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\">",
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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": "2019-10-26T21:57:22.406521",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006941/EBI-a-GCST006941_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-GCST006941/EBI-a-GCST006941.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006941/EBI-a-GCST006941_data.vcf.gz; Date=Sat Oct 26 22:18:10 2019",
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    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006941/ebi-a-GCST006941.vcf.gz; Date=Sat May  9 15:37:47 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-GCST006941/EBI-a-GCST006941.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-GCST006941/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 22:42:34 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006941/EBI-a-GCST006941.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 22:43:49 2019
Total time elapsed: 1.0m:14.15s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9589,
    "inflation_factor": 1.2037,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 10824680,
    "n_clumped_hits": 38,
    "n_p_sig": 3998,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 429027,
    "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 FALSE
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 47 0 10809014 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 10809015 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 8.526288e+00 5.701037e+00 1.0000000 4.000000e+00 7.000000e+00 1.200000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.942138e+07 5.593375e+07 828.0000000 3.358288e+07 7.035976e+07 1.147888e+08 2.492251e+08 ▇▇▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.009000e-04 2.023120e-02 -0.3492140 -4.487200e-03 -5.320000e-05 4.340200e-03 4.161800e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.256050e-02 1.570400e-02 0.0023366 2.919000e-03 5.716200e-03 1.616240e-02 1.337980e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.694470e-01 2.975403e-01 0.0000000 2.037999e-01 4.592995e-01 7.273007e-01 9.996000e-01 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.694469e-01 2.975402e-01 0.0000000 2.037291e-01 4.592999e-01 7.270897e-01 9.992021e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 1.801870e-01 2.534002e-01 0.0000894 5.471600e-03 4.879580e-02 2.651760e-01 9.998900e-01 ▇▂▁▁▁
numeric AF_reference 429027 0.9603084 NA NA NA NA NA NA NA 1.908490e-01 2.453544e-01 0.0000000 1.337860e-02 7.488020e-02 2.825480e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 715265 rs12184267 C T -0.0018034 0.0062619 0.7737007 0.7733467 0.0366377 0.0275559 NA
1 715367 rs12184277 A G -0.0018994 0.0062480 0.7614002 0.7611278 0.0367695 0.0281550 NA
1 717485 rs12184279 C A -0.0025280 0.0062729 0.6867995 0.6869478 0.0364631 NA NA
1 720381 rs116801199 G T -0.0038588 0.0062139 0.5346001 0.5346001 0.0371104 0.0359425 NA
1 721290 rs12565286 G C -0.0034948 0.0062075 0.5734004 0.5734347 0.0372080 0.0371406 NA
1 723891 rs2977670 G C 0.0035981 0.0054188 0.5070000 0.5066903 0.9503520 0.7799520 NA
1 726794 rs28454925 C G -0.0039953 0.0062525 0.5230004 0.5228231 0.0366130 0.0279553 NA
1 729632 rs116720794 C T -0.0029577 0.0062267 0.6350004 0.6347867 0.0368931 0.0099840 NA
1 729679 rs4951859 C G 0.0001910 0.0031827 0.9518000 0.9521556 0.8316140 0.6399760 NA
1 752478 rs146277091 G A -0.0037842 0.0062240 0.5430002 0.5431876 0.0367696 0.0277556 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0060424 0.0058438 0.3011002 0.3011360 0.0427541 0.0473243 NA
22 51219766 rs182321900 C T -0.0005323 0.0204724 0.9796000 0.9792574 0.0032748 NA NA
22 51220025 rs148808236 G A 0.0007570 0.0280359 0.9785000 0.9784597 0.0017399 0.0215655 NA
22 51220146 rs868950473 C T 0.0101635 0.0203677 0.6175996 0.6177788 0.0033097 NA NA
22 51221731 rs115055839 T C -0.0061511 0.0044866 0.1703001 0.1703749 0.0732027 0.0625000 NA
22 51222100 rs114553188 G T 0.0108521 0.0050901 0.0329997 0.0330067 0.0568181 0.0880591 NA
22 51223637 rs375798137 G A 0.0114387 0.0051318 0.0258101 0.0258141 0.0558736 0.0788738 NA
22 51224208 rs116656403 G A -0.0084975 0.0279524 0.7609007 0.7611282 0.0017501 0.0209665 NA
22 51224267 rs138354270 G A -0.0079962 0.0280568 0.7757004 0.7756441 0.0017376 0.0209665 NA
22 51229805 rs9616985 T C -0.0055514 0.0044805 0.2154001 0.2153454 0.0735865 0.0730831 NA

bcf preview

1   715265  rs12184267  C   T   .   PASS    AF=0.0366377    ES:SE:LP:AF:ID  -0.00180343:0.00626191:0.111427:0.0366377:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.0367695    ES:SE:LP:AF:ID  -0.00189939:0.00624799:0.118387:0.0367695:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.0364631    ES:SE:LP:AF:ID  -0.00252799:0.00627292:0.16317:0.0364631:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.0371104    ES:SE:LP:AF:ID  -0.00385884:0.00621392:0.271971:0.0371104:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037208 ES:SE:LP:AF:ID  -0.00349483:0.00620751:0.241542:0.037208:rs12565286
1   723891  rs2977670   G   C   .   PASS    AF=0.950352 ES:SE:LP:AF:ID  0.00359807:0.00541878:0.294992:0.950352:rs2977670
