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-GCST006946,TotalVariants=10824870,VariantsNotRead=0,HarmonisedVariants=10824870,VariantsNotHarmonised=0,SwitchedAlleles=0,TotalControls=367725.0,StudyType=Continuous>",
"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": "2019-10-26T21:48:43.844244",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006946/EBI-a-GCST006946_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-GCST006946/EBI-a-GCST006946.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006946/EBI-a-GCST006946_data.vcf.gz; Date=Sat Oct 26 22:11:28 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-GCST006946/ebi-a-GCST006946.vcf.gz; Date=Sat May 9 17:02:32 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-GCST006946/EBI-a-GCST006946.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-GCST006946/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 Sat Oct 26 22:30:38 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006946/EBI-a-GCST006946.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 Sat Oct 26 22:31:49 2019
Total time elapsed: 1.0m:11.26sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9589,
"inflation_factor": 1.2096,
"mean_EFFECT": -0.0002,
"n": "-Inf",
"n_snps": 10824870,
"n_clumped_hits": 21,
"n_p_sig": 903,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 429024,
"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 | TRUE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| 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 | 1.0000000 | 3 | 47 | 0 | 10809200 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 10809201 | 0.0000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.526274e+00 | 5.701042e+00 | 1.0000000 | 4.000000e+00 | 7.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.942102e+07 | 5.593342e+07 | 828.0000000 | 3.358311e+07 | 7.035950e+07 | 1.147885e+08 | 2.492251e+08 | ▇▇▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.017000e-04 | 2.028690e-02 | -0.4156410 | -4.513000e-03 | -3.220000e-05 | 4.345900e-03 | 3.994560e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.254360e-02 | 1.568240e-02 | 0.0023334 | 2.915100e-03 | 5.708500e-03 | 1.614080e-02 | 1.233940e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.684327e-01 | 2.975186e-01 | 0.0000000 | 2.025998e-01 | 4.581999e-01 | 7.264003e-01 | 9.996000e-01 | ▇▆▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.684327e-01 | 2.975185e-01 | 0.0000000 | 2.026632e-01 | 4.580877e-01 | 7.263387e-01 | 9.992021e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.801865e-01 | 2.534003e-01 | 0.0000894 | 5.471400e-03 | 4.879650e-02 | 2.651710e-01 | 9.998900e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 429024 | 0.9603094 | NA | NA | NA | NA | NA | NA | NA | 1.908478e-01 | 2.453544e-01 | 0.0000000 | 1.337860e-02 | 7.488020e-02 | 2.825480e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 715265 | rs12184267 | C | T | 0.0047028 | 0.0062537 | 0.4519995 | 0.4520515 | 0.0366377 | 0.0275559 | NA |
| 1 | 715367 | rs12184277 | A | G | 0.0061212 | 0.0062397 | 0.3263999 | 0.3265924 | 0.0367695 | 0.0281550 | NA |
| 1 | 717485 | rs12184279 | C | A | 0.0062458 | 0.0062646 | 0.3190002 | 0.3187642 | 0.0364631 | NA | NA |
| 1 | 720381 | rs116801199 | G | T | 0.0071859 | 0.0062054 | 0.2467999 | 0.2468638 | 0.0371104 | 0.0359425 | NA |
| 1 | 721290 | rs12565286 | G | C | 0.0065030 | 0.0061992 | 0.2942002 | 0.2941781 | 0.0372080 | 0.0371406 | NA |
| 1 | 723891 | rs2977670 | G | C | -0.0039937 | 0.0054115 | 0.4606998 | 0.4605147 | 0.9503520 | 0.7799520 | NA |
