish to use xarray as a backend to compute LD matrices,
you may need to install some of the optional dependencies for magenpy, including e.g. rechunker. In this case,
it is recommended that you install all the dependencies listed in requirements-optional.txt. If you wish
to use plink as a backend, you may need to configure the paths for plink as explained in Section 5 below.
In either case, to compute an LD matrix using magenpy, you can invoke the .compute_ld() method
of all GWADataLoader-derived objects, as follows:
# Using xarray:
import magenpy as mgp
gdl = mgp.GWADataLoader(mgp.tgp_eur_data_path())
gdl.compute_ld(estimator='windowed',
output_dir='output/ldl/',
window_size=100)This creates a windowed LD matrix where we only measure the correlation between the focal SNP and the nearest
100 from either side. As stated above, the LD matrix will be stored on-disk and that is why we must
specify the output directory when we call .compute_ld(). To use plink to compute the LD matrix,
we can invoke a similar command:
# Using plink:
import magenpy as mgp
gdl = mgp.GWADataLoader(mgp.tgp_eur_data_path(),
backend='plink')
gdl.compute_ld(estimator='windowed',
output_dir='output/ld/',
cm_window_size=3.)In this case, we are computing a windowed LD matrix where we only measure the correlation between
SNPs that are at most 3 centi Morgan (cM) apart along the chromosome. For this small 1000G dataset, computing
the LD matrix takes about a minute. The LD matrices in Zarr format will be written to the path
specified in output_dir, so ensure that this argument is set to the desired directory.
To facilitate working with LD matrices stored in Zarr format, we created a data structure in cython called LDMatrix,
which acts as an intermediary and provides various features. For example, to compute LD scores
using this LD matrix, you can invoke the command .compute_ld_scores() on it:
gdl.ld[22]
# <LDMatrix.LDMatrix at 0x7fcec882e350>
gdl.ld[22].compute_ld_scores()
# array([1.60969673, 1.84471792, 1.59205322, ..., 3.3126724 , 3.42234106,
# 2.97252452])You can also get a table that lists the properties of the SNPs included in the LD matrix:
gdl.ld[22].to_snp_table()
# CHR SNP POS A1 MAF
# 0 22 rs9605903 17054720 C 0.260736
# 1 22 rs5746647 17057138 G 0.060327
# 2 22 rs16980739 17058616 T 0.131902
# 3 22 rs9605927 17067005 C 0.033742
# 4 22 rs5746664 17074622 A 0.066462
# ... ... ... ... .. ...
# 14880 22 rs8137951 51165664 A 0.284254
# 14881 22 rs2301584 51171497 A 0.183027
# 14882 22 rs3810648 51175626 G 0.065440
# 14883 22 rs2285395 51178090 A 0.061350
# 14884 22 rs28729663 51219006 A 0.159509
#
# [14885 rows x 5 columns]Finally, note that the LDMatrix object supports an iterator interface, so in principle
you can iterate over rows of the LD matrix without loading the entire thing into memory.
The following example shows the first 10 entries of the first row of the matrix:
np.array(next(gdl.ld[22]))[:10]
# array([ 1.00000262, -0.14938791, -0.27089083, 0.33311111, 0.35015815,
# -0.08077946, -0.08077946, 0.0797345 , -0.16252513, -0.23680465])Finally, as of magenpy>=0.0.2, now you can export the Zarr array into a scipy sparse csr
matrix as follows:
gdl.ld[22].to_csr_matrix()
# <15938x15938 sparse matrix of type '<class 'numpy.float64'>'
# with 24525854 stored elements in Compressed Sparse Row format>magenpy supports computing LD matrices using 4 different estimators that are commonly used
in statistical genetics applications.
