Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std plot

beta_std_plot

beta_std_plot

Metadata

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    "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\">",
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    "FORMAT.6": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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    "FORMAT.8": "<ID=SS,Number=A,Type=Float,Description=\"Sample size used to estimate genetic effect\">",
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    "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.\">",
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    "file_date": "2019-10-26T21:47:18.422433",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005350/EBI-a-GCST005350_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-GCST005350/EBI-a-GCST005350.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005350/EBI-a-GCST005350_data.vcf.gz; Date=Sat Oct 26 22:06:22 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST005350/ebi-a-GCST005350.vcf.gz; Date=Sat May  9 14:37:22 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-GCST005350/EBI-a-GCST005350.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-GCST005350/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 22:30:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005350/EBI-a-GCST005350.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 22:31:38 2019
Total time elapsed: 1.0m:6.12s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9569,
    "inflation_factor": 1.0615,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 10304110,
    "n_clumped_hits": 21,
    "n_p_sig": 1265,
    "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": 244836,
    "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 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 between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations 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 expect ratio_se_n to 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 of diff and the mean of diff2 (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<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 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: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_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 59 0 10279773 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 10279814 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 8.957172e+00 6.067024e+00 1.0000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.852476e+07 5.624218e+07 828.0000 3.221605e+07 6.926399e+07 1.145155e+08 2.492393e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.800000e-05 4.681520e-02 -0.9252 -1.500000e-02 -1.000000e-04 1.510000e-02 7.383000e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.410900e-02 3.161340e-02 0.0104 1.280000e-02 2.000000e-02 4.340000e-02 3.059000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.899183e-01 2.908821e-01 0.0000 2.360000e-01 4.870999e-01 7.418994e-01 9.998000e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.899168e-01 2.908818e-01 0.0000 2.359442e-01 4.871316e-01 7.417931e-01 9.996663e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.244817e-01 2.567404e-01 0.0050 2.460000e-02 1.087000e-01 3.533000e-01 9.948000e-01 ▇▂▁▁▁
numeric AF_reference 244836 0.9761828 NA NA NA NA NA NA NA 2.256728e-01 2.523590e-01 0.0000 2.016770e-02 1.246010e-01 3.556310e-01 1.000000e+00 ▇▂▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 11012 rs544419019 C G -0.0345 0.0323 0.2865999 0.2854702 0.0941 0.0880591 NA
1 13110 rs540538026 G A 0.0064 0.0533 0.9048000 0.9044237 0.0580 0.0267572 NA
1 13116 rs62635286 T G -0.0256 0.0318 0.4216004 0.4208015 0.1820 0.0970447 NA
1 13118 rs200579949 A G -0.0256 0.0318 0.4216004 0.4208015 0.1820 0.0970447 NA
1 13273 rs531730856 G C -0.0463 0.0373 0.2138001 0.2144998 0.1337 0.0950479 NA
1 14599 rs531646671 T A -0.0269 0.0309 0.3844997 0.3839998 0.1813 0.1475640 NA
1 14604 rs541940975 A G -0.0269 0.0309 0.3843997 0.3839998 0.1813 0.1475640 NA
1 14930 rs75454623 A G -0.0034 0.0227 0.8797000 0.8809384 0.5339 0.4822280 NA
1 15211 rs78601809 T G 0.0285 0.0253 0.2602999 0.2599615 0.7177 0.6090260 NA
1 18849 rs533090414 C G 0.1029 0.0690 0.1357000 0.1358816 0.9810 0.9518770 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154918383 rs641588 G T -0.0029 0.0155 0.8521001 0.8515848 0.2457 0.3565560 NA
23 154918624 rs5940558 A G -0.0092 0.0324 0.7762006 0.7764482 0.0524 0.1290070 NA
23 154921127 rs58486544 T C -0.0069 0.0323 0.8303999 0.8308416 0.0526 0.1451660 NA
23 154923311 rs141127553 C T -0.0097 0.0328 0.7681995 0.7674350 0.0499 0.0309934 NA
23 154925045 rs509981 C T -0.0032 0.0156 0.8385001 0.8374720 0.2458 0.3634440 NA
23 154925895 rs538470 C T -0.0014 0.0159 0.9296999 0.9298367 0.2501 0.3634440 NA
23 154927199 rs645904 C T -0.0030 0.0156 0.8477000 0.8475012 0.2462 0.3674170 NA
23 154927581 rs644138 G A -0.0037 0.0148 0.8004000 0.8025873 0.2967 0.4635760 NA
23 154929412 rs557132 C T -0.0025 0.0156 0.8735000 0.8726791 0.2454 0.3568210 NA
23 154930230 rs781880 A G -0.0027 0.0156 0.8649000 0.8625910 0.2446 0.3618540 NA

