echolocatoR
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Published
20 hours ago •
RajLabMSSM
RajLabMSSM
MAF cannot be inferred
I just got this running
Note that: MAF is not inferred even though I am passing Freq col, and I am seeting maf to calculate
Code
columnsnames = echodata::construct_colmap(munged= FALSE,
CHR = "CHR", POS = "POS",
SNP = "SNP", P = "P",
Effect = "BETA", StdErr = "SE",
A1 = "A1", A2 = "A2", Freq = "FREQ",
N = "N", MAF = "calculate",)
# Pass the sample size as "N" column
# compute_n will do all what is in the docu f N does not exist
finemap_loci(# GENERAL ARGUMENTS
topSNPs = topSNPs,
results_dir = fullRS_path,
loci = topSNPs$Locus,
dataset_name = "LID_COX",
dataset_type = "GWAS",
force_new_subset = TRUE,
force_new_LD = FALSE,
force_new_finemap = TRUE,
remove_tmps = FALSE,
finemap_methods = c("ABF","FINEMAP","SUSIE", "POLYFUN_SUSIE"),
# Munge full sumstats first
munged = FALSE,
colmap = columnsnames,
# SUMMARY STATS ARGUMENTS
fullSS_path = newSS_name_colmap,
fullSS_genome_build = "hg19",
query_by ="tabix",
#compute_n = 3500,
bp_distance = 10000,#500000*2,
min_MAF = 0.001,
trim_gene_limits = FALSE,
case_control = FALSE,
# FINE-MAPPING ARGUMENTS
## General
n_causal = 5,
credset_thresh = .95,
consensus_thresh = 2,
# LD ARGUMENTS
LD_reference = "1KGphase3",#"UKB",
superpopulation = "EUR",
download_method = "axel",
LD_genome_build = "hg19",
leadSNP_LD_block = FALSE,
#### PLotting args ####
plot_types = c("simple"),
show_plot = TRUE,
zoom = "1x",
tx_biotypes = NULL,
nott_epigenome = FALSE,
nott_show_placseq = FALSE,
nott_binwidth = 200,
nott_bigwig_dir = NULL,
xgr_libnames = NULL,
roadmap = FALSE,
roadmap_query = NULL,
#### General args ####
seed = 2022,
nThread = 20,
verbose = TRUE
)
Output
PolyFun submodule already installed.
┌─────────────────────────────────────────────────┐
│ │
│ )))> 🦇 RP11-240A16.1 [locus 1 / 3] 🦇 <((( │
│ │
└─────────────────────────────────────────────────┘
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 1 ▶▶▶ Query 🔎 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+ Query Method: tabix
Constructing GRanges query using min/max ranges within a single chromosome.
query_dat is already a GRanges object. Returning directly.
========= echotabix::convert =========
Converting full summary stats file to tabix format for fast querying.
Inferred format: 'table'
Explicit format: 'table'
Inferring comment_char from tabular header: 'SNP'
Determining chrom type from file header.
Chromosome format: 1
Detecting column delimiter.
Identified column separator: \t
Sorting rows by coordinates via bash.
Searching for header row with grep.
( grep ^'SNP' .../QC_SNPs_COLMAP.txt; grep
-v ^'SNP' .../QC_SNPs_COLMAP.txt | sort
-k2,2n
-k3,3n ) > .../file2fb2fcecd3b_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_SNPs_COLMAP.txt.bgz
Set force_new=TRUE to override this.
Tabix-indexing file using: Rsamtools
Data successfully converted to bgzip-compressed, tabix-indexed format.
========= echotabix::query =========
query_dat is already a GRanges object. Returning directly.
Inferred format: 'table'
Querying tabular tabix file using: Rsamtools.
Checking query chromosome style is correct.
Chromosome format: 1
Retrieving data.
Converting query results to data.table.
Processing query: 4:32425284-32445284
Adding 'query' column to results.
Retrieved data with 76 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/RP11-240A16.1_LID_COX_subset.tsv.gz
+ Query: 76 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ Preparing N_cases,N_controls cols.
++ Preparing proportion_cases col.
++ proportion_cases not included in data subset.
Preparing sample size column (N).
Using existing 'N' column.
+ Imputing t-statistic from Effect and StdErr.
+ leadSNP missing. Assigning new one by min p-value.
++ Ensuring Effect,StdErr,P are numeric.
++ Ensuring 1 SNP per row and per genomic coordinate.
++ Removing extra whitespace
+ Standardized query: 76 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/RP11-240A16.1_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/LD/RP11-240A16.1.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 76 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
Using existing 'N' column.
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF
SUSIE
✅ All required columns present.
