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-GCST006093,TotalVariants=558253,VariantsNotRead=0,HarmonisedVariants=558246,VariantsNotHarmonised=7,SwitchedAlleles=195,TotalControls=3941.0,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": "2019-10-26T09:28:53.875204",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006093/EBI-a-GCST006093_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-GCST006093/EBI-a-GCST006093.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006093/EBI-a-GCST006093_data.vcf.gz; Date=Sat Oct 26 09:43:35 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-GCST006093/ebi-a-GCST006093.vcf.gz; Date=Sun May 10 01:39:33 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-GCST006093/EBI-a-GCST006093.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-GCST006093/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 10:01:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006093/EBI-a-GCST006093.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 10:01:32 2019
Total time elapsed: 3.53sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8479,
"inflation_factor": 1.0383,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 558246,
"n_clumped_hits": 2,
"n_p_sig": 292,
"n_mono": 0,
"n_ns": 4900,
"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": 5120,
"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 | 1.0000000 | 4 | 38 | 0 | 556752 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 44 | 0 | 446 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 31 | 0 | 289 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 556753 | 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 | 8.794880e+00 | 6.068220e+00 | 1.000e+00 | 3.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.873837e+07 | 5.618588e+07 | 3.587e+03 | 3.133642e+07 | 7.104690e+07 | 1.136810e+08 | 2.492107e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.313000e-04 | 7.220810e-02 | -1.094e+00 | -4.258000e-02 | -1.802000e-04 | 4.248000e-02 | 1.081000e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.561940e-02 | 2.252540e-02 | 4.803e-02 | 5.178000e-02 | 5.831000e-02 | 7.200000e-02 | 1.416000e+00 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.936163e-01 | 2.904732e-01 | 0.000e+00 | 2.406999e-01 | 4.919002e-01 | 7.449995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.936162e-01 | 2.904732e-01 | 0.000e+00 | 2.406534e-01 | 4.919083e-01 | 7.449787e-01 | 9.999995e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.670948e-01 | 2.465832e-01 | 2.537e-04 | 1.560760e-01 | 3.070440e-01 | 5.466840e-01 | 9.956730e-01 | ▇▆▅▃▂ |
| numeric | AF_reference | 5120 | 0.9908038 | NA | NA | NA | NA | NA | NA | NA | 3.380696e-01 | 2.301240e-01 | 1.997e-04 | 1.489620e-01 | 2.911340e-01 | 5.000000e-01 | 9.974040e-01 | ▇▇▅▃▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 752566 | rs3094315 | G | A | 0.133200 | 0.06756 | 0.0487203 | 0.0486575 | 0.8448450 | 0.7182510 | NA |
| 1 | 990417 | rs2465136 | T | C | -0.005197 | 0.06752 | 0.9386001 | 0.9386476 | 0.1625320 | 0.3839860 | NA |
| 1 | 1018704 | rs9442372 | A | G | -0.005319 | 0.06327 | 0.9329999 | 0.9330021 | 0.8119550 | 0.6110220 | NA |
| 1 | 1036959 | rs11579015 | T | C | 0.042070 | 0.06151 | 0.4940001 | 0.4940043 | 0.2039570 | 0.1569490 | NA |
| 1 | 1046164 | rs6666280 | C | T | 0.041420 | 0.06155 | 0.5009000 | 0.5009799 | 0.2033310 | 0.2823480 | NA |
