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.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.\">",
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    "file_date": "2019-10-27T07:28:57.398627",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005810/EBI-a-GCST005810_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",
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    "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-GCST005810/EBI-a-GCST005810.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005810/EBI-a-GCST005810_data.vcf.gz; Date=Sun Oct 27 07:56:05 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST005810/ebi-a-GCST005810.vcf.gz; Date=Sun May 10 11:37:27 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-GCST005810/EBI-a-GCST005810.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-GCST005810/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 08:22:01 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005810/EBI-a-GCST005810.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 08:23:45 2019
Total time elapsed: 1.0m:43.74s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9553,
    "inflation_factor": 0.9644,
    "mean_EFFECT": -0.0003,
    "n": "-Inf",
    "n_snps": 15543680,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1394415,
    "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": 468425,
    "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 1.0000000 3 58 0 15520568 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 84 0 66632 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 63 0 34207 0 NA NA NA NA NA NA NA NA NA NA
logical N 15521131 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 8.769916e+00 5.807564e+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 7.836083e+07 5.621572e+07 56.0000000 3.224883e+07 6.884319e+07 1.137980e+08 2.492397e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.275000e-04 2.479662e-01 -8.1659000 -5.874530e-02 1.720200e-03 6.503530e-02 7.034610e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.900242e-01 2.890786e-01 0.0316581 4.216010e-02 9.946850e-02 3.000450e-01 2.150410e+01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 5.048043e-01 2.889569e-01 0.0000001 2.551779e-01 5.077232e-01 7.553861e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 5.063263e-01 2.878437e-01 0.0000000 2.586293e-01 5.091404e-01 7.555279e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 1.648066e-01 2.461007e-01 0.0010000 3.753400e-03 3.063820e-02 2.366795e-01 9.989990e-01 ▇▁▁▁▁
numeric AF_reference 468425 0.9698202 NA NA NA NA NA NA NA 1.678114e-01 2.389264e-01 0.0000000 1.996800e-03 4.353040e-02 2.509980e-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 0.1002850 0.0462299 0.0303438 0.0300624 0.3932930 0.4253190 NA
1 10352 rs555500075 T TA -0.0676942 0.0492497 0.1682500 0.1692834 0.3878300 0.4375000 NA
1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C -0.1059600 0.2909860 0.7192734 0.7157526 0.9947080 0.9930110 NA
1 11012 rs544419019 C G 0.0141324 0.0808825 0.8615399 0.8612937 0.0846656 0.0880591 NA
1 13110 rs540538026 G A -0.1157700 0.1096840 0.2833101 0.2912028 0.0611845 0.0267572 NA
1 13116 rs62635286 T G -0.1570190 0.0666911 0.0166223 0.0185515 0.1926570 0.0970447 NA
1 13118 rs200579949 A G -0.1570190 0.0666911 0.0166223 0.0185515 0.1926570 0.0970447 NA
1 13273 rs531730856 G C 0.1321250 0.0699654 0.0622644 0.0589678 0.1326330 0.0950479 NA
1 13453 rs568927457 T C 0.1023720 0.2976780 0.7352483 0.7309201 0.0059674 0.0007987 NA
