Summary statistics for this section:¶

Trait number	Trait	Filename
1	Subjective well-being (SWB)	`1_OA2016_hm3samp_SWB.txt`
2	Neuroticism (NEUR)	`1_OA2016_hm3samp_NEUR.txt`

Publicly-available sumstats from:

Okbay et al. "Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses." Nature Genetics 48, 624–633 (2016).

Sample (~10%) of Hapmap 3 SNPs
Found in the data folder

A quick primer on Linux¶

We will only go over the minimum detail needed to follow along in the workshop. There are many good resources for working with Unix available online.

Simple commands¶

cd [path]: change directory to [path] (can be relative or absolute)
ls [path]: list the files and folder in [path]
head -n [N] [file]: view the first [N] lines of [file]. tail lets you view the last [N] lines.
python [path] [-options]: run [path] as a Python script with some options specified. This is how we run MTAG.

Running Bash/Shell scripts¶

You can write a sequence of commands (and comments) in a .bash or .sh script
bash [file.bash] executes these commands

This is the easiest way to run the MTAG commands in this tutorial

A quick primer on Linux¶

Helpful shortcuts¶

Press the "UP" key to automatically re-type past commands
Use "CTRL+C" to kill processes in the foreground
Auto-completion: the TAB key is your friend!
"." refers to the current directory
".." refers to the parent of the current directory

Listing options (`-h`)¶

General format: python [PATH_OF_MTAG_CODE] -h
For, example in the mtag folder:

For convenience, most commands that are shown can be run by calling `bash` on the parenthetical script name.¶

The slide will display the contents inside each script.¶

Viewing MTAG options¶

Check that you are in the `code` directory by typing `pwd`¶

Then, type:¶

bash 1.1_access_help.bash

`bash 1.1_access_help.bash`¶

Here are the contents of `1.1_access_help.bash`¶

In [1]:

# 1.1_access_help.bash
!python ../mtag/mtag.py -h

usage: mtag.py [-h] [--sumstats [{File1},{File2}...]]
               [--gencov_path FILE_PATH] [--residcov_path FILE_PATH]
               [--out DIR/PREFIX] [--make_full_path] [--snp_name SNP_NAME]
               [--z_name Z_NAME] [--n_name N_NAME] [--eaf_name EAF_NAME]
               [--chr_name CHR_NAME] [--bpos_name BPOS_NAME]
               [--a1_name A1_NAME] [--a2_name A2_NAME]
               [--include SNPLIST1,SNPLIST2,..]
               [--exclude SNPLIST1,SNPLIST2,..] [--only_chr CHR_A,CHR_B,..]
               [--homogNs_frac FRAC] [--homogNs_dist D] [--maf_min MAF_MIN]
               [--n_min N_MIN] [--n_max N_MAX] [--info_min INFO_MIN]
               [--drop_ambig_snps] [--no_allele_flipping] [--analytic_omega]
               [--no_overlap] [--perfect_gencov] [--equal_h2] [--fdr]
               [--grid_file GRID_FILE] [--fit_ss] [--intervals INTERVALS]
               [--cores CORES] [--p_sig P_SIG] [--n_approx]
               [--ld_ref_panel FOLDER_PATH] [--time_limit TIME_LIMIT]
               [--std_betas] [--tol TOL] [--numerical_omega] [--verbose]
               [--chunksize CHUNKSIZE] [--stream_stdout]

**mtag: Multitrait Analysis of GWAS** This program is the implementation of
MTAG method described by Turley et. al. Requires the input of a comma-
separated list of GWAS summary statistics with identical columns. It is
recommended to pass the column names manually to the program using the options
below. The implementation of MTAG makes use of the LD Score Regression (ldsc)
for cleaning the data and estimating residual variance-covariance matrix, so
the input must also be compatible ./munge_sumstats.py command in the ldsc
distribution included with mtag. The default estimation method for the genetic
covariance matrix Omega is GMM (as described in the paper). Note below: any
list of passed to the options below must be comma-separated without
whitespace.

optional arguments:
  -h, --help            show this help message and exit

Input Files:
  Input files to be used by MTAG. The --sumstats option is required, while
  using the other two options take priority of their corresponding
  estimation routines, if used.

