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": "2019-10-27T07:30:19.298803",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST007557/EBI-a-GCST007557_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-GCST007557/EBI-a-GCST007557.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST007557/EBI-a-GCST007557_data.vcf.gz; Date=Sun Oct 27 07:55:45 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST007557/ebi-a-GCST007557.vcf.gz; Date=Sun May 10 08:31:53 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-GCST007557/EBI-a-GCST007557.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-GCST007557/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 08:20:30 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST007557/EBI-a-GCST007557.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 08:22:12 2019
Total time elapsed: 1.0m:41.58s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9508,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0.0002,
    "n": "-Inf",
    "n_snps": 15914883,
    "n_clumped_hits": 50,
    "n_p_sig": 2281,
    "n_mono": 15111,
    "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": 4829530,
    "n_miss_AF_reference": 945540,
    "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 59 0 15813655 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 15828003 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 8.725677e+00 5.849319e+00 1.0000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.865447e+07 5.632227e+07 56.0000 3.240807e+07 6.923678e+07 1.144404e+08 2.492405e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.973000e-04 4.779540e-02 -0.9057 -1.020000e-02 -1.000000e-04 9.900000e-03 9.232000e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.341500e-02 3.373330e-02 0.0034 5.600000e-03 1.670000e-02 5.830000e-02 4.607000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.823445e-01 2.924343e-01 0.0000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.823451e-01 2.924099e-01 0.0000 2.252283e-01 4.773778e-01 7.357136e-01 9.995053e-01 ▇▇▇▇▇
numeric AF 4829530 0.6948743 NA NA NA NA NA NA NA 2.063996e-01 2.574661e-01 0.0010 1.400000e-02 8.100000e-02 3.200000e-01 1.000000e+00 ▇▂▁▁▁
numeric AF_reference 945540 0.9402616 NA NA NA NA NA NA NA 1.519775e-01 2.319349e-01 0.0000 1.397800e-03 2.655750e-02 2.134580e-01 1.000000e+00 ▇▁▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10511 rs534229142 G A 0.0335 0.1112 0.7600007 0.7632170 NA 0.0001997 NA
1 11012 rs544419019 C G 0.0198 0.0135 0.1400000 0.1424668 0.09 0.0880591 NA
1 13110 rs540538026 G A 0.0039 0.0175 0.8300000 0.8236467 0.06 0.0267572 NA
1 13116 rs62635286 T G -0.0038 0.0107 0.7199992 0.7224846 0.19 0.0970447 NA
1 13118 rs200579949 A G -0.0038 0.0107 0.7199992 0.7224846 0.19 0.0970447 NA
1 13273 rs531730856 G C 0.0056 0.0123 0.6499995 0.6489045 0.13 0.0950479 NA
1 13453 rs568927457 T C -0.0516 0.0478 0.2800000 0.2803658 0.01 0.0007987 NA
1 13483 rs554760071 G C -0.0864 0.0537 0.1100001 0.1076298 0.01 0.0019968 NA
1 13494 rs574697788 A G -0.0117 0.0740 0.8700001 0.8743716 NA 0.0013978 NA
