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": "2019-10-26T21:46:25.777770",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005345/EBI-a-GCST005345_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-GCST005345/EBI-a-GCST005345.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005345/EBI-a-GCST005345_data.vcf.gz; Date=Sat Oct 26 22:03:46 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-GCST005345/ebi-a-GCST005345.vcf.gz; Date=Sat May  9 23:16:51 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-GCST005345/EBI-a-GCST005345.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-GCST005345/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 22:27:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005345/EBI-a-GCST005345.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 22:28:31 2019
Total time elapsed: 59.53s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9596,
    "inflation_factor": 1.0216,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9351693,
    "n_clumped_hits": 8,
    "n_p_sig": 243,
    "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": 232780,
    "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 59 0 9329910 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 9329928 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 8.947394e+00 6.054375e+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 7.856315e+07 5.619142e+07 828.0000 3.224102e+07 6.941443e+07 1.145184e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.770000e-05 4.178480e-02 -0.7556 -1.610000e-02 1.000000e-04 1.610000e-02 7.127000e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.302020e-02 2.581150e-02 0.0125 1.490000e-02 2.150000e-02 4.220000e-02 2.580000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.964667e-01 2.892095e-01 0.0000 2.455998e-01 4.954000e-01 7.473000e-01 9.998000e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.964657e-01 2.892079e-01 0.0000 2.455987e-01 4.953539e-01 7.472844e-01 9.995929e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.365570e-01 2.555515e-01 0.0051 3.450000e-02 1.273000e-01 3.708000e-01 9.947000e-01 ▇▂▂▁▁
numeric AF_reference 232780 0.9750502 NA NA NA NA NA NA NA 2.380285e-01 2.534266e-01 0.0000 3.214860e-02 1.401760e-01 3.734030e-01 1.000000e+00 ▇▂▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 30923 rs806731 G T 0.0349 0.0701 0.6179993 0.6185826 0.7692 0.8724040 NA
1 51479 rs116400033 T A 0.1209 0.0733 0.0991905 0.0990686 0.2121 0.1281950 NA
1 54490 rs141149254 G A 0.1258 0.0797 0.1142999 0.1144694 0.1668 0.0960463 NA
1 54676 rs2462492 C T 0.0948 0.0867 0.2739000 0.2742070 0.1502 NA NA
1 55299 rs10399749 C T -0.0035 0.0527 0.9465000 0.9470485 0.1920 NA NA
1 58814 rs114420996 G A 0.0414 0.0697 0.5521003 0.5525294 0.0998 0.1090260 NA
1 59040 rs62637815 T C 0.0047 0.1153 0.9676001 0.9674847 0.0740 0.0615016 NA
1 61987 rs76735897 A G 0.0667 0.0657 0.3102001 0.3100006 0.2977 NA NA
1 61989 rs77573425 G C 0.0694 0.0656 0.2901997 0.2900888 0.2977 NA NA
1 63671 rs80011619 G A 0.0041 0.0533 0.9390000 0.9386847 0.1800 0.1875000 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154918266 rs642043 C T 0.0046 0.0155 0.7681995 0.7666389 0.3185 0.478675 NA