  --sumstats [{File1},{File2}...]
                        Specify the list of summary statistics files to
                        perform multitrait analysis. Multiple files paths must
                        be separated by ",". Please read the documentation to
                        find the up-to-date set of acceptable file formats. A
                        general guideline is that any files you pass into MTAG
                        should also be parsable by ldsc and you should take
                        the additional step of specifying the names of the
                        main columns below to avoid reading errors.
  --gencov_path FILE_PATH
                        If specified, will read in the genetic covariance
                        matrix saved in the file path below and skip the
                        estimation routine. The rows and columns of the matrix
                        must correspond to the order of the GWAS input files
                        specified. FIles can either be in whitespace-delimited
                        .txt or .npy format. Use with caution as the genetic
                        covariance matrix specified will be weakly nonoptimal.
  --residcov_path FILE_PATH
                        If specified, will read in the residual covariance
                        matrix saved in the file path below and skip the
                        estimation routine. The rows and columns of the matrix
                        must correspond to the order of the GWAS input files
                        specified. FIles can either be in .txt or .npy format.
                        Use with caution as the genetic covariance matrix
                        specified will be weakly nonoptimal. File must either
                        be in whitespace-delimited .txt or .npy

Output formatting:
  Set the output directory and common name of prefix files.

  --out DIR/PREFIX      Specify the directory and name prefix to output MTAG
                        results. All mtag results will be prefixed with the
                        corresponding tag. Default is ./mtag_results
  --make_full_path      option to make output path specified in -out if it
                        does not exist.

Column names of input files:
  These options manually pass the names of the relevant summary statistics
  columns used by MTAG. It is recommended to pass these names because only
  narrow searches for these columns are performed in the default cases.
  Moreover, it is necessary that these input files be readable by ldsc's
  munge_sumstats command.

  --snp_name SNP_NAME   Name of the single column that provides the unique
                        identifier for SNPs in the GWAS summary statistics
                        across all GWAS results. Default is "snpid". This the
                        index that will be used to merge the GWAS summary
                        statistics. Any SNP lists passed to ---include or
                        --exclude should also contain the same name.
  --z_name Z_NAME       The common name of the column of Z scores across all
                        input files. Default is to search for columns
                        beginning with the lowercase letter z.
  --n_name N_NAME       the common name of the column of sample sizes in the
                        GWAS summary statistics files. Default is to search
                        for columns beginning with the lowercase letter n.
  --eaf_name EAF_NAME   The common name of the column of minor allele
                        frequencies (MAF) in the GWAS input files. The default
                        is "freq".
  --chr_name CHR_NAME   Name of the column containing the chromosome of each
                        SNP in the GWAS input. Default is "chr".
  --bpos_name BPOS_NAME
                        Name of the column containing the base pair of each
                        SNP in the GWAS input. Default is "bpos".
  --a1_name A1_NAME     Name of the column containing the effect allele of
                        each SNP in the GWAS input. Default is "a1".
  --a2_name A2_NAME     Name of the column containing the non-effect allele of
                        each SNP in the GWAS input. Default is "a2".

