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": "2019-10-26T21:44:55.753611",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006366/EBI-a-GCST006366_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-GCST006366/EBI-a-GCST006366.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006366/EBI-a-GCST006366_data.vcf.gz; Date=Sat Oct 26 21:59:02 2019",
    "bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006366/ebi-a-GCST006366.vcf.gz; Date=Sat May  9 20:39:39 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-GCST006366/EBI-a-GCST006366.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-GCST006366/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 22:18:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006366/EBI-a-GCST006366.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 22:19:26 2019
Total time elapsed: 54.16s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9405,
    "inflation_factor": 1.064,
    "mean_EFFECT": -0.005,
    "n": "-Inf",
    "n_snps": 8131508,
    "n_clumped_hits": 26,
    "n_p_sig": 1355,
    "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": 223064,
    "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 1.000000 3 59 0 8112224 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 8112305 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.669749e+00 5.762267e+00 1.0000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.850927e+07 5.643765e+07 828.0000 3.208020e+07 6.897003e+07 1.143215e+08 2.492223e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -5.028500e-03 1.171836e+00 -19.3235 -4.738000e-01 -2.000000e-03 4.681000e-01 1.890130e+01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 9.176636e-01 6.961161e-01 0.3681 4.398000e-01 6.128000e-01 1.135700e+00 5.820100e+00 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.895691e-01 2.921834e-01 0.0000 2.340998e-01 4.865999e-01 7.430996e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.895691e-01 2.921834e-01 0.0000 2.340755e-01 4.866395e-01 7.430650e-01 9.999821e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.605156e-01 2.603576e-01 0.0101 4.740000e-02 1.577000e-01 4.092000e-01 9.897000e-01 ▇▂▂▁▁
numeric AF_reference 223064 0.972503 NA NA NA NA NA NA NA 2.628727e-01 2.545406e-01 0.0000 5.451280e-02 1.741210e-01 4.101440e-01 1.000000e+00 ▇▃▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 73841 rs143773730 C T -0.4405 1.3915 0.7516004 0.7515738 0.2357 NA NA
1 84002 rs28850140 G A 3.2619 1.8846 0.0834795 0.0834842 0.1110 NA NA
1 162463 rs143399298 T C 1.3128 1.9597 0.5029004 0.5029225 0.0973 NA NA
1 240789 rs71222827 T G 5.2838 3.6298 0.1455000 0.1454832 0.0216 NA NA
1 662622 rs61769339 G A 1.0394 1.8237 0.5686997 0.5687182 0.0991 0.1475640 NA
1 693731 rs12238997 A G -0.7765 1.6622 0.6403995 0.6403912 0.1164 0.1417730 NA
1 704637 rs142559957 G A 0.3447 2.6454 0.8963000 0.8963278 0.0521 0.0473243 NA
1 705882 rs72631875 G A 1.5150 2.2135 0.4936999 0.4936996 0.0620 0.0315495 NA
1 706368 rs55727773 A G 0.8985 1.1460 0.4330998 0.4330217 0.3967 0.2751600 NA
1 722670 rs116030099 T C -0.6590 1.9990 0.7416995 0.7416532 0.0920 0.0413339 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51224718 rs189454695 T G -0.9212 1.3675 0.5006002 0.5005415 0.1523 NA NA
