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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    "file_date": "2019-10-26T21:47:17.297649",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006701/EBI-a-GCST006701_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-GCST006701/EBI-a-GCST006701.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006701/EBI-a-GCST006701_data.vcf.gz; Date=Sat Oct 26 22:07:26 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-GCST006701/ebi-a-GCST006701.vcf.gz; Date=Sun May 10 11:16:09 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-GCST006701/EBI-a-GCST006701.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-GCST006701/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 22:32:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006701/EBI-a-GCST006701.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 22:33:33 2019
Total time elapsed: 1.0m:16.73s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9528,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -4.1799e-06,
    "n": "-Inf",
    "n_snps": 11489836,
    "n_clumped_hits": 16,
    "n_p_sig": 478,
    "n_mono": 0,
    "n_ns": 2138,
    "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": 571989,
    "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 11458765 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 60 0 401 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 50 0 352 0 NA NA NA NA NA NA NA NA NA NA
logical N 11472723 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 8.857641e+00 5.769100e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▆▅▃▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.421516e+07 5.206992e+07 173.0000000 3.148281e+07 6.642492e+07 1.080961e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.200000e-06 1.618580e-02 -0.2920860 -4.305700e-03 1.360000e-05 4.329100e-03 3.107790e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.126270e-02 1.072680e-02 0.0021592 2.789200e-03 6.144500e-03 1.747750e-02 1.388210e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.731572e-01 2.957284e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.731749e-01 2.957283e-01 0.0000000 2.101456e-01 4.635744e-01 7.292017e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 1.663793e-01 2.446090e-01 0.0010010 4.429000e-03 3.528100e-02 2.391940e-01 9.990000e-01 ▇▁▁▁▁
numeric AF_reference 571989 0.9501436 NA NA NA NA NA NA NA 1.741701e-01 2.396234e-01 0.0000000 2.995200e-03 5.630990e-02 2.605830e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 13169 rs1436530583 A G -0.0099694 0.0295329 0.7700005 0.7356865 0.997275 NA NA
1 15850 rs575961614 G A 0.0162274 0.0107386 0.1400000 0.1307558 0.021314 0.0005990 NA
1 15893 rs555382915 T C -0.0004269 0.0033337 0.8900000 0.8981161 0.778527 0.0001997 NA
1 30912 rs571608907 C T 0.0621145 0.0309059 0.0460002 0.0444530 0.996866 0.0001997 NA
1 49298 rs200943160 T C -0.0050645 0.0038608 0.2000000 0.1896054 0.623653 0.7821490 NA
1 54676 rs2462492 C T 0.0063507 0.0038243 0.1000000 0.0967950 0.400806 NA NA
1 54712 rs568927205 T TTTTC 0.0028396 0.0030244 0.3500000 0.3477799 0.585682 NA NA
1 55326 rs3107975 T C -0.0218180 0.0214018 0.3100002 0.3079908 0.008387 0.0459265 NA
1 55389 rs1190986229 T C 0.0029572 0.0214595 0.8700001 0.8903970 0.004150 NA NA
1 70728 rs1259734071 C T -0.0371011 0.0386951 0.3500000 0.3376564 0.002154 NA NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51226692 rs150189434 G A -0.0096342 0.0288873 0.6999999 0.7387492 0.001674 0.0155751 NA