Filter Options:
  The input summary statistics files can be filtered using the options
  below. Note that there is some default filtering according to sample size
  and allele frequency, following the recommendations we make in the
  corresponding paper. All of these column-based options allow a list of
  values to be passed of the same length as the number of traits

  --include SNPLIST1,SNPLIST2,..
                        Restricts MTAG analysis to the union of snps in the
                        list of snplists provided. The header line must match
                        the SNP index that will be used to merge the GWAS
                        input files.
  --exclude SNPLIST1,SNPLIST2,.., --excludeSNPs SNPLIST1,SNPLIST2,..
                        Similar to the --include option, except that the union
                        of SNPs found in the specified files will be excluded
                        from MTAG. Both -exclude and -include may be
                        simultaneously specified, but -exclude will take
                        precedent (i.e., SNPs found in both the -include and
                        -exclude SNP lists will be excluded).
  --only_chr CHR_A,CHR_B,..
                        Restrict MTAG to SNPs on one of the listed, comma-
                        separated chromosome. Can be specified simultaneously
                        with --include and --exclude, but will take precedent
                        over both. Not generally recommended. Multiple
                        chromosome numbers should be separated by commas
                        without whitespace. If this option is specified, the
                        GWAS summary statistics must also list the chromosome
                        of each SNPs in a column named \`chr\`.
  --homogNs_frac FRAC   Restricts to SNPs within FRAC of the mode of sample
                        sizes for the SNPs as given by (N-Mode)/Mode < FRAC.
                        This filter is not applied by default.
  --homogNs_dist D      Restricts to SNPs within DIST (in sample size) of the
                        mode of sample sizes for the SNPs. This filter is not
                        applied by default.
  --maf_min MAF_MIN     set the threshold below SNPs with low minor allele
                        frequencies will be dropped. Default is 0.01. Set to 0
                        to skip MAF filtering.
  --n_min N_MIN         set the minimum threshold for SNP sample size in input
                        data. Default is 2/3*(90th percentile). Any SNP that
                        does not pass this threshold for all of the GWAS input
                        statistics will not be included in MTAG.
  --n_max N_MAX         set the maximum threshold for SNP sample size in input
                        data. Not used by default. Any SNP that does not pass
                        this threshold for any of the GWAS input statistics
                        will not be included in MTAG.
  --info_min INFO_MIN   Minimim info score for filtering SNPs for MTAG.
  --drop_ambig_snps     Drop strand ambiguous SNPs when performing MTAG (they
                        are already not used to estimate Omega or Sigma.
  --no_allele_flipping  Prevents flipping the effect sizes of summary
                        statistics when the effect and non-effect alleles are
                        reversed (reletive the first summary statistics file.

Special Cases:
  These options deal with notable special cases of MTAG that yield
  improvements in runtime. However, they should be used with caution as they
  will yield non-optimal results if the assumptions implicit in each option
  are violated.

  --analytic_omega      Option to turn off the numerical estimation of the
                        genetic VCV matrix in the presence of constant sample
                        size within each GWAS, for which a closed-form
                        solution exists. The default is to typically use the
                        closed form solution as the starting point for the
                        numerical solution to the maximum-likelihood genetic
                        VCV, Use with caution! If any input GWAS does not have
                        constant sample size, then the analytic solution
                        employed here will not be a maximizer of the
                        likelihood function.
  --no_overlap          Imposes the assumption that there is no sample overlap
                        between the input GWAS summary statistics. MTAG is
                        performed with the off-diagonal terms on the residual
                        covariance matrix set to 0.
  --perfect_gencov      Imposes the assumption that all traits used are
                        perfectly genetically correlated with each other. The
                        off-diagonal terms of the genetic covariance matrix
                        are set to the square root of the product of the
                        heritabilities
  --equal_h2            Imposes the assumption that all traits passed to MTAG
                        have equal heritability. The diagonal terms of the
                        genetic covariance matrix are set equal to each other.
                        Can only be used in conjunction with --perfect_gencov

Max FDR calculation:
  These options are used for the calculation of an upper bound on the false
  disovery under the model described in Supplementary Note 1.1.4 of Turley
  et al. (2017). Note that there is one of three ways to define the space of
  grid points over which the upper bound is searched.