✅ All optional columns present.
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
Using existing 'N' column.
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../RP11-240A16.1 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
------------
FINE-MAPPING (1/1)
------------
- GWAS summary stats : FINEMAP/data.z
- SNP correlations : FINEMAP/data.ld
- Causal SNP stats : FINEMAP/data.snp
- Causal configurations : FINEMAP/data.config
- Credible sets : FINEMAP/data.cred
- Log file : FINEMAP/data.log_sss
- Reading input : done!
- Updated prior SD of effect sizes : 0.05 0.0528 0.0558 0.0589
- Number of GWAS samples : 2687
- Number of SNPs : 76
- Prior-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.584
2 -> 0.292
3 -> 0.096
4 -> 0.0234
5 -> 0.00449
- 1800 configurations evaluated (0.122/100%) : converged after 122 iterations
- Computing causal SNP statistics : done!
- Regional SNP heritability : 0.0276 (SD: 0.00441 ; 95% CI: [0.0196,0.0371])
- Log10-BF of >= one causal SNP : 24.4
- Post-expected # of causal SNPs : 4.74
- Post-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 9.4e-21
2 -> 2.73e-11
3 -> 1.41e-07
4 -> 0.265
5 -> 0.735
- Writing output : done!
- Run time : 0 hours, 0 minutes, 0 seconds
2 data.cred* file(s) found in the same subfolder.
Selected file based on postPr_k: data.cred5
Importing conditional probabilities (.cred file).
No configurations were causal at PP>=0.95.
Importing marginal probabilities (.snp file).
Importing configuration probabilities (.config file).
FINEMAP was unable to identify any credible sets at PP>=0.95.
++ Credible Set SNPs identified = 0
++ Merging FINEMAP results with multi-finemap data.
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
Preparing sample size column (N).
Using existing 'N' column.
+ SUSIE:: sample_size=2,687
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
+ SUSIE:: Using `susie_rss()` from susieR v0.12.27
+ SUSIE:: Extracting Credible Sets.
++ Credible Set SNPs identified = 2
++ Merging SUSIE results with multi-finemap data.
+++ Multi-finemap:: POLYFUN_SUSIE +++
PolyFun submodule already installed.
PolyFun:: Fine-mapping with method=SUSIE
PolyFun:: Using priors from mode=precomputed
Unable to find conda binary. Is Anaconda installed?Locus RP11-240A16.1 complete in: 0.33 min
┌─────────────────────────────────────────┐
│ │
│ )))> 🦇 XYLT1 [locus 2 / 3] 🦇 <((( │
│ │
└─────────────────────────────────────────┘
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 1 ▶▶▶ Query 🔎 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+ Query Method: tabix
Constructing GRanges query using min/max ranges within a single chromosome.
query_dat is already a GRanges object. Returning directly.
========= echotabix::convert =========
Converting full summary stats file to tabix format for fast querying.
Inferred format: 'table'
Explicit format: 'table'
Inferring comment_char from tabular header: 'SNP'
Determining chrom type from file header.
Chromosome format: 1
Detecting column delimiter.
Identified column separator: \t
Sorting rows by coordinates via bash.
Searching for header row with grep.
( grep ^'SNP' .../QC_SNPs_COLMAP.txt; grep
-v ^'SNP' .../QC_SNPs_COLMAP.txt | sort
-k2,2n
-k3,3n ) > .../file2fb33669f7f_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_SNPs_COLMAP.txt.bgz
Set force_new=TRUE to override this.
Tabix-indexing file using: Rsamtools
Data successfully converted to bgzip-compressed, tabix-indexed format.
========= echotabix::query =========
query_dat is already a GRanges object. Returning directly.
Inferred format: 'table'
Querying tabular tabix file using: Rsamtools.
Checking query chromosome style is correct.
Chromosome format: 1
Retrieving data.
Converting query results to data.table.
Processing query: 16:17034975-17054975
Adding 'query' column to results.
Retrieved data with 80 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/XYLT1_LID_COX_subset.tsv.gz
+ Query: 80 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ Preparing N_cases,N_controls cols.
++ Preparing proportion_cases col.
++ proportion_cases not included in data subset.
Preparing sample size column (N).
Using existing 'N' column.
+ Imputing t-statistic from Effect and StdErr.
+ leadSNP missing. Assigning new one by min p-value.
++ Ensuring Effect,StdErr,P are numeric.
++ Ensuring 1 SNP per row and per genomic coordinate.
++ Removing extra whitespace
+ Standardized query: 80 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/XYLT1_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/LD/XYLT1.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 78 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 78 SNPs.
+ dat = 78 SNPs.