1 13550 rs554008981 G A -0.0445 0.0765 0.5600000 0.5607692 NA 0.0033946 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154891554 rs526172 G T -0.0057 0.0073 0.4299995 0.4349072 0.760 0.6423840 NA
23 154900717 rs628761 C A -0.0052 0.0072 0.4700002 0.4701579 0.760 0.6378810 NA
23 154903802 rs138894319 G A 0.0066 0.0139 0.6300007 0.6349152 0.073 0.0185430 NA
23 154910802 rs147364233 C T -0.0057 0.0184 0.7600007 0.7567263 0.043 0.1070200 NA
23 154915730 rs143362946 C T 0.0345 0.0279 0.2200002 0.2162508 0.022 0.0307285 NA
23 154916845 rs669237 G T 0.0090 0.0075 0.2300001 0.2301393 0.240 0.4166890 NA
23 154918383 rs641588 G T 0.0053 0.0072 0.4700002 0.4616631 0.240 0.3565560 NA
23 154929412 rs557132 C T 0.0028 0.0076 0.7099994 0.7125593 0.240 0.3568210 NA
23 155069592 rs35519384 C T 0.0104 0.0118 0.3800004 0.3781252 0.067 0.0565096 NA
23 155189982 rs5940638 C T 0.0006 0.0059 0.9199999 0.9189988 0.450 0.2739620 NA

bcf preview

1   10511   rs534229142 G   A   .   PASS    .   ES:SE:LP:ID 0.0335:0.1112:0.119186:rs534229142
1   11012   rs544419019 C   G   .   PASS    AF=0.09 ES:SE:LP:AF:ID  0.0198:0.0135:0.853872:0.09:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.06 ES:SE:LP:AF:ID  0.0039:0.0175:0.0809219:0.06:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.19 ES:SE:LP:AF:ID  -0.0038:0.0107:0.142668:0.19:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.19 ES:SE:LP:AF:ID  -0.0038:0.0107:0.142668:0.19:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.13 ES:SE:LP:AF:ID  0.0056:0.0123:0.187087:0.13:rs531730856
1   13453   rs568927457 T   C   .   PASS    AF=0.01 ES:SE:LP:AF:ID  -0.0516:0.0478:0.552842:0.01:rs568927457
1   13483   rs554760071 G   C   .   PASS    AF=0.01 ES:SE:LP:AF:ID  -0.0864:0.0537:0.958607:0.01:rs554760071
1   13494   rs574697788 A   G   .   PASS    .   ES:SE:LP:ID -0.0117:0.074:0.0604807:rs574697788
1   13550   rs554008981 G   A   .   PASS    .   ES:SE:LP:ID -0.0445:0.0765:0.251812:rs554008981
1   14464   rs546169444 A   T   .   PASS    AF=0.16 ES:SE:LP:AF:ID  -0.0175:0.0112:0.920819:0.16:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.19 ES:SE:LP:AF:ID  0.0046:0.0103:0.187087:0.19:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.19 ES:SE:LP:AF:ID  0.0046:0.0103:0.187087:0.19:rs541940975
1   14930   rs6682385   A   G   .   PASS    AF=0.47 ES:SE:LP:AF:ID  -0.0044:0.008:0.236572:0.47:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.05 ES:SE:LP:AF:ID  0.0076:0.019:0.161151:0.05:rs199856693
1   15211   rs3982632   T   G   .   PASS    AF=0.74 ES:SE:LP:AF:ID  0.0075:0.0091:0.387216:0.74:rs3982632
1   15245   rs576044687 C   T   .   PASS    .   ES:SE:LP:ID 0.0921:0.1049:0.420216:rs576044687
1   15585   rs533630043 G   A   .   PASS    AF=0.01 ES:SE:LP:AF:ID  0.0765:0.0494:0.920819:0.01:rs533630043
1   15644   rs564003018 G   A   .   PASS    .   ES:SE:LP:ID 0.0159:0.07:0.0861861:rs564003018
1   15774   rs374029747 G   A   .   PASS    AF=0.01 ES:SE:LP:AF:ID  -0.0328:0.0591:0.236572:0.01:rs374029747
