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\">",
    "INFO": "<ID=AF,Number=A,Type=Float,Description=\"Allele Frequency\">",
    "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.5": "<ID=TotalVariants,Number=1,Type=Integer,Description=\"Total number of variants in input\">",
    "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": "2019-10-26T21:45:55.681509",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005353/EBI-a-GCST005353_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-GCST005353/EBI-a-GCST005353.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005353/EBI-a-GCST005353_data.vcf.gz; Date=Sat Oct 26 21:58:27 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-GCST005353/ebi-a-GCST005353.vcf.gz; Date=Sun May 10 10:48:45 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-GCST005353/EBI-a-GCST005353.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-GCST005353/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 22:21:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005353/EBI-a-GCST005353.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 140, in read_vcf
    o = [[rec.id, rec.samples[sample]['ES'][0]/rec.samples[sample]['SE'][0]] for rec in vcf_in.fetch()]
ZeroDivisionError: float division by zero

Analysis finished at Sat Oct 26 22:21:49 2019
Total time elapsed: 0.45s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9,
    "inflation_factor": 1.1118,
    "mean_EFFECT": -0.0004,
    "n": "-Inf",
    "n_snps": 6463530,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 459086,
    "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": 332142,
    "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 6441878 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 25 0 14405 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 25 0 12305 0 NA NA NA NA NA NA NA NA NA NA
logical N 6442494 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 9.062274e+00 6.144661e+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 7.853104e+07 5.634929e+07 302.0000000 3.199851e+07 6.934810e+07 1.146711e+08 2.492298e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.593000e-04 1.617620e-01 -15.7897000 -9.727000e-02 -4.200000e-04 9.629000e-02 1.444210e+01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.385984e-01 4.314750e-02 0.0000000 1.059100e-01 1.238400e-01 1.619100e-01 1.562660e+00 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.835761e-01 2.921123e-01 0.0000018 2.262440e-01 4.769721e-01 7.360291e-01 9.999942e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.705015e-01 2.946229e-01 0.0000000 2.080180e-01 4.578012e-01 7.248036e-01 9.999734e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.341909e-01 2.461896e-01 0.0500100 1.236200e-01 2.615900e-01 5.010600e-01 9.499900e-01 ▇▃▂▂▂
numeric AF_reference 332142 0.9484451 NA NA NA NA NA NA NA 3.313113e-01 2.419700e-01 0.0000000 1.317890e-01 2.703670e-01 4.948080e-01 1.000000e+00 ▇▆▃▂▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 54421 rs146477069 A G -0.22778 0.36695 0.6880056 0.5347717 0.08284 NA NA
1 55249 rs200769871 C CTATGG -0.07369 0.36665 0.8870192 0.8407128 0.06373 NA NA
1 55299 rs10399749 C T -0.11758 0.20942 0.7147119 0.5744871 0.20671 NA NA
1 57321 rs1244179854 G A -4.92046 0.00000 0.9298498 0.0000000 0.15383 NA NA
1 77874 rs62641297 G A 0.20467 0.30362 0.6648903 0.5002482 0.07767 0.0547125 NA
1 84005 rs202079949 AG A -0.08858 0.26267 0.8213034 0.7359441 0.06338 NA NA