+ 78 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
Using existing 'N' column.
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF
SUSIE
✅ All required columns present.
✅ All optional columns present.
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
Using existing 'N' column.
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 78 SNPs.
+ dat = 78 SNPs.
+ 78 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../XYLT1 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
------------
FINE-MAPPING (1/1)
------------
- GWAS summary stats : FINEMAP/data.z
- SNP correlations : FINEMAP/data.ld
- Causal SNP stats : FINEMAP/data.snp
- Causal configurations : FINEMAP/data.config
- Credible sets : FINEMAP/data.cred
- Log file : FINEMAP/data.log_sss
- Reading input : done!
- Updated prior SD of effect sizes : 0.05 0.0522 0.0545 0.0568
- Number of GWAS samples : 2687
- Number of SNPs : 78
- Prior-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.584
2 -> 0.292
3 -> 0.0961
4 -> 0.0234
5 -> 0.0045
- 1077 configurations evaluated (0.198/100%) : converged after 198 iterations
- Computing causal SNP statistics : done!
- Regional SNP heritability : 0.0119 (SD: 0.00385 ; 95% CI: [0.00536,0.0204])
- Log10-BF of >= one causal SNP : 4.46
- Post-expected # of causal SNPs : 1.96
- Post-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.245
2 -> 0.548
3 -> 0.204
4 -> 0.00238
5 -> 0
- Writing output : done!
- Run time : 0 hours, 0 minutes, 0 seconds
3 data.cred* file(s) found in the same subfolder.
Selected file based on postPr_k: data.cred2
Importing conditional probabilities (.cred file).
No configurations were causal at PP>=0.95.
Importing marginal probabilities (.snp file).
Importing configuration probabilities (.config file).
FINEMAP was unable to identify any credible sets at PP>=0.95.
++ Credible Set SNPs identified = 0
++ Merging FINEMAP results with multi-finemap data.
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
In addition: Warning messages:
1: In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
2: In susie_suff_stat(XtX = XtX, Xty = Xty, n = n, yty = (n - 1) * :
IBSS algorithm did not converge in 100 iterations!
Please check consistency between summary statistics and LD matrix.
See https://stephenslab.github.io/susieR/articles/susierss_diagnostic.html
Preparing sample size column (N).
Using existing 'N' column.
+ SUSIE:: sample_size=2,687
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 78 SNPs.
+ dat = 78 SNPs.
+ 78 SNPs in common.
Converting obj to sparseMatrix.
+ SUSIE:: Using `susie_rss()` from susieR v0.12.27
+ SUSIE:: Extracting Credible Sets.
++ Credible Set SNPs identified = 1
++ Merging SUSIE results with multi-finemap data.
+++ Multi-finemap:: POLYFUN_SUSIE +++
PolyFun submodule already installed.
PolyFun:: Fine-mapping with method=SUSIE
PolyFun:: Using priors from mode=precomputed
Unable to find conda binary. Is Anaconda installed?Locus XYLT1 complete in: 0.32 min
┌────────────────────────────────────────┐
│ │
│ )))> 🦇 LRP8 [locus 3 / 3] 🦇 <((( │
│ │
└────────────────────────────────────────┘
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 1 ▶▶▶ Query 🔎 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+ Query Method: tabix
Constructing GRanges query using min/max ranges within a single chromosome.
query_dat is already a GRanges object. Returning directly.
========= echotabix::convert =========
Converting full summary stats file to tabix format for fast querying.
Inferred format: 'table'
Explicit format: 'table'
Inferring comment_char from tabular header: 'SNP'
Determining chrom type from file header.
Chromosome format: 1
Detecting column delimiter.
Identified column separator: \t
Sorting rows by coordinates via bash.
Searching for header row with grep.
( grep ^'SNP' .../QC_SNPs_COLMAP.txt; grep
-v ^'SNP' .../QC_SNPs_COLMAP.txt | sort
-k2,2n
-k3,3n ) > .../file2fb4113b218_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_SNPs_COLMAP.txt.bgz
Set force_new=TRUE to override this.
Tabix-indexing file using: Rsamtools
Data successfully converted to bgzip-compressed, tabix-indexed format.
========= echotabix::query =========
query_dat is already a GRanges object. Returning directly.
Inferred format: 'table'
Querying tabular tabix file using: Rsamtools.
Checking query chromosome style is correct.
Chromosome format: 1
Retrieving data.
Converting query results to data.table.
Processing query: 1:53768300-53788300
Adding 'query' column to results.
Retrieved data with 52 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LRP8_LID_COX_subset.tsv.gz
+ Query: 52 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ Preparing N_cases,N_controls cols.
