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": "2019-10-28T00:05:26.507296",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004615/EBI-a-GCST004615_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-GCST004615/EBI-a-GCST004615.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004615/EBI-a-GCST004615_data.vcf.gz; Date=Mon Oct 28 00:49:47 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST004615/ebi-a-GCST004615.vcf.gz; Date=Sun May 10 23:26:20 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-GCST004615/EBI-a-GCST004615.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-GCST004615/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 Mon Oct 28 01:18:51 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004615/EBI-a-GCST004615.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 Mon Oct 28 01:22:18 2019
Total time elapsed: 3.0m:26.34s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9629,
    "inflation_factor": 1,
    "mean_EFFECT": -9.5297e-06,
    "n": "-Inf",
    "n_snps": 29167036,
    "n_clumped_hits": 126,
    "n_p_sig": 31496,
    "n_mono": 0,
    "n_ns": 2186623,
    "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": 3828767,
    "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 58 0 29141449 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 93 0 107698 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 103 0 46321 0 NA NA NA NA NA NA NA NA NA NA
logical N 29141915 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 8.646842e+00 5.784635e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.917868e+07 5.638565e+07 56.0000000 3.277873e+07 6.999146e+07 1.150723e+08 2.492398e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -9.500000e-06 7.966160e-02 -1.1039900 -2.184080e-02 -3.340000e-05 2.175090e-02 1.156140e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.232530e-02 5.483720e-02 0.0031276 9.226300e-03 5.135580e-02 1.018180e-01 4.622770e-01 ▇▃▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.987490e-01 2.907018e-01 0.0000000 2.472999e-01 5.000000e-01 7.506993e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.987490e-01 2.907019e-01 0.0000000 2.473347e-01 4.999569e-01 7.507410e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 9.255560e-02 2.049318e-01 0.0001000 5.000000e-04 1.800000e-03 4.660000e-02 9.999000e-01 ▇▁▁▁▁
numeric AF_reference 3828767 0.8686165 NA NA NA NA NA NA NA 1.073479e-01 2.076393e-01 0.0000000 3.994000e-04 6.190100e-03 9.524760e-02 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 -0.0015314 0.0048905 0.7542008 0.7541702 0.3923 0.4253190 NA
1 10352 rs555500075 T TA 0.0000321 0.0050262 0.9949000 0.9949098 0.3853 0.4375000 NA
1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C 0.0223319 0.0321406 0.4871997 0.4871687 0.9942 0.9930110 NA
1 11012 rs544419019 C G -0.0070422 0.0085152 0.4082000 0.4082308 0.0845 0.0880591 NA
1 13110 rs540538026 G A -0.0035835 0.0107932 0.7399000 0.7398775 0.0591 0.0267572 NA
1 13116 rs62635286 T G -0.0015506 0.0065211 0.8121000 0.8120535 0.1870 0.0970447 NA
1 13118 rs200579949 A G -0.0038344 0.0065035 0.5554999 0.5554656 0.1883 0.0970447 NA
1 13273 rs531730856 G C -0.0052240 0.0074472 0.4829999 0.4830079 0.1346 0.0950479 NA
1 13445 rs558318514 C G 0.1769380 0.0974729 0.0694800 0.0694850 0.0006 0.0005990 NA
1 13453 rs568927457 T C 0.0295240 0.0298710 0.3230004 0.3229649 0.0063 0.0007987 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51238364 rs564490465 C G -0.0171883 0.0358275 0.6314006 0.6314040 0.0048 0.0005990 NA