  --fdr                 Perform max FDR calculations
  --grid_file GRID_FILE
                        Pre-set list of grid points. Users can define a list
                        of grid points over which the search is conducted. The
                        list of grid points should be passed in text file as a
                        white-space delimited matrix of dimnesions, G x S,
                        where G is the number of grid points and S = 2^T is
                        the number of possible causal states for SNPs. States
                        are ordered according to a tree-like recursive
                        structure from right to left. For example, for 3
                        traits, with the triple TFT denoting the state for
                        which SNPs are causal for State 1, not causal for
                        state 2, and causal for state 3, then the column
                        ordering of probabilities should be: FFF FFT FTF FTT
                        TFF TFT TTF TTT There should be no headers, or row
                        names in the file. Any rows for which (i) the
                        probabilities do not sum to 1, the prior of a SNP
                        being is causal is 0 for any of the traits, and (iii)
                        the resulting genetic correlation matrix is non
                        positive definite will excluded in the search.
  --fit_ss              This estimates the prior probability that a SNP is
                        null for each trait and then proceeds to restrict the
                        grid search to the set of probability vectors that sum
                        to the prior null for each trait. This is useful for
                        restrict the search space of larger-dimensional
                        traits.
  --intervals INTERVALS
                        Number of intervals that you would like to partition
                        the [0,1] interval. For example example, with two
                        traits and --intervals set 10, then maxFDR will
                        calculated over the set of feasible points in {0.,
                        0.1, 0.2,..,0.9,1.0}^2.
  --cores CORES         Number of threads/cores use to compute the FDR grid
                        points for each trait.
  --p_sig P_SIG         P-value threshold used for statistical signifiance.
                        Default is p=5.0e-8 (genome-wide significance).
  --n_approx            Speed up FDR calculation by replacing the sample size
                        of a SNP for each trait by the mean across SNPs (for
                        each trait). Recommended.

Winner's curse adjustment:
  Options related to the winner's curse adjustment of estimates of effect
  sizes from MTAG that could be used when replicating analyses.

Miscellaneous:
  --ld_ref_panel FOLDER_PATH
                        Specify folder of the ld reference panel (split by
                        chromosome) that will be used in the estimation of the
                        error VCV (sigma). This option is passed to --ref-ld-
                        chr and --w-ld-chr when running LD score regression.
                        The default is to use the reference panel of LD scores
                        computed from 1000 Genomes European subjects
                        (eur_w_ld_chr) that is included with the distribution
                        of MTAG
  --time_limit TIME_LIMIT
                        Set time limit (hours) on the numerical estimation of
                        the variance covariance matrix for MTAG, after which
                        the optimization routine will complete its current
                        iteration and perform MTAG using the last iteration of
                        the genetic VCV.
  --std_betas           Results files will have standardized effect sizes,
                        i.e., the weights 1/sqrt(2*MAF*(1-MAF)) are not
                        applied when outputting MTAG results, where MAF is the
                        minor allele frequency.
  --tol TOL             Set the relative (x) tolerance when numerically
                        estimating the genetic variance-covariance matrix. Not
                        recommended to change unless you are facing strong
                        runtime constraints for a large number of traits.
  --numerical_omega     Option to use the MLE estimator of the genetic VCV
                        matrix, implemented through a numerical routine.
  --verbose             When used, will include output from running ldsc
                        scripts as well additional information (such as
                        optimization routine information.
  --chunksize CHUNKSIZE
                        Chunksize for reading in data.
  --stream_stdout       Will streat mtag processing on console in addition to
                        writing to log file.