For a more thorough description of the estimators and their properties, consult our manuscript
and the citations therein. The LD estimators are:
windowed(recommended): The windowed estimator computes the pairwise correlation coefficient between SNPs that are within a pre-defined distance along the chromosome from each other. In many statistical genetics applications, the recommended distance is between 1 and 3 centi Morgan (cM). As ofmagenpy==0.0.2, now you can customize the distance based on three criteria: (1) A window size based on the number neighboring variants, (2) distance threshold in kilobases (kb), and (3) distance threshold in centi Morgan (cM). When defining the boundaries for each SNP,magenpytakes the intersection of the boundaries defined by each window.
import magenpy as mgp
gdl = mgp.GWADataLoader(mgp.tgp_eur_data_path(),
backend='plink')
gdl.compute_ld('windowed', output_dir='output/ld/',
window_size=100, kb_window_size=1000, cm_window_size=2.)
gdl.cleanup()block: The block estimator estimates the pairwise correlation coefficient between variants that are in the same LD block, as defined by, e.g. LDetect. Given an LD block file, we can compute a block-based LD matrix as follows:
import magenpy as mgp
ld_block_url = "https://bitbucket.org/nygcresearch/ldetect-data/raw/ac125e47bf7ff3e90be31f278a7b6a61daaba0dc/EUR/fourier_ls-all.bed"
gdl = mgp.GWADataLoader(mgp.tgp_eur_data_path(),
backend='plink')
gdl.compute_ld('block', output_dir='output/ld/',
ld_blocks_file=ld_block_url)
gdl.cleanup()If you have the LD blocks file on your system, you can also pass the path to the file instead.
shrinkage: For the shrinkage estimator, we shrink the entries of the LD matrix by a quantity related to the distance between SNPs along the chromosome + some additional information related to the sample from which the genetic map was estimated. In particular, we need to specify the effective population size and the sample size used to estimate the genetic map. Also, to make the matrix sparse, we often specify a threshold value below which we consider the correlation to be zero. Here's an example for the 1000G sample:
import magenpy as mgp
gdl = mgp.GWADataLoader(mgp.tgp_eur_data_path(),
backend='plink')
gdl.compute_ld('shrinkage', output_dir='output/ld/',
genetic_map_ne=11400, # effective population size (Ne)
genetic_map_sample_size=183, # Sample size
threshold=1e-3) # The cutoff value
gdl.cleanup()sample: This estimator computes the pairwise correlation coefficient between all SNPs on the same chromosome and thus results in a dense matrix. Thus, it is rarely used in practice and we include it here for testing/debugging purposes mostly. To compute the sample LD matrix, you only need to specify the correct estimator:
import magenpy as mgp
gdl = mgp.GWADataLoader(mgp.tgp_eur_data_path(),
backend='plink')
gdl.compute_ld('sample', output_dir='output/ld/')
gdl.cleanup()There are many different statistical genetics data sources and formats out there. One of the goals of
magenpy is to create a friendly interface for matching and merging these data sources for
downstream analyses. For example, for summary statistics-based methods, we often need
to merge the LD matrix derived from a reference panel with the GWAS summary statistics estimated
in a different cohort. While this is a simple task, it can be tricky sometimes, e.g. in
cases where the effect allele is flipped between the two cohort.
The functionalities that we provide for this are minimal at this stage and mainly geared towards
harmonizing Zarr-formatted LD matrices with GWAS summary statistics. The following example
shows how to do this in a simple case:
import magenpy as mgp
# First, generate some summary statistics from a simulation:
g_sim = mgp.GWASimulator(mgp.tgp_eur_data_path())
g_sim.simulate()
g_sim.to_summary_statistics_table().to_csv(
"chr_22.sumstats", sep="\t", index=False
)
# Then load those summary statistics and match them with previously
# computed windowed LD matrix for chromosome 22:
gdl = mgp.GWADataLoader(ld_store_files='output/windowed_ld/chr_22/',
sumstats_files='chr_22.sumstats',
sumstats_format='magenpy')Here, the GWADataLoader object takes care of the harmonization step by
automatically invoking the .harmonize_data() method. When you read or update
any of the data sources, we recommend that you invoke the .harmonize_data() method again
to make sure that all the data sources are aligned properly. In the near future,
we are planning to add many other functionalities in this space. Stay tuned.
Many of the functionalities that magenpy supports require access to and performing linear algebra
operations on the genotype matrix. By default, magenpy uses xarray and dask
to carry out these operations, as these are the tools supported by our main dependency: pandas-plink.
However, dask can be quite slow and inefficient when deployed on large-scale genotype matrices. To get
around this difficulty, for many operations, such as linear scoring or computing minor allele frequency,
we support (and recommend) using plink as a backend.