| 1 | 726794 | rs28454925 | C | G | 0.0077738 | 0.0062440 | 0.2130002 | 0.2131320 | 0.0366130 | 0.0279553 | NA |
| 1 | 729632 | rs116720794 | C | T | 0.0078476 | 0.0062184 | 0.2071000 | 0.2069486 | 0.0368931 | 0.0099840 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0030862 | 0.0031784 | 0.3315004 | 0.3315478 | 0.8316140 | 0.6399760 | NA |
| 1 | 752478 | rs146277091 | G | A | 0.0058179 | 0.0062157 | 0.3493003 | 0.3492730 | 0.0367696 | 0.0277556 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0027253 | 0.0058358 | 0.6402005 | 0.6404996 | 0.0427541 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0282137 | 0.0204447 | 0.1675001 | 0.1675864 | 0.0032748 | NA | NA |
| 22 | 51220025 | rs148808236 | G | A | -0.0095754 | 0.0279982 | 0.7322005 | 0.7323507 | 0.0017399 | 0.0215655 | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0185299 | 0.0203402 | 0.3621004 | 0.3622959 | 0.0033097 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0001389 | 0.0044806 | 0.9752999 | 0.9752694 | 0.0732027 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0064459 | 0.0050835 | 0.2049000 | 0.2047979 | 0.0568181 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0061092 | 0.0051251 | 0.2330999 | 0.2332616 | 0.0558736 | 0.0788738 | NA |
| 22 | 51224208 | rs116656403 | G | A | 0.0121987 | 0.0279147 | 0.6621997 | 0.6621120 | 0.0017501 | 0.0209665 | NA |
| 22 | 51224267 | rs138354270 | G | A | 0.0043710 | 0.0280191 | 0.8761000 | 0.8760333 | 0.0017376 | 0.0209665 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0004027 | 0.0044746 | 0.9286000 | 0.9282873 | 0.0735865 | 0.0730831 | NA |
bcf preview
1 715265 rs12184267 C T . PASS AF=0.0366377 ES:SE:LP:AF:ID 0.0047028:0.00625373:0.344862:0.0366377:rs12184267
1 715367 rs12184277 A G . PASS AF=0.0367695 ES:SE:LP:AF:ID 0.00612115:0.0062397:0.48625:0.0367695:rs12184277
1 717485 rs12184279 C A . PASS AF=0.0364631 ES:SE:LP:AF:ID 0.00624585:0.00626464:0.496209:0.0364631:rs12184279
1 720381 rs116801199 G T . PASS AF=0.0371104 ES:SE:LP:AF:ID 0.00718588:0.00620542:0.607655:0.0371104:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037208 ES:SE:LP:AF:ID 0.00650295:0.00619919:0.531357:0.037208:rs12565286
1 723891 rs2977670 G C . PASS AF=0.950352 ES:SE:LP:AF:ID -0.00399367:0.00541148:0.336582:0.950352:rs2977670
1 726794 rs28454925 C G . PASS AF=0.036613 ES:SE:LP:AF:ID 0.0077738:0.00624402:0.67162:0.036613:rs28454925
1 729632 rs116720794 C T . PASS AF=0.0368931 ES:SE:LP:AF:ID 0.00784765:0.00621842:0.68382:0.0368931:rs116720794
1 729679 rs4951859 C G . PASS AF=0.831614 ES:SE:LP:AF:ID 0.00308622:0.00317839:0.479516:0.831614:rs4951859
1 752478 rs146277091 G A . PASS AF=0.0367696 ES:SE:LP:AF:ID 0.00581786:0.00621566:0.456801:0.0367696:rs146277091
1 752566 rs3094315 G A . PASS AF=0.829189 ES:SE:LP:AF:ID 0.00240591:0.0031044:0.358427:0.829189:rs3094315
1 752721 rs3131972 A G . PASS AF=0.826243 ES:SE:LP:AF:ID 0.00256816:0.00307933:0.393189:0.826243:rs3131972
1 753405 rs3115860 C A . PASS AF=0.860653 ES:SE:LP:AF:ID 0.00442135:0.00338024:0.719194:0.860653:rs3115860
1 753541 rs2073813 G A . PASS AF=0.140455 ES:SE:LP:AF:ID -0.00489219:0.0033716:0.832978:0.140455:rs2073813
1 754063 rs12184312 G T . PASS AF=0.0371833 ES:SE:LP:AF:ID 0.00460882:0.00616977:0.341702:0.0371833:rs12184312
1 754105 rs12184325 C T . PASS AF=0.0374306 ES:SE:LP:AF:ID 0.0059993:0.00614683:0.482804:0.0374306:rs12184325
1 754182 rs3131969 A G . PASS AF=0.857265 ES:SE:LP:AF:ID 0.00459282:0.00334754:0.76904:0.857265:rs3131969