1   726794  rs28454925  C   G   .   PASS    AF=0.036613 ES:SE:LP:AF:ID  -0.00399532:0.00625246:0.281498:0.036613:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.0368931    ES:SE:LP:AF:ID  -0.00295768:0.00622669:0.197226:0.0368931:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.831614 ES:SE:LP:AF:ID  0.000190964:0.00318273:0.0214543:0.831614:rs4951859
1   752478  rs146277091 G   A   .   PASS    AF=0.0367696    ES:SE:LP:AF:ID  -0.00378416:0.00622395:0.2652:0.0367696:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.829189 ES:SE:LP:AF:ID  -0.0008735:0.00310854:0.108518:0.829189:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.826243 ES:SE:LP:AF:ID  -0.000477939:0.00308348:0.0570499:0.826243:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.860653 ES:SE:LP:AF:ID  -0.0013031:0.00338467:0.154964:0.860653:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.140455 ES:SE:LP:AF:ID  0.000165426:0.00337605:0.017367:0.140455:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.0371833    ES:SE:LP:AF:ID  -0.0054862:0.00617815:0.426548:0.0371833:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.0374306    ES:SE:LP:AF:ID  -0.00470866:0.00615512:0.352617:0.0374306:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.857265 ES:SE:LP:AF:ID  -0.00114972:0.00335195:0.135667:0.857265:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.857357 ES:SE:LP:AF:ID  -0.00105284:0.003353:0.123032:0.857357:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.0374202    ES:SE:LP:AF:ID  -0.00568777:0.00616226:0.44855:0.0374202:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.857369 ES:SE:LP:AF:ID  -0.00112999:0.0033531:0.133063:0.857369:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.826634 ES:SE:LP:AF:ID  -0.000919673:0.00309654:0.115431:0.826634:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.0374193    ES:SE:LP:AF:ID  -0.00518441:0.00616458:0.397397:0.0374193:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.827271 ES:SE:LP:AF:ID  -0.00106158:0.00310405:0.135252:0.827271:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.831031 ES:SE:LP:AF:ID  9.55794e-06:0.00318598:0.00121773:0.831031:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.860232 ES:SE:LP:AF:ID  -0.000998285:0.00337259:0.114922:0.860232:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.85979  ES:SE:LP:AF:ID  -0.00129215:0.00336496:0.154344:0.85979:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.854521 ES:SE:LP:AF:ID  -0.00123051:0.00331674:0.148314:0.854521:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.859889 ES:SE:LP:AF:ID  -0.00133671:0.00336703:0.160145:0.859889:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.859935 ES:SE:LP:AF:ID  -0.00134702:0.00336754:0.161781:0.859935:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.859942 ES:SE:LP:AF:ID  -0.00138072:0.00336762:0.16647:0.859942:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.860464 ES:SE:LP:AF:ID  -0.00138751:0.00337594:0.166662:0.860464:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.0374737    ES:SE:LP:AF:ID  -0.0055919:0.00615171:0.439854:0.0374737:rs114525117
1   760912  rs1048488   C   T   .   PASS    AF=0.82959  ES:SE:LP:AF:ID  -0.000705041:0.00311965:0.0853924:0.82959:rs1048488
1   760998  rs148828841 C   A   .   PASS    AF=0.0369479    ES:SE:LP:AF:ID  -0.00638616:0.00620618:0.518128:0.0369479:rs148828841
1   761147  rs3115850   T   C   .   PASS    AF=0.829712 ES:SE:LP:AF:ID  -0.000627333:0.00312106:0.0751524:0.829712:rs3115850
1   761606  rs377377186 G   A   .   PASS    AF=0.0358125    ES:SE:LP:AF:ID  -0.00614278:0.00632624:0.479516:0.0358125:rs377377186
1   761732  rs2286139   C   T   .   PASS    AF=0.848492 ES:SE:LP:AF:ID  -0.00119686:0.00333388:0.142728:0.848492:rs2286139
1   761881  rs374493323 A   C   .   PASS    AF=0.0342849    ES:SE:LP:AF:ID  -0.00542331:0.00647946:0.395342:0.0342849:rs374493323
1   766007  rs61768174  A   C   .   PASS    AF=0.105478 ES:SE:LP:AF:ID  0.00203029:0.00385255:0.222936:0.105478:rs61768174
1   767096  rs115991721 A   G   .   PASS    AF=0.00259125   ES:SE:LP:AF:ID  -0.0212117:0.0229813:0.448306:0.00259125:rs115991721
1   768253  rs2977608   A   C   .   PASS    AF=0.744125 ES:SE:LP:AF:ID  -0.000606563:0.00268391:0.0855511:0.744125:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.111263 ES:SE:LP:AF:ID  0.000219208:0.00371539:0.0210438:0.111263:rs12562034
1   768680  rs140605359 C   T   .   PASS    AF=0.00057169   ES:SE:LP:AF:ID  -0.0846707:0.0488861:1.07972:0.00057169:rs140605359
1   768819  rs12562811  C   T   .   PASS    AF=0.0121867    ES:SE:LP:AF:ID  -0.00231952:0.010689:0.0819697:0.0121867:rs12562811
1   769223  rs60320384  C   G   .   PASS    AF=0.135131 ES:SE:LP:AF:ID  0.000564763:0.00342281:0.0609302:0.135131:rs60320384
1   770504  rs150664068 C   G   .   PASS    AF=0.000562164  ES:SE:LP:AF:ID  -0.078139:0.0492991:0.946922:0.000562164:rs150664068
1   771823  rs2977605   T   C   .   PASS    AF=0.859693 ES:SE:LP:AF:ID  -0.00105774:0.00336859:0.122744:0.859693:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.136281 ES:SE:LP:AF:ID  0.000474071:0.00341058:0.0507076:0.136281:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.859685 ES:SE:LP:AF:ID  -0.00105099:0.00336856:0.122053:0.859685:rs2905039
1   776556  rs151160018 C   T   .   PASS    AF=0.00833475   ES:SE:LP:AF:ID  0.00478625:0.0128663:0.148619:0.00833475:rs151160018