To use plink as a backend for magenpy, first you may need to configure the paths
on your system. By default, magenpy assumes that, in the shell, the name plink2 invokes the plink2
executable and plink invokes plink1.9 software. To change this behavior, you can update the
configuration file as follows. First, let's see the default configurations that ship with magenpy:
import magenpy as mgp
mgp.print_options()
# -> Section: DEFAULT
# ---> plink1.9_path: plink
# ---> plink2_path: plink2The above shows the default configurations for the plink1.9 and plink2 paths. To change
the path for plink2, for example, you can use the set_option() function:
mgp.set_option("plink2_path", "~/software/plink2/plink2")
mgp.print_options()
# -> Section: USER
# ---> plink2_path: ~/software/plink2/plink2
# ---> plink1.9_path: plink
# -> Section: DEFAULT
# ---> plink1.9_path: plink
# ---> plink2_path: plink2As you can see, this added a new section to the configuration file, named USER, that has the
new path for the plink2 software. Now, every time magenpy needs to invoke plink2, it calls
the executable stored at ~/software/plink2/. Note that you only need to do this once on any particular
machine or system, as this preference is now recorded in the configuration file and will be taken into
account for all future operations.
Note that for most of the operations, we assume that the user has plink2 installed. We only
use plink1.9 for some operations that are currently not supported by plink2, especially for
e.g. LD computation. This behavior may change in the near future.
Once the paths are configured, to use plink as a backend for the various computations and
tools, make sure that you specify the backend='plink' flag in GWADataLoader and all of its
derived data structures (including all the GWASimulator classes):
import magenpy as mgp
gdl = mgp.GWADataLoader(mgp.tgp_eur_data_path(),
backend='plink')If you are not comfortable programming in python and would like to access some of the functionalities
of magenpy with minimal interaction with python code, we packaged a number of commandline
scripts that can be useful for some downstream applications.
The binaries that are currently supported are:
magenpy_ld: For computing LD matrices and storing them inZarrformat.magenpy_simulate: For simulating complex traits with various genetic architectures.
Once you install magenpy via pip, these two scripts will be added to the system PATH
and you can invoke them directly from the commandline, as follows:
$ magenpy_ld -h
**********************************************
_ __ ___ __ _ __ _ ___ _ __ _ __ _ _
| '_ ` _ \ / _` |/ _` |/ _ \ '_ \| '_ \| | | |
| | | | | | (_| | (_| | __/ | | | |_) | |_| |
|_| |_| |_|\__,_|\__, |\___|_| |_| .__/ \__, |
|___/ |_| |___/
Modeling and Analysis of Genetics data in python
Version: 0.0.10 | Release date: May 2022
Author: Shadi Zabad, McGill University
**********************************************
< Compute LD matrix and output in Zarr format >
usage: magenpy_ld [-h] [--estimator {block,shrinkage,sample,windowed}] --bfile BED_FILE [--keep KEEP_FILE] [--extract EXTRACT_FILE] [--backend {plink,xarray}] [--temp-dir TEMP_DIR]
--output-dir OUTPUT_DIR [--min-maf MIN_MAF] [--min-mac MIN_MAC] [--ld-window LD_WINDOW] [--ld-window-kb LD_WINDOW_KB] [--ld-window-cm LD_WINDOW_CM] [--ld-blocks LD_BLOCKS]
[--genmap-Ne GENMAP_NE] [--genmap-sample-size GENMAP_SS] [--shrinkage-cutoff SHRINK_CUTOFF]
Commandline arguments for LD matrix computation
optional arguments:
-h, --help show this help message and exit
--estimator {block,shrinkage,sample,windowed}
The LD estimator (windowed, shrinkage, block, sample)
--bfile BED_FILE The path to a plink BED file
--keep KEEP_FILE A plink-style keep file to select a subset of individuals to compute the LD matrices.
--extract EXTRACT_FILE
A plink-style extract file to select a subset of SNPs to compute the LD matrix for.
--backend {plink,xarray}
The backend software used to compute the Linkage-Disequilibrium between variants.
--temp-dir TEMP_DIR The temporary directory where we store intermediate files.
--output-dir OUTPUT_DIR
The output directory where the Zarr formatted LD matrices will be stored.
--min-maf MIN_MAF The minimum minor allele frequency for variants included in the LD matrix.
