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=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
    "FORMAT.2": "<ID=LP,Number=A,Type=Float,Description=\"-log10 p-value for effect estimate\">",
    "FORMAT.3": "<ID=AF,Number=A,Type=Float,Description=\"Alternate allele frequency in the association study\">",
    "FORMAT.4": "<ID=SS,Number=A,Type=Float,Description=\"Sample size used to estimate genetic effect\">",
    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_5386.vcf.gz --id UKB-b:1998 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5386.txt.gz --cohort_controls 38371 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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}

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/public/UKB-b-1998/UKB-b-1998_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1998/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1998/UKB-b-1998_data.vcf.gz ...
Read summary statistics for 7866408 SNPs.
Dropped 5856 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1281202 SNPs remain.
After merging with regression SNP LD, 1281202 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0367 (0.0126)
Lambda GC: 1.0284
Mean Chi^2: 1.0258
Intercept: 0.9974 (0.0064)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:43:37 2019
Total time elapsed: 1.0m:31.53s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9423,
    "inflation_factor": 1,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 73327,
    "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": 1281202,
    "ldsc_nsnp_merge_regression_ld": 1281202,
    "ldsc_observed_scale_h2_beta": 0.0367,
    "ldsc_observed_scale_h2_se": 0.0126,
    "ldsc_intercept_beta": 0.9974,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0284,
    "ldsc_mean_chisq": 1.0258,
    "ldsc_ratio": -0.1008
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq FALSE
n_p_sig FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio FALSE
ldsc_intercept_beta FALSE
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 7860579 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 7866408 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 8.661094e+00 5.763689e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.869958e+07 5.642689e+07 828.0000000 3.223575e+07 6.916925e+07 1.145575e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 8.270000e-05 1.506640e-02 -0.1549090 -7.040800e-03 1.840000e-05 7.164800e-03 1.555060e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.293180e-02 7.551600e-03 0.0060240 6.978700e-03 9.608000e-03 1.691900e-02 7.295900e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.965366e-01 2.895398e-01 0.0000001 2.399999e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.965378e-01 2.895151e-01 0.0000001 2.445756e-01 4.952130e-01 7.474101e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.492725e-01 2.607841e-01 0.0091220 3.801000e-02 1.418180e-01 3.944510e-01 9.908780e-01 ▇▂▂▁▁
numeric AF_reference 73327 0.9906785 NA NA NA NA NA NA NA 2.484601e-01 2.526347e-01 0.0000000 4.093450e-02 1.569490e-01 3.895770e-01 1.000000e+00 ▇▃▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C -0.0130900 0.0110510 0.2399999 0.2362120 0.621440 0.7821490 NA
1 54676 rs2462492 C T 0.0162021 0.0110146 0.1400000 0.1413004 0.400172 NA NA
1 86028 rs114608975 T C 0.0156881 0.0172783 0.3599996 0.3638965 0.105372 0.0277556 NA
1 91536 rs6702460 G T -0.0036608 0.0108699 0.7400005 0.7362803 0.455148 0.4207270 NA
1 234313 rs8179466 C T -0.0185482 0.0217585 0.3900004 0.3939601 0.073653 NA NA
1 534192 rs6680723 C T -0.0004392 0.0123267 0.9699999 0.9715785 0.243499 NA NA
1 546697 rs12025928 A G 0.0001628 0.0154341 0.9900000 0.9915814 0.913440 NA NA
1 693731 rs12238997 A G -0.0096668 0.0104014 0.3500000 0.3526958 0.117493 0.1417730 NA
1 705882 rs72631875 G A 0.0020587 0.0151721 0.8900000 0.8920649 0.067372 0.0315495 NA
1 706368 rs55727773 A G 0.0118673 0.0076731 0.1199999 0.1219571 0.514334 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0087527 0.0093372 0.3500000 0.3485519 0.137210 0.2052720 NA
22 51219387 rs9616832 T C -0.0082295 0.0121322 0.5000000 0.4975685 0.072216 0.0654952 NA
22 51219704 rs147475742 G A -0.0131857 0.0162007 0.4199997 0.4157040 0.041079 0.0473243 NA
22 51221190 rs369304721 G A -0.0021944 0.0162060 0.8900000 0.8922918 0.048874 NA NA
22 51221731 rs115055839 T C -0.0091266 0.0121367 0.4500005 0.4520629 0.071769 0.0625000 NA
22 51222100 rs114553188 G T -0.0124932 0.0140348 0.3700002 0.3733807 0.055228 0.0880591 NA
22 51223637 rs375798137 G A -0.0128497 0.0141262 0.3599996 0.3630145 0.054726 0.0788738 NA
22 51229805 rs9616985 T C -0.0084400 0.0121800 0.4899999 0.4883487 0.071644 0.0730831 NA
22 51232488 rs376461333 A G 0.0186675 0.0282840 0.5099998 0.5092525 0.020414 NA NA