| 1 | 1062638 | rs9442373 | C | A | -0.030350 | 0.04930 | 0.5382004 | 0.5381462 | 0.5396500 | 0.5742810 | NA |
| 1 | 1216195 | rs55834051 | C | A | 0.001382 | 0.05179 | 0.9787000 | 0.9787112 | 0.6441210 | 0.3182910 | NA |
| 1 | 1217011 | rs1262894 | A | C | 0.013120 | 0.07617 | 0.8631999 | 0.8632439 | 0.1219990 | 0.2096650 | NA |
| 1 | 1217058 | rs3753340 | G | A | 0.107400 | 0.20690 | 0.6037998 | 0.6036970 | 0.0144225 | 0.0547125 | NA |
| 1 | 1218086 | rs6603788 | C | T | -0.012120 | 0.05115 | 0.8127001 | 0.8126955 | 0.6326950 | 0.4694490 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 155075318 | rs28673993 | A | G | -0.036560 | 0.05020 | 0.4664005 | 0.4664380 | 0.550435 | 0.494010 | NA |
| 23 | 155093036 | rs5940611 | A | G | 0.035540 | 0.05055 | 0.4820000 | 0.4820145 | 0.455459 | 0.518171 | NA |
| 23 | 155096106 | rs7056030 | C | T | 0.070060 | 0.05102 | 0.1696001 | 0.1696942 | 0.383539 | 0.509385 | NA |
| 23 | 155101861 | rs5940431 | T | C | -0.041230 | 0.05015 | 0.4110002 | 0.4110009 | 0.530342 | 0.646765 | NA |
| 23 | 155104608 | rs5940618 | G | A | -0.055290 | 0.05108 | 0.2791001 | 0.2790661 | 0.349194 | 0.339457 | NA |
| 23 | 155123237 | rs4893101 | C | T | 0.008931 | 0.05205 | 0.8638000 | 0.8637638 | 0.644919 | 0.592851 | NA |
| 23 | 155158490 | rs5983829 | A | G | 0.001700 | 0.05241 | 0.9740999 | 0.9741239 | 0.664322 | 0.637780 | NA |
| 23 | 155184866 | rs2889416 | T | C | 0.042620 | 0.05765 | 0.4597005 | 0.4597316 | 0.248887 | 0.370407 | NA |
| 23 | 155223705 | rs7051412 | T | G | -0.084700 | 0.05789 | 0.1435000 | 0.1434346 | 0.743987 | 0.552117 | NA |
| 23 | 155227607 | rs3093457 | T | G | -0.079290 | 0.05694 | 0.1638000 | 0.1637655 | 0.729838 | 0.512580 | NA |
bcf preview
1 752566 rs3094315 G A . PASS AF=0.844845 ES:SE:LP:AF:ID 0.1332:0.06756:1.31229:0.844845:rs3094315
1 990417 rs2465136 T C . PASS AF=0.162532 ES:SE:LP:AF:ID -0.005197:0.06752:0.0275194:0.162532:rs2465136
1 1018704 rs9442372 A G . PASS AF=0.811955 ES:SE:LP:AF:ID -0.005319:0.06327:0.0301184:0.811955:rs9442372
1 1036959 rs11579015 T C . PASS AF=0.203957 ES:SE:LP:AF:ID 0.04207:0.06151:0.306273:0.203957:rs11579015
1 1046164 rs6666280 C T . PASS AF=0.203331 ES:SE:LP:AF:ID 0.04142:0.06155:0.300249:0.203331:rs6666280
1 1062638 rs9442373 C A . PASS AF=0.53965 ES:SE:LP:AF:ID -0.03035:0.0493:0.269056:0.53965:rs9442373
1 1216195 rs55834051 C A . PASS AF=0.644121 ES:SE:LP:AF:ID 0.001382:0.05179:0.00935041:0.644121:rs55834051
1 1217011 rs1262894 A C . PASS AF=0.121999 ES:SE:LP:AF:ID 0.01312:0.07617:0.0638886:0.121999:rs1262894
1 1217058 rs3753340 G A . PASS AF=0.0144225 ES:SE:LP:AF:ID 0.1074:0.2069:0.219107:0.0144225:rs3753340
1 1218086 rs6603788 C T . PASS AF=0.632695 ES:SE:LP:AF:ID -0.01212:0.05115:0.0900697:0.632695:rs6603788
1 1220136 rs2144440 A G . PASS AF=0.652452 ES:SE:LP:AF:ID -0.0131:0.05165:0.0970186:0.652452:rs2144440
1 1220954 rs12751100 G A . PASS AF=0.63732 ES:SE:LP:AF:ID -0.002286:0.05137:0.0156978:0.63732:rs12751100
1 1221138 rs11260578 G A . PASS AF=0.622697 ES:SE:LP:AF:ID -0.02047:0.05114:0.161844:0.622697:rs11260578
1 1222958 rs111819661 C T . PASS AF=0.0144934 ES:SE:LP:AF:ID 0.01141:0.2101:0.0192242:0.0144934:rs111819661
1 1223529 rs78481580 G C . PASS AF=0.182145 ES:SE:LP:AF:ID 0.04027:0.06433:0.274578:0.182145:rs78481580
1 1225959 rs1570867 C G . PASS AF=0.922551 ES:SE:LP:AF:ID 0.1319:0.09049:0.838333:0.922551:rs1570867
1 1227244 rs2239609 G A . PASS AF=0.274785 ES:SE:LP:AF:ID -0.0006034:0.05486:0.00383871:0.274785:rs2239609
