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": "2019-10-27T07:19:51.596860",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003435/EBI-a-GCST003435_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-GCST003435/EBI-a-GCST003435.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003435/EBI-a-GCST003435_data.vcf.gz; Date=Sun Oct 27 07:24:57 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST003435/ebi-a-GCST003435.vcf.gz; Date=Sun May 10 13:08:58 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-GCST003435/EBI-a-GCST003435.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-GCST003435/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 07:47:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003435/EBI-a-GCST003435.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 07:47:27 2019
Total time elapsed: 20.56s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9493,
    "inflation_factor": 1.0122,
    "mean_EFFECT": -0.002,
    "n": "-Inf",
    "n_snps": 3206638,
    "n_clumped_hits": 10,
    "n_p_sig": 230,
    "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": 28789,
    "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 42 0 3198634 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 3198639 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 8.598011e+00 5.683501e+00 1.0000 4.000000e+00 8.000000e+00 1.200000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.892800e+07 5.586057e+07 6689.0000 3.268053e+07 7.007209e+07 1.144234e+08 2.492190e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.979900e-03 2.055875e+00 -1553.1400 -6.800000e-03 1.000000e-04 6.900000e-03 9.578250e+02 ▁▁▁▇▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.609500e-02 2.727097e+00 0.0045 6.000000e-03 8.000000e-03 1.880000e-02 1.065310e+03 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.970508e-01 2.882262e-01 0.0000 2.477000e-01 4.974003e-01 7.470007e-01 9.999000e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.970505e-01 2.882252e-01 0.0000 2.476962e-01 4.974095e-01 7.470129e-01 9.999437e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.334624e-01 2.751068e-01 0.0007 1.015000e-01 2.443000e-01 5.231000e-01 9.993000e-01 ▇▃▂▂▂
numeric AF_reference 28789 0.9909996 NA NA NA NA NA NA NA 3.264312e-01 2.781282e-01 0.0000 8.925720e-02 2.440100e-01 5.163740e-01 1.000000e+00 ▇▅▂▂▂

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 721290 rs12565286 G C -0.0198 0.0198 0.3174001 0.3173105 0.0521 0.0371406 NA
1 723819 rs11804171 T A 0.0121 0.0255 0.6350998 0.6351364 0.0609 0.1345850 NA
1 723891 rs2977670 G C -0.0221 0.0225 0.3260003 0.3259904 0.9149 0.7799520 NA
1 750235 rs12138618 G A 0.0470 0.0267 0.0783592 0.0783570 0.0648 NA NA
1 752566 rs3094315 G A -0.0044 0.0085 0.6047000 0.6047045 0.8170 0.7182510 NA
1 752721 rs3131972 A G -0.0177 0.0406 0.6629000 0.6628653 0.8031 0.6533550 NA
1 753405 rs3115860 C A -0.0063 0.0425 0.8822001 0.8821571 0.8587 0.7517970 NA
1 753541 rs2073813 G A 0.0159 0.0176 0.3663001 0.3663088 0.1388 0.3019170 NA
1 754192 rs3131968 A G -0.0006 0.0102 0.9531001 0.9530927 0.8262 0.6785140 NA
1 761732 rs2286139 C T -0.0263 0.0217 0.2255002 0.2255194 0.8497 0.6257990 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51212875 rs2238837 A C 0.0009 0.0102 0.9296999 0.9296897 0.3816 0.3724040 NA
