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\">",
    "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.7": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
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    "file_date": "2019-10-27T07:24:08.124225",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003392/EBI-a-GCST003392_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-GCST003392/EBI-a-GCST003392.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003392/EBI-a-GCST003392_data.vcf.gz; Date=Sun Oct 27 07:38:35 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST003392/ebi-a-GCST003392.vcf.gz; Date=Sun May 10 11:36:20 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-GCST003392/EBI-a-GCST003392.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-GCST003392/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 08:02:20 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003392/EBI-a-GCST003392.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 08:03:22 2019
Total time elapsed: 1.0m:2.02s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9423,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9481845,
    "n_clumped_hits": 1,
    "n_p_sig": 34,
    "n_mono": 0,
    "n_ns": 1083062,
    "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": 195792,
    "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 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 9447187 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 45 0 46862 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 45 0 28940 0 NA NA NA NA NA NA NA NA NA NA
logical N 9460401 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 8.669292e+00 5.775284e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.864280e+07 5.657574e+07 302.0000000 3.196346e+07 6.910224e+07 1.144941e+08 2.492393e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -4.190000e-05 1.078850e-02 -0.1265310 -5.250800e-03 -5.080000e-05 5.166700e-03 1.370990e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 9.157300e-03 5.285400e-03 0.0040153 5.077900e-03 6.819400e-03 1.177950e-02 4.194690e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.890728e-01 2.915406e-01 0.0000000 2.300001e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.890722e-01 2.915158e-01 0.0000000 2.341405e-01 4.851936e-01 7.412909e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.611296e-01 2.628873e-01 0.0100010 4.380300e-02 1.581010e-01 4.157400e-01 9.899990e-01 ▇▂▂▁▁
numeric AF_reference 195792 0.979304 NA NA NA NA NA NA NA 2.605844e-01 2.537652e-01 0.0000000 4.972040e-02 1.737220e-01 4.109420e-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 -0.0045508 0.0066609 0.4899999 0.4944774 0.402314 0.4253190 NA
1 10352 rs555500075 T TA -0.0081098 0.0069301 0.2399999 0.2419115 0.390095 0.4375000 NA
1 11012 rs544419019 C G -0.0018841 0.0115483 0.8700001 0.8703986 0.086367 0.0880591 NA
1 13110 rs540538026 G A 0.0033456 0.0147842 0.8200001 0.8209694 0.059597 0.0267572 NA
1 13116 rs62635286 T G 0.0007276 0.0090807 0.9400001 0.9361373 0.188535 0.0970447 NA
1 13118 rs200579949 A G 0.0007276 0.0090807 0.9400001 0.9361373 0.188535 0.0970447 NA
1 13273 rs531730856 G C -0.0104917 0.0104064 0.3100002 0.3133600 0.133959 0.0950479 NA
1 14464 rs546169444 A T -0.0057434 0.0094689 0.5400003 0.5441486 0.155098 0.0958466 NA
1 14599 rs531646671 T A 0.0036009 0.0086883 0.6800001 0.6785437 0.193476 0.1475640 NA