1 1278237 rs307361 T C . PASS AF=0.86929 ES:SE:LP:AF:ID 0.0299:0.07467:0.161844:0.86929:rs307361
1 1287040 rs182532 T C . PASS AF=0.869056 ES:SE:LP:AF:ID 0.01809:0.07488:0.0919978:0.869056:rs182532
1 1295323 rs34389364 G A . PASS AF=0.808131 ES:SE:LP:AF:ID -0.05499:0.06424:0.406714:0.808131:rs34389364
1 1314015 rs2649588 C T . PASS AF=0.826185 ES:SE:LP:AF:ID 0.02905:0.06691:0.177767:0.826185:rs2649588
1 1335218 rs2291889 G A . PASS AF=0.121077 ES:SE:LP:AF:ID -0.07378:0.0765:0.475085:0.121077:rs2291889
1 1487059 rs1887284 G A . PASS AF=0.262417 ES:SE:LP:AF:ID 0.01623:0.05679:0.110642:0.262417:rs1887284
1 1489928 rs7366884 T C . PASS AF=0.716754 ES:SE:LP:AF:ID -0.08232:0.05656:0.836839:0.716754:rs7366884
1 1500941 rs6603791 A G . PASS AF=0.824429 ES:SE:LP:AF:ID -0.1576:0.06772:1.70006:0.824429:rs6603791
1 1599161 rs6604981 A G . PASS AF=0.621014 ES:SE:LP:AF:ID 0.09443:0.05052:1.21035:0.621014:rs6604981
1 1647686 rs909823 A C . PASS AF=0.869063 ES:SE:LP:AF:ID 0.001765:0.07008:0.00881824:0.869063:rs909823
1 1687625 rs34306661 T C . PASS AF=0.332064 ES:SE:LP:AF:ID 0.0064:0.05223:0.0445528:0.332064:rs34306661
1 1706160 rs7531583 A G . PASS AF=0.70011 ES:SE:LP:AF:ID -0.07085:0.05376:0.726999:0.70011:rs7531583
1 1721479 rs2272908 C T . PASS AF=0.502984 ES:SE:LP:AF:ID -0.05843:0.04934:0.62672:0.502984:rs2272908
1 1722585 rs3737626 C A . PASS AF=0.0381004 ES:SE:LP:AF:ID -0.3974:0.124:2.87031:0.0381004:rs3737626
1 1722932 rs3737628 C T . PASS AF=0.502721 ES:SE:LP:AF:ID -0.05647:0.04936:0.597567:0.502721:rs3737628
1 1723031 rs9660180 G A . PASS AF=0.504401 ES:SE:LP:AF:ID -0.05836:0.04947:0.623058:0.504401:rs9660180
1 1725016 rs80220232 G A . PASS AF=0.066223 ES:SE:LP:AF:ID 0.02349:0.09884:0.0903905:0.066223:rs80220232
1 1725626 rs9970652 C T . PASS AF=0.206243 ES:SE:LP:AF:ID 0.03928:0.06145:0.281747:0.206243:rs9970652
1 1733219 rs10907185 A G . PASS AF=0.710327 ES:SE:LP:AF:ID -0.04897:0.05489:0.429107:0.710327:rs10907185
1 1734337 rs75622028 T C . PASS AF=0.103286 ES:SE:LP:AF:ID 0.02972:0.08181:0.144844:0.103286:rs75622028
1 1737900 rs17363334 C T . PASS AF=0.0685096 ES:SE:LP:AF:ID 0.04995:0.09768:0.215311:0.0685096:rs17363334
1 1738984 rs76117314 C A . PASS AF=0.0598594 ES:SE:LP:AF:ID 0.0212:0.1044:0.0761863:0.0598594:rs76117314
1 1745726 rs16825336 G A . PASS AF=0.266627 ES:SE:LP:AF:ID -0.04667:0.05608:0.392223:0.266627:rs16825336
1 1746694 rs12742323 G T . PASS AF=0.207488 ES:SE:LP:AF:ID 0.03077:0.06133:0.21049:0.207488:rs12742323
1 1747318 rs59787372 T C . PASS AF=0.0356951 ES:SE:LP:AF:ID -0.1118:0.1312:0.404283:0.0356951:rs59787372
1 1748734 rs2180311 T C . PASS AF=0.505353 ES:SE:LP:AF:ID -0.05511:0.04934:0.578232:0.505353:rs2180311
1 1752955 rs4648726 C T . PASS AF=0.843103 ES:SE:LP:AF:ID -0.08912:0.06859:0.712422:0.843103:rs4648726
1 1759026 rs9786963 T C . PASS AF=0.908969 ES:SE:LP:AF:ID -0.07768:0.08595:0.4364:0.908969:rs9786963
1 1759054 rs10907187 G A . PASS AF=0.128451 ES:SE:LP:AF:ID -0.02314:0.07415:0.122111:0.128451:rs10907187
1 1759213 rs9786942 A G . PASS AF=0.505091 ES:SE:LP:AF:ID -0.05235:0.04942:0.538502:0.505091:rs9786942
1 1760937 rs77726987 G A . PASS AF=0.0353723 ES:SE:LP:AF:ID -0.06273:0.134:0.193956:0.0353723:rs77726987
1 1765583 rs6603797 T C . PASS AF=0.908316 ES:SE:LP:AF:ID -0.06572:0.08527:0.35566:0.908316:rs6603797
1 1766094 rs6663586 A C . PASS AF=0.195172 ES:SE:LP:AF:ID 0.01417:0.06313:0.0849169:0.195172:rs6663586