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.7": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
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    "file_date": "2019-10-26T21:54:43.703016",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003116/EBI-a-GCST003116_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-GCST003116/EBI-a-GCST003116.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003116/EBI-a-GCST003116_data.vcf.gz; Date=Sat Oct 26 22:07:57 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST003116/ebi-a-GCST003116.vcf.gz; Date=Sun May 10 05:14:38 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-GCST003116/EBI-a-GCST003116.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-GCST003116/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 Sat Oct 26 22:28:03 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003116/EBI-a-GCST003116.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 Sat Oct 26 22:28:59 2019
Total time elapsed: 56.34s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9612,
    "inflation_factor": 1.0026,
    "mean_EFFECT": 0.0006,
    "n": "-Inf",
    "n_snps": 8597751,
    "n_clumped_hits": 42,
    "n_p_sig": 2032,
    "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": 210895,
    "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 8579878 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 8579912 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 8.615756e+00 5.736666e+00 1.000000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.873476e+07 5.629107e+07 828.000000 3.243873e+07 6.927640e+07 1.143963e+08 2.492223e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 5.960000e-04 2.529910e-02 -0.288636 -1.058900e-02 3.290000e-04 1.135200e-02 5.503510e-01 ▁▇▁▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.129020e-02 1.509600e-02 0.009053 1.060970e-02 1.463870e-02 2.643460e-02 1.697250e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.984255e-01 2.906604e-01 0.000000 2.469420e-01 4.994454e-01 7.501377e-01 9.999892e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.984255e-01 2.906604e-01 0.000000 2.469420e-01 4.994448e-01 7.501367e-01 9.999892e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.441898e-01 2.509679e-01 0.005774 4.004500e-02 1.431200e-01 3.865690e-01 9.933000e-01 ▇▂▂▁▁
numeric AF_reference 210895 0.9754199 NA NA NA NA NA NA NA 2.498501e-01 2.542272e-01 0.000000 4.213260e-02 1.565500e-01 3.917730e-01 1.000000e+00 ▇▃▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 751756 rs143225517 T C 0.013006 0.0173240 0.4528016 0.4528029 0.158264 0.242212 NA
1 752566 rs3094315 G A -0.005243 0.0157652 0.7394605 0.7394605 0.763018 0.718251 NA
1 752721 rs3131972 A G -0.003032 0.0156381 0.8462652 0.8462656 0.740969 0.653355 NA
1 752894 rs3131971 T C 0.004640 0.0162377 0.7750666 0.7750660 0.744287 0.753195 NA
1 753405 rs3115860 C A -0.006291 0.0167080 0.7065258 0.7065257 0.775368 0.751797 NA
1 753474 rs2073814 C G 0.000407 0.0157456 0.9793782 0.9793782 0.716742 0.611422 NA
1 753541 rs2073813 G A 0.005802 0.0167808 0.7295297 0.7295289 0.194804 0.301917 NA
1 754182 rs3131969 A G -0.006522 0.0165571 0.6936478 0.6936478 0.760434 0.678514 NA
1 754192 rs3131968 A G -0.006791 0.0165668 0.6818675 0.6818674 0.759886 0.678514 NA
1 754334 rs3131967 T C -0.009343 0.0168482 0.5792086 0.5792094 0.747971 0.684305 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51216564 rs9616970 T C -0.013838 0.0201787 0.4928571 0.4928565 0.113187 0.1563500 NA
22 51216731 rs5771014 T C -0.016457 0.0394971 0.6769241 0.6769246 0.252603 0.2593850 NA
22 51218377 rs2519461 G C 0.037570 0.0289743 0.1947458 0.1947460 0.070224 0.0826677 NA
22 51219006 rs28729663 G A 0.010715 0.0181365 0.5546564 0.5546561 0.172818 0.2052720 NA
22 51222100 rs114553188 G T -0.047162 0.0291731 0.1059588 0.1059590 0.051709 0.0880591 NA
22 51222766 rs139240849 G A -0.064096 0.0328328 0.0509143 0.0509152 0.038431 0.0321486 NA
23 46256106 rs12007097 G C 0.006000 0.0372300 0.8719671 0.8719671 0.973551 0.6821190 NA
23 83855186 rs5923048 G T -0.029983 0.0765511 0.6952996 0.6952998 0.993300 0.7075500 NA
23 100784211 rs188350543 C A 0.020365 0.0139109 0.1432043 0.1432049 0.746040 0.6498010 NA
23 147407824 rs28859988 G A 0.013580 0.0477756 0.7762221 0.7762222 0.012571 NA NA

bcf preview

1   751756  rs28527770  T   C   .   PASS    AF=0.158264 ES:SE:LP:AF:ID  0.013006:0.017324:0.344092:0.158264:rs28527770
1   752566  rs3094315   G   A   .   PASS    AF=0.763018 ES:SE:LP:AF:ID  -0.005243:0.0157652:0.131085:0.763018:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.740969 ES:SE:LP:AF:ID  -0.003032:0.0156381:0.0724935:0.740969:rs3131972
1   752894  rs3131971   T   C   .   PASS    AF=0.744287 ES:SE:LP:AF:ID  0.00464:0.0162377:0.110661:0.744287:rs3131971
1   753405  rs3115860   C   A   .   PASS    AF=0.775368 ES:SE:LP:AF:ID  -0.006291:0.016708:0.150872:0.775368:rs3115860
1   753474  rs2073814   C   G   .   PASS    AF=0.716742 ES:SE:LP:AF:ID  0.000407:0.0157456:0.00904957:0.716742:rs2073814
