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": "2019-10-26T21:46:53.971408",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006572/EBI-a-GCST006572_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-GCST006572/EBI-a-GCST006572.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006572/EBI-a-GCST006572_data.vcf.gz; Date=Sat Oct 26 22:04:28 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-GCST006572/ebi-a-GCST006572.vcf.gz; Date=Sun May 10 08:45:55 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-GCST006572/EBI-a-GCST006572.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-GCST006572/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 22:28:31 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006572/EBI-a-GCST006572.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 22:29:37 2019
Total time elapsed: 1.0m:5.47s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9525,
    "inflation_factor": 1.4111,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 10066414,
    "n_clumped_hits": 147,
    "n_p_sig": 13679,
    "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": 2628,
    "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 TRUE
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 1.0000000 3 58 0 10049938 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 10049954 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 8.631825e+00 5.763125e+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 7.920078e+07 5.643283e+07 828.0000000 3.281876e+07 6.996503e+07 1.150544e+08 2.492251e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 5.560000e-05 2.276420e-02 -0.6409700 -5.390000e-03 -6.000000e-05 5.300000e-03 6.123400e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.291320e-02 1.829060e-02 0.0028500 3.410000e-03 6.020000e-03 1.468000e-02 1.532700e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.432368e-01 3.034898e-01 0.0000000 1.660000e-01 4.229996e-01 7.069993e-01 9.990000e-01 ▇▆▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.432325e-01 3.034926e-01 0.0000000 1.660805e-01 4.229522e-01 7.065068e-01 9.994795e-01 ▇▅▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 1.949720e-01 2.551226e-01 0.0017010 1.020000e-02 6.633000e-02 2.993000e-01 9.982990e-01 ▇▂▁▁▁
numeric AF_reference 2628 0.9997385 NA NA NA NA NA NA NA 1.965597e-01 2.460989e-01 0.0001997 9.784400e-03 8.526360e-02 3.005190e-01 9.996010e-01 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 693731 rs12238997 A G -0.00343 0.00444 0.4400003 0.4398050 0.102000 0.1417730 NA
1 713092 rs4565649 G A 0.02162 0.03519 0.5390003 0.5389649 0.008503 0.0249601 NA
1 715205 rs141090730 C G 0.03701 0.03430 0.2809998 0.2805838 0.008503 0.0299521 NA
1 715265 rs12184267 C T -0.00107 0.00758 0.8870000 0.8877428 0.040820 0.0275559 NA
1 715367 rs12184277 A G -0.00062 0.00757 0.9350000 0.9347244 0.040820 0.0281550 NA
1 717474 rs141784362 C T 0.03958 0.03519 0.2610003 0.2606944 0.008503 0.0249601 NA
1 717587 rs144155419 G A -0.01206 0.01120 0.2819999 0.2815760 0.008503 0.0045926 NA
1 720381 rs116801199 G T -0.00114 0.00754 0.8800001 0.8798230 0.040820 0.0359425 NA
1 721290 rs12565286 G C -0.00097 0.00753 0.8969999 0.8975016 0.042520 0.0371406 NA
1 722559 rs150361918 T C 0.04207 0.03430 0.2200002 0.2199991 0.008503 0.0373403 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.00341 0.00548 0.5339997 0.5337691 0.091840 0.0826677 NA
22 51219006 rs28729663 G A -0.00149 0.00414 0.7179993 0.7189194 0.176900 0.2052720 NA
22 51219387 rs9616832 T C -0.00229 0.00547 0.6760005 0.6754740 0.090140 0.0654952 NA
22 51219704 rs147475742 G A -0.01145 0.00715 0.1089999 0.1092887 0.044220 0.0473243 NA
22 51221731 rs115055839 T C -0.00342 0.00549 0.5329997 0.5333168 0.091840 0.0625000 NA
22 51222100 rs114553188 G T 0.00303 0.00628 0.6300007 0.6294621 0.074830 0.0880591 NA
22 51229805 rs9616985 T C -0.00281 0.00550 0.6100002 0.6094147 0.091840 0.0730831 NA
22 51237063 rs3896457 T C -0.00149 0.00311 0.6330004 0.6318677 0.226200 0.2050720 NA
23 83855186 rs5923048 G T -0.03612 0.03348 0.2809998 0.2806532 0.996599 0.7075500 NA
23 147407824 rs28859988 G A -0.01097 0.02633 0.6770005 0.6769454 0.006803 NA NA

bcf preview

1   693731  rs12238997  A   G   .   PASS    AF=0.102    ES:SE:LP:AF:ID  -0.00343:0.00444:0.356547:0.102:rs12238997
1   713092  rs4565649   G   A   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.02162:0.03519:0.268411:0.008503:rs4565649
1   715205  rs141090730 C   G   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.03701:0.0343:0.551294:0.008503:rs141090730
1   715265  rs12184267  C   T   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.00107:0.00758:0.0520764:0.04082:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.00062:0.00757:0.0291884:0.04082:rs12184277
1   717474  rs141784362 C   T   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.03958:0.03519:0.583359:0.008503:rs141784362