22 51227766 rs186062720 T C 0.0319964 0.0276794 0.2500000 0.2476957 0.002275 0.0005990 NA
22 51229805 rs9616985 T C 0.0027210 0.0041974 0.5400003 0.5168260 0.073185 0.0730831 NA
22 51230673 rs555680442 G C -0.0079235 0.0290407 0.8000000 0.7849742 0.002158 0.0017971 NA
22 51231424 rs539541647 A G 0.0447880 0.0339902 0.1800002 0.1876128 0.001287 0.0011981 NA
22 51232488 rs376461333 A G 0.0060559 0.0098726 0.5600000 0.5396079 0.020134 NA NA
22 51234033 rs764597437 C T -0.0941931 0.0375938 0.0109999 0.0122262 0.001190 NA NA
22 51237063 rs3896457 T C -0.0025047 0.0025666 0.3300000 0.3291267 0.298643 0.2050720 NA
22 51239586 rs535432390 T G -0.0019990 0.0229641 0.8800001 0.9306330 0.003039 0.0001997 NA
23 65456737 rs77978087 G C 0.0050120 0.0026228 0.0629999 0.0560092 0.506955 NA NA

bcf preview

1   13169   rs1436530583    A   G   .   PASS    AF=0.997275 ES:SE:LP:AF:ID  -0.00996945:0.0295329:0.113509:0.997275:rs1436530583
1   15850   rs575961614 G   A   .   PASS    AF=0.021314 ES:SE:LP:AF:ID  0.0162274:0.0107386:0.853872:0.021314:rs575961614
1   15893   rs555382915 T   C   .   PASS    AF=0.778527 ES:SE:LP:AF:ID  -0.000426851:0.00333369:0.05061:0.778527:rs555382915
1   30912   rs571608907 C   T   .   PASS    AF=0.996866 ES:SE:LP:AF:ID  0.0621145:0.0309059:1.33724:0.996866:rs571608907
1   49298   rs10399793  T   C   .   PASS    AF=0.623653 ES:SE:LP:AF:ID  -0.00506446:0.00386085:0.69897:0.623653:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400806 ES:SE:LP:AF:ID  0.00635066:0.00382433:1:0.400806:rs2462492
1   54712   rs568927205 T   TTTTC   .   PASS    AF=0.585682 ES:SE:LP:AF:ID  0.00283965:0.00302443:0.455932:0.585682:rs568927205
1   55326   rs3107975   T   C   .   PASS    AF=0.008387 ES:SE:LP:AF:ID  -0.021818:0.0214018:0.508638:0.008387:rs3107975
1   55389   rs1190986229    T   C   .   PASS    AF=0.00415  ES:SE:LP:AF:ID  0.00295716:0.0214595:0.0604807:0.00415:rs1190986229
1   70728   rs1259734071    C   T   .   PASS    AF=0.002154 ES:SE:LP:AF:ID  -0.0371011:0.0386951:0.455932:0.002154:rs1259734071
1   74356   rs1374516807    T   C   .   PASS    AF=0.996729 ES:SE:LP:AF:ID  0.0675652:0.0292501:1.61979:0.996729:rs1374516807
1   79033   rs2462495   A   G   .   PASS    AF=0.998734 ES:SE:LP:AF:ID  0.0508263:0.0496876:0.49485:0.998734:rs2462495
1   81120   rs1180184210    G   C   .   PASS    AF=0.998455 ES:SE:LP:AF:ID  -0.0669506:0.0384376:1.09691:0.998455:rs1180184210
1   82103   rs2020400   T   C   .   PASS    AF=0.925076 ES:SE:LP:AF:ID  0.000165179:0.00536837:0.0222764:0.925076:rs2020400
1   86028   rs114608975 T   C   .   PASS    AF=0.103491 ES:SE:LP:AF:ID  0.00248334:0.00612151:0.167491:0.103491:rs114608975
1   88166   rs1329210408    T   C   .   PASS    AF=0.997842 ES:SE:LP:AF:ID  0.0173272:0.0357836:0.187087:0.997842:rs1329210408
1   91311   rs1354585492    T   C   .   PASS    AF=0.997676 ES:SE:LP:AF:ID  0.0307522:0.0306539:0.49485:0.997676:rs1354585492
1   91536   rs6702460   G   T   .   PASS    AF=0.457215 ES:SE:LP:AF:ID  0.00267825:0.00376738:0.327902:0.457215:rs6702460
1   115967  rs1310910178    C   T   .   PASS    AF=0.002524 ES:SE:LP:AF:ID  -0.0257731:0.0380415:0.318759:0.002524:rs1310910178
1   121759  rs975013924 C   A   .   PASS    AF=0.007649 ES:SE:LP:AF:ID  -0.0186619:0.0152089:0.619789:0.007649:rs975013924
1   123130  rs1235743598    T   C   .   PASS    AF=0.994506 ES:SE:LP:AF:ID  -0.0242667:0.0229151:0.552842:0.994506:rs1235743598