22 51228910 rs145146472 G A -0.0450 0.9522 0.9623000 0.9623068 0.2801 0.2276360 NA
22 51229805 rs9616985 T C -1.5667 1.5346 0.3073003 0.3072935 0.0806 0.0730831 NA
22 51229855 rs144549712 G A -0.1331 1.4605 0.9274000 0.9273868 0.1045 0.1160140 NA
22 51233300 rs9616839 C T 0.0911 0.9590 0.9243001 0.9243190 0.3031 0.3146960 NA
22 51237063 rs3896457 T C 0.2797 1.0031 0.7803995 0.7803710 0.2552 0.2050720 NA
22 51238249 rs149733995 A C -0.3400 1.5533 0.8266999 0.8267367 0.0860 NA NA
22 51243297 rs2905159 A T 0.2618 1.4368 0.8553999 0.8554176 0.1361 NA NA
23 46256106 rs12007097 G C 0.4607 1.8266 0.8009000 0.8008732 0.9689 0.6821190 NA
23 100784211 rs188350543 C A -0.8295 0.8025 0.3012999 0.3013022 0.7579 0.6498010 NA

bcf preview

1   73841   rs143773730 C   T   .   PASS    AF=0.2357   ES:SE:LP:AF:ID  -0.4405:1.3915:0.124013:0.2357:rs143773730
1   84002   rs28850140  G   A   .   PASS    AF=0.111    ES:SE:LP:AF:ID  3.2619:1.8846:1.07842:0.111:rs28850140
1   162463  rs143399298 T   C   .   PASS    AF=0.0973   ES:SE:LP:AF:ID  1.3128:1.9597:0.298518:0.0973:rs143399298
1   240789  rs71222827  T   G   .   PASS    AF=0.0216   ES:SE:LP:AF:ID  5.2838:3.6298:0.837137:0.0216:rs71222827
1   662622  rs61769339  G   A   .   PASS    AF=0.0991   ES:SE:LP:AF:ID  1.0394:1.8237:0.245117:0.0991:rs61769339
1   693731  rs12238997  A   G   .   PASS    AF=0.1164   ES:SE:LP:AF:ID  -0.7765:1.6622:0.193549:0.1164:rs12238997
1   704637  rs142559957 G   A   .   PASS    AF=0.0521   ES:SE:LP:AF:ID  0.3447:2.6454:0.0475466:0.0521:rs142559957
1   705882  rs72631875  G   A   .   PASS    AF=0.062    ES:SE:LP:AF:ID  1.515:2.2135:0.306537:0.062:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.3967   ES:SE:LP:AF:ID  0.8985:1.146:0.363412:0.3967:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.092    ES:SE:LP:AF:ID  -0.659:1.999:0.129772:0.092:rs116030099
1   729679  rs4951859   C   G   .   PASS    AF=0.8085   ES:SE:LP:AF:ID  -0.1991:1.2469:0.058936:0.8085:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.1289   ES:SE:LP:AF:ID  0.2995:1.4585:0.0771189:0.1289:rs58276399
1   732801  rs144022023 A   G   .   PASS    AF=0.0319   ES:SE:LP:AF:ID  -1.2156:3.2271:0.150949:0.0319:rs144022023
1   734349  rs141242758 T   C   .   PASS    AF=0.1244   ES:SE:LP:AF:ID  0.0725:1.4856:0.0172314:0.1244:rs141242758
1   734462  rs12564807  G   A   .   PASS    AF=0.7586   ES:SE:LP:AF:ID  0.3638:1.3697:0.102043:0.7586:rs12564807
1   736289  rs79010578  T   A   .   PASS    AF=0.1338   ES:SE:LP:AF:ID  -0.1567:1.4581:0.0388638:0.1338:rs79010578
1   739210  rs2427917   A   G   .   PASS    AF=0.8155   ES:SE:LP:AF:ID  0.2698:1.4905:0.0673233:0.8155:rs2427917
1   741579  rs3115865   T   C   .   PASS    AF=0.949    ES:SE:LP:AF:ID  -2.578:2.5974:0.49363:0.949:rs3115865
1   748141  rs2427893   G   A   .   PASS    AF=0.0359   ES:SE:LP:AF:ID  1.1062:3.037:0.145269:0.0359:rs2427893
1   748878  rs2519043   G   T   .   PASS    AF=0.7397   ES:SE:LP:AF:ID  -0.166:1.1321:0.0537934:0.7397:rs2519043
1   750055  rs11240771  T   C   .   PASS    AF=0.0887   ES:SE:LP:AF:ID  4.0494:2.0294:1.33724:0.0887:rs11240771
