Summary
Summary {data-width=650}
Manhattan plot
manhattan_plot
QQ plot
qq_plot
AF plot
af_plot
P-Z plot
pz_plot
beta_std plot
beta_std_plot
Metadata
{
"fileformat": "VCFv4.2",
"FILTER": "<ID=PASS,Description=\"All filters passed\">",
"FORMAT": "<ID=AF,Number=A,Type=Float,Description=\"Alternate allele frequency in the association study\">",
"FORMAT.1": "<ID=ES,Number=A,Type=Float,Description=\"Effect size estimate relative to the alternative allele\">",
"FORMAT.2": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
"FORMAT.3": "<ID=ID,Number=1,Type=String,Description=\"Study variant identifier\">",
"FORMAT.4": "<ID=LP,Number=A,Type=Float,Description=\"-log10 p-value for effect estimate\">",
"FORMAT.5": "<ID=NC,Number=A,Type=Float,Description=\"Number of cases used to estimate genetic effect\">",
"FORMAT.6": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
"FORMAT.7": "<ID=SI,Number=A,Type=Float,Description=\"Accuracy score of summary data imputation\">",
"FORMAT.8": "<ID=SS,Number=A,Type=Float,Description=\"Sample size used to estimate genetic effect\">",
"INFO": "<ID=AF,Number=A,Type=Float,Description=\"Allele Frequency\">",
"INFO.1": "<ID=ReverseComplementedAlleles,Number=0,Type=Flag,Description=\"The REF and the ALT alleles have been reverse complemented in liftover since the mapping from the previous reference to the current one was on the negative strand.\">",
"INFO.2": "<ID=SwappedAlleles,Number=0,Type=Flag,Description=\"The REF and the ALT alleles have been swapped in liftover due to changes in the reference. It is possible that not all INFO annotations reflect this swap, and in the genotypes, only the GT, PL, and AD fields have been modified. You should check the TAGS_TO_REVERSE parameter that was used during the LiftOver to be sure.\">",
"META": "<ID=HarmonisedVariants,Number=1,Type=Integer,Description=\"Total number of harmonised variants\">",
"META.1": "<ID=StudyType,Number=1,Type=String,Description=\"Type of GWAS study [Continuous or CaseControl]\">",
"META.2": "<ID=SwitchedAlleles,Number=1,Type=Integer,Description=\"Total number of variants strand switched\">",
"META.3": "<ID=TotalCases,Number=1,Type=Integer,Description=\"Total number of cases in the association study\">",
"META.4": "<ID=TotalControls,Number=1,Type=Integer,Description=\"Total number of controls in the association study\">",
"META.5": "<ID=TotalVariants,Number=1,Type=Integer,Description=\"Total number of variants in input\">",
"META.6": "<ID=VariantsNotHarmonised,Number=1,Type=Integer,Description=\"Total number of variants that could not be harmonised\">",
"META.7": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
"SAMPLE": "<ID=EBI-a-GCST005811,TotalVariants=15870475,VariantsNotRead=0,HarmonisedVariants=15870473,VariantsNotHarmonised=2,SwitchedAlleles=2,TotalControls=50898.0,TotalCases=12658.0,StudyType=CaseControl>",
"contig": "<ID=1,length=249250621>",
"contig.1": "<ID=2,length=243199373>",
"contig.2": "<ID=3,length=198022430>",
"contig.3": "<ID=4,length=191154276>",
"contig.4": "<ID=5,length=180915260>",
"contig.5": "<ID=6,length=171115067>",
"contig.6": "<ID=7,length=159138663>",
"contig.7": "<ID=8,length=146364022>",
"contig.8": "<ID=9,length=141213431>",
"contig.9": "<ID=10,length=135534747>",
"contig.10": "<ID=11,length=135006516>",
"contig.11": "<ID=12,length=133851895>",
"contig.12": "<ID=13,length=115169878>",
"contig.13": "<ID=14,length=107349540>",
"contig.14": "<ID=15,length=102531392>",
"contig.15": "<ID=16,length=90354753>",
"contig.16": "<ID=17,length=81195210>",
"contig.17": "<ID=18,length=78077248>",
"contig.18": "<ID=19,length=59128983>",
"contig.19": "<ID=20,length=63025520>",
"contig.20": "<ID=21,length=48129895>",
"contig.21": "<ID=22,length=51304566>",
"contig.22": "<ID=X,length=155270560>",
"contig.23": "<ID=Y,length=59373566>",
"contig.24": "<ID=MT,length=16569>",
"contig.25": "<ID=GL000207.1,length=4262>",
"contig.26": "<ID=GL000226.1,length=15008>",
