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 :                            |
| - finemap@christianbenner.com        |
| - matti.pirinen@helsinki.fi          |
|--------------------------------------|

--------
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 :                            |
| - finemap@christianbenner.com        |
| - matti.pirinen@helsinki.fi          |
|--------------------------------------|

--------
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 :                            |
| - finemap@christianbenner.com        |
| - matti.pirinen@helsinki.fi          |
|--------------------------------------|

--------
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 https://github.com/RajLabMSSM/echolocatoR/issues/114#issuecomment-1252447380

[bschilder]

ABF does not get to if proportion_cases is missing

• [x] 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).