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\">",
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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_2804.vcf.gz --id UKB-b:4206 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2804.txt.gz --cohort_controls 180068 --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-4206/UKB-b-4206_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4206/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:03 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4206/UKB-b-4206_data.vcf.gz ...
Read summary statistics for 9433511 SNPs.
Dropped 11108 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, 1288224 SNPs remain.
After merging with regression SNP LD, 1288224 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0297 (0.0033)
Lambda GC: 1.1061
Mean Chi^2: 1.1211
Intercept: 1.0158 (0.0072)
Ratio: 0.1304 (0.0592)
Analysis finished at Thu Oct 17 14:45:47 2019
Total time elapsed: 1.0m:43.84s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9489,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 34,
    "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": 125342,
    "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": 1288224,
    "ldsc_nsnp_merge_regression_ld": 1288224,
    "ldsc_observed_scale_h2_beta": 0.0297,
    "ldsc_observed_scale_h2_se": 0.0033,
    "ldsc_intercept_beta": 1.0158,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.1061,
    "ldsc_mean_chisq": 1.1211,
    "ldsc_ratio": 0.1305
}

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 9422461 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 9433511 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 8.630419e+00 5.752824e+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 7.883448e+07 5.631397e+07 828.0000000 3.252876e+07 6.941442e+07 1.145702e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.010000e-05 1.307710e-02 -0.1736990 -4.654500e-03 -5.800000e-06 4.641900e-03 1.651100e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.953800e-03 8.165800e-03 0.0032054 3.881100e-03 6.214000e-03 1.358640e-02 1.117060e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.882134e-01 2.925038e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.882138e-01 2.924774e-01 0.0000000 2.316388e-01 4.843667e-01 7.420938e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.111397e-01 2.574827e-01 0.0019440 1.633300e-02 8.870400e-02 3.307820e-01 9.980560e-01 ▇▂▁▁▁
numeric AF_reference 125342 0.9867131 NA NA NA NA NA NA NA 2.125732e-01 2.492247e-01 0.0000000 1.377800e-02 1.080270e-01 3.316690e-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 -0.0038204 0.0059149 0.5199996 0.5183496 0.623031 0.7821490 NA
1 54676 rs2462492 C T 0.0042806 0.0058483 0.4600002 0.4642037 0.401388 NA NA
1 86028 rs114608975 T C -0.0037576 0.0093652 0.6899999 0.6882517 0.103527 0.0277556 NA
1 91536 rs6702460 G T -0.0042606 0.0057622 0.4600002 0.4596629 0.456976 0.4207270 NA
1 234313 rs8179466 C T 0.0035323 0.0113775 0.7600007 0.7562091 0.074523 NA NA
1 534192 rs6680723 C T -0.0018545 0.0065750 0.7800007 0.7779045 0.241512 NA NA
1 546697 rs12025928 A G 0.0064167 0.0082302 0.4400003 0.4356003 0.913616 NA NA
1 693731 rs12238997 A G -0.0128116 0.0055086 0.0200000 0.0200312 0.116649 0.1417730 NA
1 705882 rs72631875 G A -0.0012631 0.0081104 0.8800001 0.8762367 0.066882 0.0315495 NA
1 706368 rs55727773 A G 0.0042283 0.0040839 0.2999998 0.3005026 0.514793 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0017514 0.0049452 0.7199992 0.7232213 0.137469 0.2052720 NA
22 51219387 rs9616832 T C 0.0004098 0.0064168 0.9500000 0.9490805 0.073312 0.0654952 NA
22 51219704 rs147475742 G A 0.0020325 0.0086069 0.8100000 0.8133200 0.041633 0.0473243 NA
22 51221190 rs369304721 G A 0.0031786 0.0085933 0.7099994 0.7114607 0.049325 NA NA
22 51221731 rs115055839 T C 0.0005428 0.0064200 0.9299999 0.9326247 0.072837 0.0625000 NA
22 51222100 rs114553188 G T 0.0012436 0.0075329 0.8700001 0.8688743 0.054556 0.0880591 NA
22 51223637 rs375798137 G A 0.0008138 0.0075677 0.9100000 0.9143659 0.054206 0.0788738 NA
22 51229805 rs9616985 T C -0.0001015 0.0064424 0.9900000 0.9874333 0.072711 0.0730831 NA
22 51232488 rs376461333 A G -0.0050956 0.0151184 0.7400005 0.7360830 0.020092 NA NA
22 51237063 rs3896457 T C 0.0008091 0.0039338 0.8400000 0.8370325 0.297203 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623031 ES:SE:LP:AF:ID  -0.00382039:0.00591491:0.283997:0.623031:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401388 ES:SE:LP:AF:ID  0.00428059:0.00584826:0.337242:0.401388:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103527 ES:SE:LP:AF:ID  -0.00375758:0.00936518:0.161151:0.103527:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456976 ES:SE:LP:AF:ID  -0.00426056:0.00576217:0.337242:0.456976:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074523 ES:SE:LP:AF:ID  0.00353229:0.0113775:0.119186:0.074523:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241512 ES:SE:LP:AF:ID  -0.00185449:0.00657502:0.107905:0.241512:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913616 