22 51216564 rs9616970 T C -0.0099 0.0130 0.4463001 0.4463355 0.1421 0.1563500 NA
22 51217134 rs117417021 A G 0.0028 0.0109 0.7973001 0.7972708 0.4312 0.2671730 NA
22 51219006 rs28729663 G A 0.0101 0.0291 0.7285007 0.7285319 0.1447 0.2052720 NA
22 51222100 rs114553188 G T -0.0244 0.0165 0.1391999 0.1391970 0.0658 0.0880591 NA
22 51223637 rs375798137 G A -0.0246 0.0166 0.1384000 0.1383595 0.0642 0.0788738 NA
22 51229805 rs9616985 T C 0.0144 0.0196 0.4625003 0.4625259 0.0804 0.0730831 NA
23 57663373 rs28962478 A G 0.1546 0.1419 0.2759002 0.2759336 0.0838 NA NA
23 91415872 rs6562597 G A -0.0031 0.0210 0.8826000 0.8826434 0.0258 0.0021192 NA
23 118495837 rs12882977 G A -0.0009 0.0055 0.8700001 0.8700174 0.4724 0.2307280 NA

bcf preview

1   721290  rs12565286  G   C   .   PASS    AF=0.0521   ES:SE:LP:AF:ID  -0.0198:0.0198:0.498393:0.0521:rs12565286
1   723819  rs11804171  T   A   .   PASS    AF=0.0609   ES:SE:LP:AF:ID  0.0121:0.0255:0.197158:0.0609:rs11804171
1   723891  rs2977670   G   C   .   PASS    AF=0.9149   ES:SE:LP:AF:ID  -0.0221:0.0225:0.486782:0.9149:rs2977670
1   750235  rs12138618  G   A   .   PASS    AF=0.0648   ES:SE:LP:AF:ID  0.047:0.0267:1.10591:0.0648:rs12138618
1   752566  rs3094315   G   A   .   PASS    AF=0.817    ES:SE:LP:AF:ID  -0.0044:0.0085:0.21846:0.817:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.8031   ES:SE:LP:AF:ID  -0.0177:0.0406:0.178552:0.8031:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.8587   ES:SE:LP:AF:ID  -0.0063:0.0425:0.0544329:0.8587:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.1388   ES:SE:LP:AF:ID  0.0159:0.0176:0.436163:0.1388:rs2073813
1   754192  rs3131968   A   G   .   PASS    AF=0.8262   ES:SE:LP:AF:ID  -0.0006:0.0102:0.0208615:0.8262:rs3131968
1   761732  rs2286139   C   T   .   PASS    AF=0.8497   ES:SE:LP:AF:ID  -0.0263:0.0217:0.646853:0.8497:rs2286139
1   768448  rs12562034  G   A   .   PASS    AF=0.1563   ES:SE:LP:AF:ID  0.0201:0.0123:0.990549:0.1563:rs12562034
1   775659  rs2905035   A   G   .   PASS    AF=0.8556   ES:SE:LP:AF:ID  -0.0089:0.0103:0.411728:0.8556:rs2905035
1   776546  rs12124819  A   G   .   PASS    AF=0.2379   ES:SE:LP:AF:ID  -0.0151:0.014:0.551603:0.2379:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.8514   ES:SE:LP:AF:ID  -0.0059:0.0095:0.271971:0.8514:rs2980319
1   779322  rs4040617   A   G   .   PASS    AF=0.1472   ES:SE:LP:AF:ID  0.0028:0.01:0.108184:0.1472:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.8548   ES:SE:LP:AF:ID  -0.0065:0.0107:0.2648:0.8548:rs2977612
1   785050  rs2905062   G   A   .   PASS    AF=0.8221   ES:SE:LP:AF:ID  -0.0044:0.0077:0.24657:0.8221:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.8242   ES:SE:LP:AF:ID  0.0012:0.0093:0.0470623:0.8242:rs2980300
1   791853  rs6684487   G   A   .   PASS    AF=0.0871   ES:SE:LP:AF:ID  -0.0519:0.0918:0.242756:0.0871:rs6684487
1   798026  rs4951864   C   T   .   PASS    AF=0.8973   ES:SE:LP:AF:ID  -0.017:0.0184:0.44916:0.8973:rs4951864
1   798801  rs12132517  G   A   .   PASS    AF=0.1115   ES:SE:LP:AF:ID  0.0213:0.0198:0.549751:0.1115:rs12132517
