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\">",
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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.\">",
    "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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    "file_date": "2019-10-26T21:48:09.837477",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006487/EBI-a-GCST006487_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-GCST006487/EBI-a-GCST006487.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006487/EBI-a-GCST006487_data.vcf.gz; Date=Sat Oct 26 22:08:29 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006487/ebi-a-GCST006487.vcf.gz; Date=Sun May 10 13:34:29 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-GCST006487/EBI-a-GCST006487.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-GCST006487/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 22:33:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006487/EBI-a-GCST006487.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 22:34:41 2019
Total time elapsed: 1.0m:24.32s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9508,
    "inflation_factor": 1.0263,
    "mean_EFFECT": 0.0003,
    "n": "-Inf",
    "n_snps": 12339141,
    "n_clumped_hits": 1,
    "n_p_sig": 5,
    "n_mono": 0,
    "n_ns": 1152229,
    "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": 407638,
    "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 12318046 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 84 0 53930 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 103 0 30743 0 NA NA NA NA NA NA NA NA NA NA
logical N 12318757 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 8.615078e+00 5.744901e+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 7.895724e+07 5.624582e+07 302.0000000 3.266916e+07 6.965011e+07 1.145108e+08 2.492383e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.292000e-04 6.799430e-02 -0.9914970 -1.828750e-02 3.220000e-05 1.848190e-02 1.084930e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.814030e-02 4.787640e-02 0.0106836 1.466080e-02 2.505030e-02 6.397390e-02 6.896980e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.953398e-01 2.902618e-01 0.0000000 2.424142e-01 4.944131e-01 7.466086e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.953398e-01 2.902618e-01 0.0000000 2.424139e-01 4.944125e-01 7.466085e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.020268e-01 2.571060e-01 0.0001000 1.044770e-02 7.483690e-02 3.185730e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 407638 0.9669092 NA NA NA NA NA NA NA 2.062821e-01 2.488698e-01 0.0000000 8.985600e-03 9.824280e-02 3.258790e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 636285 rs545945172 T C -0.0059637 0.0231454 0.7966669 0.7966678 0.0995027 0.0956470 NA
1 649192 rs201942322 A T 0.0020722 0.0212404 0.9222800 0.9222803 0.1186820 0.1369810 NA
1 662622 rs61769339 G A 0.0006799 0.0214207 0.9746790 0.9746804 0.1104060 0.1475640 NA
1 692794 rs530212009 CA C -0.0052654 0.0213938 0.8055880 0.8055927 0.1114590 0.1894970 NA
1 693731 rs12238997 A G 0.0019864 0.0214424 0.9261930 0.9261916 0.1109680 0.1417730 NA
1 693823 rs61769351 G C 0.0024254 0.0214158 0.9098330 0.9098316 0.1112910 0.1491610 NA
1 707522 rs371890604 G C 0.0038906 0.0219433 0.8592741 0.8592698 0.1002150 0.1293930 NA
1 711310 rs200531508 G A -0.0021518 0.0320927 0.9465409 0.9465425 0.0476890 0.0736821 NA
1 727841 rs116587930 G A -0.0299494 0.0274080 0.2745137 0.2745147 0.0570351 0.0127796 NA