1 754192 rs3131968 A G . PASS AF=0.857357 ES:SE:LP:AF:ID 0.00457083:0.00334859:0.763967:0.857357:rs3131968
1 754211 rs12184313 G A . PASS AF=0.0374202 ES:SE:LP:AF:ID 0.00558162:0.00615394:0.438541:0.0374202:rs12184313
1 754334 rs3131967 T C . PASS AF=0.857369 ES:SE:LP:AF:ID 0.00444371:0.00334869:0.734239:0.857369:rs3131967
1 754503 rs3115859 G A . PASS AF=0.826634 ES:SE:LP:AF:ID 0.00287906:0.00309244:0.453828:0.826634:rs3115859
1 754629 rs10454459 A G . PASS AF=0.0374193 ES:SE:LP:AF:ID 0.00514046:0.00615624:0.393619:0.0374193:rs10454459
1 754964 rs3131966 C T . PASS AF=0.827271 ES:SE:LP:AF:ID 0.00271864:0.00309994:0.419874:0.827271:rs3131966
1 755775 rs3131965 A G . PASS AF=0.831031 ES:SE:LP:AF:ID 0.00311823:0.00318187:0.485585:0.831031:rs3131965
1 755890 rs3115858 A T . PASS AF=0.860232 ES:SE:LP:AF:ID 0.00384296:0.00336806:0.595166:0.860232:rs3115858
1 756604 rs3131962 A G . PASS AF=0.85979 ES:SE:LP:AF:ID 0.00305799:0.00336043:0.440213:0.85979:rs3131962
1 757640 rs3115853 G A . PASS AF=0.854521 ES:SE:LP:AF:ID 0.00352763:0.00331233:0.542118:0.854521:rs3115853
1 757734 rs4951929 C T . PASS AF=0.859889 ES:SE:LP:AF:ID 0.00328183:0.00336253:0.482804:0.859889:rs4951929
1 757936 rs4951862 C A . PASS AF=0.859935 ES:SE:LP:AF:ID 0.0032756:0.00336304:0.481749:0.859935:rs4951862
1 758144 rs3131956 A G . PASS AF=0.859942 ES:SE:LP:AF:ID 0.00326222:0.00336311:0.4786:0.859942:rs3131956
1 758626 rs3131954 C T . PASS AF=0.860464 ES:SE:LP:AF:ID 0.00423118:0.00337146:0.679231:0.860464:rs3131954
1 759036 rs114525117 G A . PASS AF=0.0374737 ES:SE:LP:AF:ID 0.00391337:0.00614343:0.280669:0.0374737:rs114525117
1 760912 rs1048488 C T . PASS AF=0.82959 ES:SE:LP:AF:ID 0.00293479:0.00311548:0.460548:0.82959:rs1048488
1 760998 rs148828841 C A . PASS AF=0.0369479 ES:SE:LP:AF:ID 0.00506987:0.00619789:0.383419:0.0369479:rs148828841
1 761147 rs3115850 T C . PASS AF=0.829712 ES:SE:LP:AF:ID 0.00305142:0.00311688:0.484524:0.829712:rs3115850
1 761606 rs377377186 G A . PASS AF=0.0358125 ES:SE:LP:AF:ID 0.00204703:0.00631799:0.127319:0.0358125:rs377377186
1 761732 rs2286139 C T . PASS AF=0.848492 ES:SE:LP:AF:ID 0.0035892:0.0033295:0.551139:0.848492:rs2286139
1 761881 rs374493323 A C . PASS AF=0.0342849 ES:SE:LP:AF:ID 0.00327433:0.00647101:0.21254:0.0342849:rs374493323
1 766007 rs61768174 A C . PASS AF=0.105478 ES:SE:LP:AF:ID -0.0018044:0.00384733:0.194567:0.105478:rs61768174
1 767096 rs115991721 A G . PASS AF=0.00259125 ES:SE:LP:AF:ID -0.00417697:0.0229504:0.0675262:0.00259125:rs115991721
1 768253 rs2977608 A C . PASS AF=0.744125 ES:SE:LP:AF:ID 0.000779973:0.00268032:0.112946:0.744125:rs2977608
1 768448 rs12562034 G A . PASS AF=0.111263 ES:SE:LP:AF:ID 0.00236351:0.00371038:0.280669:0.111263:rs12562034
1 768680 rs140605359 C T . PASS AF=0.00057169 ES:SE:LP:AF:ID -0.0406676:0.0488206:0.392867:0.00057169:rs140605359
1 768819 rs12562811 C T . PASS AF=0.0121867 ES:SE:LP:AF:ID 0.0150725:0.0106745:0.801618:0.0121867:rs12562811
1 769223 rs60320384 C G . PASS AF=0.135131 ES:SE:LP:AF:ID -0.00320624:0.00341816:0.458047:0.135131:rs60320384
1 770504 rs150664068 C G . PASS AF=0.000562164 ES:SE:LP:AF:ID -0.0246165:0.049233:0.209715:0.000562164:rs150664068
1 771823 rs2977605 T C . PASS AF=0.859693 ES:SE:LP:AF:ID 0.00324298:0.00336409:0.475085:0.859693:rs2977605
1 771967 rs59066358 G A . PASS AF=0.136281 ES:SE:LP:AF:ID -0.00333785:0.00340597:0.485187:0.136281:rs59066358
1 772755 rs2905039 A C . PASS AF=0.859685 ES:SE:LP:AF:ID 0.0030882:0.00336406:0.445632:0.859685:rs2905039
1 776556 rs151160018 C T . PASS AF=0.00833475 ES:SE:LP:AF:ID -0.00146477:0.0128489:0.0414839:0.00833475:rs151160018