1

users,

I hope some of you have an idea on how I can fix the following issue.

What I am trying to do: I am trying to install/use a software called BOLT-LMM from a *.tar.gz file downloaded here as described in the manual (the BOLT-LMM link).

What is the issue: When executing the ./bolt command (in the extracted tar directory), I get the following error:

$ ./bolt
-bash: ./bolt: cannot execute binary file

The software and machine seems to be compatible:

$ uname -a
Darwin ***-************.local 18.2.0 Darwin Kernel Version 18.2.0: Mon Nov 12 20:24:46 PST 2018; root:xnu-4903.231.4~2/RELEASE_X86_64 x86_64  

$ file ./bolt
./bolt: ELF 64-bit LSB executable, x86-64, version 1 (GNU/Linux), dynamically linked, interpreter /lib64/ld-linux-x86-64.so.2, for GNU/Linux 2.6.32, BuildID[sha1]=93d69585dd693546b12df2b859882a6ec6eaf571, with debug_info, not stripped

I have a feeling that it has something to do with my $PATH (I am absolutely no expert when it comes to this):

$ echo $PATH
/Users/birni/bin:/Users/birni/anaconda3/bin:/Users/birni/anaconda3/bin:/Users/birni/miniconda3/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/opt/X11/bin

Maybe some of you are able to see what is wrong? Or may have a solution? I will be grateful for every advice in fixing this issue!

Thanks!

Best wishes, Birgitte

2

You actually need to compile the /src folder on your System in order to run the executable. There are several dependencies that you need to fulfill first:

  • BLAS/LAPACK numerical libraries.
  • Boost C++ libraries.
  • NLopt numerical optimization library

In my opinion, rather than compiling the src for OS X, it is far simpler to run the program within a Docker interactive session. There are roughly three steps:

  1. Install Docker for Mac.
  2. Enter into the Terminal: docker run -it --rm ubuntu
  3. Install BOLT-LMM for here.

I tested it, and it seems to work fine:

root@817555a92572:/usr/local/BOLT-LMM_v2.3.2# cd example
root@817555a92572:/usr/local/BOLT-LMM_v2.3.2/example# ./run_example.sh

                      +-----------------------------+
                      |                       ___   |
                      |   BOLT-LMM, v2.3.2   /_ /   |
                      |   March 10, 2018      /_/   |
                      |   Po-Ru Loh            //   |
                      |                        /    |
                      +-----------------------------+

Copyright (C) 2014-2018 Harvard University.
Distributed under the GNU GPLv3 open source license.

Compiled with USE_SSE: fast aligned memory access
Compiled with USE_MKL: Intel Math Kernel Library linear algebra
Boost version: 1_58

Command line options:

../bolt \
    --bfile=EUR_subset \
    --remove=EUR_subset.remove \
    --exclude=EUR_subset.exclude \
    --exclude=EUR_subset.exclude2 \
    --phenoFile=EUR_subset.pheno2.covars \
    --phenoCol=PHENO \
    --covarFile=EUR_subset.pheno2.covars \
    --covarCol=CAT_COV \
    --qCovarCol=QCOV{1:2} \
    --modelSnps=EUR_subset.modelSnps \
    --lmm \
    --LDscoresFile=../tables/LDSCORE.1000G_EUR.tab.gz \
    --numThreads=2 \
    --statsFile=example.stats \
    --dosageFile=EUR_subset.dosage.chr17first100 \
    --dosageFile=EUR_subset.dosage.chr22last100.gz \
    --dosageFidIidFile=EUR_subset.dosage.indivs \
    --statsFileDosageSnps=example.dosageSnps.stats \
    --impute2FileList=EUR_subset.impute2FileList.txt \
    --impute2FidIidFile=EUR_subset.impute2.indivs \
    --statsFileImpute2Snps=example.impute2Snps.stats \
    --dosage2FileList=EUR_subset.dosage2FileList.txt \
    --statsFileDosage2Snps=example.dosage2Snps.stats 

Verifying contents of --dosage2FileList: EUR_subset.dosage2FileList.txt
Checking map file EUR_subset.dosage2.chr17first100.map and 2-dosage genotype file EUR_subset.dosage2.chr17first100.gz
Checking map file EUR_subset.dosage2.chr17second100.map and 2-dosage genotype file EUR_subset.dosage2.chr17second100
Checking map file EUR_subset.dosage2.chr22last100.map and 2-dosage genotype file EUR_subset.dosage2.chr22last100.gz