1   126133  rs531293975 G   T   .   PASS    AF=0.998591 ES:SE:LP:AF:ID  0.0564039:0.0455445:0.657577:0.998591:rs531293975
1   157899  rs1339985321    G   C   .   PASS    AF=0.001275 ES:SE:LP:AF:ID  -0.0511475:0.0496044:0.537602:0.001275:rs1339985321
1   174747  rs1399732465    C   T   .   PASS    AF=0.025782 ES:SE:LP:AF:ID  0.00848651:0.0107409:0.337242:0.025782:rs1399732465
1   234313  rs8179466   C   T   .   PASS    AF=0.074608 ES:SE:LP:AF:ID  0.00343728:0.00741494:0.173925:0.074608:rs8179466
1   526736  rs28863004  C   G   .   PASS    AF=0.005836 ES:SE:LP:AF:ID  0.0239885:0.0258851:0.455932:0.005836:rs28863004
1   534192  rs6680723   C   T   .   PASS    AF=0.240899 ES:SE:LP:AF:ID  -0.00033516:0.00430484:0.0315171:0.240899:rs6680723
1   534583  rs6683466   C   G   .   PASS    AF=0.006682 ES:SE:LP:AF:ID  -0.0095427:0.0243833:0.173925:0.006682:rs6683466
1   544584  rs576404767 C   T   .   PASS    AF=0.001868 ES:SE:LP:AF:ID  -0.0112199:0.0350866:0.130768:0.001868:rs576404767
1   546697  rs12025928  A   G   .   PASS    AF=0.913378 ES:SE:LP:AF:ID  -0.00132568:0.00535763:0.0705811:0.913378:rs12025928
1   564862  rs1988726   T   C   .   PASS    AF=0.001321 ES:SE:LP:AF:ID  -0.0318211:0.0523951:0.236572:0.001321:rs1988726
1   565111  rs573042692 T   C   .   PASS    AF=0.001579 ES:SE:LP:AF:ID  0.122438:0.0421393:2.40894:0.001579:rs573042692
1   565130  rs371431021 G   A   .   PASS    AF=0.00453  ES:SE:LP:AF:ID  0.00458019:0.0268761:0.05061:0.00453:rs371431021
1   565196  rs538567606 T   C   .   PASS    AF=0.002314 ES:SE:LP:AF:ID  -0.0165708:0.0369578:0.187087:0.002314:rs538567606
1   565469  rs554127336 C   T   .   PASS    AF=0.001444 ES:SE:LP:AF:ID  -0.0272134:0.0499929:0.236572:0.001444:rs554127336
1   565470  rs544876160 G   A   .   PASS    AF=0.001017 ES:SE:LP:AF:ID  -0.0871707:0.0434613:1.284:0.001017:rs544876160
1   565490  rs7349153   T   C   .   PASS    AF=0.001368 ES:SE:LP:AF:ID  -0.0689802:0.0526274:0.69897:0.001368:rs7349153
1   565870  rs9326619   T   C   .   PASS    AF=0.001518 ES:SE:LP:AF:ID  -0.0680251:0.0505813:0.721246:0.001518:rs9326619
1   566024  rs6421779   G   A   .   PASS    AF=0.001304 ES:SE:LP:AF:ID  -0.0373007:0.0526906:0.283997:0.001304:rs6421779
1   566371  rs1832731   A   G   .   PASS    AF=0.001491 ES:SE:LP:AF:ID  -0.0430067:0.0520512:0.356547:0.001491:rs1832731
1   566792  rs9283152   T   C   .   PASS    AF=0.002097 ES:SE:LP:AF:ID  -0.0367999:0.0455311:0.366532:0.002097:rs9283152
1   566933  rs113120793 A   G   .   PASS    AF=0.001312 ES:SE:LP:AF:ID  -0.0637211:0.053743:0.585027:0.001312:rs113120793
1   566960  rs2185540   T   C   .   PASS    AF=0.001403 ES:SE:LP:AF:ID  -0.05667:0.0532203:0.508638:0.001403:rs2185540
1   567006  rs565235853 G   T   .   PASS    AF=0.003014 ES:SE:LP:AF:ID  -0.00501689:0.0240622:0.0705811:0.003014:rs565235853
1   567867  rs2000096   A   G   .   PASS    AF=0.002937 ES:SE:LP:AF:ID  -0.012204:0.0359707:0.119186:0.002937:rs2000096
1   568072  rs2853820   A   G   .   PASS    AF=0.001531 ES:SE:LP:AF:ID  -0.0617964:0.0484929:0.657577:0.001531:rs2853820
1   568800  rs375217967 G   A   .   PASS    AF=0.016903 ES:SE:LP:AF:ID  0.0133352:0.0147653:0.455932:0.016903:rs375217967
1   569204  rs112660509 T   C   .   PASS    AF=0.001865 ES:SE:LP:AF:ID  -0.0327538:0.0483418:0.275724:0.001865:rs112660509
1   569543  rs538153094 G   A   .   PASS    AF=0.001549 ES:SE:LP:AF:ID  -0.0410693:0.0506758:0.408935:0.001549:rs538153094
1   569604  rs9645429   G   A   .   PASS    AF=0.001684 ES:SE:LP:AF:ID  -0.0123333:0.0430828:0.102373:0.001684:rs9645429