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.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": "2019-10-26T21:56:43.519806",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005327/EBI-a-GCST005327_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-GCST005327/EBI-a-GCST005327.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005327/EBI-a-GCST005327_data.vcf.gz; Date=Sat Oct 26 22:11:06 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST005327/ebi-a-GCST005327.vcf.gz; Date=Sun May 10 10:19:07 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-GCST005327/EBI-a-GCST005327.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-GCST005327/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 22:30:34 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005327/EBI-a-GCST005327.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 22:31:27 2019
Total time elapsed: 52.7s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9471,
    "inflation_factor": 1.5903,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 8106987,
    "n_clumped_hits": 9,
    "n_p_sig": 2704,
    "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": 5722,
    "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 TRUE
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 58 0 8088722 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 8088769 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 8.659490e+00 5.766683e+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 7.876037e+07 5.644459e+07 828.000000 3.247033e+07 6.935780e+07 1.145135e+08 2.492330e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.830000e-05 3.270000e-03 -0.081000 -2.000000e-03 1.000000e-03 2.000000e-03 8.000000e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.622500e-03 7.887000e-04 0.002000 2.000000e-03 3.000000e-03 3.000000e-03 3.700000e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.029921e-01 2.527475e-01 0.000000 1.780000e-01 3.949999e-01 6.210006e-01 9.820000e-01 ▇▆▆▆▂
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 3.787052e-01 2.492765e-01 0.000000 1.336144e-01 3.173105e-01 6.170751e-01 9.653203e-01 ▇▅▃▇▁
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.288574e-01 2.600651e-01 0.001701 2.381000e-02 1.139000e-01 3.639000e-01 9.982990e-01 ▇▂▁▁▁
numeric AF_reference 5722 0.9992926 NA NA NA NA NA NA NA 2.283747e-01 2.516375e-01 0.000000 2.176520e-02 1.295930e-01 3.608230e-01 9.996010e-01 ▇▂▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 51479 rs116400033 T A 0.010 0.015 0.4839996 0.5049851 0.282300 0.1281950 NA
1 52238 rs2691277 T G -0.009 0.023 0.7010000 0.6955723 0.984690 0.9217250 NA
1 54490 rs141149254 G A -0.002 0.015 0.9030001 0.8939298 0.202400 0.0960463 NA
1 58814 rs114420996 G A 0.012 0.015 0.4030000 0.4237108 0.059520 0.1090260 NA
1 63671 rs80011619 G A 0.025 0.023 0.2789999 0.2770560 0.096940 0.1875000 NA
1 77874 rs62641297 G A 0.016 0.015 0.2979998 0.2861224 0.044220 0.0547125 NA
1 83084 rs181193408 T A -0.006 0.023 0.7919997 0.7941931 0.998299 0.9201280 NA
1 88338 rs55700207 G A -0.003 0.020 0.8960000 0.8807646 0.049320 0.0940495 NA
1 89946 rs138808727 A T 0.018 0.015 0.2160001 0.2301393 0.294200 0.1226040 NA