A. The two options that must always be specified¶

--sumstats [File1],[File2],..
- Comma-separated list of summary statistics files to apply MTAG. Currently, files must be whitespace-delimited and must either have the default column names or column names that are provided through options (see below).
- Should also make sure that files can be read by ldsc (included in the mtag folder)
--out [output_folder][prefix]
- The path provided consists of a folder [output_folder] (e.g., ../output/ or ./)
- The [prefix] (e.g., igss_mtag_tutorial_) is prepended to the files produced by MTAG

B. Sumstats file formatting¶

The column names must be consistent across all sumstats files.
- At present mtag does not "guess" the column names (will hopefully do that soon).
- Column order does not matter.
- All files must be whitespace (tab) delimited

The workshop files provided contain the "default" column names for MTAG

`bash 1.2_view_sumstats.bash`¶

In [2]:

# 1.2 view sample sumstats files (for format)
!head ../data/1_OA2016_hm3samp_NEUR.txt # 1.2

snpid	chr	bpos	a1	a2	freq	z	pval	n
rs2736372	8	11106041	T	C	0.4179	-7.71614161262	1.209e-14	111111.111111
rs2060465	8	11162609	T	C	0.6194	7.69444599845	1.422e-14	62500.0
rs10096421	8	10831868	T	G	0.4646	-7.561098219	3.989e-14	111111.111111
rs2409722	8	11039816	T	G	0.4627	-7.38261601808	1.553e-13	111111.111111
rs11991118	8	10939273	T	G	0.5056	7.32202915636	2.443e-13	111111.111111
rs2736371	8	11105529	A	G	0.3806	-7.32009158327	2.478e-13	62500.0
rs2736313	8	11086942	T	C	0.4646	-7.24228035161	4.411e-13	111111.111111
rs876954	8	8310923	A	G	0.4813	-7.15791677597	8.191e-13	62500.0
rs1533059	8	8684953	A	G	0.4478	-7.07412856424	1.504e-12	62500.0

All of the columns shown above are necessary for MTAG to run

In [12]:

!tail -5 ../data/1_OA2016_hm3samp_NEUR.txt

rs165119	18	75317995	T	C	0.1101	0.0	1.0	27777.7777778
rs1916337	3	165252023	T	C	0.2444	0.0	1.0	62500.0
rs4878187	9	38227021	T	C	0.8713	0.0	1.0	40000.0
rs8080313	17	35952983	A	G	0.8396	0.0	1.0	40000.0
rs851997	6	151985550	A	G	0.3787	0.0	1.0	62500.0

In [3]:

!sed -n '103995,104000p' ../data/1_OA2016_hm3samp_NEUR.txt

rs322382	5	172198952	T	C	0.3041	-0.648595291061	0.5166	62500.0
rs3795732	1	156564640	A	G	0.7108	-0.648595291061	0.5166	62500.0
rs4111166	9	26524416	T	C	0.4739	0.648595291061	0.5166	111111.111111
rs4491677	2	128789204	A	G	0.2276	-0.648595291061	0.5166	62500.0
rs622272	17	14400878	T	G	0.5	-0.648595291061	0.5166	111111.111111
rs842938	2	201404694	T	C	0.459	0.648595291061	0.5166	111111.111111

Running MTAG with the defaults¶

Using mtag with the default options implements the following steps:

Read in each summary statistics file in the list and "munges" (filters/clean) them, in particular MTAG:
- Only keeps SNPs with Minor Allele Frequency (MAF) $\geq 0.01$
- Only keeps SNPs with sample size $N \geq \frac{2}{3} F^{-1}_N(90)$ (greater than two-thirds of the 90th percentile)
Merge the filtered GWAS summary statistics results together, taking the intersection of available SNPs (accounting for lists of SNPs to include and/or exclude).
Estimate the residual covariance matrix, $\Sigma_{LD}$ , using the intercept of LD Score regression (ldsc, longest step).
Estimate the genetic covariance matrix, $\Omega$ .
Calculate MTAG-adjusted statistics and output results.

Running MTAG with the defaults¶

Call the mtag.py script via python

Note: we use the --stream_stdout so that messages from MTAG are printed to the console in addition to the log file.