Setting number of threads to 2
fam: EUR_subset.fam
bim(s): EUR_subset.bim
bed(s): EUR_subset.bed

=== Reading genotype data ===

Total indivs in PLINK data: Nbed = 379
Reading remove file (indivs to remove): EUR_subset.remove
Removed 6 individual(s)
Total indivs stored in memory: N = 373
Reading bim file #1: EUR_subset.bim
    Read 54051 snps
Total snps in PLINK data: Mbed = 54051
Reading exclude file (SNPs to exclude): EUR_subset.exclude
Excluded 5405 SNP(s)
Reading exclude file (SNPs to exclude): EUR_subset.exclude2
Excluded 43171 SNP(s)
Reading list of SNPs to include in model (i.e., GRM): EUR_subset.modelSnps
WARNING: SNP has been excluded: rs1882989
WARNING: SNP has been excluded: rs112221137
WARNING: SNP has been excluded: rs35840960
WARNING: SNP has been excluded: rs62057022
WARNING: SNP has been excluded: rs1882990
Included 2431 SNP(s) in model in 1 variance component(s)
WARNING: 24594 SNP(s) had been excluded

Breakdown of SNP pre-filtering results:
  2431 SNPs to include in model (i.e., GRM)
  3044 additional non-GRM SNPs loaded
  48576 excluded SNPs
Allocating 2431 x 376/4 bytes to store genotypes
Reading genotypes and performing QC filtering on snps and indivs...
Reading bed file #1: EUR_subset.bed
    Expecting 5134845 (+3) bytes for 379 indivs, 54051 snps
Total indivs after QC: 373
Total post-QC SNPs: M = 2431
  Variance component 1: 2431 post-QC SNPs (name: 'modelSnps')
Time for SnpData setup = 0.353741 sec

=== Reading phenotype and covariate data ===

Read data for 373 indivs (ignored 0 without genotypes) from:
  EUR_subset.pheno2.covars
Read data for 373 indivs (ignored 0 without genotypes) from:
  EUR_subset.pheno2.covars
Number of indivs with no missing phenotype(s) to use: 369
NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
WARNING: 3 of 369 samples passing previous QC have missing covariates
  --covarUseMissingIndic is not set, so these samples will be removed
Number of individuals used in analysis: Nused = 366
Singular values of covariate matrix:
    S[0] = 39.4151
    S[1] = 13.5249
    S[2] = 6.56744
    S[3] = 4.65936
    S[4] = 6.61483e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           362.015344
Dimension of all-1s proj space (Nused-1): 365
Time for covariate data setup + Bolt initialization = 0.022151 sec

Phenotype 1:   N = 366   mean = 0.00450586   std = 1.0273

=== Computing linear regression (LINREG) stats ===

Time for computing LINREG stats = 0.00499105 sec

=== Estimating variance parameters ===

Using CGtol of 0.005 for this step
Using default number of random trials: 15 (for Nused = 366)

Estimating MC scaling f_REML at log(delta) = 1.09865, h2 = 0.25...
  Batch-solving 16 systems of equations using conjugate gradient iteration
  iter 1:  time=0.00  rNorms/orig: (0.1,0.1)  res2s: 767.193..199.099
  iter 2:  time=0.01  rNorms/orig: (0.01,0.03)  res2s: 791.087..208.371
  iter 3:  time=0.01  rNorms/orig: (0.002,0.004)  res2s: 791.958..209.121
  Converged at iter 3: rNorms/orig all < CGtol=0.005
  Time breakdown: dgemm = 43.1%, memory/overhead = 56.9%
  MCscaling: logDelta = 1.10, h2 = 0.250, f = 0.0583786

Estimating MC scaling f_REML at log(delta) = 4.23869e-05, h2 = 0.5...
  Batch-solving 16 systems of equations using conjugate gradient iteration
  iter 1:  time=0.01  rNorms/orig: (0.2,0.3)  res2s: 157.403..82.5002
  iter 2:  time=0.01  rNorms/orig: (0.04,0.1)  res2s: 176.427..94.685
  iter 3:  time=0.01  rNorms/orig: (0.01,0.02)  res2s: 178.429..97.6069
  iter 4:  time=0.00  rNorms/orig: (0.004,0.005)  res2s: 178.791..97.8407
  Converged at iter 4: rNorms/orig all < CGtol=0.005
  Time breakdown: dgemm = 30.1%, memory/overhead = 69.9%
  MCscaling: logDelta = 0.00, h2 = 0.500, f = 0.00362986

