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.4": "<ID=TotalControls,Number=1,Type=Integer,Description=\"Total number of controls in the association study\">",
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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": "2019-10-27T07:18:50.231581",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006328/EBI-a-GCST006328_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-GCST006328/EBI-a-GCST006328.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006328/EBI-a-GCST006328_data.vcf.gz; Date=Sun Oct 27 07:22:55 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006328/ebi-a-GCST006328.vcf.gz; Date=Sun May 10 03:57:38 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-GCST006328/EBI-a-GCST006328.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-GCST006328/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 07:45:23 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006328/EBI-a-GCST006328.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 07:45:38 2019
Total time elapsed: 14.6s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.063,
    "inflation_factor": 1.0155,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 2300113,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 17111,
    "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 TRUE
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 1.0000000 3 42 0 2294142 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 2294147 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 8.564079e+00 5.642283e+00 1.0000e+00 4.000000e+00 8.000000e+00 1.200000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.890076e+07 5.560885e+07 2.9443e+04 3.278564e+07 7.037349e+07 1.142828e+08 2.492107e+08 ▇▇▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.147000e-04 1.165923e-01 -1.1760e+00 -6.770000e-02 2.000000e-04 6.800000e-02 1.136600e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.083055e-01 4.072970e-02 7.0200e-02 8.040000e-02 9.250000e-02 1.203000e-01 6.009000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.977998e-01 2.890324e-01 3.0000e-07 2.474002e-01 4.966998e-01 7.483004e-01 9.998000e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.977997e-01 2.890324e-01 3.0000e-07 2.473801e-01 4.966648e-01 7.482678e-01 9.997411e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.908984e-01 2.969044e-01 1.0000e-02 2.170000e-01 4.830000e-01 7.650000e-01 9.900000e-01 ▇▆▆▆▇
numeric AF_reference 17111 0.9925415 NA NA NA NA NA NA NA 3.708246e-01 2.491936e-01 1.9970e-04 1.615420e-01 3.142970e-01 5.521170e-01 1.000000e+00 ▇▇▅▃▂

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 990380 rs3121561 C T -0.0071 0.1350 0.9579001 0.9580565 0.267 0.343450 NA
1 998501 rs3813193 G C 0.0096 0.1269 0.9394001 0.9396975 0.150 0.206669 NA
1 1005806 rs3934834 C T 0.0311 0.1261 0.8052001 0.8051948 0.119 0.223442 NA
1 1017170 rs3766193 C G -0.0075 0.0946 0.9365000 0.9368090 0.569 0.575479 NA
1 1017197 rs3766192 C T -0.0011 0.0907 0.9901000 0.9903236 0.424 0.511182 NA
1 1017587 rs3766191 C T 0.0502 0.1284 0.6954998 0.6958226 0.096 0.171126 NA
1 1018562 rs9442371 C T 0.0109 0.0898 0.9034000 0.9033894 0.408 0.530152 NA
1 1018704 rs9442372 A G 0.0157 0.0896 0.8607000 0.8609042 0.602 0.611022 NA