"contig.27": "<ID=GL000229.1,length=19913>",
"contig.28": "<ID=GL000231.1,length=27386>",
"contig.29": "<ID=GL000210.1,length=27682>",
"contig.30": "<ID=GL000239.1,length=33824>",
"contig.31": "<ID=GL000235.1,length=34474>",
"contig.32": "<ID=GL000201.1,length=36148>",
"contig.33": "<ID=GL000247.1,length=36422>",
"contig.34": "<ID=GL000245.1,length=36651>",
"contig.35": "<ID=GL000197.1,length=37175>",
"contig.36": "<ID=GL000203.1,length=37498>",
"contig.37": "<ID=GL000246.1,length=38154>",
"contig.38": "<ID=GL000249.1,length=38502>",
"contig.39": "<ID=GL000196.1,length=38914>",
"contig.40": "<ID=GL000248.1,length=39786>",
"contig.41": "<ID=GL000244.1,length=39929>",
"contig.42": "<ID=GL000238.1,length=39939>",
"contig.43": "<ID=GL000202.1,length=40103>",
"contig.44": "<ID=GL000234.1,length=40531>",
"contig.45": "<ID=GL000232.1,length=40652>",
"contig.46": "<ID=GL000206.1,length=41001>",
"contig.47": "<ID=GL000240.1,length=41933>",
"contig.48": "<ID=GL000236.1,length=41934>",
"contig.49": "<ID=GL000241.1,length=42152>",
"contig.50": "<ID=GL000243.1,length=43341>",
"contig.51": "<ID=GL000242.1,length=43523>",
"contig.52": "<ID=GL000230.1,length=43691>",
"contig.53": "<ID=GL000237.1,length=45867>",
"contig.54": "<ID=GL000233.1,length=45941>",
"contig.55": "<ID=GL000204.1,length=81310>",
"contig.56": "<ID=GL000198.1,length=90085>",
"contig.57": "<ID=GL000208.1,length=92689>",
"contig.58": "<ID=GL000191.1,length=106433>",
"contig.59": "<ID=GL000227.1,length=128374>",
"contig.60": "<ID=GL000228.1,length=129120>",
"contig.61": "<ID=GL000214.1,length=137718>",
"contig.62": "<ID=GL000221.1,length=155397>",
"contig.63": "<ID=GL000209.1,length=159169>",
"contig.64": "<ID=GL000218.1,length=161147>",
"contig.65": "<ID=GL000220.1,length=161802>",
"contig.66": "<ID=GL000213.1,length=164239>",
"contig.67": "<ID=GL000211.1,length=166566>",
"contig.68": "<ID=GL000199.1,length=169874>",
"contig.69": "<ID=GL000217.1,length=172149>",
"contig.70": "<ID=GL000216.1,length=172294>",
"contig.71": "<ID=GL000215.1,length=172545>",
"contig.72": "<ID=GL000205.1,length=174588>",
"contig.73": "<ID=GL000219.1,length=179198>",
"contig.74": "<ID=GL000224.1,length=179693>",
"contig.75": "<ID=GL000223.1,length=180455>",
"contig.76": "<ID=GL000195.1,length=182896>",
"contig.77": "<ID=GL000212.1,length=186858>",
"contig.78": "<ID=GL000222.1,length=186861>",
"contig.79": "<ID=GL000200.1,length=187035>",
"contig.80": "<ID=GL000193.1,length=189789>",
"contig.81": "<ID=GL000194.1,length=191469>",
"contig.82": "<ID=GL000225.1,length=211173>",
"contig.83": "<ID=GL000192.1,length=547496>",
"file_date": "2019-10-27T07:29:16.739493",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005811/EBI-a-GCST005811_data.json --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/hg38/hg38.fa; 1.1.1",
"reference": "file:/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/b37/human_g1k_v37.fasta",
"bcftools_annotateVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
"bcftools_annotateCommand": "annotate -a /mnt/storage/home/gh13047/mr-eve/vcf-reference-datasets/dbsnp/dbsnp.v153.b37.vcf.gz -c ID -o /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005811/EBI-a-GCST005811.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005811/EBI-a-GCST005811_data.vcf.gz; Date=Sun Oct 27 07:55:45 2019",
"bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST005811/ebi-a-GCST005811.vcf.gz; Date=Sun May 10 08:03:31 2020"
}
LDSC
*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call:
./ldsc.py \
--h2 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005811/EBI-a-GCST005811.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005811/ldsc.txt \
--snplist /data/ref/snplist.gz \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Sun Oct 27 08:21:31 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005811/EBI-a-GCST005811.vcf.gz ...
and extracting SNPs specified in /data/ref/snplist.gz ...