Estimating MC scaling f_REML at log(delta) = -0.0727959, h2 = 0.518202...
  Batch-solving 16 systems of equations using conjugate gradient iteration
  iter 1:  time=0.00  rNorms/orig: (0.2,0.3)  res2s: 140.004..76.2204
  iter 2:  time=0.00  rNorms/orig: (0.04,0.1)  res2s: 158.154..88.1446
  iter 3:  time=0.01  rNorms/orig: (0.01,0.03)  res2s: 160.162..91.1652
  iter 4:  time=0.01  rNorms/orig: (0.004,0.006)  res2s: 160.548..91.4234
  iter 5:  time=0.00  rNorms/orig: (0.0008,0.001)  res2s: 160.575..91.4401
  Converged at iter 5: rNorms/orig all < CGtol=0.005
  Time breakdown: dgemm = 30.4%, memory/overhead = 69.6%
  MCscaling: logDelta = -0.07, h2 = 0.518, f = -0.000114364

Secant iteration for h2 estimation converged in 1 steps
Estimated (pseudo-)heritability: h2g = 0.518
To more precisely estimate variance parameters and estimate s.e., use --reml
Variance params: sigma^2_K = 0.539611, logDelta = -0.072796, f = -0.000114364

Time for fitting variance components = 0.105714 sec

=== Computing mixed model assoc stats (inf. model) ===

Selected 30 SNPs for computation of prospective stat
Tried 30; threw out 0 with GRAMMAR chisq > 5
Assigning SNPs to 6 chunks for leave-out analysis
Each chunk is excluded when testing SNPs belonging to the chunk
  Batch-solving 36 systems of equations using conjugate gradient iteration
  iter 1:  time=0.01  rNorms/orig: (0.2,0.3)  res2s: 77.2766..87.3902
  iter 2:  time=0.01  rNorms/orig: (0.05,0.1)  res2s: 91.4012..100.112
  iter 3:  time=0.01  rNorms/orig: (0.01,0.03)  res2s: 94.9553..101.227
  iter 4:  time=0.01  rNorms/orig: (0.003,0.008)  res2s: 95.3511..101.387
  iter 5:  time=0.01  rNorms/orig: (0.0008,0.002)  res2s: 95.3793..101.413
  iter 6:  time=0.01  rNorms/orig: (0.0003,0.0004)  res2s: 95.381..101.415
  Converged at iter 6: rNorms/orig all < CGtol=0.0005
  Time breakdown: dgemm = 47.8%, memory/overhead = 52.2%

AvgPro: 1.016   AvgRetro: 0.998   Calibration: 1.018 (0.008)   (30 SNPs)
Ratio of medians: 1.020   Median of ratios: 1.015

Time for computing infinitesimal model assoc stats = 0.060806 sec

=== Estimating chip LD Scores using 400 indivs ===

WARNING: Only 373 indivs available; using all
Reducing sample size to 368 for memory alignment

Time for estimating chip LD Scores = 0.0121329 sec

=== Reading LD Scores for calibration of Bayesian assoc stats ===

Looking up LD Scores...
  Looking for column header 'SNP': column number = 1
  Looking for column header 'LDSCORE': column number = 5
Found LD Scores for 2431/2431 SNPs

Estimating inflation of LINREG chisq stats using MLMe as reference...
Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01
# of SNPs passing filters before outlier removal: 2427/2431
Masking windows around outlier snps (chisq > 20.0)
# of SNPs remaining after outlier window removal: 2409/2427
Intercept of LD Score regression for ref stats:   1.042 (0.048)
Estimated attenuation: 0.428 (0.415)
Intercept of LD Score regression for cur stats: 1.094 (0.048)
Calibration factor (ref/cur) to multiply by:      0.952 (0.018)
LINREG intercept inflation = 1.05058

=== Estimating mixture parameters by cross-validation ===

Setting maximum number of iterations to 250 for this step
Max CV folds to compute = 5 (to have > 10000 samples)