1   753541  rs2073813   G   A   .   PASS    AF=0.194804 ES:SE:LP:AF:ID  0.005802:0.0167808:0.136957:0.194804:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.760434 ES:SE:LP:AF:ID  -0.006522:0.0165571:0.158861:0.760434:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.759886 ES:SE:LP:AF:ID  -0.006791:0.0165668:0.1663:0.759886:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.747971 ES:SE:LP:AF:ID  -0.009343:0.0168482:0.237165:0.747971:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.742041 ES:SE:LP:AF:ID  -0.000146:0.0158271:0.00320832:0.742041:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.727432 ES:SE:LP:AF:ID  0.002221:0.0162312:0.0500439:0.727432:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.71467  ES:SE:LP:AF:ID  0.006636:0.0164734:0.162997:0.71467:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.776798 ES:SE:LP:AF:ID  -0.008914:0.0170133:0.221619:0.776798:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.756247 ES:SE:LP:AF:ID  -0.004858:0.0168188:0.111988:0.756247:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.749175 ES:SE:LP:AF:ID  -0.008785:0.0168699:0.220013:0.749175:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.782655 ES:SE:LP:AF:ID  -0.014073:0.0170176:0.389067:0.782655:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.78203  ES:SE:LP:AF:ID  -0.014716:0.0170263:0.41182:0.78203:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.767599 ES:SE:LP:AF:ID  -0.006877:0.0164376:0.170262:0.767599:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.781067 ES:SE:LP:AF:ID  -0.013634:0.0171545:0.369833:0.781067:rs3131954
1   759700  rs3115852   T   C   .   PASS    AF=0.736839 ES:SE:LP:AF:ID  -0.002354:0.0165581:0.0521013:0.736839:rs3115852
1   759837  rs3115851   T   A   .   PASS    AF=0.784208 ES:SE:LP:AF:ID  -0.014084:0.017224:0.383492:0.784208:rs3115851
1   760912  rs1048488   C   T   .   PASS    AF=0.73858  ES:SE:LP:AF:ID  0.005562:0.0164788:0.133286:0.73858:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.740961 ES:SE:LP:AF:ID  4.8e-05:0.0166255:0.00100159:0.740961:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.726374 ES:SE:LP:AF:ID  0.005293:0.0171078:0.12089:0.726374:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.769413 ES:SE:LP:AF:ID  -0.006653:0.0175564:0.15198:0.769413:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.745318 ES:SE:LP:AF:ID  -0.007391:0.0177444:0.169396:0.745318:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.085269 ES:SE:LP:AF:ID  -0.008002:0.0278761:0.11122:0.085269:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.74118  ES:SE:LP:AF:ID  0.002249:0.017916:0.0457075:0.74118:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.736188 ES:SE:LP:AF:ID  0.00606:0.017904:0.133708:0.736188:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.733172 ES:SE:LP:AF:ID  0.002579:0.0178932:0.0528628:0.733172:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.740222 ES:SE:LP:AF:ID  0.002908:0.0178193:0.0602981:0.740222:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.155298 ES:SE:LP:AF:ID  -0.000659:0.018596:0.0124541:0.155298:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.117535 ES:SE:LP:AF:ID  -0.0006:0.0210901:0.00997046:0.117535:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.648619 ES:SE:LP:AF:ID  -0.010842:0.0146235:0.338713:0.648619:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.133117 ES:SE:LP:AF:ID  -0.00342:0.0172229:0.07438:0.133117:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.147952 ES:SE:LP:AF:ID  0.002734:0.0187665:0.0534641:0.147952:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.124842 ES:SE:LP:AF:ID  -0.001891:0.0184102:0.0370677:0.124842:rs7518545
1   771823  rs2977605   T   C   .   PASS    AF=0.78219  ES:SE:LP:AF:ID  -0.006408:0.0179203:0.142272:0.78219:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.148426 ES:SE:LP:AF:ID  0.003761:0.0184819:0.0763688:0.148426:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.780253 ES:SE:LP:AF:ID  -0.00819:0.0177487:0.19079:0.780253:rs2905039
1   775181  rs61768182  A   G   .   PASS    AF=0.154809 ES:SE:LP:AF:ID  0.001659:0.0177118:0.0336827:0.154809:rs61768182
1   777122  rs2980319   A   T   .   PASS    AF=0.785305 ES:SE:LP:AF:ID  2.8e-05:0.0173153:0.000560732:0.785305:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.065846 ES:SE:LP:AF:ID  0.035269:0.029713:0.628505:0.065846:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.147126 ES:SE:LP:AF:ID  -0.002725:0.01808:0.0554199:0.147126:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.152134 ES:SE:LP:AF:ID  -0.002915:0.017778:0.0606018:0.152134:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.769873 ES:SE:LP:AF:ID  0.001497:0.0167895:0.0320065:0.769873:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.096234 ES:SE:LP:AF:ID  -0.006469:0.0224577:0.111648:0.096234:rs61768199
1   783318  rs6686696   A   G   .   PASS    AF=0.138281 ES:SE:LP:AF:ID  -0.005518:0.018798:0.114012:0.138281:rs6686696
1   785050  rs2905062   G   A   .   PASS    AF=0.763925 ES:SE:LP:AF:ID  -0.000143:0.0165277:0.00300846:0.763925:rs2905062