Traceback (most recent call last):
File "./ldsc/ldsc.py", line 647, in <module>
sumstats.estimate_h2(args, log)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
args, log, args.h2)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 246, in _read_ld_sumstats
sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 165, in _read_sumstats
sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna, slh=args.snplist)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 85, in sumstats
x = read_vcf(fh, alleles, slh)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 161, in read_vcf
with gzip.open(slh) as f:
File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 34, in open
return GzipFile(filename, mode, compresslevel)
File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 94, in __init__
fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
IOError: [Errno 2] No such file or directory: '/data/ref/snplist.gz'
Analysis finished at Sun Oct 27 08:23:18 2019
Total time elapsed: 1.0m:46.75sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9559,
"inflation_factor": 1.0237,
"mean_EFFECT": 0.0004,
"n": "-Inf",
"n_snps": 15870473,
"n_clumped_hits": 1,
"n_p_sig": 2,
"n_mono": 0,
"n_ns": 1407138,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 503542,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NaN",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": "NA",
"ldsc_nsnp_merge_regression_ld": "NA",
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": "NA",
"ldsc_intercept_se": "NA",
"ldsc_lambda_gc": "NA",
"ldsc_mean_chisq": "NA",
"ldsc_ratio": "NA"
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| n_p_sig | FALSE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | TRUE |
| ldsc_intercept_beta | TRUE |
| n_clumped_hits | FALSE |
| r2_sum1 | FALSE |
| r2_sum2 | FALSE |
| r2_sum3 | FALSE |
| r2_sum4 | FALSE |
Definitions
General metrics
af_correlation: Correlation coefficient betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.n: Maximum value of reported sample size across all SNPs, \(n\).n_clumped_hits: Number of clumped hits.n_snps: Number of SNPsn_p_sig: Number of SNPs with pvalue below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(\(2 \times N \times MAF\)) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations for<*>column.
se_n metrics
n_est: Estimated sample size value, \(\widehat{n}\).ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expectratio_se_nto be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.- \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
- \({z}_j = \frac{\beta_j}{{se}_j}\),
- and \(\beta_j\) is the reported effect size.
ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean ofdiffand the mean ofdiff2(expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.- \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
- \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.- \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
- \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
- and \({se}_j\) is the reported standard error.
sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.- \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
- \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),
r2 metrics
Sum of variance explained, calculated from the clumped top hits sample.
r2_sum<*>:r2statistics under various assumptions1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).
LDSC metrics
Metrics from LD regression
ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.ldsc_observed_scale_h2_{beta,se}Coefficient value and SE for total observed scale h2.ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.ldsc_lambda_gc: Lambda GC statistics.ldsc_mean_chisq: Mean \(\chi^2\) statistics.ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).
Flags
When a metric needs attention, the flag should return TRUE.