23 154918383 rs641588 G T 0.0028 0.0162 0.8649000 0.8627776 0.2563 0.356556 NA
23 154918624 rs5940558 A G 0.0085 0.0331 0.7973999 0.7973350 0.0668 0.129007 NA
23 154921127 rs58486544 T C 0.0116 0.0327 0.7213998 0.7227843 0.0685 0.145166 NA
23 154925045 rs509981 C T 0.0045 0.0162 0.7830004 0.7811830 0.2559 0.363444 NA
23 154925895 rs538470 C T 0.0016 0.0168 0.9248000 0.9241257 0.2552 0.363444 NA
23 154927199 rs645904 C T 0.0054 0.0162 0.7361003 0.7388827 0.2558 0.367417 NA
23 154927581 rs644138 G A 0.0047 0.0156 0.7629007 0.7631994 0.3168 0.463576 NA
23 154929412 rs557132 C T 0.0033 0.0162 0.8372000 0.8385850 0.2550 0.356821 NA
23 154930230 rs781880 A G 0.0050 0.0162 0.7589008 0.7575939 0.2549 0.361854 NA

bcf preview

1   30923   rs806731    G   T   .   PASS    AF=0.7692   ES:SE:LP:AF:ID  0.0349:0.0701:0.209012:0.7692:rs806731
1   51479   rs116400033 T   A   .   PASS    AF=0.2121   ES:SE:LP:AF:ID  0.1209:0.0733:1.00353:0.2121:rs116400033
1   54490   rs141149254 G   A   .   PASS    AF=0.1668   ES:SE:LP:AF:ID  0.1258:0.0797:0.941954:0.1668:rs141149254
1   54676   rs2462492   C   T   .   PASS    AF=0.1502   ES:SE:LP:AF:ID  0.0948:0.0867:0.562408:0.1502:rs2462492
1   55299   rs10399749  C   T   .   PASS    AF=0.192    ES:SE:LP:AF:ID  -0.0035:0.0527:0.0238794:0.192:rs10399749
1   58814   rs114420996 G   A   .   PASS    AF=0.0998   ES:SE:LP:AF:ID  0.0414:0.0697:0.257982:0.0998:rs114420996
1   59040   rs62637815  T   C   .   PASS    AF=0.074    ES:SE:LP:AF:ID  0.0047:0.1153:0.0143041:0.074:rs62637815
1   61987   rs76735897  A   G   .   PASS    AF=0.2977   ES:SE:LP:AF:ID  0.0667:0.0657:0.508358:0.2977:rs76735897
1   61989   rs77573425  G   C   .   PASS    AF=0.2977   ES:SE:LP:AF:ID  0.0694:0.0656:0.537303:0.2977:rs77573425
1   63671   rs80011619  G   A   .   PASS    AF=0.18 ES:SE:LP:AF:ID  0.0041:0.0533:0.0273344:0.18:rs80011619
1   66162   rs62639105  A   T   .   PASS    AF=0.3044   ES:SE:LP:AF:ID  0.0319:0.0651:0.204746:0.3044:rs62639105
1   69511   rs2691305   A   G   .   PASS    AF=0.6674   ES:SE:LP:AF:ID  0.055:0.0457:0.639975:0.6674:rs2691305
1   82163   rs139113303 G   A   .   PASS    AF=0.0591   ES:SE:LP:AF:ID  -0.1215:0.085:0.816445:0.0591:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.0599   ES:SE:LP:AF:ID  -0.1216:0.0844:0.825359:0.0599:rs149189449
1   82734   rs4030331   T   C   .   PASS    AF=0.1718   ES:SE:LP:AF:ID  -0.0909:0.0806:0.586365:0.1718:rs4030331
1   86028   rs114608975 T   C   .   PASS    AF=0.0709   ES:SE:LP:AF:ID  -0.1264:0.1101:0.600672:0.0709:rs114608975
1   86065   rs116504101 G   C   .   PASS    AF=0.0711   ES:SE:LP:AF:ID  -0.1246:0.1101:0.588886:0.0711:rs116504101
1   87190   rs1524602   G   A   .   PASS    AF=0.1739   ES:SE:LP:AF:ID  -0.0182:0.0558:0.128019:0.1739:rs1524602
1   87409   rs139490478 C   T   .   PASS    AF=0.0711   ES:SE:LP:AF:ID  -0.1239:0.1101:0.583859:0.0711:rs139490478
1   88169   rs940550    C   T   .   PASS    AF=0.163    ES:SE:LP:AF:ID  -0.0517:0.057:0.438422:0.163:rs940550
1   88172   rs940551    G   A   .   PASS    AF=0.0589   ES:SE:LP:AF:ID  0.1025:0.1271:0.376854:0.0589:rs940551
1   88177   rs143215837 G   C   .   PASS    AF=0.0587   ES:SE:LP:AF:ID  0.1096:0.1272:0.410274:0.0587:rs143215837
