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.7": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
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    "file_date": "2019-10-27T07:24:09.470208",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005314/EBI-a-GCST005314_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-GCST005314/EBI-a-GCST005314.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005314/EBI-a-GCST005314_data.vcf.gz; Date=Sun Oct 27 07:37:34 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST005314/ebi-a-GCST005314.vcf.gz; Date=Sat May  9 22:24:41 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-GCST005314/EBI-a-GCST005314.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-GCST005314/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 Sun Oct 27 08:00:49 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005314/EBI-a-GCST005314.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 Sun Oct 27 08:01:44 2019
Total time elapsed: 55.22s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9369,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 5.1166e-06,
    "n": "-Inf",
    "n_snps": 8662228,
    "n_clumped_hits": 10,
    "n_p_sig": 86,
    "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": 92719,
    "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 8624730 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 8636144 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 8.668567e+00 5.771491e+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 7.838113e+07 5.650814e+07 828.0000 3.192191e+07 6.866509e+07 1.142241e+08 2.492405e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 5.100000e-06 2.028800e-02 -4.4768 -7.100000e-03 -1.000000e-04 7.000000e-03 4.708000e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.323620e-02 1.398140e-02 0.0047 6.700000e-03 9.300000e-03 1.690000e-02 3.133800e+00 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.869264e-01 2.905420e-01 0.0000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.869209e-01 2.905244e-01 0.0000 2.324663e-01 4.831660e-01 7.388827e-01 9.996170e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.554425e-01 2.595716e-01 0.0100 4.200000e-02 1.500000e-01 4.100000e-01 9.900000e-01 ▇▂▂▁▁
numeric AF_reference 92719 0.9892638 NA NA NA NA NA NA NA 2.526224e-01 2.529666e-01 0.0000 4.412940e-02 1.621410e-01 3.971650e-01 1.000000e+00 ▇▃▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 11012 rs544419019 C G -0.0019 0.0161 0.9000000 0.9060578 0.090 0.0880591 NA
1 12986 rs1398513748 G C 0.5144 0.3691 0.1600000 0.1634202 0.056 NA NA
1 13110 rs540538026 G A -0.0178 0.0208 0.3900004 0.3921254 0.060 0.0267572 NA
1 13116 rs62635286 T G 0.0020 0.0127 0.8800001 0.8748663 0.190 0.0970447 NA
1 13118 rs200579949 A G 0.0020 0.0127 0.8800001 0.8748663 0.190 0.0970447 NA
1 13273 rs531730856 G C -0.0063 0.0144 0.6600001 0.6617488 0.140 0.0950479 NA
1 13797 rs1347340604 A C -0.1072 0.1560 0.4899999 0.4919696 0.790 NA NA
1 14464 rs546169444 A T 0.0198 0.0132 0.1299999 0.1336144 0.160 0.0958466 NA
1 14599 rs531646671 T A 0.0093 0.0121 0.4400003 0.4421338 0.190 0.1475640 NA