1   717587  rs144155419 G   A   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  -0.01206:0.0112:0.549751:0.008503:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.00114:0.00754:0.0555173:0.04082:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.04252  ES:SE:LP:AF:ID  -0.00097:0.00753:0.0472076:0.04252:rs12565286
1   722559  rs150361918 T   C   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.04207:0.0343:0.657577:0.008503:rs150361918
1   722603  rs138029171 T   C   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.03129:0.0372:0.39794:0.008503:rs138029171
1   722980  rs114222710 C   T   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.04316:0.03519:0.657577:0.008503:rs114222710
1   723891  rs2977670   G   C   .   PASS    AF=0.95238  ES:SE:LP:AF:ID  0.00076:0.00708:0.0390538:0.95238:rs2977670
1   723918  rs144434834 G   A   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.04722:0.03519:0.744727:0.008503:rs144434834
1   726794  rs28454925  C   G   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.00124:0.00756:0.0609802:0.04082:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.001701 ES:SE:LP:AF:ID  -0.00322:0.00754:0.173925:0.001701:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.8554   ES:SE:LP:AF:ID  0.00428:0.00392:0.560667:0.8554:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05272  ES:SE:LP:AF:ID  -0.00188:0.00621:0.118045:0.05272:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.1071   ES:SE:LP:AF:ID  -0.00554:0.00435:0.692504:0.1071:rs58276399
1   732120  rs114572157 T   C   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.03405:0.0343:0.493495:0.008503:rs114572157
1   732801  rs144022023 A   G   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.04831:0.0343:0.798603:0.008503:rs144022023
1   734349  rs141242758 T   C   .   PASS    AF=0.1054   ES:SE:LP:AF:ID  -0.00607:0.00436:0.785156:0.1054:rs141242758
1   736186  rs147133636 G   A   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.03261:0.0396:0.387216:0.008503:rs147133636
1   736289  rs79010578  T   A   .   PASS    AF=0.1241   ES:SE:LP:AF:ID  -0.00469:0.00421:0.576754:0.1241:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  -0.00089:0.01395:0.0227338:0.008503:rs181876450
1   738539  rs147999235 T   C   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.05948:0.03519:1.04096:0.008503:rs147999235
1   739608  rs145267859 T   C   .   PASS    AF=0.001701 ES:SE:LP:AF:ID  0.07833:0.10838:0.327902:0.001701:rs145267859
1   740284  rs61770167  C   T   .   PASS    AF=0.005102 ES:SE:LP:AF:ID  -0.0275:0.02001:0.772113:0.005102:rs61770167
1   741833  rs148581628 C   T   .   PASS    AF=0.008503 ES:SE:LP:AF:ID  0.04339:0.03616:0.638272:0.008503:rs148581628
1   746189  rs139221807 A   G   .   PASS    AF=0.001701 ES:SE:LP:AF:ID  0.13657:0.05421:1.92812:0.001701:rs139221807
1   748445  rs183774475 T   C   .   PASS    AF=0.001701 ES:SE:LP:AF:ID  0.11936:0.10838:0.567031:0.001701:rs183774475
1   748446  rs188307914 G   A   .   PASS    AF=0.001701 ES:SE:LP:AF:ID  0.08003:0.10838:0.337242:0.001701:rs188307914
1   752478  rs146277091 G   A   .   PASS    AF=0.03912  ES:SE:LP:AF:ID  -0.00204:0.00757:0.103474:0.03912:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.8486   ES:SE:LP:AF:ID  0.00399:0.0039:0.5157:0.8486:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.8486   ES:SE:LP:AF:ID  0.00311:0.00391:0.37059:0.8486:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.8741   ES:SE:LP:AF:ID  0.00426:0.00425:0.500313:0.8741:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.1156   ES:SE:LP:AF:ID  -0.00435:0.00425:0.5157:0.1156:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03061  ES:SE:LP:AF:ID  -0.00331:0.00752:0.180456:0.03061:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03741  ES:SE:LP:AF:ID  -0.00244:0.00754:0.127261:0.03741:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.8759   ES:SE:LP:AF:ID  0.00356:0.00423:0.39794:0.8759:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.8759   ES:SE:LP:AF:ID  0.00435:0.00425:0.514279:0.8759:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.0031:0.00756:0.166216:0.04082:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.8759   ES:SE:LP:AF:ID  0.00379:0.00425:0.430626:0.8759:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005102 ES:SE:LP:AF:ID  0.02759:0.02152:0.69897:0.005102:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005102 ES:SE:LP:AF:ID  0.02759:0.02152:0.69897:0.005102:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.8486   ES:SE:LP:AF:ID  0.00321:0.0039:0.387216:0.8486:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.04082  ES:SE:LP:AF:ID  -0.00363:0.00751:0.20204:0.04082:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.8469   ES:SE:LP:AF:ID  0.00305:0.00391:0.360514:0.8469:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.0119   ES:SE:LP:AF:ID  -0.00389:0.01264:0.120331:0.0119:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005102 ES:SE:LP:AF:ID  -0.00647:0.02072:0.122053:0.005102:rs184270342