用户，
我希望你们中的一些人知道我该如何解决以下问题。
我正在尝试做的事情： 我正在尝试安装/使用一款名为螺栓从下载的 *.tar.gz 文件这里如手册中所述（BOLT-LMM 链接）。
问题是什么： 执行 ./bolt 命令时（在解压的 tar 目录中），出现以下错误：
$ ./bolt
-bash: ./bolt: cannot execute binary file
该软件和机器似乎兼容：
$ 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
我感觉它与我的 $PATH 有关（在这方面我绝对不是专家）：
$ 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
也许你们中的一些人能够看出哪里出了问题？或者可能有解决方案？我将非常感激每一条解决这个问题的建议！
谢谢！
祝一切顺利，Birgitte
答案1
实际上，您需要/src在系统上编译该文件夹才能运行可执行文件。您需要先满足几个依赖关系：
- BLAS/LAPACK 数值库。
- Boost C++ 库。
- NLopt 数值优化库
在我看来，与编译 OS X 的 src 相比，在 Docker 交互式会话中运行程序要简单得多。大致有三个步骤：
- 安装适用于 Mac 的 Docker。
- 进入终端：
docker run -it --rm ubuntu - 在此安装 BOLT-LMM。
我测试了一下，它似乎运行良好：
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
答案2
是什么让您认为文件和操作系统应该兼容？您正在使用 Darwin 并尝试执行 Linux 程序。
从 的输出中可以看出file，解释器是/lib64/ld-linux-x86-64.so.2。它可能不存在于您的机器上。即使存在，您也需要额外的动态库。然后还有一个问题，即 Darwin 和 Linux 在系统调用接口级别是否兼容。
答案3
您正尝试在 Mac OS X 操作系统上运行 Linux 二进制文件...我对 Mac OS X 了解不多，但我很确定它无法“开箱即用”...
另一个好的信息来源是运行ldd这个二进制文件......