1 14604 rs541940975 A G 0.0093 0.0121 0.4400003 0.4421338 0.190 0.1475640 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154395148 rs113038688 C T 0.0043 0.0047 0.3599996 0.3602475 0.020 0.0259603 NA
23 154451378 rs4893072 C G -0.0028 0.0049 0.5700002 0.5677092 0.290 0.3766890 NA
23 154462321 rs150901018 C G 0.0047 0.0047 0.3200000 0.3173105 0.063 0.0161589 NA
23 154523008 rs113313431 G T -0.0064 0.0047 0.1700000 0.1732919 0.058 0.0177483 NA
23 154616509 rs28826658 C G 0.0026 0.0047 0.5800000 0.5801323 0.965 0.8752320 NA
23 154617838 rs28867467 T G -0.0013 0.0047 0.7800007 0.7820905 0.023 0.0307285 NA
23 154810333 rs7064354 G A -0.0006 0.0047 0.9000000 0.8984184 0.033 0.0913907 NA
23 154865915 rs601290 G A -0.0001 0.0047 0.9800000 0.9830250 0.750 0.6381460 NA
23 155069592 rs35519384 C T 0.0067 0.0047 0.1499999 0.1540035 0.071 0.0565096 NA
23 155189982 rs5940638 C T -0.0040 0.0047 0.3900004 0.3947339 0.450 0.2739620 NA

bcf preview

1   11012   rs544419019 C   G   .   PASS    AF=0.09 ES:SE:LP:AF:ID  -0.0019:0.0161:0.0457575:0.09:rs544419019
1   12986   rs1398513748    G   C   .   PASS    AF=0.056    ES:SE:LP:AF:ID  0.5144:0.3691:0.79588:0.056:rs1398513748
1   13110   rs540538026 G   A   .   PASS    AF=0.06 ES:SE:LP:AF:ID  -0.0178:0.0208:0.408935:0.06:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.19 ES:SE:LP:AF:ID  0.002:0.0127:0.0555173:0.19:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.19 ES:SE:LP:AF:ID  0.002:0.0127:0.0555173:0.19:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.14 ES:SE:LP:AF:ID  -0.0063:0.0144:0.180456:0.14:rs531730856
1   13797   rs1347340604    A   C   .   PASS    AF=0.79 ES:SE:LP:AF:ID  -0.1072:0.156:0.309804:0.79:rs1347340604
1   14464   rs546169444 A   T   .   PASS    AF=0.16 ES:SE:LP:AF:ID  0.0198:0.0132:0.886057:0.16:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.19 ES:SE:LP:AF:ID  0.0093:0.0121:0.356547:0.19:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.19 ES:SE:LP:AF:ID  0.0093:0.0121:0.356547:0.19:rs541940975
1   14867   rs1361043022    A   G   .   PASS    AF=0.58 ES:SE:LP:AF:ID  -0.1005:0.0587:1.06048:0.58:rs1361043022
1   14930   rs6682385   A   G   .   PASS    AF=0.47 ES:SE:LP:AF:ID  0.0012:0.0095:0.0457575:0.47:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.047    ES:SE:LP:AF:ID  0.0146:0.0227:0.283997:0.047:rs199856693
1   15031   rs568188357 G   A   .   PASS    AF=0.97 ES:SE:LP:AF:ID  -1.226:0.4675:2.06048:0.97:rs568188357
1   15211   rs3982632   T   G   .   PASS    AF=0.74 ES:SE:LP:AF:ID  0.002:0.0108:0.0705811:0.74:rs3982632
1   15339   rs1419228067    C   T   .   PASS    AF=0.028    ES:SE:LP:AF:ID  1.2196:0.4816:1.95861:0.028:rs1419228067
1   15761   rs1269189092    A   G   .   PASS    AF=0.23 ES:SE:LP:AF:ID  0.0927:0.0838:0.568636:0.23:rs1269189092
1   15811   rs1359290710    T   C   .   PASS    AF=0.58 ES:SE:LP:AF:ID  0.1407:0.0665:1.45593:0.58:rs1359290710
1   15820   rs2691315   G   T   .   PASS    AF=0.27 ES:SE:LP:AF:ID  0.0081:0.0111:0.327902:0.27:rs2691315
1   15850   rs575961614 G   A   .   PASS    AF=0.027    ES:SE:LP:AF:ID  0.0502:0.2433:0.0757207:0.027:rs575961614
1   15893   rs555382915 T   C   .   PASS    AF=0.77 ES:SE:LP:AF:ID  -0.0927:0.0838:0.568636:0.77:rs555382915