af_correlation:abs(af_correlation)< 0.7.inflation_factor:inflation_factor> 1.2.n:n(max reported sample size) < 10000.is_snpid_non_unique: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_mean_chisq< 0.7.n_p_sig:n_p_sig> 1000.miss_<*>:n_miss_<*>/n_snps> 0.01.ldsc_ratio:ldsc_ratio> 0.5ldsc_intercept_beta:ldsc_intercept_beta> 1.5n_clumped_hits:n_clumped_hits> 1000r2_sum<*>:r2_sum<*>> 0.5
Plots
- Manhattan plot
- Red line: \(-log_{10}^{5 \times 10^{-8}}\)
- Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
- QQ plot
- AF plot
- P-Z plot
- beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)
Diagnostics
Details
Summary stats
| skim_type | skim_variable | n_missing | complete_rate | character.min | character.max | character.empty | character.n_unique | character.whitespace | logical.mean | logical.count | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.000000 | 3 | 58 | 0 | 15847544 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 84 | 0 | 67620 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 63 | 0 | 34449 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 15848125 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.780969e+00 | 5.811487e+00 | 1.000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▃▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.836022e+07 | 5.621528e+07 | 56.000000 | 3.222386e+07 | 6.888263e+07 | 1.137764e+08 | 2.492397e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.241000e-04 | 1.107008e-01 | -1.847210 | -2.768630e-02 | 4.081000e-04 | 2.936650e-02 | 1.158960e+00 | ▁▁▂▇▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.431350e-02 | 7.954840e-02 | 0.013941 | 1.859530e-02 | 4.619990e-02 | 1.397950e-01 | 9.283890e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.964180e-01 | 2.902947e-01 | 0.000000 | 2.441912e-01 | 4.949667e-01 | 7.484090e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.966810e-01 | 2.900833e-01 | 0.000000 | 2.448177e-01 | 4.951965e-01 | 7.484389e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.605900e-01 | 2.444362e-01 | 0.001000 | 3.311600e-03 | 2.669000e-02 | 2.266360e-01 | 9.989990e-01 | ▇▁▁▁▁ |
| numeric | AF_reference | 503542 | 0.968227 | NA | NA | NA | NA | NA | NA | NA | 1.639440e-01 | 2.374700e-01 | 0.000000 | 1.797100e-03 | 3.853830e-02 | 2.424120e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 10177 | rs367896724 | A | AC | 0.0191220 | 0.0203942 | 0.3486551 | 0.3484400 | 0.3997500 | 0.4253190 | NA |
| 1 | 10352 | rs555500075 | T | TA | -0.0219211 | 0.0212741 | 0.3024747 | 0.3028164 | 0.3899480 | 0.4375000 | NA |
| 1 | 10616 | rs376342519 | CCGCCGTTGCAAAGGCGCGCCG | C | 0.0052437 | 0.1470910 | 0.9716300 | 0.9715618 | 0.9952540 | 0.9930110 | NA |
| 1 | 11012 | rs544419019 | C | G | 0.0068388 | 0.0350959 | 0.8456320 | 0.8455020 | 0.0857256 | 0.0880591 | NA |
| 1 | 13110 | rs540538026 | G | A | -0.0329803 | 0.0465210 | 0.4763794 | 0.4783657 | 0.0604217 | 0.0267572 | NA |
| 1 | 13116 | rs62635286 | T | G | 0.0012293 | 0.0278320 | 0.9648600 | 0.9647706 | 0.1909880 | 0.0970447 | NA |
| 1 | 13118 | rs200579949 | A | G | 0.0012293 | 0.0278320 | 0.9648600 | 0.9647706 | 0.1909880 | 0.0970447 | NA |
| 1 | 13273 | rs531730856 | G | C | -0.0418774 | 0.0322351 | 0.1917949 | 0.1939013 | 0.1348690 | 0.0950479 | NA |
| 1 | 13453 | rs568927457 | T | C | -0.0143314 | 0.1247340 | 0.9073660 | 0.9085278 | 0.0065598 | 0.0007987 | NA |