1   751756  rs28527770  T   C   .   PASS    AF=0.1448   ES:SE:LP:AF:ID  -0.796:0.8524:0.455436:0.1448:rs28527770
1   752566  rs3094315   G   A   .   PASS    AF=0.8285   ES:SE:LP:AF:ID  -0.0578:0.6455:0.0321246:0.8285:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.7866   ES:SE:LP:AF:ID  0.1807:0.7145:0.0967472:0.7866:rs3131972
1   752894  rs3131971   T   C   .   PASS    AF=0.7932   ES:SE:LP:AF:ID  0.0955:0.7607:0.0457092:0.7932:rs3131971
1   753405  rs3115860   C   A   .   PASS    AF=0.8361   ES:SE:LP:AF:ID  0.7219:0.8514:0.401757:0.8361:rs3115860
1   753474  rs2073814   C   G   .   PASS    AF=0.7523   ES:SE:LP:AF:ID  0.0933:0.7625:0.0445047:0.7523:rs2073814
1   753541  rs2073813   G   A   .   PASS    AF=0.1915   ES:SE:LP:AF:ID  -0.1844:0.84:0.0828623:0.1915:rs2073813
1   753849  rs58324164  G   T   .   PASS    AF=0.1714   ES:SE:LP:AF:ID  1.095:1.5001:0.332174:0.1714:rs58324164
1   754182  rs3131969   A   G   .   PASS    AF=0.8019   ES:SE:LP:AF:ID  0.0462:0.8167:0.0200421:0.8019:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.8013   ES:SE:LP:AF:ID  0.0676:0.8196:0.0295137:0.8013:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.7833   ES:SE:LP:AF:ID  -0.3015:0.8155:0.147764:0.7833:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.7859   ES:SE:LP:AF:ID  0.0542:0.764:0.0252581:0.7859:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.7637   ES:SE:LP:AF:ID  -0.0918:0.7872:0.042297:0.7637:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.7746   ES:SE:LP:AF:ID  0.3689:0.7697:0.19942:0.7746:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.8317   ES:SE:LP:AF:ID  0.3129:0.8187:0.153477:0.8317:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.8093   ES:SE:LP:AF:ID  0.7991:0.7993:0.498256:0.8093:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.7954   ES:SE:LP:AF:ID  0.2774:0.8696:0.125112:0.7954:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.8397   ES:SE:LP:AF:ID  0.3374:0.8203:0.166917:0.8397:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.8385   ES:SE:LP:AF:ID  0.3721:0.8225:0.186419:0.8385:rs4951862
1   758048  rs3131957   A   G   .   PASS    AF=0.1127   ES:SE:LP:AF:ID  0.5508:1.7704:0.121651:0.1127:rs3131957
1   758144  rs3131956   A   G   .   PASS    AF=0.8233   ES:SE:LP:AF:ID  0.3328:0.7869:0.172372:0.8233:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.8375   ES:SE:LP:AF:ID  0.3867:0.8419:0.189767:0.8375:rs3131954
1   759700  rs3115852   T   C   .   PASS    AF=0.7935   ES:SE:LP:AF:ID  1.0538:0.8782:0.637895:0.7935:rs3115852
1   759837  rs3115851   T   A   .   PASS    AF=0.8429   ES:SE:LP:AF:ID  0.8227:0.9358:0.421017:0.8429:rs3115851
1   760912  rs1048488   C   T   .   PASS    AF=0.7883   ES:SE:LP:AF:ID  0.2423:0.7987:0.118273:0.7883:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.7933   ES:SE:LP:AF:ID  0.1507:0.7999:0.0702746:0.7933:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.7882   ES:SE:LP:AF:ID  -0.3101:0.988:0.122859:0.7882:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.8308   ES:SE:LP:AF:ID  0.6179:0.8587:0.326242:0.8308:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.818    ES:SE:LP:AF:ID  0.9274:1.0443:0.426548:0.818:rs3115849