1 88169 rs940550 C T -0.06506 0.24409 0.8654720 0.7898226 0.17881 0.2993210 NA
1 88338 rs55700207 G A 0.07204 0.30069 0.8754600 0.8106541 0.10143 0.0940495 NA
1 89946 rs138808727 A T -0.31236 0.16219 0.2134460 0.0541182 0.23900 0.1226040 NA
1 92858 rs147061536 G T -0.35013 0.16379 0.1737101 0.0325430 0.22950 0.1349840 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154918383 rs641588 G T 0.04554 0.10724 0.6752911 0.6710882 0.19562 0.356556 NA
23 154925045 rs509981 C T 0.04549 0.10738 0.6762262 0.6718315 0.19527 0.363444 NA
23 154925893 rs201173655 C CAT -0.06337 0.22977 0.8501137 0.7827036 0.06163 NA NA
23 154925895 rs538470 C T 0.00787 0.11569 0.9486218 0.9457644 0.19549 0.363444 NA
23 154927185 rs185685661 T C 0.22448 0.21767 0.4356252 0.3024068 0.10682 0.179603 NA
23 154927197 rs200614200 TAC T 0.04497 0.10780 0.6814343 0.6765603 0.19424 NA NA
23 154927199 rs645904 C T 0.04549 0.10737 0.6762356 0.6718027 0.19527 0.367417 NA
23 154927581 rs644138 G A 0.02709 0.11113 0.8220882 0.8074101 0.22898 0.463576 NA
23 154929412 rs557132 C T 0.04589 0.10748 0.6750361 0.6694062 0.19631 0.356821 NA
23 154930230 rs781880 A G 0.04625 0.10913 0.6781988 0.6717070 0.19423 0.361854 NA

bcf preview

1   54421   rs146477069 A   G   .   PASS    AF=0.08284  ES:SE:LP:AF:ID  -0.22778:0.36695:0.162408:0.08284:rs146477069
1   55249   rs200769871 C   CTATGG  .   PASS    AF=0.06373  ES:SE:LP:AF:ID  -0.07369:0.36665:0.052067:0.06373:rs200769871
1   55299   rs10399749  C   T   .   PASS    AF=0.20671  ES:SE:LP:AF:ID  -0.11758:0.20942:0.145869:0.20671:rs10399749
1   57321   rs1244179854    G   A   .   PASS    AF=0.15383  ES:SE:LP:AF:ID  -4.92046:0:0.0315872:0.15383:rs1244179854
1   77874   rs62641297  G   A   .   PASS    AF=0.07767  ES:SE:LP:AF:ID  0.20467:0.30362:0.17725:0.07767:rs62641297
1   84005   rs202079949 AG  A   .   PASS    AF=0.06338  ES:SE:LP:AF:ID  -0.08858:0.26267:0.0854964:0.06338:rs202079949
1   88169   rs940550    C   T   .   PASS    AF=0.17881  ES:SE:LP:AF:ID  -0.06506:0.24409:0.062747:0.17881:rs940550
1   88338   rs55700207  G   A   .   PASS    AF=0.10143  ES:SE:LP:AF:ID  0.07204:0.30069:0.0577637:0.10143:rs55700207
1   89946   rs138808727 A   T   .   PASS    AF=0.239    ES:SE:LP:AF:ID  -0.31236:0.16219:0.670712:0.239:rs138808727
1   92858   rs147061536 G   T   .   PASS    AF=0.2295   ES:SE:LP:AF:ID  -0.35013:0.16379:0.760175:0.2295:rs147061536
1   94421   rs200856736 TC  T   .   PASS    AF=0.09237  ES:SE:LP:AF:ID  -0.16356:0.31017:0.135116:0.09237:rs200856736
1   98974   rs12184307  A   G   .   PASS    AF=0.06021  ES:SE:LP:AF:ID  0.25638:0.49029:0.132341:0.06021:rs12184307
1   103905  rs62642112  A   G   .   PASS    AF=0.07194  ES:SE:LP:AF:ID  0.01758:0.3816:0.0105813:0.07194:rs62642112
1   138694  rs1221604280    A   G   .   PASS    AF=0.19798  ES:SE:LP:AF:ID  1.2372:0:0.00879723:0.19798:rs1221604280
1   138756  rs1201779331    G   A   .   PASS    AF=0.05974  ES:SE:LP:AF:ID  -15.7897:0:0.0459202:0.05974:rs1201779331
1   233473  rs112455420 C   G   .   PASS    AF=0.14157  ES:SE:LP:AF:ID  -0.20348:0.20911:0.301304:0.14157:rs112455420
1   534192  rs6680723   C   T   .   PASS    AF=0.21284  ES:SE:LP:AF:ID  -0.04002:0.17899:0.0553029:0.21284:rs6680723
1   534198  rs6680725   C   T   .   PASS    AF=0.09122  ES:SE:LP:AF:ID  -0.19127:0.312:0.175474:0.09122:rs6680725
1   534545  rs145971835 G   A   .   PASS    AF=0.11485  ES:SE:LP:AF:ID  0.00075:0.27729:0.000675767:0.11485:rs145971835