| 1 | 13483 | rs554760071 | G | C | -0.2184770 | 0.1582050 | 0.1516981 | 0.1672869 | 0.0050317 | 0.0019968 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51238328 | rs553081191 | A | C | 0.2208420 | 0.1749560 | 0.2185732 | 0.2068511 | 0.0019518 | 0.0005990 | NA |
| 22 | 51238364 | rs564490465 | C | G | -0.0116499 | 0.1421720 | 0.9342571 | 0.9346926 | 0.0054613 | 0.0005990 | NA |
| 22 | 51238394 | rs149712012 | C | T | 0.0507221 | 0.1586730 | 0.7508047 | 0.7492228 | 0.0033183 | 0.0033946 | NA |
| 22 | 51239281 | rs8138215 | G | C | 0.2854510 | 0.2073840 | 0.1812650 | 0.1686864 | 0.0014243 | 0.0111821 | NA |
| 22 | 51239296 | rs8137179 | T | C | 0.2854510 | 0.2073840 | 0.1812650 | 0.1686864 | 0.0014243 | 0.0111821 | NA |
| 22 | 51239304 | rs8142977 | C | T | 0.2854510 | 0.2073840 | 0.1812650 | 0.1686864 | 0.0014243 | 0.0111821 | NA |
| 22 | 51239586 | rs535432390 | T | G | 0.0413893 | 0.2220940 | 0.8530180 | 0.8521629 | 0.0017510 | 0.0001997 | NA |
| 22 | 51239794 | rs561893765 | C | A | 0.1758750 | 0.2221010 | 0.4377982 | 0.4284368 | 0.0016647 | 0.0299521 | NA |
| 22 | 51240820 | rs202228854 | C | T | 0.0211943 | 0.0572660 | 0.7119738 | 0.7113060 | 0.0252822 | 0.1267970 | NA |
| 22 | 51244237 | rs575160859 | C | T | -0.0085179 | 0.0867618 | 0.9216300 | 0.9217929 | 0.0131893 | 0.0037939 | NA |
bcf preview
1 10177 rs367896724 A AC . PASS AF=0.39975 ES:SE:LP:AF:ID 0.019122:0.0203942:0.457604:0.39975:rs367896724
1 10352 rs555500075 T TA . PASS AF=0.389948 ES:SE:LP:AF:ID -0.0219211:0.0212741:0.519311:0.389948:rs555500075
1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C . PASS AF=0.995254 ES:SE:LP:AF:ID 0.00524372:0.147091:0.0124991:0.995254:rs376342519
1 11012 rs544419019 C G . PASS AF=0.0857256 ES:SE:LP:AF:ID 0.00683881:0.0350959:0.0728186:0.0857256:rs544419019
1 13110 rs540538026 G A . PASS AF=0.0604217 ES:SE:LP:AF:ID -0.0329803:0.046521:0.322047:0.0604217:rs540538026
1 13116 rs62635286 T G . PASS AF=0.190988 ES:SE:LP:AF:ID 0.00122928:0.027832:0.0155357:0.190988:rs62635286
1 13118 rs62028691 A G . PASS AF=0.190988 ES:SE:LP:AF:ID 0.00122928:0.027832:0.0155357:0.190988:rs62028691
1 13273 rs531730856 G C . PASS AF=0.134869 ES:SE:LP:AF:ID -0.0418774:0.0322351:0.717163:0.134869:rs531730856
1 13453 rs568927457 T C . PASS AF=0.00655975 ES:SE:LP:AF:ID -0.0143314:0.124734:0.0422175:0.00655975:rs568927457
1 13483 rs554760071 G C . PASS AF=0.00503166 ES:SE:LP:AF:ID -0.218477:0.158205:0.81902:0.00503166:rs554760071
1 14464 rs546169444 A T . PASS AF=0.156315 ES:SE:LP:AF:ID 0.0436624:0.0288854:0.878434:0.156315:rs546169444
1 14599 rs707680 T A . PASS AF=0.19309 ES:SE:LP:AF:ID -0.019911:0.0268163:0.340135:0.19309:rs707680
1 14604 rs541940975 A G . PASS AF=0.19309 ES:SE:LP:AF:ID -0.019911:0.0268163:0.340135:0.19309:rs541940975
1 14930 rs6682385 A G . PASS AF=0.470997 ES:SE:LP:AF:ID -0.0424693:0.0208793:1.37866:0.470997:rs6682385
1 14933 rs199856693 G A . PASS AF=0.0480576 ES:SE:LP:AF:ID -0.105306:0.0509972:1.44337:0.0480576:rs199856693
1 15211 rs3982632 T G . PASS AF=0.743721 ES:SE:LP:AF:ID -0.0188042:0.0238563:0.365374:0.743721:rs3982632
1 15245 rs576044687 C T . PASS AF=0.00115332 ES:SE:LP:AF:ID 0.223507:0.263815:0.388726:0.00115332:rs576044687
1 15644 rs564003018 G A . PASS AF=0.00341452 ES:SE:LP:AF:ID 0.0319748:0.186731:0.063136:0.00341452:rs564003018
1 15820 rs2691315 G T . PASS AF=0.270052 ES:SE:LP:AF:ID 0.0368954:0.0243105:0.88606:0.270052:rs2691315
