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.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": "2019-10-27T07:19:15.773191",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005218/EBI-a-GCST005218_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-GCST005218/EBI-a-GCST005218.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005218/EBI-a-GCST005218_data.vcf.gz; Date=Sun Oct 27 07:23:25 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST005218/ebi-a-GCST005218.vcf.gz; Date=Sun May 10 03:10:08 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-GCST005218/EBI-a-GCST005218.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-GCST005218/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 07:45:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005218/EBI-a-GCST005218.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 07:46:01 2019
Total time elapsed: 15.44s

QC metrics

Metrics

Metrics

{
    "af_correlation": -0.3898,
    "inflation_factor": 1.0221,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 2428482,
    "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": 1931,
    "n_miss_AF_reference": 19083,
    "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 2422204 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 2422206 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 8.589827e+00 5.657306e+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 7.884510e+07 5.567461e+07 1.1523e+04 3.268025e+07 7.029970e+07 1.143323e+08 2.492190e+08 ▇▇▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.337000e-04 3.948210e-02 -7.7000e-01 -2.100000e-02 -5.800000e-05 2.100000e-02 7.400000e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.451530e-02 1.877240e-02 2.1000e-02 2.300000e-02 2.800000e-02 3.700000e-02 4.300000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.968015e-01 2.891248e-01 6.0000e-07 2.458018e-01 4.952973e-01 7.469835e-01 9.999900e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.968273e-01 2.891771e-01 6.0000e-07 2.451696e-01 4.953539e-01 7.469018e-01 9.999914e-01 ▇▇▇▇▇
numeric AF 1931 0.9992028 NA NA NA NA NA NA NA 5.696657e-01 2.989613e-01 4.0000e-03 3.080000e-01 6.060000e-01 8.500000e-01 9.960000e-01 ▅▅▅▅▇
numeric AF_reference 19083 0.9921216 NA NA NA NA NA NA NA 3.627056e-01 2.543931e-01 1.9970e-04 1.489620e-01 3.015180e-01 5.461260e-01 1.000000e+00 ▇▆▅▃▂

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 721290 rs12565286 G C -0.110 0.073 0.1465210 0.1318493 0.968 0.0371406 NA
1 723819 rs11804171 T A -0.110 0.073 0.1442139 0.1318493 0.963 0.1345850 NA
1 723891 rs2977670 G C 0.130 0.085 0.1231530 0.1261624 0.071 0.7799520 NA
1 750235 rs12138618 G A -0.140 0.110 0.2008968 0.2031148 0.942 NA NA
1 752566 rs3094315 G A 0.034 0.029 0.2283779 0.2410310 0.835 0.7182510 NA
1 754192 rs3131968 A G 0.040 0.047 0.3993852 0.3947339 0.875 0.6785140 NA
1 768448 rs12562034 G A -0.097 0.061 0.1123650 0.1117979 0.102 0.1918930 NA
1 775659 rs2905035 A G 0.026 0.033 0.4298501 0.4307676 0.875 0.7450080 NA
1 777122 rs2980319 A T 0.026 0.033 0.4327838 0.4307676 0.866 0.7472040 NA
1 779322 rs4040617 A G -0.025 0.033 0.4507586 0.4487050 0.133 0.2264380 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51178090 rs2285395 G A 0.047 0.045 0.2990047 0.2962798 0.934 0.0666933 NA
22 51183017 rs58651371 C T 0.022 0.058 0.7010694 0.7044574 0.733 0.4137380 NA
22 51196164 rs8136603 A T 0.044 0.056 0.4252614 0.4320349 0.925 0.1427720 NA
22 51211392 rs3888396 T C 0.033 0.098 0.7346576 0.7363169 0.894 0.1641370 NA
22 51212875 rs2238837 A C -0.044 0.036 0.2155952 0.2216236 0.615 0.3724040 NA
22 51216564 rs9616970 T C 0.022 0.049 0.6536948 0.6534464 0.850 0.1563500 NA
22 51217134 rs117417021 A G -0.041 0.037 0.2647872 0.2678151 0.558 0.2671730 NA
22 51223637 rs375798137 G A 0.060 0.056 0.2807573 0.2839768 0.933 0.0788738 NA
22 51229805 rs9616985 T C -0.016 0.100 0.8786510 0.8728811 0.908 0.0730831 NA
23 118495837 rs12882977 G A 0.056 0.022 0.0121899 0.0109136 0.491 0.2307280 NA