1   589082  rs187234864 G   A   .   PASS    AF=0.0941   ES:SE:LP:AF:ID  -0.00518:0.25855:0.00465654:0.0941:rs187234864
1   618463  rs4108074   G   A   .   PASS    AF=0.09334  ES:SE:LP:AF:ID  0.18874:0.36168:0.13516:0.09334:rs4108074
1   662622  rs61769339  G   A   .   PASS    AF=0.09078  ES:SE:LP:AF:ID  -0.18966:0.23173:0.275907:0.09078:rs61769339
1   691541  rs201999089 AT  A   .   PASS    AF=0.21852  ES:SE:LP:AF:ID  -0.01748:0.19057:0.0233332:0.21852:rs201999089
1   693731  rs12238997  A   G   .   PASS    AF=0.12456  ES:SE:LP:AF:ID  -0.27901:0.1844:0.674292:0.12456:rs12238997
1   706368  rs12029736  A   G   .   PASS    AF=0.39335  ES:SE:LP:AF:ID  0.35903:0.14444:1.03582:0.39335:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.115    ES:SE:LP:AF:ID  0.13438:0.25495:0.158169:0.115:rs116030099
1   727841  rs116587930 G   A   .   PASS    AF=0.07046  ES:SE:LP:AF:ID  -0.3363:0.26856:0.541932:0.07046:rs116587930
1   729679  rs4951859   C   G   .   PASS    AF=0.83337  ES:SE:LP:AF:ID  0.25512:0.17174:0.656296:0.83337:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.07048  ES:SE:LP:AF:ID  -0.33619:0.26843:0.542369:0.07048:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12641  ES:SE:LP:AF:ID  -0.27483:0.1802:0.723656:0.12641:rs58276399
1   732809  rs12131618  T   C   .   PASS    AF=0.05892  ES:SE:LP:AF:ID  -0.26123:0.22448:0.531893:0.05892:rs12131618
1   734349  rs141242758 T   C   .   PASS    AF=0.12423  ES:SE:LP:AF:ID  -0.27853:0.1808:0.73158:0.12423:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.12226  ES:SE:LP:AF:ID  -0.28129:0.18259:0.739827:0.12226:rs79010578
1   748878  rs2519043   G   T   .   PASS    AF=0.7576   ES:SE:LP:AF:ID  0.15093:0.15421:0.334487:0.7576:rs2519043
1   749592  rs4606254   G   A   .   PASS    AF=0.16033  ES:SE:LP:AF:ID  -0.08591:0.19287:0.112925:0.16033:rs4606254
1   751756  rs28527770  T   C   .   PASS    AF=0.14079  ES:SE:LP:AF:ID  -0.19219:0.14836:0.690429:0.14079:rs28527770
1   752566  rs3094315   G   A   .   PASS    AF=0.85393  ES:SE:LP:AF:ID  0.16902:0.14361:0.621215:0.85393:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.85153  ES:SE:LP:AF:ID  0.17635:0.14508:0.641808:0.85153:rs3131972
1   752894  rs3131971   T   C   .   PASS    AF=0.8066   ES:SE:LP:AF:ID  0.13487:0.14174:0.374912:0.8066:rs3131971
1   753405  rs3115860   C   A   .   PASS    AF=0.85127  ES:SE:LP:AF:ID  0.17832:0.14827:0.60403:0.85127:rs3115860
1   753474  rs2073814   C   G   .   PASS    AF=0.8383   ES:SE:LP:AF:ID  0.15828:0.13669:0.577754:0.8383:rs2073814
1   753541  rs2073813   G   A   .   PASS    AF=0.14378  ES:SE:LP:AF:ID  -0.19133:0.14885:0.68049:0.14378:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.8556   ES:SE:LP:AF:ID  0.19079:0.14939:0.67387:0.8556:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.85554  ES:SE:LP:AF:ID  0.19072:0.14939:0.673427:0.85554:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.82033  ES:SE:LP:AF:ID  0.1639:0.14879:0.468729:0.82033:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.8512   ES:SE:LP:AF:ID  0.17299:0.14523:0.622635:0.8512:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.82439  ES:SE:LP:AF:ID  0.14221:0.14497:0.418885:0.82439:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.79203  ES:SE:LP:AF:ID  0.19702:0.15209:0.591082:0.79203:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.85372  ES:SE:LP:AF:ID  0.18542:0.14874:0.639834:0.85372:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.83353  ES:SE:LP:AF:ID  0.10759:0.14518:0.29539:0.83353:rs3131962