22 51238394 rs149712012 C T 0.0537455 0.0364340 0.1402000 0.1401731 0.0034 0.0033946 NA
22 51239281 rs8138215 G C 0.0372478 0.0455789 0.4137995 0.4138051 0.0025 0.0111821 NA
22 51239296 rs8137179 T C 0.0372478 0.0455789 0.4137995 0.4138051 0.0025 0.0111821 NA
22 51239304 rs8142977 C T 0.0372478 0.0455789 0.4137995 0.4138051 0.0025 0.0111821 NA
22 51239586 rs535432390 T G 0.0485385 0.0503299 0.3347997 0.3348420 0.0018 0.0001997 NA
22 51239652 rs564418969 G T 0.1118570 0.0994349 0.2605998 0.2606200 0.0008 0.0011981 NA
22 51239794 rs561893765 C A 0.0173087 0.0505657 0.7320993 0.7321242 0.0020 0.0299521 NA
22 51240820 rs202228854 C T 0.0012684 0.0132432 0.9236999 0.9236949 0.0264 0.1267970 NA
22 51244237 rs575160859 C T -0.0051872 0.0205335 0.8006000 0.8005612 0.0123 0.0037939 NA

bcf preview

1   10177   rs367896724 A   AC  .   PASS    AF=0.3923   ES:SE:LP:AF:ID  -0.00153145:0.00489054:0.122513:0.3923:rs367896724
1   10352   rs555500075 T   TA  .   PASS    AF=0.3853   ES:SE:LP:AF:ID  3.20658e-05:0.00502623:0.00222057:0.3853:rs555500075
1   10616   rs376342519 CCGCCGTTGCAAAGGCGCGCCG  C   .   PASS    AF=0.9942   ES:SE:LP:AF:ID  0.0223319:0.0321406:0.312293:0.9942:rs376342519
1   11012   rs544419019 C   G   .   PASS    AF=0.0845   ES:SE:LP:AF:ID  -0.00704219:0.00851523:0.389127:0.0845:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.0591   ES:SE:LP:AF:ID  -0.00358351:0.0107932:0.130827:0.0591:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.187    ES:SE:LP:AF:ID  -0.00155057:0.00652109:0.0903905:0.187:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.1883   ES:SE:LP:AF:ID  -0.00383441:0.00650352:0.255316:0.1883:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.1346   ES:SE:LP:AF:ID  -0.00522401:0.0074472:0.316053:0.1346:rs531730856
1   13445   rs558318514 C   G   .   PASS    AF=0.0006   ES:SE:LP:AF:ID  0.176938:0.0974729:1.15814:0.0006:rs558318514
1   13453   rs568927457 T   C   .   PASS    AF=0.0063   ES:SE:LP:AF:ID  0.029524:0.029871:0.490797:0.0063:rs568927457
1   13483   rs554760071 G   C   .   PASS    AF=0.0049   ES:SE:LP:AF:ID  0.0346038:0.0339282:0.511731:0.0049:rs554760071
1   14464   rs546169444 A   T   .   PASS    AF=0.1535   ES:SE:LP:AF:ID  0.00850792:0.00692486:0.659159:0.1535:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.1915   ES:SE:LP:AF:ID  -0.00446199:0.00629364:0.3203:0.1915:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.1911   ES:SE:LP:AF:ID  -0.00648176:0.00630081:0.517698:0.1911:rs541940975
1   14930   rs6682385   A   G   .   PASS    AF=0.4631   ES:SE:LP:AF:ID  -0.00415833:0.0049315:0.398918:0.4631:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.0475   ES:SE:LP:AF:ID  0.0118423:0.0116584:0.509059:0.0475:rs199856693
1   15211   rs3982632   T   G   .   PASS    AF=0.726    ES:SE:LP:AF:ID  -0.00636203:0.00549801:0.606952:0.726:rs3982632
1   15245   rs576044687 C   T   .   PASS    AF=0.0014   ES:SE:LP:AF:ID  -0.101217:0.0624046:0.979639:0.0014:rs576044687
1   15644   rs564003018 G   A   .   PASS    AF=0.004    ES:SE:LP:AF:ID  -0.107023:0.0418273:1.9784:0.004:rs564003018
1   15820   rs2691315   G   T   .   PASS    AF=0.268    ES:SE:LP:AF:ID  -0.00186098:0.00574547:0.127261:0.268:rs2691315
1   15903   rs557514207 G   GC  .   PASS    AF=0.4014   ES:SE:LP:AF:ID  0.00896494:0.00486518:1.18456:0.4014:rs557514207
