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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    "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_4288.vcf.gz --id UKB-b:14360 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4288.txt.gz --cohort_controls 152605 --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-14360/UKB-b-14360_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14360/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:25 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14360/UKB-b-14360_data.vcf.gz ...
Read summary statistics for 9331187 SNPs.
Dropped 10464 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, 1288023 SNPs remain.
After merging with regression SNP LD, 1288023 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0413 (0.0044)
Lambda GC: 1.2518
Mean Chi^2: 1.26
Intercept: 1.1383 (0.0077)
Ratio: 0.5321 (0.0296)
Analysis finished at Thu Oct 17 14:45:05 2019
Total time elapsed: 1.0m:40.0s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9488,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 1489,
    "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": 114147,
    "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": 1288023,
    "ldsc_nsnp_merge_regression_ld": 1288023,
    "ldsc_observed_scale_h2_beta": 0.0413,
    "ldsc_observed_scale_h2_se": 0.0044,
    "ldsc_intercept_beta": 1.1383,
    "ldsc_intercept_se": 0.0077,
    "ldsc_lambda_gc": 1.2518,
    "ldsc_mean_chisq": 1.26,
    "ldsc_ratio": 0.5319
}

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 TRUE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio TRUE
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 9320775 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 9331187 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 8.634416e+00 5.753927e+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 7.881347e+07 5.630879e+07 828.0000000 3.250562e+07 6.939334e+07 1.145427e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -7.790000e-05 1.415550e-02 -0.1711500 -5.247700e-03 -4.140000e-05 5.159600e-03 1.644980e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.028170e-02 8.171000e-03 0.0034234 4.123600e-03 6.530100e-03 1.408870e-02 1.189440e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.684551e-01 2.965734e-01 0.0000000 2.000000e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.684552e-01 2.965486e-01 0.0000000 2.042343e-01 4.561750e-01 7.250895e-01 9.999996e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.132743e-01 2.577553e-01 0.0022940 1.727200e-02 9.179200e-02 3.347880e-01 9.977060e-01 ▇▂▁▁▁
numeric AF_reference 114147 0.9877672 NA NA NA NA NA NA NA 2.143599e-01 2.495347e-01 0.0000000 1.457670e-02 1.104230e-01 3.348640e-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 0.0038337 0.0063102 0.5400003 0.5434919 0.623679 0.7821490 NA
1 54676 rs2462492 C T 0.0055237 0.0062707 0.3800004 0.3783898 0.399292 NA NA
1 86028 rs114608975 T C -0.0198933 0.0099882 0.0460002 0.0464066 0.103814 0.0277556 NA
1 91536 rs6702460 G T 0.0071594 0.0061774 0.2500000 0.2464703 0.456198 0.4207270 NA
1 234313 rs8179466 C T -0.0130458 0.0121871 0.2800000 0.2844124 0.074549 NA NA
1 534192 rs6680723 C T 0.0174247 0.0070599 0.0140001 0.0135822 0.241219 NA NA
1 546697 rs12025928 A G 0.0012445 0.0087588 0.8900000 0.8870130 0.913055 NA NA
1 693731 rs12238997 A G -0.0057990 0.0058820 0.3200000 0.3241891 0.117053 0.1417730 NA
1 705882 rs72631875 G A -0.0023569 0.0086041 0.7800007 0.7841446 0.067648 0.0315495 NA
1 706368 rs55727773 A G -0.0002059 0.0043573 0.9599999 0.9623024 0.514892 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0035357 0.0052840 0.5000000 0.5034117 0.137136 0.2052720 NA
22 51219387 rs9616832 T C -0.0026796 0.0068708 0.6999999 0.6965393 0.072809 0.0654952 NA
22 51219704 rs147475742 G A -0.0005887 0.0091677 0.9500000 0.9487956 0.041735 0.0473243 NA
22 51221190 rs369304721 G A -0.0019654 0.0091950 0.8300000 0.8307413 0.049157 NA NA
22 51221731 rs115055839 T C -0.0030737 0.0068760 0.6499995 0.6548668 0.072283 0.0625000 NA
22 51222100 rs114553188 G T -0.0041603 0.0080528 0.6100002 0.6054143 0.054450 0.0880591 NA
22 51223637 rs375798137 G A -0.0046067 0.0080939 0.5700002 0.5692497 0.054062 0.0788738 NA
22 51229805 rs9616985 T C -0.0031790 0.0069013 0.6499995 0.6450559 0.072137 0.0730831 NA
22 51232488 rs376461333 A G -0.0064629 0.0161585 0.6899999 0.6891802 0.020185 NA NA