1 729679 rs4951859 C G 0.0014916 0.0190002 0.9374299 0.9374276 0.8486730 0.6399760 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51228888 rs201882178 T G 0.0089018 0.0143272 0.5343871 0.5343894 0.3546310 0.2929310 NA
22 51228910 rs145146472 G A 0.0061716 0.0149477 0.6796886 0.6796934 0.3002780 0.2276360 NA
22 51229455 rs534865882 G C 0.0138303 0.0293344 0.6373016 0.6373049 0.0536577 0.0483227 NA
22 51229656 rs564838851 G A 0.0208659 0.0255934 0.4149110 0.4149095 0.0729534 0.0774760 NA
22 51229717 rs200078515 A T 0.0067576 0.0150434 0.6532810 0.6532828 0.2965530 0.2294330 NA
22 51229805 rs9616985 T C 0.0220461 0.0254911 0.3871177 0.3871185 0.0715926 0.0730831 NA
22 51231220 rs368226325 A G 0.0106933 0.0293253 0.7153787 0.7153770 0.0537110 0.0601038 NA
22 51232581 rs200771213 T C 0.0077974 0.0150506 0.6044008 0.6044031 0.3002050 0.2270370 NA
22 51236013 rs200507571 A AT -0.0028666 0.0156748 0.8548899 0.8548951 0.2545810 0.1487620 NA
22 51237712 rs370652263 G A 0.0031572 0.0290579 0.9134791 0.9134796 0.0557662 0.0690895 NA

bcf preview

1   636285  rs545945172 T   C   .   PASS    AF=0.0995027    ES:SE:LP:AF:ID  -0.00596369:0.0231454:0.0987232:0.0995027:rs545945172
1   649192  rs201942322 A   T   .   PASS    AF=0.118682 ES:SE:LP:AF:ID  0.00207225:0.0212404:0.0351372:0.118682:rs201942322
1   662622  rs61769339  G   A   .   PASS    AF=0.110406 ES:SE:LP:AF:ID  0.000679867:0.0214207:0.0111384:0.110406:rs61769339
1   692794  rs530212009 CA  C   .   PASS    AF=0.111459 ES:SE:LP:AF:ID  -0.00526535:0.0213938:0.093887:0.111459:rs530212009
1   693731  rs12238997  A   G   .   PASS    AF=0.110968 ES:SE:LP:AF:ID  0.00198637:0.0214424:0.0332985:0.110968:rs12238997
1   693823  rs61769351  G   C   .   PASS    AF=0.111291 ES:SE:LP:AF:ID  0.00242536:0.0214158:0.0410383:0.111291:rs61769351
1   707522  rs371890604 G   C   .   PASS    AF=0.100215 ES:SE:LP:AF:ID  0.00389063:0.0219433:0.0658683:0.100215:rs371890604
1   711310  rs200531508 G   A   .   PASS    AF=0.047689 ES:SE:LP:AF:ID  -0.00215179:0.0320927:0.0238606:0.047689:rs200531508
1   727841  rs116587930 G   A   .   PASS    AF=0.0570351    ES:SE:LP:AF:ID  -0.0299494:0.027408:0.561436:0.0570351:rs116587930
1   729679  rs4951859   C   G   .   PASS    AF=0.848673 ES:SE:LP:AF:ID  0.00149158:0.0190002:0.0280612:0.848673:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.0571799    ES:SE:LP:AF:ID  -0.0321841:0.0276671:0.611322:0.0571799:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12273  ES:SE:LP:AF:ID  -0.00471738:0.0201026:0.0891244:0.12273:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.12171  ES:SE:LP:AF:ID  -0.002059:0.0201288:0.03691:0.12171:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121518 ES:SE:LP:AF:ID  -0.00103892:0.020139:0.0182471:0.121518:rs141242758
1   735985  rs12405651  G   A   .   PASS    AF=0.0319687    ES:SE:LP:AF:ID  -0.0138866:0.0394707:0.139678:0.0319687:rs12405651
1   736289  rs79010578  T   A   .   PASS    AF=0.128535 ES:SE:LP:AF:ID  -0.00575769:0.0202358:0.110137:0.128535:rs79010578
1   736736  rs10454457  A   G   .   PASS    AF=0.0318969    ES:SE:LP:AF:ID  -0.00902689:0.0393135:0.087037:0.0318969:rs10454457
1   740285  rs193160839 G   A   .   PASS    AF=0.0260411    ES:SE:LP:AF:ID  -0.0201107:0.043906:0.189145:0.0260411:rs193160839
1   740578  rs7521650   G   C   .   PASS    AF=0.00124956   ES:SE:LP:AF:ID  -0.0298362:0.189554:0.0580277:0.00124956:rs7521650
1   745642  rs200097270 AC  A   .   PASS    AF=0.030476 ES:SE:LP:AF:ID  -0.0156375:0.0399777:0.157589:0.030476:rs200097270
1   746211  rs201075335 A   AG  .   PASS    AF=0.0348924    ES:SE:LP:AF:ID  -0.0294716:0.0365693:0.376446:0.0348924:rs201075335