1 13483 rs554760071 G C 0.1075390 0.3229520 0.7438922 0.7391437 0.0051827 0.0019968 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51238328 rs553081191 A C 0.4053040 0.399525 0.3290561 0.3103611 0.0017116 0.0005990 NA
22 51238364 rs564490465 C G 0.5195100 0.290416 0.0885870 0.0736393 0.0049424 0.0005990 NA
22 51238394 rs149712012 C T -0.6814410 0.476514 0.1064030 0.1527014 0.0035693 0.0033946 NA
22 51239281 rs8138215 G C 0.3149740 0.446349 0.4961580 0.4803949 0.0016429 0.0111821 NA
22 51239296 rs8137179 T C 0.3149740 0.446349 0.4961580 0.4803949 0.0016429 0.0111821 NA
22 51239304 rs8142977 C T 0.3149740 0.446349 0.4961580 0.4803949 0.0016429 0.0111821 NA
22 51239586 rs535432390 T G -1.7004700 1.729580 0.0749290 0.3255241 0.0018701 0.0001997 NA
22 51239794 rs561893765 C A -0.6077290 0.690309 0.3314317 0.3786576 0.0018358 0.0299521 NA
22 51240820 rs202228854 C T 0.0152015 0.126881 0.9054759 0.9046344 0.0262359 0.1267970 NA
22 51244237 rs575160859 C T -0.0306624 0.201600 0.8785871 0.8791118 0.0132401 0.0037939 NA

bcf preview

1   10177   rs367896724 A   AC  .   PASS    AF=0.393293 ES:SE:LP:AF:ID  0.100285:0.0462299:1.51793:0.393293:rs367896724
1   10352   rs555500075 T   TA  .   PASS    AF=0.38783  ES:SE:LP:AF:ID  -0.0676942:0.0492497:0.774045:0.38783:rs555500075
1   10616   rs376342519 CCGCCGTTGCAAAGGCGCGCCG  C   .   PASS    AF=0.994708 ES:SE:LP:AF:ID  -0.10596:0.290986:0.143106:0.994708:rs376342519
1   11012   rs544419019 C   G   .   PASS    AF=0.0846656    ES:SE:LP:AF:ID  0.0141324:0.0808825:0.0647246:0.0846656:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.0611845    ES:SE:LP:AF:ID  -0.11577:0.109684:0.547738:0.0611845:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.192657 ES:SE:LP:AF:ID  -0.157019:0.0666911:1.77931:0.192657:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.192657 ES:SE:LP:AF:ID  -0.157019:0.0666911:1.77931:0.192657:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.132633 ES:SE:LP:AF:ID  0.132125:0.0699654:1.20576:0.132633:rs531730856
1   13453   rs568927457 T   C   .   PASS    AF=0.0059674    ES:SE:LP:AF:ID  0.102372:0.297678:0.133566:0.0059674:rs568927457
1   13483   rs554760071 G   C   .   PASS    AF=0.0051827    ES:SE:LP:AF:ID  0.107539:0.322952:0.12849:0.0051827:rs554760071
1   14464   rs546169444 A   T   .   PASS    AF=0.158163 ES:SE:LP:AF:ID  -0.0193693:0.0661443:0.113951:0.158163:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.192988 ES:SE:LP:AF:ID  0.0789058:0.0598588:0.720388:0.192988:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.192988 ES:SE:LP:AF:ID  0.0789058:0.0598588:0.720388:0.192988:rs541940975
1   14930   rs6682385   A   G   .   PASS    AF=0.46955  ES:SE:LP:AF:ID  0.00958963:0.0478477:0.0751162:0.46955:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.0491667    ES:SE:LP:AF:ID  -0.0279575:0.111032:0.0966587:0.0491667:rs199856693
1   15211   rs3982632   T   G   .   PASS    AF=0.745228 ES:SE:LP:AF:ID  -0.0881682:0.054192:0.976299:0.745228:rs3982632
1   15245   rs576044687 C   T   .   PASS    AF=0.00117504   ES:SE:LP:AF:ID  0.535115:0.551153:0.453193:0.00117504:rs576044687
1   15644   rs564003018 G   A   .   PASS    AF=0.00346843   ES:SE:LP:AF:ID  -0.420602:0.463931:0.47949:0.00346843:rs564003018
1   15820   rs2691315   G   T   .   PASS    AF=0.272828 ES:SE:LP:AF:ID  -0.0883886:0.0567234:0.93255:0.272828:rs2691315
1   15903   rs557514207 G   GC  .   PASS    AF=0.407492 ES:SE:LP:AF:ID  0.0229205:0.0466352:0.205324:0.407492:rs557514207
1   16142   rs548165136 G   A   .   PASS    AF=0.00311841   ES:SE:LP:AF:ID  -0.116027:0.426389:0.106471:0.00311841:rs548165136
