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": "2019-10-26T21:55:54.563817",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005346/EBI-a-GCST005346_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-GCST005346/EBI-a-GCST005346.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005346/EBI-a-GCST005346_data.vcf.gz; Date=Sat Oct 26 22:12:11 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST005346/ebi-a-GCST005346.vcf.gz; Date=Sun May 10 12:19:20 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-GCST005346/EBI-a-GCST005346.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-GCST005346/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 22:36:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005346/EBI-a-GCST005346.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 22:37:27 2019
Total time elapsed: 1.0m:1.28s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9626,
    "inflation_factor": 1.033,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9656698,
    "n_clumped_hits": 9,
    "n_p_sig": 440,
    "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": 230937,
    "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 64 0 9634039 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 9634059 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 8.938735e+00 6.049202e+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 7.856558e+07 5.617654e+07 828.0000 3.228915e+07 6.933272e+07 1.145283e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -8.180000e-05 5.252850e-02 -0.8478 -1.930000e-02 -3.000000e-04 1.900000e-02 9.306000e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.041450e-02 3.302410e-02 0.0138 1.730000e-02 2.580000e-02 5.170000e-02 2.973000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.945509e-01 2.894839e-01 0.0000 2.429003e-01 4.930001e-01 7.452003e-01 9.998000e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.945504e-01 2.894835e-01 0.0000 2.428934e-01 4.930066e-01 7.452494e-01 9.996328e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.338675e-01 2.558620e-01 0.0051 3.240000e-02 1.230000e-01 3.663000e-01 9.947000e-01 ▇▂▂▁▁
numeric AF_reference 230937 0.9760291 NA NA NA NA NA NA NA 2.346756e-01 2.534170e-01 0.0000 2.875400e-02 1.355830e-01 3.686100e-01 1.000000e+00 ▇▂▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 82163 rs139113303 G A -0.1163 0.0708 0.1004001 0.1004543 0.0679 0.0327476 NA
1 82609 rs149189449 C G -0.0981 0.0707 0.1655000 0.1652732 0.0685 0.0327476 NA
1 86065 rs116504101 G C -0.0810 0.0713 0.2558003 0.2559378 0.0686 0.0375399 NA
1 87409 rs139490478 C T -0.1018 0.0705 0.1487000 0.1487470 0.0702 0.0393371 NA
1 88710 rs186575039 C G -0.1052 0.0706 0.1360000 0.1362019 0.0691 0.0379393 NA
1 91190 rs143856811 G A -0.1167 0.0720 0.1051001 0.1050534 0.0684 0.0391374 NA
1 92633 rs149776517 C T -0.0124 0.0898 0.8899000 0.8901735 0.0463 0.0139776 NA
1 249276 rs115018998 T C 0.1154 0.1356 0.3946999 0.3947513 0.0163 0.0055910 NA
1 662622 rs61769339 G A 0.0063 0.0394 0.8722000 0.8729610 0.1111 0.1475640 NA
1 693625 rs190214723 T C -0.0541 0.0859 0.5285000 0.5288241 0.0357 0.0187700 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154918383 rs641588 G T -0.0035 0.0230 0.8798001 0.8790498 0.2547 0.3565560 NA