1   15777   rs2691317   A   G   .   PASS    AF=0.01 ES:SE:LP:AF:ID  -0.0773:0.0382:1.36653:0.01:rs2691317
1   15820   rs2691315   G   T   .   PASS    AF=0.27 ES:SE:LP:AF:ID  -0.0079:0.0094:0.39794:0.27:rs2691315
1   16142   rs548165136 G   A   .   PASS    .   ES:SE:LP:ID -0.0896:0.075:0.638272:rs548165136
1   16949   rs199745162 A   C   .   PASS    AF=0.02 ES:SE:LP:AF:ID  0.0647:0.0279:1.69897:0.02:rs199745162
1   18643   rs564023708 G   A   .   PASS    AF=0.01 ES:SE:LP:AF:ID  -0.0483:0.0511:0.468521:0.01:rs564023708
1   18849   rs533090414 C   G   .   PASS    AF=0.975    ES:SE:LP:AF:ID  0.0001:0.0235:-0:0.975:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.904    ES:SE:LP:AF:ID  0.0025:0.0141:0.0655015:0.904:rs806731
1   47159   rs540662756 T   C   .   PASS    AF=0.07 ES:SE:LP:AF:ID  -0.042:0.0168:1.92082:0.07:rs540662756
1   49298   rs10399793  T   C   .   PASS    AF=0.84 ES:SE:LP:AF:ID  0.0159:0.0112:0.79588:0.84:rs10399793
1   49315   rs567788405 T   A   .   PASS    .   ES:SE:LP:ID -0.0203:0.1241:0.0604807:rs567788405
1   49318   rs536836601 A   G   .   PASS    .   ES:SE:LP:ID -0.0564:0.0976:0.251812:rs536836601
1   49343   rs553572247 T   C   .   PASS    .   ES:SE:LP:ID -0.0918:0.0895:0.522879:rs553572247
1   49554   rs539322794 A   G   .   PASS    AF=0.1  ES:SE:LP:AF:ID  0.0055:0.0138:0.161151:0.1:rs539322794
1   50891   rs542415070 T   C   .   PASS    .   ES:SE:LP:ID -0.1444:0.076:1.24413:rs542415070
1   51047   rs559500163 A   T   .   PASS    .   ES:SE:LP:ID 0.0268:0.102:0.102373:rs559500163
1   51049   rs528344458 A   C   .   PASS    .   ES:SE:LP:ID 0.0268:0.102:0.102373:rs528344458
1   51050   rs551668143 A   T   .   PASS    .   ES:SE:LP:ID 0.0268:0.102:0.102373:rs551668143
1   51053   rs565211799 G   T   .   PASS    .   ES:SE:LP:ID 0.0268:0.102:0.102373:rs565211799
1   51479   rs116400033 T   A   .   PASS    AF=0.21 ES:SE:LP:AF:ID  -0.0025:0.0099:0.09691:0.21:rs116400033
1   51762   rs559190862 A   G   .   PASS    AF=0.01 ES:SE:LP:AF:ID  0.0383:0.0453:0.39794:0.01:rs559190862
1   51765   rs575564077 C   G   .   PASS    AF=0.01 ES:SE:LP:AF:ID  0.0383:0.0454:0.39794:0.01:rs575564077
1   52238   rs2691277   T   G   .   PASS    AF=0.978    ES:SE:LP:AF:ID  0.0519:0.0292:1.12494:0.978:rs2691277
1   52253   rs530867301 C   G   .   PASS    AF=0.01 ES:SE:LP:AF:ID  -0.1075:0.059:1.16115:0.01:rs530867301
1   54353   rs140052487 C   A   .   PASS    .   ES:SE:LP:ID -0.0732:0.08:0.443698:rs140052487
1   54354   rs569165477 C   T   .   PASS    .   ES:SE:LP:ID 0.0038:0.0747:0.0177288:rs569165477
1   54490   rs141149254 G   A   .   PASS    AF=0.15 ES:SE:LP:AF:ID  -0.0007:0.0111:0.0222764:0.15:rs141149254
1   54591   rs561234294 A   G   .   PASS    .   ES:SE:LP:ID -0.0257:0.0839:0.119186:rs561234294
1   54712   rs552304420 T   C   .   PASS    AF=0.01 ES:SE:LP:AF:ID  0.0869:0.0405:1.49485:0.01:rs552304420
1   54712   rs552304420 T   C   .   PASS    AF=0.01 ES:SE:LP:AF:ID  0.0869:0.0405:1.49485:0.01:rs552304420
1   54716   rs569128616 C   T   .   PASS    AF=0.43 ES:SE:LP:AF:ID  0.0047:0.0083:0.236572:0.43:rs569128616