1   798959  rs11240777  G   A   .   PASS    AF=0.2377   ES:SE:LP:AF:ID  -0.0002:0.0103:0.00678428:0.2377:rs11240777
1   799463  rs4245756   T   C   .   PASS    AF=0.9266   ES:SE:LP:AF:ID  0.0217:0.1505:0.0528605:0.9266:rs4245756
1   838555  rs4970383   C   A   .   PASS    AF=0.2622   ES:SE:LP:AF:ID  -0.0152:0.0351:0.177178:0.2622:rs4970383
1   846808  rs4475691   C   T   .   PASS    AF=0.1891   ES:SE:LP:AF:ID  0.0428:0.0265:0.973467:0.1891:rs4475691
1   846864  rs950122    G   C   .   PASS    AF=0.1745   ES:SE:LP:AF:ID  -0.0035:0.0249:0.0514892:0.1745:rs950122
1   854250  rs7537756   A   G   .   PASS    AF=0.194    ES:SE:LP:AF:ID  0.0234:0.0394:0.257589:0.194:rs7537756
1   861808  rs13302982  A   G   .   PASS    AF=0.9595   ES:SE:LP:AF:ID  0.0357:0.0828:0.176265:0.9595:rs13302982
1   873558  rs1110052   G   T   .   PASS    AF=0.6858   ES:SE:LP:AF:ID  -0.0363:0.0346:0.531505:0.6858:rs1110052
1   879317  rs7523549   C   T   .   PASS    AF=0.0201   ES:SE:LP:AF:ID  -0.0862:0.2012:0.175029:0.0201:rs7523549
1   882033  rs2272756   G   A   .   PASS    AF=0.2473   ES:SE:LP:AF:ID  -0.007:0.014:0.209644:0.2473:rs2272756
1   888659  rs3748597   T   C   .   PASS    AF=0.9098   ES:SE:LP:AF:ID  0.0139:0.0277:0.21056:0.9098:rs3748597
1   891945  rs13303106  A   G   .   PASS    AF=0.634    ES:SE:LP:AF:ID  0.0105:0.0328:0.125576:0.634:rs13303106
1   894573  rs13303010  G   A   .   PASS    AF=0.9062   ES:SE:LP:AF:ID  0.1111:0.062:1.13585:0.9062:rs13303010
1   900505  rs28705211  G   C   .   PASS    AF=0.4168   ES:SE:LP:AF:ID  0.024:0.0311:0.356251:0.4168:rs28705211
1   900730  rs3935066   G   A   .   PASS    AF=0.9279   ES:SE:LP:AF:ID  0.1535:0.097:0.945004:0.9279:rs3935066
1   903104  rs6696281   C   T   .   PASS    AF=0.0372   ES:SE:LP:AF:ID  -0.1072:0.1009:0.540608:0.0372:rs6696281
1   910935  rs2340592   G   A   .   PASS    AF=0.1922   ES:SE:LP:AF:ID  0.1105:0.0457:1.8066:0.1922:rs2340592
1   915227  rs13303355  A   G   .   PASS    AF=0.9806   ES:SE:LP:AF:ID  0.0957:0.1983:0.201073:0.9806:rs13303355
1   918384  rs13303118  G   T   .   PASS    AF=0.5743   ES:SE:LP:AF:ID  0.006:0.0171:0.139243:0.5743:rs13303118
1   918573  rs2341354   A   G   .   PASS    AF=0.5776   ES:SE:LP:AF:ID  0.0137:0.019:0.327071:0.5776:rs2341354
1   922009  rs6693747   G   A   .   PASS    AF=0.2146   ES:SE:LP:AF:ID  -0.0161:0.0602:0.102868:0.2146:rs6693747
1   924898  rs6665000   C   A   .   PASS    AF=0.9687   ES:SE:LP:AF:ID  0.057:0.0808:0.318307:0.9687:rs6665000
1   926431  rs4970403   A   T   .   PASS    AF=0.9629   ES:SE:LP:AF:ID  0.0409:0.0554:0.336865:0.9629:rs4970403
1   927309  rs2341362   T   C   .   PASS    AF=0.9687   ES:SE:LP:AF:ID  0.0595:0.08:0.340084:0.9687:rs2341362
1   928836  rs9777703   C   T   .   PASS    AF=0.9693   ES:SE:LP:AF:ID  -0.0201:0.0586:0.135726:0.9693:rs9777703
1   932457  rs1891910   G   A   .   PASS    AF=0.2317   ES:SE:LP:AF:ID  -0.007:0.0368:0.0710412:0.2317:rs1891910
1   940203  rs35940137  G   A   .   PASS    AF=0.0619   ES:SE:LP:AF:ID  0.0102:0.0721:0.0518316:0.0619:rs35940137
1   943468  rs3121567   T   C   .   PASS    AF=0.9715   ES:SE:LP:AF:ID  -0.1209:0.0788:0.90309:0.9715:rs3121567
1   944564  rs3128117   T   C   .   PASS    AF=0.4193   ES:SE:LP:AF:ID  0.0299:0.0282:0.539102:0.4193:rs3128117