1 91536 rs6702460 G T -0.012 0.023 0.5979997 0.6018520 0.636100 0.4207270 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51233300 rs9616839 C T 0.003 0.003 0.2610003 0.3173105 0.28230 0.3146960 NA
22 51233312 rs62240043 A G 0.002 0.003 0.5990002 0.5049851 0.20580 0.2134580 NA
22 51233347 rs62240044 T C 0.002 0.003 0.5990002 0.5049851 0.20580 0.2134580 NA
22 51234799 rs191117135 G A 0.001 0.003 0.6480000 0.7388827 0.02381 0.0059904 NA
22 51235959 rs200189535 T C -0.001 0.003 0.6240006 0.7388827 0.18710 0.1996810 NA
22 51237063 rs3896457 T C 0.003 0.003 0.3030003 0.3173105 0.22620 0.2050720 NA
22 51237712 rs370652263 G A 0.006 0.003 0.0769999 0.0455003 0.07993 0.0690895 NA
23 46256106 rs12007097 G C -0.005 0.003 0.1000000 0.0955807 0.98129 0.6821190 NA
23 75822996 rs147134110 C T 0.002 0.003 0.6089996 0.5049851 0.02041 0.0773510 NA
23 100784211 rs188350543 C A -0.004 0.003 0.1080001 0.1824224 0.72620 0.6498010 NA

bcf preview

1   51479   rs116400033 T   A   .   PASS    AF=0.2823   ES:SE:LP:AF:ID  0.01:0.015:0.315155:0.2823:rs116400033
1   52238   rs2691277   T   G   .   PASS    AF=0.98469  ES:SE:LP:AF:ID  -0.009:0.023:0.154282:0.98469:rs2691277
1   54490   rs141149254 G   A   .   PASS    AF=0.2024   ES:SE:LP:AF:ID  -0.002:0.015:0.0443122:0.2024:rs141149254
1   58814   rs114420996 G   A   .   PASS    AF=0.05952  ES:SE:LP:AF:ID  0.012:0.015:0.394695:0.05952:rs114420996
1   63671   rs80011619  G   A   .   PASS    AF=0.09694  ES:SE:LP:AF:ID  0.025:0.023:0.554396:0.09694:rs80011619
1   77874   rs62641297  G   A   .   PASS    AF=0.04422  ES:SE:LP:AF:ID  0.016:0.015:0.525784:0.04422:rs62641297
1   83084   rs4030326   T   A   .   PASS    AF=0.998299 ES:SE:LP:AF:ID  -0.006:0.023:0.101275:0.998299:rs4030326
1   88338   rs55700207  G   A   .   PASS    AF=0.04932  ES:SE:LP:AF:ID  -0.003:0.02:0.047692:0.04932:rs55700207
1   89946   rs138808727 A   T   .   PASS    AF=0.2942   ES:SE:LP:AF:ID  0.018:0.015:0.665546:0.2942:rs138808727
1   91536   rs6702460   G   T   .   PASS    AF=0.6361   ES:SE:LP:AF:ID  -0.012:0.023:0.223299:0.6361:rs6702460
1   91581   rs1524604   G   A   .   PASS    AF=0.6361   ES:SE:LP:AF:ID  -0.012:0.023:0.213249:0.6361:rs1524604
1   92858   rs147061536 G   T   .   PASS    AF=0.3061   ES:SE:LP:AF:ID  0.019:0.015:0.675718:0.3061:rs147061536
1   526840  rs60396226  T   C   .   PASS    AF=0.02721  ES:SE:LP:AF:ID  0.001:0.017:0.0218195:0.02721:rs60396226
1   649192  rs201942322 A   T   .   PASS    AF=0.1122   ES:SE:LP:AF:ID  0.001:0.003:0.0872467:0.1122:rs201942322
1   662414  rs371628865 C   T   .   PASS    AF=0.1122   ES:SE:LP:AF:ID  -0.001:0.003:0.107349:0.1122:rs371628865
1   662622  rs61769339  G   A   .   PASS    AF=0.1003   ES:SE:LP:AF:ID  0.001:0.003:0.161781:0.1003:rs61769339
1   665266  rs539032812 T   C   .   PASS    AF=0.03061  ES:SE:LP:AF:ID  -0.001:0.003:0.140261:0.03061:rs539032812
1   693731  rs12238997  A   G   .   PASS    AF=0.102    ES:SE:LP:AF:ID  0.001:0.003:0.140862:0.102:rs12238997
1   704637  rs142559957 G   A   .   PASS    AF=0.05612  ES:SE:LP:AF:ID  0.013:0.018:0.317855:0.05612:rs142559957
1   706368  rs12029736  A   G   .   PASS    AF=0.5323   ES:SE:LP:AF:ID  0.008:0.009:0.424812:0.5323:rs12029736
1   706778  rs113462541 G   A   .   PASS    AF=0.7874   ES:SE:LP:AF:ID  -0.001:0.003:0.106793:0.7874:rs113462541
