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\">",
    "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\">",
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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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    "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\">",
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    "file_date": "2019-10-27T07:30:03.796256",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006288/EBI-a-GCST006288_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",
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    "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-GCST006288/EBI-a-GCST006288.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006288/EBI-a-GCST006288_data.vcf.gz; Date=Sun Oct 27 07:56:05 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006288/ebi-a-GCST006288.vcf.gz; Date=Sun May 10 14:13:44 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-GCST006288/EBI-a-GCST006288.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-GCST006288/ldsc.txt \
--snplist /data/ref/snplist.gz \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Sun Oct 27 08:22:01 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006288/EBI-a-GCST006288.vcf.gz ...
and extracting SNPs specified in /data/ref/snplist.gz ...
Traceback (most recent call last):
  File "./ldsc/ldsc.py", line 647, in <module>
    sumstats.estimate_h2(args, log)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
    args, log, args.h2)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 246, in _read_ld_sumstats
    sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 165, in _read_sumstats
    sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna, slh=args.snplist)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 85, in sumstats
    x = read_vcf(fh, alleles, slh)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 161, in read_vcf
    with gzip.open(slh) as f:
  File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 34, in open
    return GzipFile(filename, mode, compresslevel)
  File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 94, in __init__
    fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
IOError: [Errno 2] No such file or directory: '/data/ref/snplist.gz'

Analysis finished at Sun Oct 27 08:24:01 2019
Total time elapsed: 1.0m:59.42s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9564,
    "inflation_factor": 1.2544,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 16957716,
    "n_clumped_hits": 261,
    "n_p_sig": 43644,
    "n_mono": 0,
    "n_ns": 1545355,
    "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": 1014944,
    "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 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 1.000000 3 58 0 16929639 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 96 0 74646 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 103 0 37471 0 NA NA NA NA NA NA NA NA NA NA
logical N 16932958 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.667426e+00 5.788080e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.878863e+07 5.641632e+07 56.0000000 3.244235e+07 6.934548e+07 1.145602e+08 2.492397e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 6.250000e-05 3.073060e-02 -0.4353540 -8.316300e-03 1.650000e-05 8.373100e-03 4.835000e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 2.179460e-02 2.009660e-02 0.0028786 4.567000e-03 1.241100e-02 3.729280e-02 1.911530e-01 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.626714e-01 2.999807e-01 0.0000000 1.900002e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.635495e-01 2.998235e-01 0.0000000 1.947411e-01 4.518749e-01 7.235127e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.000000 NA NA NA NA NA NA NA 1.533475e-01 2.414493e-01 0.0010000 2.722000e-03 2.107900e-02 2.085710e-01 9.990000e-01 ▇▁▁▁▁