====> Starting CV fold 1 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 292
Singular values of covariate matrix:
    S[0] = 35.2135
    S[1] = 12.0776
    S[2] = 5.84295
    S[3] = 4.11065
    S[4] = 1.02073e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           288.024349
Dimension of all-1s proj space (Nused-1): 291
  Beginning variational Bayes
  iter 1:  time=0.01 for 18 active reps
  iter 2:  time=0.01 for 18 active reps  approxLL diffs: (14.01,24.97)
  iter 3:  time=0.01 for 18 active reps  approxLL diffs: (0.54,2.37)
  iter 4:  time=0.01 for 18 active reps  approxLL diffs: (0.08,0.82)
  iter 5:  time=0.01 for 18 active reps  approxLL diffs: (0.01,0.62)
  iter 6:  time=0.01 for 11 active reps  approxLL diffs: (0.00,0.71)
  iter 7:  time=0.01 for  7 active reps  approxLL diffs: (0.00,0.59)
  iter 8:  time=0.00 for  6 active reps  approxLL diffs: (0.00,0.30)
  iter 9:  time=0.00 for  4 active reps  approxLL diffs: (0.01,0.17)
  iter 10:  time=0.00 for  3 active reps  approxLL diffs: (0.00,0.09)
  iter 11:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.04)
  iter 12:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.02)
  iter 13:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  iter 14:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 14: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 23.5%, memory/overhead = 76.5%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00770092 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.3, p=0.01: 0.126476
 f2=0.5, p=0.01: 0.115832
 f2=0.3, p=0.02: 0.114885
            ...
 f2=0.1, p=0.01: 0.061449

====> End CV fold 1: 18 remaining param pair(s) <====

Estimated proportion of variance explained using inf model: 0.066
Relative improvement in prediction MSE using non-inf model: 0.064

====> Starting CV fold 2 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.5041
    S[1] = 12.0959
    S[2] = 5.91229
    S[3] = 4.11948
    S[4] = 2.68583e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           289.038063
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.02 for 18 active reps
  Converged at iter 23: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 26.9%, memory/overhead = 73.1%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00608587 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.3, p=0.01: 0.110938
 f2=0.3, p=0.02: 0.099200
 f2=0.5, p=0.01: 0.094056
            ...
 f2=0.1, p=0.01: 0.033146

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 1.01771
  Absolute prediction MSE using standard LMM:         0.996793
  Absolute prediction MSE, fold-best f2=0.3, p=0.01:  0.920624
    Absolute pred MSE using   f2=0.5, p=0.5: 0.996793

====> End CV fold 2: 3 remaining param pair(s) <====

====> Starting CV fold 3 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.1358
    S[1] = 12.1017
    S[2] = 5.88329
    S[3] = 4.16419
    S[4] = 4.06329e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           288.977885
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.00 for  3 active reps
  iter 2:  time=0.00 for  3 active reps  approxLL diffs: (16.59,19.92)
  Converged at iter 10: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 21.7%, memory/overhead = 78.3%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00236201 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.5, p=0.01: 0.090904
 f2=0.3, p=0.01: 0.065602
 f2=0.1, p=0.02: 0.049509

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 1.13673
  Absolute prediction MSE, fold-best f2=0.5, p=0.01:  1.04056
    Absolute pred MSE using  f2=0.5, p=0.01: 1.040557
    Absolute pred MSE using  f2=0.3, p=0.01: 1.165222
    Absolute pred MSE using  f2=0.1, p=0.02: 1.168803

====> End CV fold 3: 3 remaining param pair(s) <====

====> Starting CV fold 4 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.366
    S[1] = 12.1033
    S[2] = 5.89805
    S[3] = 4.20734
    S[4] = 2.03806e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           289.016478
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.01 for  3 active reps
  iter 2:  time=0.00 for  3 active reps  approxLL diffs: (19.58,23.11)
  Converged at iter 31: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 23.5%, memory/overhead = 76.5%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00351691 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.5, p=0.01: 0.087902
 f2=0.3, p=0.01: 0.050466
 f2=0.1, p=0.02: 0.023887

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 0.941491
  Absolute prediction MSE, fold-best f2=0.5, p=0.01:  0.867212
    Absolute pred MSE using  f2=0.5, p=0.01: 0.867212
    Absolute pred MSE using  f2=0.3, p=0.01: 0.936730
    Absolute pred MSE using  f2=0.1, p=0.02: 0.991367

====> End CV fold 4: 3 remaining param pair(s) <====

====> Starting CV fold 5 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.0554
    S[1] = 12.1063
    S[2] = 5.808
    S[3] = 4.21359
    S[4] = 1.41518e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           288.978200
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.01 for  3 active reps
  iter 2:  time=0.01 for  3 active reps  approxLL diffs: (25.07,26.60)
  iter 3:  time=0.01 for  3 active reps  approxLL diffs: (3.20,5.69)
  Converged at iter 9: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 27.0%, memory/overhead = 73.0%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00459003 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.5, p=0.01: 0.056417
 f2=0.3, p=0.01: 0.014181
 f2=0.1, p=0.02: -0.003485