`bash 1.3_mtag_default.bash`¶

In [5]:

# 1.3_mtag_default.bash  
!python ../mtag/mtag.py  \
        --sumstats ../data/1_OA2016_hm3samp_NEUR.txt,../data/1_OA2016_hm3samp_SWB.txt \
        --out ../output/1_basics/1.3_mtag_default_NS \
        --stream_stdout

<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<>
<> MTAG: Multitrait Analysis of GWAS 
<> Version: 1.0.3
<> (C) 2017 Omeed Maghzian, Raymond Walters, and Patrick Turley
<> Harvard University Department of Economics / Broad Institute of MIT and Harvard
<> GNU General Public License v3
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<> Note:  It is recommended to run your own QC on the input before using this program. 
<> Software-related correspondence: maghzian@nber.org 
<> All other correspondence: paturley@broadinstitute.org 
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>


Calling ./mtag.py \
--stream-stdout  \
--sumstats ../data/1_OA2016_hm3samp_NEUR.txt,../data/1_OA2016_hm3samp_SWB.txt \
--out ../output/1_basics/1.3_mtag_default_NS 

Beginning MTAG analysis...
Read in Trait 1 summary statistics (185458 SNPs) from ../data/1_OA2016_hm3samp_NEUR.txt ...
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Munging Trait 1  <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Interpreting column names as follows:
snpid:	Variant ID (e.g., rs number)
n:	Sample size
a1:	Allele 1, interpreted as ref allele for signed sumstat.
pval:	p-Value
a2:	Allele 2, interpreted as non-ref allele for signed sumstat.
freq:	Allele frequency
z:	Directional summary statistic as specified by --signed-sumstats.

Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time.
. done
Read 185458 SNPs from --sumstats file.
Removed 0 SNPs with missing values.
Removed 0 SNPs with INFO <= None.
Removed 0 SNPs with MAF <= 0.01.
Removed 0 SNPs with out-of-bounds p-values.
Removed 0 variants that were not SNPs. Note: strand ambiguous SNPs were not dropped.
185458 SNPs remain.
Removed 0 SNPs with duplicated rs numbers (185458 SNPs remain).
Removed 162680 SNPs with N < 74074.074074 (22778 SNPs remain).
Median value of SIGNED_SUMSTATS was 0.0, which seems sensible.
Dropping snps with null values

Metadata:
Mean chi^2 = 1.467
Lambda GC = 1.366
Max chi^2 = 59.523
20 Genome-wide significant SNPs (some may have been removed by filtering).

Conversion finished at Thu Oct 19 02:03:44 2017
Total time elapsed: 0.7s
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Munging of Trait 1 complete. SNPs remaining:	 22778
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Read in Trait 2 summary statistics (167911 SNPs) from ../data/1_OA2016_hm3samp_SWB.txt ...
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Munging Trait 2  <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Interpreting column names as follows:
snpid:	Variant ID (e.g., rs number)
n:	Sample size
a1:	Allele 1, interpreted as ref allele for signed sumstat.
pval:	p-Value
a2:	Allele 2, interpreted as non-ref allele for signed sumstat.
freq:	Allele frequency
z:	Directional summary statistic as specified by --signed-sumstats.

Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time.
. done
Read 167911 SNPs from --sumstats file.
Removed 0 SNPs with missing values.
Removed 0 SNPs with INFO <= None.
Removed 0 SNPs with MAF <= 0.01.
Removed 0 SNPs with out-of-bounds p-values.
Removed 0 variants that were not SNPs. Note: strand ambiguous SNPs were not dropped.
167911 SNPs remain.
Removed 0 SNPs with duplicated rs numbers (167911 SNPs remain).
Removed 102537 SNPs with N < 74074.074074 (65374 SNPs remain).
Median value of SIGNED_SUMSTATS was 0.0, which seems sensible.
Dropping snps with null values

Metadata:
Mean chi^2 = 1.208
Lambda GC = 1.188
Max chi^2 = 29.353
1 Genome-wide significant SNPs (some may have been removed by filtering).