1   16949   rs199745162 A   C   .   PASS    AF=0.02 ES:SE:LP:AF:ID  -0.0623:0.0336:1.20066:0.02:rs199745162
1   18849   rs533090414 C   G   .   PASS    AF=0.975    ES:SE:LP:AF:ID  -0.0158:0.0277:0.244125:0.975:rs533090414
1   19161   rs1459947110    G   A   .   PASS    AF=0.74 ES:SE:LP:AF:ID  -0.2298:0.0914:1.92082:0.74:rs1459947110
1   20210   rs1341566117    G   A   .   PASS    AF=0.15 ES:SE:LP:AF:ID  0.4555:0.2102:1.52288:0.15:rs1341566117
1   29269   rs1439806364    C   T   .   PASS    AF=0.94 ES:SE:LP:AF:ID  -0.3392:0.2263:0.886057:0.94:rs1439806364
1   29554   rs1261304906    G   A   .   PASS    AF=0.033    ES:SE:LP:AF:ID  0.6402:0.4569:0.79588:0.033:rs1261304906
1   30923   rs806731    G   T   .   PASS    AF=0.9  ES:SE:LP:AF:ID  -0.0007:0.0167:0.0177288:0.9:rs806731
1   38814   rs1463170359    G   A   .   PASS    AF=0.011    ES:SE:LP:AF:ID  1.1009:1.5141:0.327902:0.011:rs1463170359
1   47159   rs540662756 T   C   .   PASS    AF=0.07 ES:SE:LP:AF:ID  0.004:0.0199:0.0757207:0.07:rs540662756
1   48015   rs1328248850    A   G   .   PASS    AF=0.73 ES:SE:LP:AF:ID  -0.3474:0.1666:1.4318:0.73:rs1328248850
1   49298   rs10399793  T   C   .   PASS    AF=0.84 ES:SE:LP:AF:ID  -0.0088:0.0132:0.30103:0.84:rs10399793
1   49554   rs539322794 A   G   .   PASS    AF=0.1  ES:SE:LP:AF:ID  0.007:0.0162:0.173925:0.1:rs539322794
1   51479   rs116400033 T   A   .   PASS    AF=0.21 ES:SE:LP:AF:ID  0.0135:0.0116:0.60206:0.21:rs116400033
1   52238   rs2691277   T   G   .   PASS    AF=0.978    ES:SE:LP:AF:ID  -0.0345:0.034:0.508638:0.978:rs2691277
1   53215   rs1361133293    G   A   .   PASS    AF=0.24 ES:SE:LP:AF:ID  0.0973:0.0838:0.60206:0.24:rs1361133293
1   54490   rs141149254 G   A   .   PASS    AF=0.15 ES:SE:LP:AF:ID  0.0131:0.013:0.49485:0.15:rs141149254
1   54712   rs552304420 T   C   .   PASS    AF=0.01 ES:SE:LP:AF:ID  0.04:0.0471:0.39794:0.01:rs552304420
1   54716   rs569128616 C   T   .   PASS    AF=0.43 ES:SE:LP:AF:ID  0.0126:0.0099:0.69897:0.43:rs569128616
1   55164   rs3091274   C   A   .   PASS    AF=0.98 ES:SE:LP:AF:ID  -0.0633:0.0375:1.04096:0.98:rs3091274
1   55326   rs3107975   T   C   .   PASS    AF=0.02 ES:SE:LP:AF:ID  -0.0183:0.0398:0.187087:0.02:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.26 ES:SE:LP:AF:ID  -0.0059:0.011:0.229148:0.26:rs28396308
1   57292   rs201418760 C   T   .   PASS    AF=0.021    ES:SE:LP:AF:ID  0.024:0.0349:0.309804:0.021:rs201418760
1   58814   rs114420996 G   A   .   PASS    AF=0.095    ES:SE:LP:AF:ID  -0.0049:0.0166:0.113509:0.095:rs114420996
1   59040   rs62637815  T   C   .   PASS    AF=0.09 ES:SE:LP:AF:ID  -0.012:0.0167:0.327902:0.09:rs62637815
1   60249   rs547227933 C   T   .   PASS    AF=0.018    ES:SE:LP:AF:ID  -0.0589:0.035:1.03152:0.018:rs547227933
1   60351   rs62637817  A   G   .   PASS    AF=0.09 ES:SE:LP:AF:ID  -0.0086:0.0173:0.207608:0.09:rs62637817
1   60574   rs1198550799    C   G   .   PASS    AF=0.14 ES:SE:LP:AF:ID  0.2356:0.1451:1:0.14:rs1198550799
1   61920   rs62637820  G   A   .   PASS    AF=0.03 ES:SE:LP:AF:ID  -0.0044:0.0278:0.0604807:0.03:rs62637820
1   62777   rs3844233   A   T   .   PASS    AF=0.44 ES:SE:LP:AF:ID  0.0062:0.0095:0.283997:0.44:rs3844233