--min-mac MIN_MAC The minimum minor allele count for variants included in the LD matrix.
--ld-window LD_WINDOW
Maximum number of neighboring SNPs to consider when computing LD.
--ld-window-kb LD_WINDOW_KB
Maximum distance (in kilobases) between pairs of variants when computing LD.
--ld-window-cm LD_WINDOW_CM
Maximum distance (in centi Morgan) between pairs of variants when computing LD.
--ld-blocks LD_BLOCKS
Path to the file with the LD block boundaries, in LDetect format (e.g. chr start stop, tab-separated)
--genmap-Ne GENMAP_NE
The effective population size for the population from which the genetic map was derived.
--genmap-sample-size GENMAP_SS
The sample size for the dataset used to infer the genetic map.
--shrinkage-cutoff SHRINK_CUTOFF
The cutoff value below which we assume that the correlation between variants is zero.And:
$ magenpy_simulate -h
**********************************************
_ __ ___ __ _ __ _ ___ _ __ _ __ _ _
| '_ ` _ \ / _` |/ _` |/ _ \ '_ \| '_ \| | | |
| | | | | | (_| | (_| | __/ | | | |_) | |_| |
|_| |_| |_|\__,_|\__, |\___|_| |_| .__/ \__, |
|___/ |_| |___/
Modeling and Analysis of Genetics data in python
Version: 0.0.10 | Release date: May 2022
Author: Shadi Zabad, McGill University
**********************************************
< Simulate complex quantitative or case-control traits >
usage: magenpy_simulate [-h] --bed-files BED_FILES [--keep KEEP_FILE] [--extract EXTRACT_FILE] [--backend {plink,xarray}] [--temp-dir TEMP_DIR] --output-file OUTPUT_FILE
[--output-simulated-effects] [--min-maf MIN_MAF] [--min-mac MIN_MAC] --h2 H2 [--mix-prop MIX_PROP] [--var-mult VAR_MULT] [--likelihood {binomial,gaussian}]
[--prevalence PREVALENCE]
Commandline arguments for the complex trait simulator
optional arguments:
-h, --help show this help message and exit
--bed-files BED_FILES
The BED files containing the genotype data. You may use a wildcard here (e.g. "data/chr_*.bed")
--keep KEEP_FILE A plink-style keep file to select a subset of individuals for simulation.
--extract EXTRACT_FILE
A plink-style extract file to select a subset of SNPs for simulation.
--backend {plink,xarray}
The backend software used for the computation.
--temp-dir TEMP_DIR The temporary directory where we store intermediate files.
--output-file OUTPUT_FILE
The path where the simulated phenotype will be stored (no extension needed).
--output-simulated-effects
Output a table with the true simulated effect size for each variant.
--min-maf MIN_MAF The minimum minor allele frequency for variants included in the simulation.
--min-mac MIN_MAC The minimum minor allele count for variants included in the simulation.
--h2 H2 Trait heritability. Ranges between 0. and 1., inclusive.
--mix-prop MIX_PROP, -p MIX_PROP
Mixing proportions for the mixture density (comma separated). For example, for the spike-and-slab mixture density, with the proportion of causal variants set to 0.1,
you can specify: "--mix-prop 0.9,0.1 --var-mult 0,1".
--var-mult VAR_MULT, -d VAR_MULT
Multipliers on the variance for each mixture component.
--likelihood {binomial,gaussian}
The likelihood for the simulated trait. Gaussian (e.g. quantitative) or binomial (e.g. case-control).
--prevalence PREVALENCE
The prevalence of cases (or proportion of positives) for binary traits. Ranges between 0. and 1.You can find examples of how to run the commandline scripts in the examples directory on GitHub.
To request other functionalities to be packaged with magenpy, please contact the developers or
open an Issue on GitHub.
Shadi Zabad, Simon Gravel, Yue Li. Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference. (2022)
@article {
Zabad2022.05.10.491396,
author = {Zabad, Shadi and Gravel, Simon and Li, Yue},
title = {Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference},
elocation-id = {2022.05.10.491396},
year = {2022},
doi = {10.1101/2022.05.10.491396},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2022/05/11/2022.05.10.491396},
journal = {bioRxiv}
}