1   16949   rs199745162 A   C   .   PASS    AF=0.0208271    ES:SE:LP:AF:ID  0.00376431:0.169775:0.00771209:0.0208271:rs199745162
1   18643   rs564023708 G   A   .   PASS    AF=0.00602273   ES:SE:LP:AF:ID  -0.248169:0.362468:0.323974:0.00602273:rs564023708
1   18849   rs533090414 C   G   .   PASS    AF=0.975361 ES:SE:LP:AF:ID  -0.122525:0.135548:0.427705:0.975361:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.904884 ES:SE:LP:AF:ID  -0.100571:0.0816593:0.650078:0.904884:rs806731
1   46285   rs545414834 ATAT    A   .   PASS    AF=0.0017292    ES:SE:LP:AF:ID  -0.546:0.62169:0.460761:0.0017292:rs545414834
1   47159   rs540662756 T   C   .   PASS    AF=0.0659771    ES:SE:LP:AF:ID  0.0231005:0.0987932:0.0884919:0.0659771:rs540662756
1   49298   rs10399793  T   C   .   PASS    AF=0.838935 ES:SE:LP:AF:ID  -0.0208664:0.0658848:0.123812:0.838935:rs10399793
1   49318   rs536836601 A   G   .   PASS    AF=0.0015893    ES:SE:LP:AF:ID  -0.457101:0.690389:0.324258:0.0015893:rs536836601
1   49343   rs553572247 T   C   .   PASS    AF=0.00231753   ES:SE:LP:AF:ID  -0.440793:0.560749:0.397584:0.00231753:rs553572247
1   49554   rs539322794 A   G   .   PASS    AF=0.0970869    ES:SE:LP:AF:ID  0.0648837:0.0807619:0.371453:0.0970869:rs539322794
1   51047   rs559500163 A   T   .   PASS    AF=0.00149565   ES:SE:LP:AF:ID  0.979865:0.480033:1.27817:0.00149565:rs559500163
1   51049   rs528344458 A   C   .   PASS    AF=0.00149565   ES:SE:LP:AF:ID  0.979865:0.480033:1.27817:0.00149565:rs528344458
1   51050   rs551668143 A   T   .   PASS    AF=0.00149565   ES:SE:LP:AF:ID  0.979865:0.480033:1.27817:0.00149565:rs551668143
1   51053   rs565211799 G   T   .   PASS    AF=0.00149565   ES:SE:LP:AF:ID  0.979865:0.480033:1.27817:0.00149565:rs565211799
1   51479   rs116400033 T   A   .   PASS    AF=0.211376 ES:SE:LP:AF:ID  0.067815:0.057876:0.612991:0.211376:rs116400033
1   51762   rs559190862 A   G   .   PASS    AF=0.0093499    ES:SE:LP:AF:ID  -0.157683:0.261467:0.270434:0.0093499:rs559190862
1   51765   rs575564077 C   G   .   PASS    AF=0.00908569   ES:SE:LP:AF:ID  -0.159228:0.2626:0.272266:0.00908569:rs575564077
1   52238   rs2691277   T   G   .   PASS    AF=0.978303 ES:SE:LP:AF:ID  0.10504:0.183968:0.250475:0.978303:rs2691277
1   54353   rs140052487 C   A   .   PASS    AF=0.00216849   ES:SE:LP:AF:ID  -0.779788:0.601046:0.871258:0.00216849:rs140052487
1   54354   rs569165477 C   T   .   PASS    AF=0.00210071   ES:SE:LP:AF:ID  0.611342:0.359041:0.973267:0.00210071:rs569165477
1   54490   rs141149254 G   A   .   PASS    AF=0.152906 ES:SE:LP:AF:ID  0.0623122:0.0644395:0.473214:0.152906:rs141149254
1   54591   rs561234294 A   G   .   PASS    AF=0.00205898   ES:SE:LP:AF:ID  0.646432:0.418164:0.820937:0.00205898:rs561234294
1   54716   rs569128616 C   T   .   PASS    AF=0.42841  ES:SE:LP:AF:ID  0.0228149:0.0496079:0.189945:0.42841:rs569128616
1   54945   rs569799965 C   A   .   PASS    AF=0.00558516   ES:SE:LP:AF:ID  0.193927:0.300445:0.276994:0.00558516:rs569799965
1   55164   rs3091274   C   A   .   PASS    AF=0.982694 ES:SE:LP:AF:ID  0.003915:0.194572:0.00714572:0.982694:rs3091274
1   55249   rs200769871 C   CTATGG  .   PASS    AF=0.00911021   ES:SE:LP:AF:ID  -0.279426:0.284662:0.514066:0.00911021:rs200769871
1   55326   rs3107975   T   C   .   PASS    AF=0.0162927    ES:SE:LP:AF:ID  -0.0646485:0.201971:0.126974:0.0162927:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.258061 ES:SE:LP:AF:ID  -0.0196984:0.0556846:0.140778:0.258061:rs28396308
1   56586   rs541979596 G   A   .   PASS    AF=0.00113089   ES:SE:LP:AF:ID  0.0629053:0.603289:0.0367143:0.00113089:rs541979596