1 1021346 rs10907177 A G 0.0366 0.1262 0.7716991 0.7718041 0.873 0.178914 NA
1 1021415 rs3737728 A G 0.0320 0.0890 0.7193993 0.7191833 0.717 0.812700 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51140316 rs739365 C T 0.0877 0.0851 0.3026997 0.3027508 0.333 0.4363020 NA
22 51146139 rs5770992 A G 0.0122 0.1225 0.9205000 0.9206684 0.836 0.1136180 NA
22 51146439 rs6010060 A G 0.2595 0.1537 0.0914197 0.0913433 0.915 0.0551118 NA
22 51147015 rs2040487 A G -0.0376 0.0874 0.6666993 0.6670458 0.575 0.4556710 NA
22 51150473 rs5770820 G A 0.1495 0.1025 0.1448001 0.1446927 0.190 0.2462060 NA
22 51151350 rs6009951 C T 0.0443 0.0886 0.6172001 0.6170751 0.387 0.5559110 NA
22 51151724 rs6010061 C T 0.0327 0.0886 0.7121000 0.7120722 0.381 0.6098240 NA
22 51162059 rs10451 G A 0.0304 0.1105 0.7832006 0.7832293 0.285 0.3865810 NA
22 51171497 rs2301584 G A 0.0272 0.1710 0.8736999 0.8736182 0.164 0.2533950 NA
23 118495837 rs12882977 G A -0.0112 0.0756 0.8826000 0.8822259 0.491 0.2307280 NA

bcf preview

1   990380  rs3121561   C   T   .   PASS    AF=0.267    ES:SE:LP:AF:ID  -0.0071:0.135:0.0186798:0.267:rs3121561
1   998501  rs3813193   G   C   .   PASS    AF=0.15 ES:SE:LP:AF:ID  0.0096:0.1269:0.0271494:0.15:rs3813193
1   1005806 rs3934834   C   T   .   PASS    AF=0.119    ES:SE:LP:AF:ID  0.0311:0.1261:0.0940962:0.119:rs3934834
1   1017170 rs3766193   C   G   .   PASS    AF=0.569    ES:SE:LP:AF:ID  -0.0075:0.0946:0.0284922:0.569:rs3766193
1   1017197 rs3766192   C   T   .   PASS    AF=0.424    ES:SE:LP:AF:ID  -0.0011:0.0907:0.00432094:0.424:rs3766192
1   1017587 rs3766191   C   T   .   PASS    AF=0.096    ES:SE:LP:AF:ID  0.0502:0.1284:0.157703:0.096:rs3766191
1   1018562 rs9442371   C   T   .   PASS    AF=0.408    ES:SE:LP:AF:ID  0.0109:0.0898:0.0441199:0.408:rs9442371
1   1018704 rs9442372   A   G   .   PASS    AF=0.602    ES:SE:LP:AF:ID  0.0157:0.0896:0.0651482:0.602:rs9442372
1   1021346 rs10907177  A   G   .   PASS    AF=0.873    ES:SE:LP:AF:ID  0.0366:0.1262:0.112552:0.873:rs10907177
1   1021415 rs3737728   A   G   .   PASS    AF=0.717    ES:SE:LP:AF:ID  0.032:0.089:0.14303:0.717:rs3737728
1   1021583 rs10907178  A   C   .   PASS    AF=0.887    ES:SE:LP:AF:ID  0.0332:0.1277:0.0997968:0.887:rs10907178
1   1021695 rs9442398   A   G   .   PASS    AF=0.725    ES:SE:LP:AF:ID  0.0172:0.0895:0.0719115:0.725:rs9442398
1   1022037 rs6701114   C   T   .   PASS    AF=0.433    ES:SE:LP:AF:ID  0.0031:0.0941:0.0114856:0.433:rs6701114
1   1026707 rs4074137   C   A   .   PASS    AF=0.4  ES:SE:LP:AF:ID  0.1114:0.0973:0.598427:0.4:rs4074137
1   1030565 rs6687776   C   T   .   PASS    AF=0.083    ES:SE:LP:AF:ID  0.1663:0.1178:0.801343:0.083:rs6687776
1   1030633 rs6678318   G   A   .   PASS    AF=0.086    ES:SE:LP:AF:ID  0.1302:0.1384:0.460171:0.086:rs6678318
1   1036959 rs11579015  T   C   .   PASS    AF=0.95 ES:SE:LP:AF:ID  0.0168:0.1438:0.0424406:0.95:rs11579015
1   1040026 rs6671356   T   C   .   PASS    AF=0.925    ES:SE:LP:AF:ID  0.0842:0.1268:0.295335:0.925:rs6671356
1   1046164 rs6666280   C   T   .   PASS    AF=0.067    ES:SE:LP:AF:ID  0.0689:0.1249:0.235749:0.067:rs6666280
1   1048955 rs4970405   A   G   .   PASS    AF=0.948    ES:SE:LP:AF:ID  -0.0234:0.141:0.0614803:0.948:rs4970405