ES:SE:LP:AF:ID  0.00641666:0.00823024:0.356547:0.913616:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116649 ES:SE:LP:AF:ID  -0.0128116:0.00550856:1.69897:0.116649:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066882 ES:SE:LP:AF:ID  -0.00126313:0.00811042:0.0555173:0.066882:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514793 ES:SE:LP:AF:ID  0.00422827:0.00408388:0.522879:0.514793:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033018 ES:SE:LP:AF:ID  0.0118732:0.0102958:0.60206:0.033018:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03658  ES:SE:LP:AF:ID  0.0106555:0.00936515:0.585027:0.03658:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036705 ES:SE:LP:AF:ID  0.0113429:0.00932931:0.657577:0.036705:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036389 ES:SE:LP:AF:ID  0.0110185:0.00939866:0.619789:0.036389:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016308 ES:SE:LP:AF:ID  -0.00368973:0.0145004:0.09691:0.016308:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036926 ES:SE:LP:AF:ID  0.0110128:0.00929427:0.619789:0.036926:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037003 ES:SE:LP:AF:ID  0.0100697:0.00926565:0.552842:0.037003:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100826 ES:SE:LP:AF:ID  0.00569255:0.00676094:0.39794:0.100826:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959139 ES:SE:LP:AF:ID  -0.00823362:0.00893196:0.443698:0.959139:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031497 ES:SE:LP:AF:ID  0.00181818:0.0161054:0.0409586:0.031497:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053303 ES:SE:LP:AF:ID  0.00352645:0.0129118:0.107905:0.053303:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036559 ES:SE:LP:AF:ID  0.00983546:0.00931893:0.537602:0.036559:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036881 ES:SE:LP:AF:ID  0.010908:0.00923469:0.619789:0.036881:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842843 ES:SE:LP:AF:ID  0.00728717:0.00478073:0.886057:0.842843:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056045 ES:SE:LP:AF:ID  -0.0231235:0.00773961:2.55284:0.056045:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122664 ES:SE:LP:AF:ID  -0.0125205:0.00522573:1.76955:0.122664:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025717 ES:SE:LP:AF:ID  -0.00916248:0.0128637:0.318759:0.025717:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121869 ES:SE:LP:AF:ID  -0.0121461:0.00522815:1.69897:0.121869:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13274  ES:SE:LP:AF:ID  -0.00839031:0.00515388:1:0.13274:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011133 ES:SE:LP:AF:ID  -0.00892356:0.0187943:0.200659:0.011133:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.00574  ES:SE:LP:AF:ID  -0.00545985:0.0240556:0.0861861:0.00574:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00222  ES:SE:LP:AF:ID  0.000523266:0.0413131:0.00436481:0.00222:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036773 ES:SE:LP:AF:ID  0.0103909:0.00914555:0.585027:0.036773:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838437 ES:SE:LP:AF:ID  0.00740821:0.00462618:0.958607:0.838437:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838016 ES:SE:LP:AF:ID  0.00717909:0.00462064:0.920819:0.838016:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869285 ES:SE:LP:AF:ID  0.0111352:0.00495523:1.60206:0.869285:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130438 ES:SE:LP:AF:ID  -0.0107552:0.00496428:1.52288:0.130438:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037204 ES:SE:LP:AF:ID  0.00959757:0.00900246:0.537602:0.037204:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03747  ES:SE:LP:AF:ID  0.0099169:0.00894213:0.568636:0.03747:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868572 ES:SE:LP:AF:ID  0.0109693:0.00494502:1.56864:0.868572:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868647 ES:SE:LP:AF:ID  0.0110771:0.00494691:1.60206:0.868647:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.03742  ES:SE:LP:AF:ID  0.00939465:0.00898212:0.522879:0.03742:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868572 ES:SE:LP:AF:ID  0.010936:0.00494486:1.56864:0.868572:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005119 ES:SE:LP:AF:ID  0.0179718:0.0254979:0.318759:0.005119:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005087 ES:SE:LP:AF:ID  0.0182712:0.0255586:0.327902:0.005087:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837475 ES:SE:LP:AF:ID  0.00706937:0.00460808:0.920819:0.837475:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037432 ES:SE:LP:AF:ID  0.00895295:0.0089955:0.49485:0.037432:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838129 ES:SE:LP:AF:ID  0.00700601:0.00462144:0.886057:0.838129:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013849 ES:SE:LP:AF:ID  -0.0267403:0.0161108:1.01323:0.013849:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005596 ES:SE:LP:AF:ID  0.0113996:0.0247759:0.187087:0.005596:rs184270342