1 14604 rs541940975 A G 0.0036009 0.0086883 0.6800001 0.6785437 0.193476 0.1475640 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51234343 rs374867791 G T 0.0207182 0.0208482 0.3200000 0.3203376 0.021048 0.1002400 NA
22 51234799 rs191117135 G A 0.0265410 0.0210541 0.2099999 0.2074495 0.015260 0.0059904 NA
22 51235959 rs200189535 T C 0.0035506 0.0068102 0.5999997 0.6021100 0.192540 0.1996810 NA
22 51235979 rs62240045 G A -0.0050866 0.0074545 0.5000000 0.4950204 0.260373 0.2400160 NA
22 51236013 rs200507571 A AT 0.0043461 0.0057942 0.4500005 0.4532113 0.252329 0.1487620 NA
22 51237063 rs3896457 T C -0.0061560 0.0055897 0.2700001 0.2707685 0.294328 0.2050720 NA
22 51237364 rs200607599 A G -0.0377196 0.0229992 0.1000000 0.1009969 0.015952 0.0187700 NA
22 51237712 rs370652263 G A -0.0139551 0.0106905 0.1900002 0.1917656 0.055591 0.0690895 NA
22 51240820 rs202228854 C T 0.0199202 0.0187742 0.2900000 0.2886712 0.027245 0.1267970 NA
22 51244237 rs575160859 C T -0.0089389 0.0283167 0.7499995 0.7522493 0.013418 0.0037939 NA

bcf preview

1   10177   rs367896724 A   AC  .   PASS    AF=0.402314 ES:SE:LP:AF:ID  -0.00455078:0.00666093:0.309804:0.402314:rs367896724
1   10352   rs555500075 T   TA  .   PASS    AF=0.390095 ES:SE:LP:AF:ID  -0.00810977:0.00693011:0.619789:0.390095:rs555500075
1   11012   rs544419019 C   G   .   PASS    AF=0.086367 ES:SE:LP:AF:ID  -0.00188413:0.0115483:0.0604807:0.086367:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.059597 ES:SE:LP:AF:ID  0.00334564:0.0147842:0.0861861:0.059597:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.188535 ES:SE:LP:AF:ID  0.000727598:0.00908071:0.0268721:0.188535:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.188535 ES:SE:LP:AF:ID  0.000727598:0.00908071:0.0268721:0.188535:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.133959 ES:SE:LP:AF:ID  -0.0104917:0.0104064:0.508638:0.133959:rs531730856
1   14464   rs546169444 A   T   .   PASS    AF=0.155098 ES:SE:LP:AF:ID  -0.0057434:0.00946894:0.267606:0.155098:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.193476 ES:SE:LP:AF:ID  0.00360086:0.00868826:0.167491:0.193476:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.193476 ES:SE:LP:AF:ID  0.00360086:0.00868826:0.167491:0.193476:rs541940975
1   14930   rs6682385   A   G   .   PASS    AF=0.469162 ES:SE:LP:AF:ID  0.00604503:0.00678239:0.431798:0.469162:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.047152 ES:SE:LP:AF:ID  0.00158274:0.0161103:0.0362122:0.047152:rs199856693
1   15211   rs3982632   T   G   .   PASS    AF=0.741111 ES:SE:LP:AF:ID  -0.00596082:0.00777292:0.356547:0.741111:rs3982632
1   15820   rs2691315   G   T   .   PASS    AF=0.27174  ES:SE:LP:AF:ID  0.0101415:0.00795189:0.69897:0.27174:rs2691315
1   15903   rs557514207 G   GC  .   PASS    AF=0.408683 ES:SE:LP:AF:ID  -0.00198132:0.00662145:0.119186:0.408683:rs557514207
1   16949   rs199745162 A   C   .   PASS    AF=0.020279 ES:SE:LP:AF:ID  0.0172853:0.0238933:0.327902:0.020279:rs199745162
1   18849   rs533090414 C   G   .   PASS    AF=0.974295 ES:SE:LP:AF:ID  -0.000333152:0.0201051:0.00436481:0.974295:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.903876 ES:SE:LP:AF:ID  0.000200262:0.0119506:0.00436481:0.903876:rs806731
1   47159   rs540662756 T   C   .   PASS    AF=0.065472 ES:SE:LP:AF:ID  0.000516338:0.0141865:0.0132283:0.065472:rs540662756
1   49298   rs10399793  T   C   .   PASS    AF=0.836907 ES:SE:LP:AF:ID  0.00938659:0.00948532:0.49485:0.836907:rs10399793
1   49554   rs539322794 A   G   .   PASS    AF=0.097473 ES:SE:LP:AF:ID  -0.00771457:0.0117225:0.29243:0.097473:rs539322794