1   16141   rs529651976 C   T   .   PASS    AF=0.0005   ES:SE:LP:AF:ID  0.0153067:0.105165:0.0534004:0.0005:rs529651976
1   16142   rs548165136 G   A   .   PASS    AF=0.003    ES:SE:LP:AF:ID  0.041412:0.0462759:0.43086:0.003:rs548165136
1   16542   rs539235482 C   A   .   PASS    AF=0.0001   ES:SE:LP:AF:ID  -0.0543016:0.175595:0.120847:0.0001:rs539235482
1   16949   rs199745162 A   C   .   PASS    AF=0.0208   ES:SE:LP:AF:ID  0.0238446:0.0171686:0.782779:0.0208:rs199745162
1   17571   rs557947346 C   T   .   PASS    AF=0.0005   ES:SE:LP:AF:ID  0.0379726:0.110622:0.135845:0.0005:rs557947346
1   17641   rs578081284 G   A   .   PASS    AF=0.0007   ES:SE:LP:AF:ID  0.0605316:0.070122:0.411168:0.0007:rs578081284
1   18643   rs564023708 G   A   .   PASS    AF=0.006    ES:SE:LP:AF:ID  -0.0204183:0.0335088:0.26576:0.006:rs564023708
1   18849   rs533090414 C   G   .   PASS    AF=0.9728   ES:SE:LP:AF:ID  -0.0100637:0.014159:0.3213:0.9728:rs533090414
1   30524   rs534702355 G   A   .   PASS    AF=0.0007   ES:SE:LP:AF:ID  -0.10257:0.0881945:0.611189:0.0007:rs534702355
1   30923   rs806731    G   T   .   PASS    AF=0.9007   ES:SE:LP:AF:ID  0.00812651:0.00833729:0.481881:0.9007:rs806731
1   46285   rs545414834 ATAT    A   .   PASS    AF=0.0018   ES:SE:LP:AF:ID  0.0215649:0.0552116:0.157328:0.0018:rs545414834
1   47159   rs540662756 T   C   .   PASS    AF=0.0646   ES:SE:LP:AF:ID  -0.00319613:0.0102543:0.121881:0.0646:rs540662756
1   48327   rs565824523 C   A   .   PASS    AF=0.0004   ES:SE:LP:AF:ID  0.027863:0.131497:0.0797723:0.0004:rs565824523
1   48328   rs528394432 A   T   .   PASS    AF=0.0004   ES:SE:LP:AF:ID  0.0249387:0.131494:0.0707855:0.0004:rs528394432
1   49298   rs10399793  T   C   .   PASS    AF=0.8332   ES:SE:LP:AF:ID  0.00289224:0.00669386:0.176721:0.8332:rs10399793
1   49318   rs536836601 A   G   .   PASS    AF=0.0016   ES:SE:LP:AF:ID  -0.0360382:0.0586655:0.268411:0.0016:rs536836601
1   49343   rs553572247 T   C   .   PASS    AF=0.0021   ES:SE:LP:AF:ID  0.00312454:0.0535391:0.0206793:0.0021:rs553572247
1   49482   rs202079915 G   A   .   PASS    AF=0.0002   ES:SE:LP:AF:ID  -0.0897571:0.191662:0.194092:0.0002:rs202079915
1   49554   rs539322794 A   G   .   PASS    AF=0.0935   ES:SE:LP:AF:ID  -0.000951902:0.00867108:0.0397195:0.0935:rs539322794
1   51047   rs559500163 A   T   .   PASS    AF=0.0018   ES:SE:LP:AF:ID  0.0908695:0.0617689:0.849858:0.0018:rs559500163
1   51049   rs528344458 A   C   .   PASS    AF=0.0018   ES:SE:LP:AF:ID  0.0932285:0.0617747:0.881735:0.0018:rs528344458
1   51050   rs551668143 A   T   .   PASS    AF=0.0018   ES:SE:LP:AF:ID  0.0932285:0.0617747:0.881735:0.0018:rs551668143
1   51053   rs565211799 G   T   .   PASS    AF=0.0018   ES:SE:LP:AF:ID  0.0932285:0.0617747:0.881735:0.0018:rs565211799
1   51479   rs116400033 T   A   .   PASS    AF=0.2085   ES:SE:LP:AF:ID  0.00974569:0.00611378:0.955068:0.2085:rs116400033
1   51762   rs559190862 A   G   .   PASS    AF=0.009    ES:SE:LP:AF:ID  -0.028663:0.0265633:0.551912:0.009:rs559190862
1   51765   rs575564077 C   G   .   PASS    AF=0.0086   ES:SE:LP:AF:ID  -0.0188152:0.0270962:0.312114:0.0086:rs575564077
1   52144   rs190291950 T   A   .   PASS    AF=0.0006   ES:SE:LP:AF:ID  -0.0970526:0.0869265:0.578067:0.0006:rs190291950
1   52238   rs2691277   T   G   .   PASS    AF=0.9777   ES:SE:LP:AF:ID  0.00549348:0.0174148:0.123551:0.9777:rs2691277
1   53254   rs547088867 T   A   .   PASS    AF=0.0002   ES:SE:LP:AF:ID  0.000276384:0.125319:0.000782434:0.0002:rs547088867