1   746727  rs144595511 G   A   .   PASS    AF=0.0305794    ES:SE:LP:AF:ID  -0.0159255:0.0398435:0.161544:0.0305794:rs144595511
1   747753  rs200313238 TGC T   .   PASS    AF=0.0303137    ES:SE:LP:AF:ID  -0.0134689:0.0400548:0.132725:0.0303137:rs200313238
1   749963  rs529266287 T   TAA .   PASS    AF=0.865847 ES:SE:LP:AF:ID  0.00417061:0.0188751:0.0834808:0.865847:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00161773   ES:SE:LP:AF:ID  -0.266934:0.163451:0.989509:0.00161773:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.126888 ES:SE:LP:AF:ID  -0.00252494:0.0187917:0.0490912:0.126888:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.14511  ES:SE:LP:AF:ID  -0.0130532:0.0185431:0.317429:0.14511:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.12701  ES:SE:LP:AF:ID  -0.00228569:0.0187682:0.0442776:0.12701:rs28527770
1   752307  rs201062411 AT  A   .   PASS    AF=0.0316732    ES:SE:LP:AF:ID  -0.0299579:0.0368684:0.380417:0.0316732:rs201062411
1   752478  rs146277091 G   A   .   PASS    AF=0.0321985    ES:SE:LP:AF:ID  -0.0301993:0.0365004:0.38931:0.0321985:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.841986 ES:SE:LP:AF:ID  0.00889315:0.0174546:0.214385:0.841986:rs3094315
1   752593  rs372531941 T   G   .   PASS    AF=0.0298827    ES:SE:LP:AF:ID  -0.0369458:0.0388449:0.466548:0.0298827:rs372531941
1   752617  rs149886465 C   A   .   PASS    AF=0.0313506    ES:SE:LP:AF:ID  -0.0272204:0.0372167:0.332983:0.0313506:rs149886465
1   752721  rs3131972   A   G   .   PASS    AF=0.84013  ES:SE:LP:AF:ID  0.0080468:0.0173052:0.192508:0.84013:rs3131972
1   752894  rs3131971   T   C   .   PASS    AF=0.840596 ES:SE:LP:AF:ID  0.00737241:0.01734:0.173463:0.840596:rs3131971
1   753405  rs3115860   C   A   .   PASS    AF=0.871372 ES:SE:LP:AF:ID  0.00305853:0.0186759:0.0605252:0.871372:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.871365 ES:SE:LP:AF:ID  0.00302183:0.0186757:0.059754:0.871365:rs3131970
1   753474  rs2073814   C   G   .   PASS    AF=0.838764 ES:SE:LP:AF:ID  0.00912772:0.0173839:0.222184:0.838764:rs2073814
1   753541  rs2073813   G   A   .   PASS    AF=0.127385 ES:SE:LP:AF:ID  -0.00215055:0.0187049:0.0416904:0.127385:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.0315548    ES:SE:LP:AF:ID  -0.0222692:0.0372896:0.25934:0.0315548:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.0357775    ES:SE:LP:AF:ID  -0.0278368:0.034044:0.383476:0.0357775:rs12184325
1   754121  rs12184335  T   G   .   PASS    AF=0.0292233    ES:SE:LP:AF:ID  -0.0342248:0.0390493:0.419321:0.0292233:rs12184335
1   754163  rs12184336  T   C   .   PASS    AF=0.0251199    ES:SE:LP:AF:ID  -0.0334641:0.044703:0.342843:0.0251199:rs12184336
1   754182  rs3131969   A   G   .   PASS    AF=0.870423 ES:SE:LP:AF:ID  0.00177447:0.0185594:0.0344075:0.870423:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870418 ES:SE:LP:AF:ID  0.00180425:0.0185594:0.0350073:0.870418:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.0275381    ES:SE:LP:AF:ID  -0.0339804:0.0415696:0.383337:0.0275381:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.870747 ES:SE:LP:AF:ID  0.00219054:0.0185789:0.0428028:0.870747:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00509872   ES:SE:LP:AF:ID  0.0574708:0.0956112:0.261392:0.00509872:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00506015   ES:SE:LP:AF:ID  0.0627896:0.0955068:0.291663:0.00506015:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.844029 ES:SE:LP:AF:ID  0.00530522:0.0176172:0.117299:0.844029:rs3115859