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-GCST003043,TotalVariants=111631,VariantsNotRead=0,HarmonisedVariants=111631,VariantsNotHarmonised=0,SwitchedAlleles=0,TotalControls=21770.0,TotalCases=12882.0,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": "2019-10-26T09:27:59.284702",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003043/EBI-a-GCST003043_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-GCST003043/EBI-a-GCST003043.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003043/EBI-a-GCST003043_data.vcf.gz; Date=Sat Oct 26 09:42:35 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-GCST003043/ebi-a-GCST003043.vcf.gz; Date=Sat May 9 17:29:26 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-GCST003043/EBI-a-GCST003043.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-GCST003043/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 10:04:38 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003043/EBI-a-GCST003043.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 10:04:39 2019
Total time elapsed: 0.65sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9373,
"inflation_factor": 3.0788,
"mean_EFFECT": 0.0002,
"n": "-Inf",
"n_snps": 111631,
"n_clumped_hits": 132,
"n_p_sig": 6615,
"n_mono": 0,
"n_ns": 0,
"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": 761,
"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 | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| n_p_sig | TRUE |
| 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 | 5 | 25 | 0 | 111248 | 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 | 111278 | 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 | 8.078263e+00 | 5.764499e+00 | 1.0000e+00 | 3.000000e+00 | 6.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.926666e+07 | 5.784720e+07 | 7.8232e+04 | 3.292257e+07 | 6.230507e+07 | 1.178745e+08 | 2.492107e+08 | ▇▅▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.161000e-04 | 5.289410e-02 | -1.1825e+00 | -2.017270e-02 | -4.654000e-04 | 1.967880e-02 | 1.259280e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.217070e-02 | 2.702490e-02 | 9.7639e-03 | 1.097710e-02 | 1.362230e-02 | 2.172370e-02 | 8.461290e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.275063e-01 | 3.154205e-01 | 0.0000e+00 | 2.389060e-02 | 2.366165e-01 | 5.865641e-01 | 9.999970e-01 | ▇▂▂▂▂ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.275063e-01 | 3.154205e-01 | 0.0000e+00 | 2.389040e-02 | 2.366154e-01 | 5.865638e-01 | 9.999970e-01 | ▇▂▂▂▂ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.877538e-01 | 2.634710e-01 | 1.4900e-05 | 6.533000e-02 | 2.028000e-01 | 4.566000e-01 | 9.999700e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 761 | 0.9931613 | NA | NA | NA | NA | NA | NA | NA | 2.885165e-01 | 2.576869e-01 | 1.9970e-04 | 7.468050e-02 | 2.090650e-01 | 4.508790e-01 | 1.000000e+00 | ▇▃▂▂▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1118275 | rs61733845 | C | T | -0.0050020 | 0.0255474 | 0.8447716 | 0.8447719 | 0.044160 | 0.185703 | NA |
| 1 | 1120431 | rs1320571 | G | A | -0.0072789 | 0.0251118 | 0.7719248 | 0.7719241 | 0.046530 | 0.185304 | NA |
| 1 | 1135242 | rs9729550 | A | C | 0.0474646 | 0.0117498 | 0.0000535 | 0.0000535 | 0.258700 | 0.551917 | NA |
| 1 | 1140435 | rs1815606 | G | T | 0.0417639 | 0.0113960 | 0.0002475 | 0.0002475 | 0.317900 | 0.712061 | NA |
| 1 | 1163804 | rs7515488 | C | T | 0.0874308 | 0.0138634 | 0.0000000 | 0.0000000 | 0.148600 | 0.186901 | NA |