numeric AF_reference 1014944 0.940061 NA NA NA NA NA NA NA 1.610497e-01 2.363796e-01 0.0000000 1.597400e-03 3.574280e-02 2.350240e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10177 rs367896724 A AC 0.0038200 0.0048710 0.4000000 0.4329022 0.400675 0.4253190 NA
1 10352 rs555500075 T TA -0.0058711 0.0050569 0.2099999 0.2456377 0.388926 0.4375000 NA
1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C 0.0348770 0.0345632 0.6899999 0.3129367 0.995295 0.9930110 NA
1 11012 rs544419019 C G -0.0055899 0.0084069 0.4299995 0.5061067 0.085638 0.0880591 NA
1 13110 rs540538026 G A -0.0088775 0.0109130 0.4400003 0.4159449 0.059831 0.0267572 NA
1 13116 rs62635286 T G 0.0031251 0.0066211 0.4400003 0.6369326 0.190466 0.0970447 NA
1 13118 rs200579949 A G 0.0031251 0.0066211 0.4400003 0.6369326 0.190466 0.0970447 NA
1 13273 rs531730856 G C -0.0097529 0.0075989 0.3300000 0.1993296 0.135060 0.0950479 NA
1 13453 rs568927457 T C -0.0369291 0.0298988 0.2999998 0.2167797 0.006476 0.0007987 NA
1 13483 rs554760071 G C -0.0249120 0.0334611 0.4199997 0.4565702 0.005187 0.0019968 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 152944250 rs6643760 C A 0.018610 0.006534 0.0043900 0.0043970 0.12030 0.1041060 NA
23 153027633 rs4898439 A C 0.008798 0.006288 0.1618002 0.1617611 0.13200 0.4471520 NA
23 153155987 rs12380966 C A 0.017400 0.014440 0.2283000 0.2282086 0.02204 NA NA
23 153207037 rs78377269 G T -0.013690 0.011070 0.2161001 0.2162075 0.03813 0.0214570 NA
23 153659164 rs4898495 T G -0.022770 0.011640 0.0505196 0.0504433 0.96535 0.9584110 NA
23 153714029 rs368872121 G T -0.009112 0.006614 0.1683000 0.1683010 0.87780 NA NA
23 154212603 rs149309160 T G 0.036940 0.015340 0.0160299 0.0160365 0.01960 0.0262252 NA
23 154523008 rs113313431 G T 0.006220 0.009103 0.4943995 0.4944228 0.05809 0.0177483 NA
23 154616509 rs28826658 C A -0.001750 0.011600 0.8801000 0.8800845 0.03502 NA NA
23 154617838 rs28867467 T G 0.003247 0.014280 0.8202001 0.8201275 0.02290 0.0307285 NA

bcf preview

1   10177   rs367896724 A   AC  .   PASS    AF=0.400675 ES:SE:LP:AF:ID  0.00382004:0.00487104:0.39794:0.400675:rs367896724
1   10352   rs555500075 T   TA  .   PASS    AF=0.388926 ES:SE:LP:AF:ID  -0.00587109:0.00505688:0.677781:0.388926:rs555500075
1   10616   rs376342519 CCGCCGTTGCAAAGGCGCGCCG  C   .   PASS    AF=0.995295 ES:SE:LP:AF:ID  0.034877:0.0345632:0.161151:0.995295:rs376342519
1   11012   rs544419019 C   G   .   PASS    AF=0.085638 ES:SE:LP:AF:ID  -0.00558985:0.0084069:0.366532:0.085638:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.059831 ES:SE:LP:AF:ID  -0.00887747:0.010913:0.356547:0.059831:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.190466 ES:SE:LP:AF:ID  0.00312511:0.00662111:0.356547:0.190466:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.190466 ES:SE:LP:AF:ID  0.00312511:0.00662111:0.356547:0.190466:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.13506  ES:SE:LP:AF:ID  -0.00975294:0.00759892:0.481486:0.13506:rs531730856
1   13453   rs568927457 T   C   .   PASS    AF=0.006476 ES:SE:LP:AF:ID  -0.0369291:0.0298988:0.522879:0.006476:rs568927457
1   13483   rs554760071 G   C   .   PASS    AF=0.005187 ES:SE:LP:AF:ID  -0.024912:0.0334611:0.376751:0.005187:rs554760071
1   14464   rs546169444 A   T   .   PASS    AF=0.155997 ES:SE:LP:AF:ID  0.00706305:0.00695903:0.346787:0.155997:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.192873 ES:SE:LP:AF:ID  0.00805722:0.00637265:1.0655:0.192873:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.192873 ES:SE:LP:AF:ID  0.00805722:0.00637265:1.0655:0.192873:rs541940975
1   14930   rs6682385   A   G   .   PASS    AF=0.470601 ES:SE:LP:AF:ID  0.00906406:0.00497218:0.79588:0.470601:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.047381 ES:SE:LP:AF:ID  0.0133132:0.0118366:0.60206:0.047381:rs199856693
1   15211   rs3982632   T   G   .   PASS    AF=0.742854 ES:SE:LP:AF:ID  -0.00911836:0.00567616:1.00436:0.742854:rs3982632
1   15245   rs576044687 C   T   .   PASS    AF=0.001239 ES:SE:LP:AF:ID  -0.0508681:0.0656978:0.431798:0.001239:rs576044687