1 15903 rs557514207 G GC . PASS AF=0.40673 ES:SE:LP:AF:ID 0.00592932:0.0203659:0.112959:0.40673:rs557514207
1 16142 rs548165136 G A . PASS AF=0.00301546 ES:SE:LP:AF:ID -0.0420084:0.190947:0.0836098:0.00301546:rs548165136
1 16949 rs199745162 A C . PASS AF=0.0207707 ES:SE:LP:AF:ID -0.0721658:0.0755723:0.475739:0.0207707:rs199745162
1 18643 rs564023708 G A . PASS AF=0.00636163 ES:SE:LP:AF:ID -0.207267:0.145676:0.848419:0.00636163:rs564023708
1 18849 rs533090414 C G . PASS AF=0.975353 ES:SE:LP:AF:ID -0.0121434:0.0616447:0.0736092:0.975353:rs533090414
1 30923 rs806731 G T . PASS AF=0.905389 ES:SE:LP:AF:ID -0.0163475:0.0366827:0.182755:0.905389:rs806731
1 46285 rs545414834 ATAT A . PASS AF=0.00169632 ES:SE:LP:AF:ID 0.09582:0.226057:0.170544:0.00169632:rs545414834
1 47159 rs540662756 T C . PASS AF=0.0666055 ES:SE:LP:AF:ID -0.038562:0.0439645:0.422596:0.0666055:rs540662756
1 49298 rs10399793 T C . PASS AF=0.838481 ES:SE:LP:AF:ID -0.0105397:0.0290244:0.144611:0.838481:rs10399793
1 49318 rs536836601 A G . PASS AF=0.00155756 ES:SE:LP:AF:ID -0.595702:0.330138:1.34346:0.00155756:rs536836601
1 49343 rs553572247 T C . PASS AF=0.00208417 ES:SE:LP:AF:ID 0.166456:0.209537:0.360125:0.00208417:rs553572247
1 49554 rs539322794 A G . PASS AF=0.0979388 ES:SE:LP:AF:ID -0.0246023:0.036321:0.303709:0.0979388:rs539322794
1 51047 rs559500163 A T . PASS AF=0.00167757 ES:SE:LP:AF:ID -0.110626:0.265214:0.172205:0.00167757:rs559500163
1 51049 rs528344458 A C . PASS AF=0.00167757 ES:SE:LP:AF:ID -0.110626:0.265214:0.172205:0.00167757:rs528344458
1 51050 rs551668143 A T . PASS AF=0.00167757 ES:SE:LP:AF:ID -0.110626:0.265214:0.172205:0.00167757:rs551668143
1 51053 rs565211799 G T . PASS AF=0.00167757 ES:SE:LP:AF:ID -0.110626:0.265214:0.172205:0.00167757:rs565211799
1 51479 rs116400033 T A . PASS AF=0.212081 ES:SE:LP:AF:ID 0.0415804:0.025567:0.978732:0.212081:rs116400033
1 51762 rs559190862 A G . PASS AF=0.00846922 ES:SE:LP:AF:ID 0.107578:0.112908:0.457944:0.00846922:rs559190862
1 51765 rs575564077 C G . PASS AF=0.00826278 ES:SE:LP:AF:ID 0.12437:0.112771:0.554547:0.00826278:rs575564077
1 52238 rs2691277 T G . PASS AF=0.97831 ES:SE:LP:AF:ID -0.0663974:0.0738162:0.428676:0.97831:rs2691277
1 54353 rs140052487 C A . PASS AF=0.00186819 ES:SE:LP:AF:ID -0.170142:0.225788:0.358343:0.00186819:rs140052487
1 54354 rs569165477 C T . PASS AF=0.0023221 ES:SE:LP:AF:ID 0.0883303:0.187201:0.192792:0.0023221:rs569165477
1 54490 rs141149254 G A . PASS AF=0.153782 ES:SE:LP:AF:ID 0.0522431:0.0285549:1.16329:0.153782:rs141149254
1 54591 rs561234294 A G . PASS AF=0.00229659 ES:SE:LP:AF:ID -0.0471002:0.209225:0.0858034:0.00229659:rs561234294
1 54716 rs569128616 C T . PASS AF=0.427441 ES:SE:LP:AF:ID -0.0228442:0.0218069:0.530835:0.427441:rs569128616
1 54945 rs569799965 C A . PASS AF=0.00616765 ES:SE:LP:AF:ID 0.164195:0.125245:0.699024:0.00616765:rs569799965
1 55164 rs3091274 C A . PASS AF=0.983068 ES:SE:LP:AF:ID -0.120507:0.0798441:0.862045:0.983068:rs3091274
1 55249 rs200769871 C CTATGG . PASS AF=0.0090811 ES:SE:LP:AF:ID 0.00477916:0.105048:0.0160658:0.0090811:rs200769871
1 55326 rs3107975 T C . PASS AF=0.0157118 ES:SE:LP:AF:ID -0.0694961:0.091034:0.356155:0.0157118:rs3107975
1 55405 rs372455836 C T . PASS AF=0.00507754 ES:SE:LP:AF:ID 0.194517:0.140093:0.751389:0.00507754:rs372455836
1 55545 rs28396308 C T . PASS AF=0.260151 ES:SE:LP:AF:ID -0.0322427:0.0245214:0.727059:0.260151:rs28396308