| 1 | 1165310 | rs11260562 | G | A | 0.0914881 | 0.0210530 | 0.0000139 | 0.0000139 | 0.055600 | 0.101837 | NA |
| 1 | 1173611 | rs6697886 | G | A | 0.0912882 | 0.0143832 | 0.0000000 | 0.0000000 | 0.137300 | 0.220647 | NA |
| 1 | 1194804 | rs11804831 | T | C | 0.0767614 | 0.0129065 | 0.0000000 | 0.0000000 | 0.185600 | 0.685903 | NA |
| 1 | 1218086 | rs6603788 | C | T | 0.0511661 | 0.0186037 | 0.0059537 | 0.0059537 | 0.076970 | 0.469449 | NA |
| 1 | 1227897 | rs3737721 | A | G | 0.0629665 | 0.0734371 | 0.3912118 | 0.3912123 | 0.002436 | 0.228035 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50966914 | rs470119 | T | C | -0.0521197 | 0.0101490 | 0.0000003 | 0.0000003 | 0.609800 | 0.5547120 | NA |
| 22 | 50971752 | rs131794 | A | C | -0.0577899 | 0.0121158 | 0.0000018 | 0.0000018 | 0.791900 | 0.8326680 | NA |
| 22 | 50988193 | rs131779 | A | G | -0.0261271 | 0.0109060 | 0.0165901 | 0.0165903 | 0.657900 | 0.5802720 | NA |
| 22 | 50999182 | rs140518 | C | T | -0.0316416 | 0.0109425 | 0.0038327 | 0.0038326 | 0.696600 | 0.7655750 | NA |
| 22 | 51078251 | rs4040041 | C | T | -0.0017874 | 0.0104564 | 0.8642739 | 0.8642737 | 0.373100 | 0.4666530 | NA |
| 22 | 51094926 | rs9616810 | C | T | 0.0162246 | 0.0121480 | 0.1816896 | 0.1816873 | 0.218600 | 0.2224440 | NA |
| 22 | 51105556 | rs9616812 | C | T | -0.0077821 | 0.0102872 | 0.4493616 | 0.4493592 | 0.483200 | 0.3628190 | NA |
| 22 | 51109992 | rs9628185 | T | C | -0.0075342 | 0.0103902 | 0.4683721 | 0.4683738 | 0.484300 | 0.4053510 | NA |
| 22 | 51134186 | rs8135777 | A | G | -0.3165570 | 0.2543200 | 0.2132348 | 0.2132350 | 0.000446 | 0.0273562 | NA |
| 22 | 51156666 | rs9628187 | C | T | -0.0037510 | 0.0130736 | 0.7741765 | 0.7741758 | 0.203200 | 0.1299920 | NA |
bcf preview
1 1118275 rs61733845 C T . PASS AF=0.04416 ES:SE:LP:AF:ID -0.00500201:0.0255474:0.0732607:0.04416:rs61733845
1 1120431 rs1320571 G A . PASS AF=0.04653 ES:SE:LP:AF:ID -0.00727888:0.0251118:0.112425:0.04653:rs1320571
1 1135242 rs9729550 A C . PASS AF=0.2587 ES:SE:LP:AF:ID 0.0474646:0.0117498:4.27133:0.2587:rs9729550
1 1140435 rs1815606 G T . PASS AF=0.3179 ES:SE:LP:AF:ID 0.0417639:0.011396:3.60637:0.3179:rs1815606
1 1163804 rs7515488 C T . PASS AF=0.1486 ES:SE:LP:AF:ID 0.0874308:0.0138634:9.54471:0.1486:rs7515488
1 1165310 rs11260562 G A . PASS AF=0.0556 ES:SE:LP:AF:ID 0.0914881:0.021053:4.85734:0.0556:rs11260562
1 1173611 rs6697886 G A . PASS AF=0.1373 ES:SE:LP:AF:ID 0.0912882:0.0143832:9.65802:0.1373:rs6697886
1 1194804 rs11804831 T C . PASS AF=0.1856 ES:SE:LP:AF:ID 0.0767614:0.0129065:8.56496:0.1856:rs11804831
1 1218086 rs6603788 C T . PASS AF=0.07697 ES:SE:LP:AF:ID 0.0511661:0.0186037:2.22521:0.07697:rs6603788
1 1227897 rs3737721 A G . PASS AF=0.002436 ES:SE:LP:AF:ID 0.0629665:0.0734371:0.407588:0.002436:rs3737721
1 1231656 rs1749951 G A . PASS AF=0.04028 ES:SE:LP:AF:ID 0.0371784:0.0264753:0.795229:0.04028:rs1749951
1 1233941 rs1739855 T C . PASS AF=0.07333 ES:SE:LP:AF:ID 0.0483781:0.0190326:1.95756:0.07333:rs1739855
1 1241529 rs1536168 A G . PASS AF=0.95394 ES:SE:LP:AF:ID -0.0405752:0.024294:1.0228:0.95394:rs1536168
1 1242468 rs2274264 G A . PASS AF=0.002467 ES:SE:LP:AF:ID 0.0542622:0.0782352:0.311628:0.002467:rs2274264
1 1247494 rs12103 T C . PASS AF=0.8166 ES:SE:LP:AF:ID -0.0867354:0.0130755:10.4841:0.8166:rs12103
1 1249187 rs12142199 G A . PASS AF=0.8026 ES:SE:LP:AF:ID -0.0789884:0.0128081:9.15753:0.8026:rs12142199