1   15644   rs564003018 G   A   .   PASS    AF=0.003463 ES:SE:LP:AF:ID  0.0382971:0.0440875:0.142668:0.003463:rs564003018
1   15820   rs2691315   G   T   .   PASS    AF=0.27054  ES:SE:LP:AF:ID  0.0139725:0.00581019:1.60206:0.27054:rs2691315
1   15903   rs557514207 G   GC  .   PASS    AF=0.408636 ES:SE:LP:AF:ID  0.00422751:0.00483696:0.207608:0.408636:rs557514207
1   16142   rs548165136 G   A   .   PASS    AF=0.002841 ES:SE:LP:AF:ID  0.0239247:0.0474328:0.585027:0.002841:rs548165136
1   16949   rs199745162 A   C   .   PASS    AF=0.020671 ES:SE:LP:AF:ID  -0.00133593:0.0173311:0.356547:0.020671:rs199745162
1   18643   rs564023708 G   A   .   PASS    AF=0.006257 ES:SE:LP:AF:ID  -0.0156614:0.0322863:0.05061:0.006257:rs564023708
1   18849   rs533090414 C   G   .   PASS    AF=0.975389 ES:SE:LP:AF:ID  0.0192544:0.0146773:0.420216:0.975389:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.904717 ES:SE:LP:AF:ID  0.0153236:0.00873394:0.79588:0.904717:rs806731
1   46285   rs545414834 ATAT    A   .   PASS    AF=0.001748 ES:SE:LP:AF:ID  0.0438517:0.0535183:0.420216:0.001748:rs545414834
1   47159   rs540662756 T   C   .   PASS    AF=0.065765 ES:SE:LP:AF:ID  0.0130395:0.0103827:0.30103:0.065765:rs540662756
1   49298   rs10399793  T   C   .   PASS    AF=0.838461 ES:SE:LP:AF:ID  0.0119314:0.00694492:0.769551:0.838461:rs10399793
1   49318   rs536836601 A   G   .   PASS    AF=0.001476 ES:SE:LP:AF:ID  -0.0201343:0.0611348:0.251812:0.001476:rs536836601
1   49343   rs553572247 T   C   .   PASS    AF=0.002093 ES:SE:LP:AF:ID  0.0477605:0.0529742:0.236572:0.002093:rs553572247
1   49554   rs539322794 A   G   .   PASS    AF=0.097562 ES:SE:LP:AF:ID  -0.0166201:0.00859443:0.79588:0.097562:rs539322794
1   51047   rs559500163 A   T   .   PASS    AF=0.001649 ES:SE:LP:AF:ID  0.129619:0.0642348:0.79588:0.001649:rs559500163
1   51049   rs528344458 A   C   .   PASS    AF=0.001649 ES:SE:LP:AF:ID  0.129619:0.0642348:0.79588:0.001649:rs528344458
1   51050   rs551668143 A   T   .   PASS    AF=0.001649 ES:SE:LP:AF:ID  0.129619:0.0642348:0.79588:0.001649:rs551668143
1   51053   rs565211799 G   T   .   PASS    AF=0.001649 ES:SE:LP:AF:ID  0.129619:0.0642348:0.79588:0.001649:rs565211799
1   51479   rs116400033 T   A   .   PASS    AF=0.211967 ES:SE:LP:AF:ID  0.00635986:0.00611702:0.356547:0.211967:rs116400033
1   51762   rs559190862 A   G   .   PASS    AF=0.008623 ES:SE:LP:AF:ID  -0.011104:0.0272474:0.0604807:0.008623:rs559190862
1   51765   rs575564077 C   G   .   PASS    AF=0.00844  ES:SE:LP:AF:ID  -0.00914637:0.0273154:0.0757207:0.00844:rs575564077
1   52238   rs2691277   T   G   .   PASS    AF=0.978005 ES:SE:LP:AF:ID  -0.00632434:0.0179854:0.09691:0.978005:rs2691277
1   54353   rs140052487 C   A   .   PASS    AF=0.001745 ES:SE:LP:AF:ID  -0.0432534:0.0509889:0.420216:0.001745:rs140052487
1   54354   rs569165477 C   T   .   PASS    AF=0.002388 ES:SE:LP:AF:ID  -0.0250237:0.045523:0.420216:0.002388:rs569165477
1   54490   rs141149254 G   A   .   PASS    AF=0.152963 ES:SE:LP:AF:ID  0.00671616:0.00687148:0.244125:0.152963:rs141149254
1   54591   rs561234294 A   G   .   PASS    AF=0.002246 ES:SE:LP:AF:ID  -0.0557425:0.0500186:0.236572:0.002246:rs561234294
1   54712   rs552304420 T   C   .   PASS    AF=0.010304 ES:SE:LP:AF:ID  0.01253:0.0251667:0.267606:0.010304:rs552304420
1   54716   rs569128616 C   T   .   PASS    AF=0.427319 ES:SE:LP:AF:ID  0.00762645:0.00517022:0.823909:0.427319:rs569128616
1   54945   rs569799965 C   A   .   PASS    AF=0.006145 ES:SE:LP:AF:ID  -0.0393134:0.0319041:0.408935:0.006145:rs569799965
1   55164   rs3091274   C   A   .   PASS    AF=0.982799 ES:SE:LP:AF:ID  -0.0136496:0.0197765:0.229148:0.982799:rs3091274
1   55249   rs200769871 C   CTATGG  .   PASS    AF=0.009143 ES:SE:LP:AF:ID  0.0405409:0.0249152:0.69897:0.009143:rs200769871
1   55326   rs3107975   T   C   .   PASS    AF=0.01581  ES:SE:LP:AF:ID  0.00207311:0.0208327:0.00436481:0.01581:rs3107975
1   55405   rs372455836 C   T   .   PASS    AF=0.005259 ES:SE:LP:AF:ID  -0.0326818:0.0356661:0.508638:0.005259:rs372455836