ABF fails to run for a qt trait

[AMCalejandro]

I just got this running

Note that:

• ABF does not get to if proportion_cases is missinng
• polyfun_susie is missing the environment.

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")
                                          #N_cases = NULL, N_controls = NULL,
                                          #proportion_cases = NULL,
                                          #MAF = "calculate",
                                          #tstat = NULL)


# 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     

Originally posted by @AMCalejandro in RajLabMSSM/echolocatoR#114 (comment)

[bschilder]

ABF does not get to if proportion_cases is missing

• I've just fixed this by passing the arg case_control to only require the "proportion_cases" col when the trait is binary. I originally assumed all GWAS were binary, which is obviously not the case.

polyfun_susie is missing the environment

• This actually seems to be a broader issue of echoconda not being able to find the conda executable: Unable to find conda binary. Is Anaconda installed?. Perhaps relates to similar issues on GitHub Actions (see discussion here).