Conversion finished at Thu Oct 19 02:03:46 2017
Total time elapsed: 1.21s
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Munging of Trait 2 complete. SNPs remaining:	 65374
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Trait 2 summary statistics: 	 20321 SNPs remaining merging with previous traits.
... Merge of GWAS summary statistics complete. Number of SNPs:	 20321
Using 20321 SNPs to estimate Omega (0 SNPs excluded due to strand ambiguity)
Estimating sigma..
Checking for positive definiteness ..
Sigma hat:
[[ 1.043 -0.129]
 [-0.129  0.966]]
Beginning estimation of Omega ...
Using GMM estimator of Omega ..
Checking for positive definiteness ..
Completed estimation of Omega ...
Beginning MTAG calculations...
 ... Completed MTAG calculations.
Writing Phenotype 1 to file ...
Writing Phenotype 2 to file ...

Summary of MTAG results:
------------------------
  Trait                 N (max)  N (mean)  # SNPs used  GWAS mean chi^2  MTAG mean chi^2  GWAS equiv. (max) N
1  ...hm3samp_NEUR.txt  111111   111111    20321        1.407            1.488            133309             
2  ..._hm3samp_SWB.txt  111111   111111    20321        1.269            1.408            168490             

Estimated Omega:
[[  3.817e-06  -2.199e-06]
 [ -2.199e-06   2.336e-06]]

Estimated Sigma:
[[ 1.043 -0.129]
 [-0.129  0.966]]

 
MTAG results saved to file.
MTAG complete. Time elapsed: 27.0710499287s

snpid	a1	a2	z	mtag_beta	mtag_se	mtag_z	mtag_pval
rs2736372	T	C	-7.71614161262	-0.0312843727759	0.00401008053375	-7.80143254297	6.12083232605e-15
rs10096421	T	G	-7.561098219	-0.0292358562051	0.0039656035404	-7.37235982046	1.67633824925e-13
rs2409722	T	G	-7.38261601808	-0.0314168522186	0.00396670505601	-7.92013819404	2.37246749e-15
rs11991118	T	G	7.32202915636	0.0295263257704	0.00395590010421	7.46387041953	8.40172307848e-14
rs2736313	T	C	-7.24228035161	-0.0296988957677	0.0039656035404	-7.48912377779	6.93349296434e-14

snpid	a1	a2	z	mtag_beta	mtag_se	mtag_z	mtag_pval
rs2736372	T	C	-7.71614161262	-0.0312843727759	0.00401008053375	-7.80143254297	6.12083232605e-15
rs10096421	T	G	-7.561098219	-0.0292358562051	0.0039656035404	-7.37235982046	1.67633824925e-13
rs2409722	T	G	-7.38261601808	-0.0314168522186	0.00396670505601	-7.92013819404	2.37246749e-15
rs11991118	T	G	7.32202915636	0.0295263257704	0.00395590010421	7.46387041953	8.40172307848e-14
rs2736313	T	C	-7.24228035161	-0.0296988957677	0.0039656035404	-7.48912377779	6.93349296434e-14

Column	option	Default
SNP ID	`--snp_name`	`snpid`
Chromosome	`--chr_name`	`chr`
Base-pair position	`--bpos_name`	`bpos`
Z-score	`--z_name`	`z`*
sample size	`--n_name`	`n`
expected allele frequency	`--eaf_name`	`freq`
Allele 1 (effect allele)	`--a1_name`	`a1`
Allele 2 (other allele)	`--a2_name`	`a2`

Analysis	$\Omega$	Genetic correlation
Regular	`[6.492e-06 4.815e-06] [4.815e-06 3.970e-06]`	`[1.000 0.949] [0.949 1.000]`
No overlap	`[6.492e-06 5.026e-06] [5.026e-06 3.970e-06]`	`[1.000 0.990] [0.990 1.000]`
Perfect genetic correlation	`[6.492e-06 5.077e-06] [5.077e-06 3.970e-06]`	`[1.000 1.000] [1.000 1.000]`

MTAG¶

Python Command Line Tool¶

Omeed Maghzian¶

Patrick Turley¶