22 51237063 rs3896457 T C -0.0032490 0.0074261 0.6600001 0.6617396 0.297005 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62144  ES:SE:LP:AF:ID  -0.01309:0.011051:0.619789:0.62144:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400172 ES:SE:LP:AF:ID  0.0162021:0.0110146:0.853872:0.400172:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.105372 ES:SE:LP:AF:ID  0.0156881:0.0172783:0.443698:0.105372:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455148 ES:SE:LP:AF:ID  -0.0036608:0.0108699:0.130768:0.455148:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073653 ES:SE:LP:AF:ID  -0.0185482:0.0217585:0.408935:0.073653:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.243499 ES:SE:LP:AF:ID  -0.000439183:0.0123267:0.0132283:0.243499:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91344  ES:SE:LP:AF:ID  0.00016285:0.0154341:0.00436481:0.91344:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117493 ES:SE:LP:AF:ID  -0.00966678:0.0104014:0.455932:0.117493:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067372 ES:SE:LP:AF:ID  0.00205873:0.0151721:0.05061:0.067372:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514334 ES:SE:LP:AF:ID  0.0118673:0.0076731:0.920819:0.514334:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033845 ES:SE:LP:AF:ID  -0.0126605:0.0191192:0.29243:0.033845:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037418 ES:SE:LP:AF:ID  -0.0124021:0.0174281:0.318759:0.037418:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037588 ES:SE:LP:AF:ID  -0.0133484:0.0173491:0.356547:0.037588:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037139 ES:SE:LP:AF:ID  -0.0143262:0.017516:0.387216:0.037139:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016488 ES:SE:LP:AF:ID  -0.021391:0.02709:0.366532:0.016488:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03782  ES:SE:LP:AF:ID  -0.0118462:0.0172846:0.309804:0.03782:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037904 ES:SE:LP:AF:ID  -0.0134959:0.0172324:0.366532:0.037904:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10127  ES:SE:LP:AF:ID  -0.00866132:0.0126432:0.309804:0.10127:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957783 ES:SE:LP:AF:ID  0.0147929:0.0165431:0.431798:0.957783:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031783 ES:SE:LP:AF:ID  -0.00742634:0.030135:0.091515:0.031783:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052361 ES:SE:LP:AF:ID  -0.0337484:0.0245832:0.769551:0.052361:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.0374   ES:SE:LP:AF:ID  -0.0152163:0.017344:0.420216:0.0374:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037711 ES:SE:LP:AF:ID  -0.0160508:0.017186:0.455932:0.037711:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840495 ES:SE:LP:AF:ID  0.0114894:0.00898534:0.69897:0.840495:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056911 ES:SE:LP:AF:ID  -0.00647898:0.0145033:0.180456:0.056911:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123693 ES:SE:LP:AF:ID  -0.00667689:0.00986686:0.30103:0.123693:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.026034 ES:SE:LP:AF:ID  0.011667:0.023962:0.200659:0.026034:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122897 ES:SE:LP:AF:ID  -0.00635851:0.00987352:0.283997:0.122897:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133847 ES:SE:LP:AF:ID  -0.010777:0.00972426:0.568636:0.133847:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010763 ES:SE:LP:AF:ID  -0.0265744:0.0363032:0.337242:0.010763:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037588 ES:SE:LP:AF:ID  -0.013553:0.0170447:0.366532:0.037588:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836253 ES:SE:LP:AF:ID  0.00670543:0.00872328:0.356547:0.836253:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.835861 ES:SE:LP:AF:ID  0.00722012:0.00871372:0.387216:0.835861:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867705 ES:SE:LP:AF:ID  0.000380954:0.00933841:0.0132283:0.867705:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131888 ES:SE:LP:AF:ID  -0.00169778:0.00936733:0.0655015:0.131888:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038107 ES:SE:LP:AF:ID  -0.0148182:0.0167523:0.420216:0.038107:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03835  ES:SE:LP:AF:ID  -0.0143476:0.0166506:0.408935:0.03835:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867032 ES:SE:LP:AF:ID  0.00083684:0.00932206:0.0315171:0.867032:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867128 ES:SE:LP:AF:ID  0.00110224:0.00932547:0.0409586:0.867128:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038305 ES:SE:LP:AF:ID  -0.0143712:0.0167116:0.408935:0.038305:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86705  ES:SE:LP:AF:ID  0.000827337:0.00932166:0.0315171:0.86705:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.83534  ES:SE:LP:AF:ID  0.00695143:0.00869498:0.376751:0.83534:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038315 ES:SE:LP:AF:ID  -0.0145123:0.0167337:0.408935:0.038315:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.835965 ES:SE:LP:AF:ID  0.00685002:0.00871987:0.366532:0.835965:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013143 ES:SE:LP:AF:ID  -0.0600459:0.0311951:1.26761:0.013143:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.836837 ES:SE:LP:AF:ID  0.00599169:0.00882449:0.30103:0.836837:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.867311 ES:SE:LP:AF:ID  0.000648651:0.00931094:0.0268721:0.867311:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.86694  ES:SE:LP:AF:ID  0.000471531:0.00929276:0.0177288:0.86694:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.865737 ES:SE:LP:AF:ID  0.00064794:0.00927161:0.0268721:0.865737:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867026 ES:SE:LP:AF:ID  0.000213921:0.00929695:0.00877392:0.867026:rs4951929