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 0.99199
  Absolute prediction MSE, fold-best f2=0.5, p=0.01:  1.06096
    Absolute pred MSE using  f2=0.5, p=0.01: 1.060956
    Absolute pred MSE using  f2=0.3, p=0.01: 1.121899
    Absolute pred MSE using  f2=0.1, p=0.02: 1.104061

====> End CV fold 5: 3 remaining param pair(s) <====

Optimal mixture parameters according to CV: f2 = 0.5, p = 0.01

Time for estimating mixture parameters = 20.4558 sec

=== Computing Bayesian mixed model assoc stats with mixture prior ===

Assigning SNPs to 6 chunks for leave-out analysis
Each chunk is excluded when testing SNPs belonging to the chunk
  Beginning variational Bayes
  iter 1:  time=0.01 for  6 active reps
  iter 2:  time=0.01 for  6 active reps  approxLL diffs: (22.70,28.54)
  iter 3:  time=0.01 for  6 active reps  approxLL diffs: (1.57,2.82)
  iter 4:  time=0.01 for  6 active reps  approxLL diffs: (0.18,0.58)
  iter 5:  time=0.01 for  6 active reps  approxLL diffs: (0.01,0.18)
  iter 6:  time=0.01 for  5 active reps  approxLL diffs: (0.02,0.06)
  iter 7:  time=0.01 for  5 active reps  approxLL diffs: (0.00,0.05)
  iter 8:  time=0.00 for  1 active reps  approxLL diffs: (0.06,0.06)
  iter 9:  time=0.00 for  1 active reps  approxLL diffs: (0.07,0.07)
  iter 10:  time=0.00 for  1 active reps  approxLL diffs: (0.07,0.07)
  iter 11:  time=0.00 for  1 active reps  approxLL diffs: (0.05,0.05)
  iter 12:  time=0.00 for  1 active reps  approxLL diffs: (0.02,0.02)
  iter 13:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 13: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 27.7%, memory/overhead = 72.3%
Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01
# of SNPs passing filters before outlier removal: 2427/2431
Masking windows around outlier snps (chisq > 20.0)
# of SNPs remaining after outlier window removal: 2409/2427
Intercept of LD Score regression for ref stats:   1.042 (0.048)
Estimated attenuation: 0.428 (0.415)
Intercept of LD Score regression for cur stats: 1.038 (0.044)
Calibration factor (ref/cur) to multiply by:      1.003 (0.015)

Time for computing Bayesian mixed model assoc stats = 0.0926819 sec

Calibration stats: mean and lambdaGC (over SNPs used in GRM)
  (note that both should be >1 because of polygenicity)
Mean BOLT_LMM_INF: 1.09877 (2431 good SNPs)   lambdaGC: 1.10376
Mean BOLT_LMM: 1.0957 (2431 good SNPs)   lambdaGC: 1.06946

=== Streaming genotypes to compute and write assoc stats at all SNPs ===

Time for streaming genotypes and writing output = 0.190873 sec


=== Streaming genotypes to compute and write assoc stats at dosage SNPs ===

Time for streaming dosage genotypes and writing output = 0.0288632 sec


=== Streaming genotypes to compute and write assoc stats at IMPUTE2 SNPs ===

Read 379 indivs; using 373 in filtered PLINK data

Time for streaming IMPUTE2 genotypes and writing output = 0.0464768 sec


=== Streaming genotypes to compute and write assoc stats at dosage2 SNPs ===

Time for streaming dosage2 genotypes and writing output = 0.064405 sec

Total elapsed time for analysis = 21.4401 sec
  • Thank you! I completely overlooked the compiling part! This works! – Biogitte Jan 3 at 12:52
1

What makes you think the file and the OS should be compatible? You are using Darwin and trying to execute a Linux program.

As you can see from the output of file, the interpreter is /lib64/ld-linux-x86-64.so.2. It probably is not present on your machine. Even if it were present, you would need additional dynamic libraries. And then there is the question whether Darwin and Linux are compatible on the system call interface level.

0

You're trying to run a Linux binary on a Mac OS X operating system... I don't know much about Mac OS X but i'm pretty sure it can not work "out of the box" ...

Another good informations source would be to run ldd on this binary ...

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