bcf preview

1   11012   rs544419019 C   G   .   PASS    AF=0.0941   ES:SE:LP:AF:ID  -0.0345:0.0323:0.542724:0.0941:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.058    ES:SE:LP:AF:ID  0.0064:0.0533:0.0434474:0.058:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.182    ES:SE:LP:AF:ID  -0.0256:0.0318:0.375099:0.182:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.182    ES:SE:LP:AF:ID  -0.0256:0.0318:0.375099:0.182:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.1337   ES:SE:LP:AF:ID  -0.0463:0.0373:0.669992:0.1337:rs531730856
1   14599   rs707680    T   A   .   PASS    AF=0.1813   ES:SE:LP:AF:ID  -0.0269:0.0309:0.415104:0.1813:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.1813   ES:SE:LP:AF:ID  -0.0269:0.0309:0.415217:0.1813:rs541940975
1   14930   rs6682385   A   G   .   PASS    AF=0.5339   ES:SE:LP:AF:ID  -0.0034:0.0227:0.0556654:0.5339:rs6682385
1   15211   rs3982632   T   G   .   PASS    AF=0.7177   ES:SE:LP:AF:ID  0.0285:0.0253:0.584526:0.7177:rs3982632
1   18849   rs533090414 C   G   .   PASS    AF=0.981    ES:SE:LP:AF:ID  0.1029:0.069:0.86742:0.981:rs533090414
1   47159   rs540662756 T   C   .   PASS    AF=0.0584   ES:SE:LP:AF:ID  -0.0042:0.0519:0.0289562:0.0584:rs540662756
1   51762   rs559190862 A   G   .   PASS    AF=0.0101   ES:SE:LP:AF:ID  -0.0899:0.109:0.387322:0.0101:rs559190862
1   51765   rs575564077 C   G   .   PASS    AF=0.01 ES:SE:LP:AF:ID  -0.0889:0.109:0.381952:0.01:rs575564077
1   55326   rs3107975   T   C   .   PASS    AF=0.0212   ES:SE:LP:AF:ID  -0.0032:0.1104:0.0101499:0.0212:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.2445   ES:SE:LP:AF:ID  0.0097:0.0292:0.13071:0.2445:rs28396308
1   61743   rs184286948 G   C   .   PASS    AF=0.0095   ES:SE:LP:AF:ID  0.0973:0.127:0.352813:0.0095:rs184286948
1   61920   rs62637820  G   A   .   PASS    AF=0.0235   ES:SE:LP:AF:ID  0.0991:0.081:0.655608:0.0235:rs62637820
1   62777   rs3844233   A   T   .   PASS    AF=0.4129   ES:SE:LP:AF:ID  0.0146:0.024:0.266081:0.4129:rs3844233
1   63671   rs80011619  G   A   .   PASS    AF=0.1503   ES:SE:LP:AF:ID  -0.0025:0.0332:0.0270108:0.1503:rs80011619
1   64649   rs181431124 A   C   .   PASS    AF=0.0223   ES:SE:LP:AF:ID  0.0881:0.1305:0.301204:0.0223:rs181431124
1   67580   rs571658168 T   A   .   PASS    AF=0.0093   ES:SE:LP:AF:ID  0.0677:0.1068:0.278767:0.0093:rs571658168
1   69428   rs140739101 T   G   .   PASS    AF=0.0479   ES:SE:LP:AF:ID  -0.0077:0.0538:0.0521743:0.0479:rs140739101
1   73490   rs558384541 T   C   .   PASS    AF=0.0207   ES:SE:LP:AF:ID  0.1088:0.0791:0.77237:0.0207:rs558384541
1   74790   rs13328700  C   G   .   PASS    AF=0.0323   ES:SE:LP:AF:ID  -0.0543:0.0643:0.400226:0.0323:rs13328700
1   74792   rs13328684  G   A   .   PASS    AF=0.0323   ES:SE:LP:AF:ID  -0.0543:0.0643:0.400226:0.0323:rs13328684