1 1254255 rs62623580 G A . PASS AF=0.007606 ES:SE:LP:AF:ID 0.0695751:0.0562643:0.665054:0.007606:rs62623580
1 1335790 rs1240708 A G . PASS AF=0.1737 ES:SE:LP:AF:ID 0.0814097:0.0132196:9.13345:0.1737:rs1240708
1 1493727 rs880051 G A . PASS AF=0.232 ES:SE:LP:AF:ID 0.0571914:0.0117629:5.93482:0.232:rs880051
1 1497824 rs2296716 C T . PASS AF=0.1208 ES:SE:LP:AF:ID 0.0378595:0.0155007:1.836:0.1208:rs2296716
1 1611995 rs4074196 A G . PASS AF=0.4414 ES:SE:LP:AF:ID -0.00916736:0.0116551:0.364977:0.4414:rs4074196
1 1706160 rs7531583 A G . PASS AF=0.77 ES:SE:LP:AF:ID -0.0357758:0.011644:2.67306:0.77:rs7531583
1 1721479 rs2272908 C T . PASS AF=0.4974 ES:SE:LP:AF:ID -0.0506909:0.00995343:6.45246:0.4974:rs2272908
1 1723031 rs9660180 G A . PASS AF=0.4974 ES:SE:LP:AF:ID -0.0516131:0.00997657:6.63858:0.4974:rs9660180
1 1781220 rs6681938 T C . PASS AF=0.2996 ES:SE:LP:AF:ID -0.0249012:0.011141:1.59499:0.2996:rs6681938
1 1838516 rs2377037 C A . PASS AF=0.2747 ES:SE:LP:AF:ID 0.0362376:0.0113843:2.83656:0.2747:rs2377037
1 1840038 rs2474461 T C . PASS AF=0.95331 ES:SE:LP:AF:ID -0.0692045:0.0237532:2.44681:0.95331:rs2474461
1 1853288 rs1884454 G T . PASS AF=0.2694 ES:SE:LP:AF:ID 0.0342632:0.0114288:2.56576:0.2694:rs1884454
1 1855319 rs2295362 C T . PASS AF=0.0452 ES:SE:LP:AF:ID 0.0452788:0.0244794:1.19138:0.0452:rs2295362
1 1871337 rs16824543 G A . PASS AF=0.04241 ES:SE:LP:AF:ID 0.0357672:0.0253662:0.79989:0.04241:rs16824543
1 1873625 rs12758705 G A . PASS AF=0.2651 ES:SE:LP:AF:ID 0.0340289:0.0115991:2.47511:0.2651:rs12758705
1 1881070 rs4648596 G A . PASS AF=0.04073 ES:SE:LP:AF:ID 0.0456525:0.0266091:1.06438:0.04073:rs4648596
1 1888193 rs3820011 C A . PASS AF=0.2698 ES:SE:LP:AF:ID 0.0329573:0.0116991:2.31458:0.2698:rs3820011
1 2024064 rs2459994 C T . PASS AF=0.1824 ES:SE:LP:AF:ID 0.0284659:0.0135275:1.45157:0.1824:rs2459994
1 2146966 rs7512482 T C . PASS AF=0.1674 ES:SE:LP:AF:ID -0.0246049:0.0143806:1.06006:0.1674:rs7512482
1 2202774 rs6673129 C T . PASS AF=0.1651 ES:SE:LP:AF:ID 0.0166108:0.0144719:0.600235:0.1651:rs6673129
1 2229478 rs12562937 C T . PASS AF=0.1998 ES:SE:LP:AF:ID 0.0220428:0.0132813:1.01332:0.1998:rs12562937
1 2283896 rs2840528 A G . PASS AF=0.4158 ES:SE:LP:AF:ID 0.000250701:0.0104941:0.0083573:0.4158:rs2840528
1 2290143 rs34587196 G A . PASS AF=0.01202 ES:SE:LP:AF:ID 0.060328:0.0475885:0.68845:0.01202:rs34587196
1 2404256 rs2494626 C T . PASS AF=0.2884 ES:SE:LP:AF:ID -0.00409066:0.0123399:0.130611:0.2884:rs2494626
1 2407781 rs78504402 C T . PASS AF=0.06862 ES:SE:LP:AF:ID 0.00566139:0.0213685:0.101793:0.06862:rs78504402
1 2408471 rs115996655 G A . PASS AF=0.007814 ES:SE:LP:AF:ID -0.037422:0.0554724:0.301094:0.007814:rs115996655
1 2408834 rs11588930 G A . PASS AF=0.101 ES:SE:LP:AF:ID 0.0134586:0.018543:0.329793:0.101:rs11588930
1 2409892 rs12727342 A G . PASS AF=0.6229 ES:SE:LP:AF:ID 0.0200619:0.0110419:1.15969:0.6229:rs12727342
1 2410789 rs11799501 C T . PASS AF=0.5834 ES:SE:LP:AF:ID 0.0320413:0.0109856:2.45125:0.5834:rs11799501
1 2412293 rs12731186 C T . PASS AF=0.1211 ES:SE:LP:AF:ID 0.0168713:0.0157379:0.547123:0.1211:rs12731186
1 2413166 rs115810747 A G . PASS AF=0.04094 ES:SE:LP:AF:ID 0.0408929:0.0284886:0.820537:0.04094:rs115810747
1 2414928 rs4995304 G A . PASS AF=0.5732 ES:SE:LP:AF:ID 0.0349407:0.0109466:2.84977:0.5732:rs4995304
1 2415108 rs114637672 T C . PASS AF=0.02017 ES:SE:LP:AF:ID -0.0177648:0.0370119:0.199803:0.02017:rs114637672
1 2415497 rs61763948 T C . PASS AF=0.5737 ES:SE:LP:AF:ID 0.0342705:0.0108671:2.79243:0.5737:rs61763948