1   707522  rs371890604 G   C   .   PASS    AF=0.09014  ES:SE:LP:AF:ID  -0.001:0.003:0.194499:0.09014:rs371890604
1   707886  rs78250985  G   C   .   PASS    AF=0.07143  ES:SE:LP:AF:ID  -0.006:0.015:0.14752:0.07143:rs78250985
1   711310  rs200531508 G   A   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.001:0.003:0.0985417:0.04082:rs200531508
1   714019  rs114983708 A   G   .   PASS    AF=0.04592  ES:SE:LP:AF:ID  -0.003:0.003:0.391474:0.04592:rs114983708
1   714427  rs12028261  G   A   .   PASS    AF=0.95408  ES:SE:LP:AF:ID  0.022:0.023:0.477556:0.95408:rs12028261
1   715265  rs12184267  C   T   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.345823:0.04082:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.258848:0.04082:rs12184277
1   717587  rs144155419 G   A   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.003:0.003:0.555955:0.008503:rs144155419
1   719914  rs187772768 C   G   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.25649:0.04082:rs187772768
1   720381  rs116801199 G   T   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.293282:0.04082:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.04252  ES:SE:LP:AF:ID  -0.002:0.003:0.236572:0.04252:rs12565286
1   721757  rs189147642 T   A   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.274088:0.04082:rs189147642
1   722670  rs116030099 T   C   .   PASS    AF=0.1344   ES:SE:LP:AF:ID  -0.014:0.012:0.5867:0.1344:rs116030099
1   723307  rs28659788  C   G   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.288193:0.04082:rs28659788
1   723742  rs28375378  T   C   .   PASS    AF=0.03912  ES:SE:LP:AF:ID  -0.002:0.003:0.329754:0.03912:rs28375378
1   723819  rs11804171  T   A   .   PASS    AF=0.04592  ES:SE:LP:AF:ID  -0.001:0.003:0.161781:0.04592:rs11804171
1   723891  rs2977670   G   C   .   PASS    AF=0.95238  ES:SE:LP:AF:ID  0.002:0.003:0.224026:0.95238:rs2977670
1   724324  rs28692873  G   A   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.317855:0.04082:rs28692873
1   725196  rs377099097 G   A   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.299296:0.04082:rs377099097
1   725389  rs375619475 C   T   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.335358:0.04082:rs375619475
1   726794  rs28454925  C   G   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.002:0.003:0.31158:0.04082:rs28454925
1   727841  rs116587930 G   A   .   PASS    AF=0.05272  ES:SE:LP:AF:ID  0.003:0.003:0.402305:0.05272:rs116587930
1   729632  rs116720794 C   T   .   PASS    AF=0.001701 ES:SE:LP:AF:ID  -0.002:0.003:0.327902:0.001701:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.8554   ES:SE:LP:AF:ID  0.001:0.003:0.0974532:0.8554:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05272  ES:SE:LP:AF:ID  0.002:0.003:0.372634:0.05272:rs148120343
1   732215  rs559238700 C   T   .   PASS    AF=0.03912  ES:SE:LP:AF:ID  -0.003:0.003:0.464706:0.03912:rs559238700
1   735985  rs12405651  G   A   .   PASS    AF=0.04592  ES:SE:LP:AF:ID  -0.001:0.003:0.148742:0.04592:rs12405651
1   736289  rs79010578  T   A   .   PASS    AF=0.1241   ES:SE:LP:AF:ID  -0.002:0.003:0.266001:0.1241:rs79010578
1   739210  rs2427917   A   G   .   PASS    AF=0.835    ES:SE:LP:AF:ID  0.025:0.015:1.04048:0.835:rs2427917