bcf preview

1   721290  rs12565286  G   C   .   PASS    AF=0.968    ES:SE:LP:AF:ID  -0.11:0.073:0.8341:0.968:rs12565286
1   723819  rs11804171  T   A   .   PASS    AF=0.963    ES:SE:LP:AF:ID  -0.11:0.073:0.840993:0.963:rs11804171
1   723891  rs2977670   G   C   .   PASS    AF=0.071    ES:SE:LP:AF:ID  0.13:0.085:0.909555:0.071:rs2977670
1   750235  rs12138618  G   A   .   PASS    AF=0.942    ES:SE:LP:AF:ID  -0.14:0.11:0.697027:0.942:rs12138618
1   752566  rs3094315   G   A   .   PASS    AF=0.835    ES:SE:LP:AF:ID  0.034:0.029:0.641346:0.835:rs3094315
1   754192  rs3131968   A   G   .   PASS    AF=0.875    ES:SE:LP:AF:ID  0.04:0.047:0.398608:0.875:rs3131968
1   768448  rs12562034  G   A   .   PASS    AF=0.102    ES:SE:LP:AF:ID  -0.097:0.061:0.949369:0.102:rs12562034
1   775659  rs2905035   A   G   .   PASS    AF=0.875    ES:SE:LP:AF:ID  0.026:0.033:0.366683:0.875:rs2905035
1   777122  rs2980319   A   T   .   PASS    AF=0.866    ES:SE:LP:AF:ID  0.026:0.033:0.363729:0.866:rs2980319
1   779322  rs4040617   A   G   .   PASS    AF=0.133    ES:SE:LP:AF:ID  -0.025:0.033:0.346056:0.133:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.866    ES:SE:LP:AF:ID  0.024:0.033:0.331931:0.866:rs2977612
1   785050  rs2905062   G   A   .   PASS    AF=0.856    ES:SE:LP:AF:ID  0.023:0.033:0.308363:0.856:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.868    ES:SE:LP:AF:ID  0.023:0.033:0.306086:0.868:rs2980300
1   798026  rs4951864   C   T   .   PASS    AF=0.1  ES:SE:LP:AF:ID  0.1:0.063:0.966685:0.1:rs4951864
1   798801  rs12132517  G   A   .   PASS    AF=0.892    ES:SE:LP:AF:ID  -0.1:0.064:0.94038:0.892:rs12132517
1   798959  rs11240777  G   A   .   PASS    AF=0.219    ES:SE:LP:AF:ID  -0.046:0.039:0.622872:0.219:rs11240777
1   962210  rs3128126   A   G   .   PASS    AF=0.575    ES:SE:LP:AF:ID  -0.0013:0.051:0.00916541:0.575:rs3128126
1   990380  rs3121561   C   T   .   PASS    AF=0.267    ES:SE:LP:AF:ID  -0.028:0.045:0.272409:0.267:rs3121561
1   998501  rs3813193   G   C   .   PASS    AF=0.85 ES:SE:LP:AF:ID  -0.037:0.038:0.470203:0.85:rs3813193
1   1003629 rs4075116   C   T   .   PASS    AF=0.734    ES:SE:LP:AF:ID  0.00022:0.025:0.00306825:0.734:rs4075116
1   1005806 rs3934834   C   T   .   PASS    AF=0.124    ES:SE:LP:AF:ID  -0.039:0.038:0.521744:0.124:rs3934834
1   1017170 rs3766193   C   G   .   PASS    AF=0.431    ES:SE:LP:AF:ID  0.0079:0.028:0.111208:0.431:rs3766193
1   1017197 rs3766192   C   T   .   PASS    AF=0.429    ES:SE:LP:AF:ID  0.019:0.025:0.339539:0.429:rs3766192
1   1017587 rs3766191   C   T   .   PASS    AF=0.124    ES:SE:LP:AF:ID  -0.035:0.039:0.435628:0.124:rs3766191
1   1018562 rs9442371   C   T   .   PASS    AF=0.408    ES:SE:LP:AF:ID  0.019:0.025:0.344697:0.408:rs9442371