1   76854   rs367666799 A   G   .   PASS    AF=0.0716   ES:SE:LP:AF:ID  0.0405:0.0479:0.400008:0.0716:rs367666799
1   82163   rs139113303 G   A   .   PASS    AF=0.0699   ES:SE:LP:AF:ID  0.0423:0.0441:0.471726:0.0699:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.0705   ES:SE:LP:AF:ID  0.0299:0.0439:0.304781:0.0705:rs149189449
1   86028   rs114608975 T   C   .   PASS    AF=0.0542   ES:SE:LP:AF:ID  -0.0257:0.0497:0.218101:0.0542:rs114608975
1   86065   rs116504101 G   C   .   PASS    AF=0.0709   ES:SE:LP:AF:ID  0.0192:0.0439:0.179076:0.0709:rs116504101
1   87409   rs139490478 C   T   .   PASS    AF=0.0725   ES:SE:LP:AF:ID  0.0288:0.0435:0.293709:0.0725:rs139490478
1   87647   rs146836579 T   C   .   PASS    AF=0.0201   ES:SE:LP:AF:ID  0.0497:0.0776:0.282912:0.0201:rs146836579
1   88169   rs940550    C   T   .   PASS    AF=0.1976   ES:SE:LP:AF:ID  0.007:0.0284:0.0936111:0.1976:rs940550
1   88710   rs186575039 C   G   .   PASS    AF=0.0717   ES:SE:LP:AF:ID  0.0304:0.0435:0.314527:0.0717:rs186575039
1   89599   rs375955515 A   T   .   PASS    AF=0.0727   ES:SE:LP:AF:ID  0.0445:0.0478:0.453581:0.0727:rs375955515
1   90051   rs7545609   C   T   .   PASS    AF=0.0207   ES:SE:LP:AF:ID  0.0484:0.0767:0.277531:0.0207:rs7545609
1   91190   rs143856811 G   A   .   PASS    AF=0.0708   ES:SE:LP:AF:ID  0.0142:0.0442:0.125634:0.0708:rs143856811
1   91421   rs28619159  T   C   .   PASS    AF=0.0108   ES:SE:LP:AF:ID  -0.0723:0.0982:0.335452:0.0108:rs28619159
1   91581   rs1524604   G   A   .   PASS    AF=0.5226   ES:SE:LP:AF:ID  -0.0285:0.034:0.395018:0.5226:rs1524604
1   92633   rs149776517 C   T   .   PASS    AF=0.0392   ES:SE:LP:AF:ID  0.0172:0.0591:0.113058:0.0392:rs149776517
1   104186  rs4288537   T   C   .   PASS    AF=0.6714   ES:SE:LP:AF:ID  -0.006:0.024:0.0964217:0.6714:rs4288537
1   108230  rs9726668   C   T   .   PASS    AF=0.0183   ES:SE:LP:AF:ID  0.0355:0.0787:0.185752:0.0183:rs9726668
1   122872  rs62642125  T   G   .   PASS    AF=0.2161   ES:SE:LP:AF:ID  -0.0206:0.0301:0.306449:0.2161:rs62642125
1   125271  rs3871807   C   T   .   PASS    AF=0.9703   ES:SE:LP:AF:ID  0.0974:0.0567:1.06834:0.9703:rs3871807
1   233473  rs112455420 C   G   .   PASS    AF=0.1437   ES:SE:LP:AF:ID  0.0736:0.0359:1.39545:0.1437:rs112455420
1   249276  rs115018998 T   C   .   PASS    AF=0.0203   ES:SE:LP:AF:ID  0.1129:0.0864:0.718739:0.0203:rs115018998
1   526736  rs28863004  C   G   .   PASS    AF=0.0417   ES:SE:LP:AF:ID  -0.0048:0.0626:0.0271032:0.0417:rs28863004
1   533197  rs201080464 A   G   .   PASS    AF=0.0735   ES:SE:LP:AF:ID  -0.0932:0.0458:1.37706:0.0735:rs201080464
1   536095  rs189259765 C   T   .   PASS    AF=0.287    ES:SE:LP:AF:ID  0.0094:0.0241:0.157453:0.287:rs189259765
1   536816  rs373360530 C   T   .   PASS    AF=0.0878   ES:SE:LP:AF:ID  0.0276:0.0432:0.280834:0.0878:rs373360530