23 154918624 rs5940558 A G -0.0750 0.0499 0.1333000 0.1328375 0.0536 0.1290070 NA
23 154921127 rs58486544 T C -0.0752 0.0497 0.1309001 0.1302597 0.0541 0.1451660 NA
23 154923311 rs141127553 C T -0.0696 0.0505 0.1679001 0.1681360 0.0511 0.0309934 NA
23 154925045 rs509981 C T -0.0043 0.0230 0.8529999 0.8516947 0.2549 0.3634440 NA
23 154925895 rs538470 C T -0.0062 0.0232 0.7905002 0.7892833 0.2597 0.3634440 NA
23 154927199 rs645904 C T -0.0058 0.0230 0.8003000 0.8009066 0.2555 0.3674170 NA
23 154927581 rs644138 G A -0.0183 0.0220 0.4063002 0.4055116 0.3056 0.4635760 NA
23 154929412 rs557132 C T -0.0049 0.0230 0.8303999 0.8312931 0.2546 0.3568210 NA
23 154930230 rs781880 A G -0.0054 0.0230 0.8137000 0.8143775 0.2543 0.3618540 NA

bcf preview

1   82163   rs139113303 G   A   .   PASS    AF=0.0679   ES:SE:LP:AF:ID  -0.1163:0.0708:0.998266:0.0679:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.0685   ES:SE:LP:AF:ID  -0.0981:0.0707:0.781202:0.0685:rs149189449
1   86065   rs116504101 G   C   .   PASS    AF=0.0686   ES:SE:LP:AF:ID  -0.081:0.0713:0.592099:0.0686:rs116504101
1   87409   rs139490478 C   T   .   PASS    AF=0.0702   ES:SE:LP:AF:ID  -0.1018:0.0705:0.827689:0.0702:rs139490478
1   88710   rs186575039 C   G   .   PASS    AF=0.0691   ES:SE:LP:AF:ID  -0.1052:0.0706:0.866461:0.0691:rs186575039
1   91190   rs143856811 G   A   .   PASS    AF=0.0684   ES:SE:LP:AF:ID  -0.1167:0.072:0.978397:0.0684:rs143856811
1   92633   rs149776517 C   T   .   PASS    AF=0.0463   ES:SE:LP:AF:ID  -0.0124:0.0898:0.0506588:0.0463:rs149776517
1   249276  rs115018998 T   C   .   PASS    AF=0.0163   ES:SE:LP:AF:ID  0.1154:0.1356:0.403733:0.0163:rs115018998
1   662622  rs61769339  G   A   .   PASS    AF=0.1111   ES:SE:LP:AF:ID  0.0063:0.0394:0.0593839:0.1111:rs61769339
1   693625  rs190214723 T   C   .   PASS    AF=0.0357   ES:SE:LP:AF:ID  -0.0541:0.0859:0.276955:0.0357:rs190214723
1   693731  rs12238997  A   G   .   PASS    AF=0.115    ES:SE:LP:AF:ID  0.0015:0.0375:0.014349:0.115:rs12238997
1   701835  rs189800799 T   C   .   PASS    AF=0.0332   ES:SE:LP:AF:ID  -0.0049:0.0777:0.0222764:0.0332:rs189800799
1   705882  rs72631875  G   A   .   PASS    AF=0.0624   ES:SE:LP:AF:ID  -0.0428:0.0562:0.350179:0.0624:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.4327   ES:SE:LP:AF:ID  0.0103:0.0275:0.149293:0.4327:rs12029736
1   707886  rs78250985  G   C   .   PASS    AF=0.0647   ES:SE:LP:AF:ID  0.0159:0.0704:0.0855511:0.0647:rs78250985
1   714019  rs114983708 A   G   .   PASS    AF=0.0741   ES:SE:LP:AF:ID  0.036:0.0562:0.282912:0.0741:rs114983708
1   714427  rs12028261  G   A   .   PASS    AF=0.9042   ES:SE:LP:AF:ID  -0.0293:0.0491:0.258848:0.9042:rs12028261
1   715265  rs12184267  C   T   .   PASS    AF=0.0391   ES:SE:LP:AF:ID  0.0046:0.071:0.0231917:0.0391:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.0379   ES:SE:LP:AF:ID  0.0102:0.072:0.0520764:0.0379:rs12184277
1   716041  rs149301622 G   A   .   PASS    AF=0.0315   ES:SE:LP:AF:ID  0.0346:0.1008:0.135726:0.0315:rs149301622
1   717587  rs144155419 G   A   .   PASS    AF=0.0161   ES:SE:LP:AF:ID  0.0903:0.103:0.419645:0.0161:rs144155419
1   719914  rs187772768 C   G   .   PASS    AF=0.0379   ES:SE:LP:AF:ID  -0.0273:0.0681:0.162159:0.0379:rs187772768