1   51479   rs116400033 T   A   .   PASS    AF=0.209986 ES:SE:LP:AF:ID  0.00160209:0.00835118:0.0705811:0.209986:rs116400033
1   52238   rs2691277   T   G   .   PASS    AF=0.976797 ES:SE:LP:AF:ID  0.0105561:0.0245501:0.173925:0.976797:rs2691277
1   54490   rs141149254 G   A   .   PASS    AF=0.151422 ES:SE:LP:AF:ID  -0.00167812:0.00936957:0.0655015:0.151422:rs141149254
1   54712   rs552304420 T   C   .   PASS    AF=0.01064  ES:SE:LP:AF:ID  -0.0034203:0.00630157:0.229148:0.01064:rs552304420
1   54716   rs569128616 C   T   .   PASS    AF=0.42454  ES:SE:LP:AF:ID  -0.0137921:0.00706044:1.29243:0.42454:rs569128616
1   55164   rs3091274   C   A   .   PASS    AF=0.981509 ES:SE:LP:AF:ID  0.00537332:0.026904:0.0757207:0.981509:rs3091274
1   55326   rs3107975   T   C   .   PASS    AF=0.01603  ES:SE:LP:AF:ID  0.0248986:0.0289904:0.408935:0.01603:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.261517 ES:SE:LP:AF:ID  -0.00275161:0.00790048:0.136677:0.261517:rs28396308
1   57292   rs201418760 C   T   .   PASS    AF=0.02082  ES:SE:LP:AF:ID  0.0288052:0.0251952:0.60206:0.02082:rs201418760
1   58814   rs114420996 G   A   .   PASS    AF=0.094084 ES:SE:LP:AF:ID  -0.00703133:0.0119751:0.251812:0.094084:rs114420996
1   59040   rs62637815  T   C   .   PASS    AF=0.091395 ES:SE:LP:AF:ID  -0.00176896:0.012043:0.0555173:0.091395:rs62637815
1   60249   rs547227933 C   T   .   PASS    AF=0.01879  ES:SE:LP:AF:ID  0.0409042:0.0244997:1.02228:0.01879:rs547227933
1   60351   rs62637817  A   G   .   PASS    AF=0.084746 ES:SE:LP:AF:ID  -0.00399361:0.0124292:0.124939:0.084746:rs62637817
1   61920   rs62637820  G   A   .   PASS    AF=0.030102 ES:SE:LP:AF:ID  -0.0149515:0.019922:0.346787:0.030102:rs62637820
1   62777   rs3844233   A   T   .   PASS    AF=0.442341 ES:SE:LP:AF:ID  -0.00843564:0.0067837:0.677781:0.442341:rs3844233
1   63268   rs28664618  T   C   .   PASS    AF=0.389478 ES:SE:LP:AF:ID  -0.0111335:0.00723028:0.920819:0.389478:rs28664618
1   63671   rs80011619  G   A   .   PASS    AF=0.160843 ES:SE:LP:AF:ID  -0.0013578:0.00915964:0.0555173:0.160843:rs80011619
1   63735   rs61158452  CCTA    C   .   PASS    AF=0.308226 ES:SE:LP:AF:ID  0.0131122:0.00731101:1.13668:0.308226:rs201888535
1   64649   rs181431124 A   C   .   PASS    AF=0.027235 ES:SE:LP:AF:ID  -0.0135569:0.0207958:0.29243:0.027235:rs181431124
1   64931   rs62639104  G   A   .   PASS    AF=0.083092 ES:SE:LP:AF:ID  -0.0054557:0.0125756:0.180456:0.083092:rs62639104
1   66219   rs181028663 A   T   .   PASS    AF=0.018879 ES:SE:LP:AF:ID  -0.0110774:0.0234231:0.19382:0.018879:rs181028663
1   68082   rs367789441 T   C   .   PASS    AF=0.072252 ES:SE:LP:AF:ID  -0.00266841:0.0133316:0.0757207:0.072252:rs367789441
1   69428   rs140739101 T   G   .   PASS    AF=0.036251 ES:SE:LP:AF:ID  -0.017649:0.0189251:0.455932:0.036251:rs140739101
1   69761   rs200505207 A   T   .   PASS    AF=0.075779 ES:SE:LP:AF:ID  0.00532292:0.0130562:0.167491:0.075779:rs200505207
1   69897   rs200676709 T   C   .   PASS    AF=0.742291 ES:SE:LP:AF:ID  0.0160314:0.00801374:1.34679:0.742291:rs200676709
1   72526   rs547237130 A   G   .   PASS    AF=0.041676 ES:SE:LP:AF:ID  -0.0107322:0.0177575:0.259637:0.041676:rs547237130
1   73490   rs558384541 T   C   .   PASS    AF=0.017948 ES:SE:LP:AF:ID  0.00270295:0.0264925:0.0362122:0.017948:rs558384541
1   74790   rs13328700  C   G   .   PASS    AF=0.035774 ES:SE:LP:AF:ID  -0.000207253:0.018291:0.00436481:0.035774:rs13328700
1   74792   rs13328684  G   A   .   PASS    AF=0.035774 ES:SE:LP:AF:ID  -0.000207253:0.018291:0.00436481:0.035774:rs13328684