++ Preparing proportion_cases col.
++ proportion_cases not included in data subset.
Preparing sample size column (N).
Using existing 'N' column.
+ Imputing t-statistic from Effect and StdErr.
+ leadSNP missing. Assigning new one by min p-value.
++ Ensuring Effect,StdErr,P are numeric.
++ Ensuring 1 SNP per row and per genomic coordinate.
++ Removing extra whitespace
+ Standardized query: 52 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LRP8_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LD/LRP8.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 51 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
Using existing 'N' column.
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF
SUSIE
✅ All required columns present.
✅ All optional columns present.
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
Using existing 'N' column.
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../LRP8 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
------------
FINE-MAPPING (1/1)
------------
- GWAS summary stats : FINEMAP/data.z
- SNP correlations : FINEMAP/data.ld
- Causal SNP stats : FINEMAP/data.snp
- Causal configurations : FINEMAP/data.config
- Credible sets : FINEMAP/data.cred
- Log file : FINEMAP/data.log_sss
- Reading input : done!
- Updated prior SD of effect sizes : 0.05 0.0517 0.0535 0.0554
- Number of GWAS samples : 2687
- Number of SNPs : 51
- Prior-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.585
2 -> 0.292
3 -> 0.0955
4 -> 0.0229
5 -> 0.00431
- 1081 configurations evaluated (0.123/100%) : converged after 123 iterations
- Computing causal SNP statistics : done!
- Regional SNP heritability : 0.0259 (SD: 0.00368 ; 95% CI: [0.0188,0.0334])
- Log10-BF of >= one causal SNP : 24.9
- Post-expected # of causal SNPs : 5
- Post-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 5.84e-22
2 -> 1.71e-17
3 -> 1.74e-11
4 -> 4.56e-06
5 -> 1
- Writing output : done!
- Run time : 0 hours, 0 minutes, 0 seconds
1 data.cred* file(s) found in the same subfolder.
Selected file based on postPr_k: data.cred5
Importing conditional probabilities (.cred file).
No configurations were causal at PP>=0.95.
Importing marginal probabilities (.snp file).
Importing configuration probabilities (.config file).
FINEMAP was unable to identify any credible sets at PP>=0.95.
++ Credible Set SNPs identified = 0
++ Merging FINEMAP results with multi-finemap data.
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
In addition: Warning message:
In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
Preparing sample size column (N).
Using existing 'N' column.
+ SUSIE:: sample_size=2,687
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
+ SUSIE:: Using `susie_rss()` from susieR v0.12.27
+ SUSIE:: Extracting Credible Sets.
++ Credible Set SNPs identified = 3
++ Merging SUSIE results with multi-finemap data.
+++ Multi-finemap:: POLYFUN_SUSIE +++
PolyFun submodule already installed.
PolyFun:: Fine-mapping with method=SUSIE
PolyFun:: Using priors from mode=precomputed
Unable to find conda binary. Is Anaconda installed?Locus LRP8 complete in: 0.33 min
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 6 ▶▶▶ Postprocess data 🎁 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Returning results as nested list.
All loci done in: 0.97 min
$`RP11-240A16.1`
NULL
$XYLT1
NULL
$LRP8
NULL
$merged_dat
Null data.table (0 rows and 0 cols)
Warning message:
In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
Session Info
> sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.22 SNPlocs.Hsapiens.dbSNP144.GRCh37_0.99.20 BSgenome_1.65.2
[4] rtracklayer_1.57.0 Biostrings_2.65.3 XVector_0.37.1
[7] GenomicRanges_1.49.1 GenomeInfoDb_1.33.5 IRanges_2.31.2
[10] S4Vectors_0.35.3 BiocGenerics_0.43.1 forcats_0.5.2
[13] stringr_1.4.1 dplyr_1.0.10 purrr_0.3.4
[16] readr_2.1.2 tidyr_1.2.0 tibble_3.1.8
[19] ggplot2_3.3.6 tidyverse_1.3.2 data.table_1.14.2
[22] echolocatoR_2.0.1
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.26 R.utils_2.12.0 tidyselect_1.1.2 RSQLite_2.2.16
[6] AnnotationDbi_1.59.1 htmlwidgets_1.5.4 grid_4.2.0 BiocParallel_1.31.12 XGR_1.1.8
[11] munsell_0.5.0 codetools_0.2-18 interp_1.1-3 DT_0.24 withr_2.5.0
[16] colorspace_2.0-3 OrganismDbi_1.39.1 Biobase_2.57.1 filelock_1.0.2 knitr_1.40
[21] supraHex_1.35.0 rstudioapi_0.14 DescTools_0.99.46 MatrixGenerics_1.9.1 GenomeInfoDbData_1.2.8
[26] mixsqp_0.3-43 bit64_4.0.5 echoconda_0.99.7 basilisk_1.9.2 vctrs_0.4.1
[31] generics_0.1.3 xfun_0.32 biovizBase_1.45.0 BiocFileCache_2.5.0 R6_2.5.1
[36] AnnotationFilter_1.21.0 bitops_1.0-7 cachem_1.0.6 reshape_0.8.9 DelayedArray_0.23.1
[41] assertthat_0.2.1 BiocIO_1.7.1 scales_1.2.1 googlesheets4_1.0.1 nnet_7.3-17
[46] rootSolve_1.8.2.3 gtable_0.3.1 lmom_2.9 ggbio_1.45.0 ensembldb_2.21.4
[51] rlang_1.0.5 MungeSumstats_1.5.13 echodata_0.99.14 splines_4.2.0 lazyeval_0.2.2
[56] gargle_1.2.0 dichromat_2.0-0.1 hexbin_1.28.2 broom_1.0.1 checkmate_2.1.0
[61] modelr_0.1.9 BiocManager_1.30.18 yaml_2.3.5 reshape2_1.4.4 snpStats_1.47.1
[66] backports_1.4.1 GenomicFeatures_1.49.6 ggnetwork_0.5.10 Hmisc_4.7-1 RBGL_1.73.0
[71] tools_4.2.0 echoplot_0.99.5 ellipsis_0.3.2 catalogueR_1.0.0 RColorBrewer_1.1-3
[76] proxy_0.4-27 coloc_5.1.0 Rcpp_1.0.9 plyr_1.8.7 base64enc_0.1-3
[81] progress_1.2.2 zlibbioc_1.43.0 RCurl_1.98-1.8 basilisk.utils_1.9.2 prettyunits_1.1.1
[86] rpart_4.1.16 deldir_1.0-6 viridis_0.6.2 haven_2.5.1 cluster_2.1.3
[91] SummarizedExperiment_1.27.2 ggrepel_0.9.1 fs_1.5.2 crul_1.2.0 magrittr_2.0.3
[96] echotabix_0.99.8 dnet_1.1.7 openxlsx_4.2.5 reprex_2.0.2 googledrive_2.0.0
[101] mvtnorm_1.1-3 ProtGenerics_1.29.0 matrixStats_0.62.0 hms_1.1.2 patchwork_1.1.2
[106] XML_3.99-0.10 jpeg_0.1-9 readxl_1.4.1 gridExtra_2.3 compiler_4.2.0
[111] biomaRt_2.53.2 crayon_1.5.1 R.oo_1.25.0 htmltools_0.5.3 echoannot_0.99.7
[116] tzdb_0.3.0 Formula_1.2-4 expm_0.999-6 Exact_3.1 lubridate_1.8.0
[121] DBI_1.1.3 dbplyr_2.2.1 MASS_7.3-58.1 rappdirs_0.3.3 boot_1.3-28
[126] Matrix_1.4-1 piggyback_0.1.3 cli_3.3.0 R.methodsS3_1.8.2 echofinemap_0.99.3
[131] parallel_4.2.0 igraph_1.3.4 pkgconfig_2.0.3 GenomicAlignments_1.33.1 dir.expiry_1.5.0
[136] RCircos_1.2.2 foreign_0.8-82 osfr_0.2.8 xml2_1.3.3 rvest_1.0.3
[141] echoLD_0.99.7 VariantAnnotation_1.43.3 digest_0.6.29 graph_1.75.0 httpcode_0.3.0
[146] cellranger_1.1.0 htmlTable_2.4.1 gld_2.6.5 restfulr_0.0.15 curl_4.3.2
[151] Rsamtools_2.13.4 rjson_0.2.21 lifecycle_1.0.1 nlme_3.1-159 jsonlite_1.8.0
[156] viridisLite_0.4.1 fansi_1.0.3 downloadR_0.99.4 pillar_1.8.1 susieR_0.12.27
[161] lattice_0.20-45 GGally_2.1.2 googleAuthR_2.0.0 KEGGREST_1.37.3 fastmap_1.1.0
[166] httr_1.4.4 survival_3.3-1 glue_1.6.2 zip_2.2.0 png_0.1-7
[171] bit_4.0.4 Rgraphviz_2.41.1 class_7.3-20 stringi_1.7.8 blob_1.2.3
[176] latticeExtra_0.6-30 memoise_2.0.1 irlba_2.3.5 e1071_1.7-11 ape_5.6-2