1   88338   rs55700207  G   A   .   PASS    AF=0.0867   ES:SE:LP:AF:ID  -0.0447:0.0731:0.266803:0.0867:rs55700207
1   88710   rs186575039 C   G   .   PASS    AF=0.0634   ES:SE:LP:AF:ID  -0.128:0.0839:0.895172:0.0634:rs186575039
1   89946   rs138808727 A   T   .   PASS    AF=0.1852   ES:SE:LP:AF:ID  0.1213:0.0738:0.999132:0.1852:rs138808727
1   91190   rs143856811 G   A   .   PASS    AF=0.065    ES:SE:LP:AF:ID  -0.1519:0.0838:1.15621:0.065:rs143856811
1   91536   rs6702460   G   T   .   PASS    AF=0.3847   ES:SE:LP:AF:ID  0.063:0.0419:0.876475:0.3847:rs6702460
1   91581   rs1524604   G   A   .   PASS    AF=0.3853   ES:SE:LP:AF:ID  0.0751:0.0422:1.12234:0.3853:rs1524604
1   92633   rs149776517 C   T   .   PASS    AF=0.039    ES:SE:LP:AF:ID  0.0632:0.1561:0.164056:0.039:rs149776517
1   92858   rs147061536 G   T   .   PASS    AF=0.1931   ES:SE:LP:AF:ID  0.0703:0.0731:0.473273:0.1931:rs147061536
1   98583   rs78255797  T   A   .   PASS    AF=0.0617   ES:SE:LP:AF:ID  0.1976:0.1349:0.84436:0.0617:rs78255797
1   98929   rs12184306  A   G   .   PASS    AF=0.0889   ES:SE:LP:AF:ID  -0.0806:0.1094:0.336205:0.0889:rs12184306
1   98974   rs12184307  A   G   .   PASS    AF=0.0804   ES:SE:LP:AF:ID  -0.037:0.1134:0.128427:0.0804:rs12184307
1   120458  rs4109820   T   C   .   PASS    AF=0.1813   ES:SE:LP:AF:ID  0.0503:0.0984:0.215026:0.1813:rs4109820
1   120983  rs182468771 C   T   .   PASS    AF=0.0866   ES:SE:LP:AF:ID  0.0613:0.1053:0.251657:0.0866:rs182468771
1   135032  rs148209574 G   A   .   PASS    AF=0.0849   ES:SE:LP:AF:ID  0.089:0.0804:0.571703:0.0849:rs148209574
1   233473  rs112455420 C   G   .   PASS    AF=0.1377   ES:SE:LP:AF:ID  -0.0402:0.0591:0.303644:0.1377:rs112455420
1   523471  rs76274635  T   C   .   PASS    AF=0.137    ES:SE:LP:AF:ID  0.0313:0.0691:0.186753:0.137:rs76274635
1   533198  rs78497331  C   T   .   PASS    AF=0.0665   ES:SE:LP:AF:ID  -0.0745:0.0869:0.407823:0.0665:rs78497331
1   534192  rs6680723   C   T   .   PASS    AF=0.2179   ES:SE:LP:AF:ID  0.003:0.072:0.0147083:0.2179:rs6680723
1   534547  rs188376087 G   A   .   PASS    AF=0.0449   ES:SE:LP:AF:ID  0.0369:0.1474:0.095555:0.0449:rs188376087
1   540975  rs80246094  G   A   .   PASS    AF=0.0814   ES:SE:LP:AF:ID  -0.1553:0.1054:0.851089:0.0814:rs80246094
1   618463  rs4108074   G   A   .   PASS    AF=0.1351   ES:SE:LP:AF:ID  0.0074:0.0587:0.046192:0.1351:rs4108074
1   645605  rs185127847 T   A   .   PASS    AF=0.0507   ES:SE:LP:AF:ID  -0.0239:0.0941:0.0972902:0.0507:rs185127847
1   662622  rs61769339  G   A   .   PASS    AF=0.115    ES:SE:LP:AF:ID  0.052:0.0551:0.461678:0.115:rs61769339
1   693625  rs190214723 T   C   .   PASS    AF=0.0369   ES:SE:LP:AF:ID  0.0043:0.1231:0.0122444:0.0369:rs190214723
1   693731  rs12238997  A   G   .   PASS    AF=0.1396   ES:SE:LP:AF:ID  0.049:0.0483:0.507938:0.1396:rs12238997
1   701835  rs189800799 T   C   .   PASS    AF=0.0172   ES:SE:LP:AF:ID  -0.2606:0.167:0.926282:0.0172:rs189800799
1   701946  rs61769353  A   G   .   PASS    AF=0.9132   ES:SE:LP:AF:ID  0.0504:0.0701:0.325782:0.9132:rs61769353
1   704637  rs142559957 G   A   .   PASS    AF=0.0587   ES:SE:LP:AF:ID  -0.0059:0.1125:0.0184985:0.0587:rs142559957