1   1049950 rs12726255  A   G   .   PASS    AF=0.933    ES:SE:LP:AF:ID  0.0929:0.1214:0.352324:0.933:rs12726255
1   1053452 rs4970409   G   A   .   PASS    AF=0.05 ES:SE:LP:AF:ID  0.0344:0.14:0.0938805:0.05:rs4970409
1   1060174 rs7548798   C   T   .   PASS    AF=0.398    ES:SE:LP:AF:ID  -0.0439:0.1188:0.147703:0.398:rs7548798
1   1060608 rs17160824  G   A   .   PASS    AF=0.058    ES:SE:LP:AF:ID  0.0647:0.1427:0.186953:0.058:rs17160824
1   1061115 rs17160826  T   C   .   PASS    AF=0.945    ES:SE:LP:AF:ID  0.0635:0.1428:0.182567:0.945:rs17160826
1   1061152 rs12748370  T   C   .   PASS    AF=0.95 ES:SE:LP:AF:ID  0.0615:0.143:0.175874:0.95:rs12748370
1   1061166 rs11807848  T   C   .   PASS    AF=0.588    ES:SE:LP:AF:ID  -0.0583:0.0956:0.266001:0.588:rs11807848
1   1062638 rs9442373   C   A   .   PASS    AF=0.448    ES:SE:LP:AF:ID  0.1183:0.0945:0.676542:0.448:rs9442373
1   1064535 rs6682475   G   C   .   PASS    AF=0.246    ES:SE:LP:AF:ID  -0.0127:0.1334:0.034375:0.246:rs6682475
1   1066403 rs10907182  T   C   .   PASS    AF=0.675    ES:SE:LP:AF:ID  0.0982:0.112:0.419189:0.675:rs10907182
1   1079198 rs11260603  T   C   .   PASS    AF=0.776    ES:SE:LP:AF:ID  -0.0328:0.1605:0.076756:0.776:rs11260603
1   1087683 rs9442380   T   C   .   PASS    AF=0.912    ES:SE:LP:AF:ID  0.0308:0.1481:0.0783135:0.912:rs9442380
1   1113121 rs12092254  G   A   .   PASS    AF=0.1  ES:SE:LP:AF:ID  0.2565:0.1961:0.718967:0.1:rs12092254
1   1119858 rs1320565   C   T   .   PASS    AF=0.092    ES:SE:LP:AF:ID  0.1531:0.1835:0.393619:0.092:rs1320565
1   1121014 rs3813204   G   A   .   PASS    AF=0.112    ES:SE:LP:AF:ID  0.2631:0.1677:0.933674:0.112:rs3813204
1   1121794 rs11260549  G   A   .   PASS    AF=0.125    ES:SE:LP:AF:ID  0.2478:0.136:1.16437:0.125:rs11260549
1   1124663 rs6684820   G   A   .   PASS    AF=0.267    ES:SE:LP:AF:ID  0.2047:0.1051:1.28836:0.267:rs6684820
1   1125553 rs10907174  A   T   .   PASS    AF=0.775    ES:SE:LP:AF:ID  0.1999:0.1152:1.08192:0.775:rs10907174
1   1129672 rs11260554  G   T   .   PASS    AF=0.125    ES:SE:LP:AF:ID  0.2516:0.136:1.19145:0.125:rs11260554
1   1130727 rs10907175  A   C   .   PASS    AF=0.9  ES:SE:LP:AF:ID  0.0673:0.1391:0.201695:0.9:rs10907175
1   1135242 rs9729550   A   C   .   PASS    AF=0.725    ES:SE:LP:AF:ID  0.2:0.1049:1.24757:0.725:rs9729550
1   1138913 rs3819001   T   C   .   PASS    AF=0.957    ES:SE:LP:AF:ID  0.2627:0.215:0.654234:0.957:rs3819001
1   1140435 rs1815606   G   T   .   PASS    AF=0.31 ES:SE:LP:AF:ID  0.1315:0.1232:0.543938:0.31:rs1815606
1   1143657 rs3753348   C   G   .   PASS    AF=0.967    ES:SE:LP:AF:ID  0.3101:0.2333:0.735418:0.967:rs3753348
1   1152631 rs11721 C   A   .   PASS    AF=0.083    ES:SE:LP:AF:ID  0.212:0.1555:0.762456:0.083:rs11721
1   1156131 rs2887286   T   C   .   PASS    AF=0.817    ES:SE:LP:AF:ID  -0.0483:0.1041:0.191857:0.817:rs2887286
1   1158277 rs3813199   G   A   .   PASS    AF=0.1  ES:SE:LP:AF:ID  0.052:0.134:0.156082:0.1:rs3813199
1   1162435 rs3766186   C   A   .   PASS    AF=0.1  ES:SE:LP:AF:ID  0.0575:0.1339:0.175419:0.1:rs3766186
1   1163804 rs7515488   C   T   .   PASS    AF=0.15 ES:SE:LP:AF:ID  0.1762:0.1239:0.810229:0.15:rs7515488
1   1165310 rs11260562  G   A   .   PASS    AF=0.052    ES:SE:LP:AF:ID  0.4015:0.2071:1.27968:0.052:rs11260562