1   1018704 rs9442372   A   G   .   PASS    AF=0.412    ES:SE:LP:AF:ID  0.019:0.025:0.361948:0.412:rs9442372
1   1021346 rs10907177  A   G   .   PASS    AF=0.137    ES:SE:LP:AF:ID  -0.036:0.041:0.421087:0.137:rs10907177
1   1021415 rs3737728   A   G   .   PASS    AF=0.265    ES:SE:LP:AF:ID  0.0086:0.028:0.119926:0.265:rs3737728
1   1021583 rs10907178  A   C   .   PASS    AF=0.113    ES:SE:LP:AF:ID  -0.036:0.041:0.420453:0.113:rs10907178
1   1021695 rs9442398   A   G   .   PASS    AF=0.274    ES:SE:LP:AF:ID  0.0038:0.028:0.0485454:0.274:rs9442398
1   1022037 rs6701114   C   T   .   PASS    AF=0.433    ES:SE:LP:AF:ID  0.018:0.03:0.262908:0.433:rs6701114
1   1025301 rs9442400   T   C   .   PASS    AF=0.053    ES:SE:LP:AF:ID  -0.15:0.14:0.549175:0.053:rs9442400
1   1026707 rs4074137   C   A   .   PASS    AF=0.6  ES:SE:LP:AF:ID  0.025:0.038:0.289768:0.6:rs4074137
1   1030565 rs6687776   C   T   .   PASS    AF=0.133    ES:SE:LP:AF:ID  -0.1:0.045:1.65716:0.133:rs6687776
1   1031540 rs9651273   A   G   .   PASS    AF=0.283    ES:SE:LP:AF:ID  -0.031:0.036:0.40537:0.283:rs9651273
1   1036959 rs11579015  T   C   .   PASS    AF=0.95 ES:SE:LP:AF:ID  -0.042:0.068:0.271932:0.95:rs11579015
1   1040026 rs6671356   T   C   .   PASS    AF=0.885    ES:SE:LP:AF:ID  -0.058:0.059:0.481759:0.885:rs6671356
1   1046164 rs6666280   C   T   .   PASS    AF=0.067    ES:SE:LP:AF:ID  -0.055:0.059:0.452257:0.067:rs6666280
1   1048955 rs4970405   A   G   .   PASS    AF=0.088    ES:SE:LP:AF:ID  -0.038:0.068:0.241022:0.088:rs4970405
1   1049950 rs12726255  A   G   .   PASS    AF=0.115    ES:SE:LP:AF:ID  -0.034:0.055:0.270266:0.115:rs12726255
1   1053452 rs4970409   G   A   .   PASS    AF=0.907    ES:SE:LP:AF:ID  -0.013:0.064:0.0773155:0.907:rs4970409
1   1060235 rs7540009   G   A   .   PASS    AF=0.983    ES:SE:LP:AF:ID  -0.025:0.089:0.109077:0.983:rs7540009
1   1060608 rs17160824  G   A   .   PASS    AF=0.903    ES:SE:LP:AF:ID  -0.018:0.065:0.104985:0.903:rs17160824
1   1061115 rs17160826  T   C   .   PASS    AF=0.945    ES:SE:LP:AF:ID  -0.017:0.065:0.097606:0.945:rs17160826
1   1061152 rs12748370  T   C   .   PASS    AF=0.95 ES:SE:LP:AF:ID  -0.018:0.065:0.105375:0.95:rs12748370
1   1061166 rs11807848  T   C   .   PASS    AF=0.611    ES:SE:LP:AF:ID  0.035:0.035:0.497604:0.611:rs11807848
1   1062015 rs9659914   C   T   .   PASS    AF=0.017    ES:SE:LP:AF:ID  -0.026:0.091:0.111678:0.017:rs9659914
1   1062638 rs9442373   C   A   .   PASS    AF=0.575    ES:SE:LP:AF:ID  0.02:0.035:0.257106:0.575:rs9442373
1   1064535 rs6682475   G   C   .   PASS    AF=0.246    ES:SE:LP:AF:ID  0.11:0.052:1.3914:0.246:rs6682475
1   1064979 rs2298217   C   T   .   PASS    AF=0.115    ES:SE:LP:AF:ID  -0.088:0.05:1.10682:0.115:rs2298217