22 51237063 rs3896457 T C 0.0024446 0.0041970 0.5600000 0.5602565 0.297660 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623679 ES:SE:LP:AF:ID  0.00383373:0.00631024:0.267606:0.623679:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399292 ES:SE:LP:AF:ID  0.00552367:0.00627072:0.420216:0.399292:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103814 ES:SE:LP:AF:ID  -0.0198933:0.00998822:1.33724:0.103814:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456198 ES:SE:LP:AF:ID  0.00715937:0.00617738:0.60206:0.456198:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074549 ES:SE:LP:AF:ID  -0.0130458:0.0121871:0.552842:0.074549:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241219 ES:SE:LP:AF:ID  0.0174247:0.00705989:1.85387:0.241219:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913055 ES:SE:LP:AF:ID  0.0012445:0.00875885:0.05061:0.913055:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117053 ES:SE:LP:AF:ID  -0.00579901:0.00588203:0.49485:0.117053:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067648 ES:SE:LP:AF:ID  -0.00235686:0.00860413:0.107905:0.067648:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514892 ES:SE:LP:AF:ID  -0.000205945:0.00435728:0.0177288:0.514892:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033533 ES:SE:LP:AF:ID  0.0013149:0.0108951:0.0457575:0.033533:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037244 ES:SE:LP:AF:ID  0.00443192:0.00989206:0.187087:0.037244:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037375 ES:SE:LP:AF:ID  0.00542301:0.00985263:0.236572:0.037375:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037028 ES:SE:LP:AF:ID  0.00542445:0.00992826:0.236572:0.037028:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016392 ES:SE:LP:AF:ID  -0.00438188:0.0154783:0.107905:0.016392:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037614 ES:SE:LP:AF:ID  0.00515514:0.00981362:0.221849:0.037614:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037709 ES:SE:LP:AF:ID  0.00540765:0.00978215:0.236572:0.037709:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10148  ES:SE:LP:AF:ID  0.00785717:0.00717766:0.568636:0.10148:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95834  ES:SE:LP:AF:ID  -0.00412288:0.00943971:0.180456:0.95834:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031655 ES:SE:LP:AF:ID  -0.00245142:0.0172726:0.05061:0.031655:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052652 ES:SE:LP:AF:ID  0.00481979:0.0139199:0.136677:0.052652:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037182 ES:SE:LP:AF:ID  0.00595128:0.00984979:0.259637:0.037182:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037512 ES:SE:LP:AF:ID  0.00515163:0.00976172:0.221849:0.037512:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841863 ES:SE:LP:AF:ID  0.00275245:0.0050919:0.229148:0.841863:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056122 ES:SE:LP:AF:ID  -0.000940266:0.0082716:0.0409586:0.056122:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122986 ES:SE:LP:AF:ID  -0.00564068:0.00558241:0.508638:0.122986:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025751 ES:SE:LP:AF:ID  -0.00863847:0.0137322:0.275724:0.025751:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122209 ES:SE:LP:AF:ID  -0.00505884:0.00558508:0.431798:0.122209:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133436 ES:SE:LP:AF:ID  -0.000960767:0.00549553:0.0655015:0.133436:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011213 ES:SE:LP:AF:ID  0.000475076:0.0199624:0.00877392:0.011213:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005844 ES:SE:LP:AF:ID  -0.0507671:0.0254852:1.33724:0.005844:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037452 ES:SE:LP:AF:ID  0.00603977:0.0096621:0.275724:0.037452:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837595 ES:SE:LP:AF:ID  0.00300727:0.00493044:0.267606:0.837595:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837206 ES:SE:LP:AF:ID  0.0031161:0.00492491:0.275724:0.837206:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868892 ES:SE:LP:AF:ID  0.00564528:0.00528577:0.537602:0.868892:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130816 ES:SE:LP:AF:ID  -0.00556187:0.00529615:0.537602:0.130816:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037946 ES:SE:LP:AF:ID  0.00553885:0.00950353:0.251812:0.037946:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.0382   ES:SE:LP:AF:ID  0.00593756:0.00944292:0.275724:0.0382:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868211 ES:SE:LP:AF:ID  0.00564094:0.00527511:0.552842:0.868211:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868317 ES:SE:LP:AF:ID  0.00571899:0.00527746:0.552842:0.868317:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038133 ES:SE:LP:AF:ID  0.00436602:0.00948395:0.187087:0.038133:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868207 ES:SE:LP:AF:ID  0.00570498:0.00527476:0.552842:0.868207:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005127 ES:SE:LP:AF:ID  0.0128283:0.0271946:0.19382:0.005127:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005097 ES:SE:LP:AF:ID  0.0131654:0.0272544:0.200659:0.005097:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836688 ES:SE:LP:AF:ID  0.00295019:0.00491214:0.259637:0.836688:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038144 ES:SE:LP:AF:ID  0.00393251:0.00949693:0.167491:0.038144:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837321 ES:SE:LP:AF:ID  0.00318863:0.00492577:0.283997:0.837321:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013274 ES:SE:LP:AF:ID  -0.00721477:0.0176181:0.167491:0.013274:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.00546  ES:SE:LP:AF:ID  0.00563531:0.0267707:0.0809219:0.00546:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838557 ES:SE:LP:AF:ID  0.00326708:0.00499323:0.29243:0.838557:rs3131965