1   720381  rs116801199 G   T   .   PASS    AF=0.0392   ES:SE:LP:AF:ID  -0.0448:0.068:0.292345:0.0392:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.0408   ES:SE:LP:AF:ID  -0.0307:0.0672:0.188894:0.0408:rs12565286
1   721757  rs189147642 T   A   .   PASS    AF=0.0383   ES:SE:LP:AF:ID  -0.0094:0.0713:0.0482255:0.0383:rs189147642
1   722670  rs116030099 T   C   .   PASS    AF=0.0935   ES:SE:LP:AF:ID  -0.008:0.0473:0.062432:0.0935:rs116030099
1   723307  rs28659788  C   G   .   PASS    AF=0.039    ES:SE:LP:AF:ID  -0.0183:0.0731:0.0956091:0.039:rs28659788
1   723742  rs28375378  T   C   .   PASS    AF=0.0388   ES:SE:LP:AF:ID  -0.0148:0.0731:0.076238:0.0388:rs28375378
1   723819  rs11804171  T   A   .   PASS    AF=0.0886   ES:SE:LP:AF:ID  0.0203:0.0513:0.159768:0.0886:rs11804171
1   723891  rs2977670   G   C   .   PASS    AF=0.8711   ES:SE:LP:AF:ID  -0.0161:0.0449:0.142848:0.8711:rs2977670
1   726794  rs28454925  C   G   .   PASS    AF=0.0379   ES:SE:LP:AF:ID  -0.0151:0.0678:0.0840728:0.0379:rs28454925
1   727142  rs28641938  T   C   .   PASS    AF=0.0334   ES:SE:LP:AF:ID  -0.0129:0.094:0.0500248:0.0334:rs28641938
1   727841  rs116587930 G   A   .   PASS    AF=0.0378   ES:SE:LP:AF:ID  -0.0309:0.0639:0.201418:0.0378:rs116587930
1   729679  rs4951859   C   G   .   PASS    AF=0.7909   ES:SE:LP:AF:ID  -0.0084:0.0288:0.113396:0.7909:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.0376   ES:SE:LP:AF:ID  -0.0314:0.064:0.205303:0.0376:rs148120343
1   731453  rs186002080 G   A   .   PASS    AF=0.0188   ES:SE:LP:AF:ID  0.0923:0.1133:0.381847:0.0188:rs186002080
1   731718  rs58276399  T   C   .   PASS    AF=0.126    ES:SE:LP:AF:ID  0.0179:0.0344:0.219539:0.126:rs58276399
1   732809  rs12131618  T   C   .   PASS    AF=0.0707   ES:SE:LP:AF:ID  -0.037:0.0529:0.314886:0.0707:rs12131618
1   734349  rs141242758 T   C   .   PASS    AF=0.1236   ES:SE:LP:AF:ID  0.0191:0.0346:0.235824:0.1236:rs141242758
1   735068  rs2427928   A   G   .   PASS    AF=0.0308   ES:SE:LP:AF:ID  0.0358:0.0768:0.193277:0.0308:rs2427928
1   735682  rs138174321 G   C   .   PASS    AF=0.0744   ES:SE:LP:AF:ID  0.0206:0.0618:0.131414:0.0744:rs138174321
1   735985  rs12405651  G   A   .   PASS    AF=0.0709   ES:SE:LP:AF:ID  0.0457:0.0572:0.372737:0.0709:rs12405651
1   736289  rs79010578  T   A   .   PASS    AF=0.1286   ES:SE:LP:AF:ID  0.006:0.0359:0.0621312:0.1286:rs79010578
1   737059  rs4040696   A   G   .   PASS    AF=0.0301   ES:SE:LP:AF:ID  0.0848:0.0968:0.419189:0.0301:rs4040696
1   737085  rs10454458  C   T   .   PASS    AF=0.0301   ES:SE:LP:AF:ID  0.0848:0.0968:0.419075:0.0301:rs10454458
1   739117  rs140206562 G   A   .   PASS    AF=0.0216   ES:SE:LP:AF:ID  0.0906:0.0925:0.484789:0.0216:rs140206562
1   739210  rs2427917   A   G   .   PASS    AF=0.8226   ES:SE:LP:AF:ID  -0.0184:0.0386:0.198528:0.8226:rs2427917
1   739528  rs3094317   G   A   .   PASS    AF=0.9276   ES:SE:LP:AF:ID  -0.0205:0.0583:0.139482:0.9276:rs3094317
1   740285  rs193160839 G   A   .   PASS    AF=0.0322   ES:SE:LP:AF:ID  0.0084:0.0689:0.0443603:0.0322:rs193160839
1   746727  rs144595511 G   A   .   PASS    AF=0.0342   ES:SE:LP:AF:ID  0.0206:0.0696:0.115091:0.0342:rs144595511