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_4429.vcf.gz --id UKB-b:20321 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4429.txt.gz --cohort_controls 34243 --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-20321/UKB-b-20321_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20321/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20321/UKB-b-20321_data.vcf.gz ...
Read summary statistics for 7706803 SNPs.
Dropped 5555 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, 1279579 SNPs remain.
After merging with regression SNP LD, 1279579 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0288 (0.0121)
Lambda GC: 1.0352
Mean Chi^2: 1.0285
Intercept: 1.0094 (0.0057)
Ratio: 0.3289 (0.2005)
Analysis finished at Thu Oct 17 14:43:46 2019
Total time elapsed: 1.0m:33.94s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9415,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "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": 71749,
    "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": 1279579,
    "ldsc_nsnp_merge_regression_ld": 1279579,
    "ldsc_observed_scale_h2_beta": 0.0288,
    "ldsc_observed_scale_h2_se": 0.0121,
    "ldsc_intercept_beta": 1.0094,
    "ldsc_intercept_se": 0.0057,
    "ldsc_lambda_gc": 1.0352,
    "ldsc_mean_chisq": 1.0285,
    "ldsc_ratio": 0.3298
}

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 7701272 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 7706803 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 8.661669e+00 5.763998e+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 7.868897e+07 5.643758e+07 828.0000000 3.220442e+07 6.914727e+07 1.145710e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.650000e-05 1.310740e-02 -0.1459600 -6.302300e-03 2.920000e-05 6.374700e-03 1.298100e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.132340e-02 6.368300e-03 0.0054704 6.290400e-03 8.547000e-03 1.470850e-02 6.527740e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.956914e-01 2.899186e-01 0.0000001 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.956926e-01 2.898921e-01 0.0000001 2.428332e-01 4.939903e-01 7.469613e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.539162e-01 2.607908e-01 0.0102220 4.143000e-02 1.485140e-01 4.013510e-01 9.897780e-01 ▇▂▂▁▁
numeric AF_reference 71749 0.9906902 NA NA NA NA NA NA NA 2.529859e-01 2.526668e-01 0.0000000 4.532750e-02 1.629390e-01 3.959660e-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 -0.0076588 0.0100929 0.4500005 0.4479522 0.623561 0.7821490 NA
1 54676 rs2462492 C T -0.0009309 0.0099997 0.9299999 0.9258262 0.398401 NA NA
1 86028 rs114608975 T C -0.0078777 0.0158661 0.6200004 0.6195327 0.104047 0.0277556 NA
1 91536 rs6702460 G T -0.0000981 0.0098407 0.9900000 0.9920479 0.455501 0.4207270 NA
1 234313 rs8179466 C T 0.0005781 0.0194651 0.9800000 0.9763086 0.074661 NA NA
1 534192 rs6680723 C T 0.0058200 0.0112785 0.6100002 0.6058374 0.240605 NA NA
1 546697 rs12025928 A G 0.0069902 0.0137855 0.6100002 0.6121074 0.911427 NA NA
1 693731 rs12238997 A G 0.0061544 0.0092945 0.5099998 0.5078719 0.118217 0.1417730 NA
1 705882 rs72631875 G A -0.0005012 0.0137106 0.9699999 0.9708367 0.068181 0.0315495 NA
1 706368 rs55727773 A G -0.0040904 0.0069265 0.5500004 0.5548302 0.514202 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0024704 0.0084701 0.7700005 0.7705464 0.136786 0.2052720 NA
22 51219387 rs9616832 T C -0.0069329 0.0109689 0.5300002 0.5273562 0.073317 0.0654952 NA
22 51219704 rs147475742 G A 0.0037181 0.0146185 0.8000000 0.7992292 0.042132 0.0473243 NA
22 51221190 rs369304721 G A -0.0023217 0.0146999 0.8700001 0.8745047 0.049526 NA NA
22 51221731 rs115055839 T C -0.0066476 0.0109706 0.5400003 0.5445519 0.072925 0.0625000 NA
22 51222100 rs114553188 G T 0.0121404 0.0130335 0.3500000 0.3516071 0.053218 0.0880591 NA
22 51223637 rs375798137 G A 0.0130937 0.0131077 0.3200000 0.3178277 0.052816 0.0788738 NA
22 51229805 rs9616985 T C -0.0071490 0.0110101 0.5199996 0.5161356 0.072781 0.0730831 NA
22 51232488 rs376461333 A G 0.0000991 0.0267515 1.0000000 0.9970432 0.019202 NA NA
22 51237063 rs3896457 T C 0.0033976 0.0067136 0.6100002 0.6128072 0.298842 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623561 ES:SE:LP:AF:ID  -0.00765883:0.0100929:0.346787:0.623561:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398401 ES:SE:LP:AF:ID  -0.000930949:0.00999974:0.0315171:0.398401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104047 ES:SE:LP:AF:ID  -0.00787772:0.0158661:0.207608:0.104047:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455501 ES:SE:LP:AF:ID  -9.80788e-05:0.00984066:0.00436481:0.455501:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074661 ES:SE:LP:AF:ID  0.000578057:0.0194651:0.00877392:0.074661:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240605 ES:SE:LP:AF:ID  0.00581998:0.0112785:0.21467:0.240605:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.911427 ES:SE:LP:AF:ID  0.00699018:0.0137855:0.21467:0.911427:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.118217 ES:SE:LP:AF:ID  0.0061544:0.0092945:0.29243:0.118217:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068181 ES:SE:LP:AF:ID  -0.000501245:0.0137106:0.0132283:0.068181:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514202 ES:SE:LP:AF:ID  -0.00409036:0.0069265:0.259637:0.514202:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033894 ES:SE:LP:AF:ID  -0.00163122:0.017335:0.0315171:0.033894:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037714 ES:SE:LP:AF:ID  0.00108197:0.0157189:0.0222764:0.037714:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037902 ES:SE:LP:AF:ID  0.0011554:0.0156419:0.0268721:0.037902:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03755  ES:SE:LP:AF:ID  0.00190695:0.0157708:0.0457575:0.03755:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01652  ES:SE:LP:AF:ID  0.0177761:0.0246139:0.327902:0.01652:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.038165 ES:SE:LP:AF:ID  -0.0010167:0.0155799:0.0222764:0.038165:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.038215 ES:SE:LP:AF:ID  -0.000479555:0.015543:0.00877392:0.038215:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101307 ES:SE:LP:AF:ID  0.0027492:0.0114549:0.091515:0.101307:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957802 ES:SE:LP:AF:ID  -0.000161242:0.0150049:0.00436481:0.957802:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031294 ES:SE:LP:AF:ID  -0.0547136:0.027882:1.30103:0.031294:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052465 ES:SE:LP:AF:ID  -0.000762162:0.0222079:0.0132283:0.052465:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03759  ES:SE:LP:AF:ID  -0.000178764:0.0156688:0.00436481:0.03759:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037916 ES:SE:LP:AF:ID  -0.00186967:0.0155242:0.0457575:0.037916:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840431 ES:SE:LP:AF:ID  -0.00282964:0.00808764:0.136677:0.840431:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056077 ES:SE:LP:AF:ID  -0.00597678:0.0131908:0.187087:0.056077:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123846 ES:SE:LP:AF:ID  0.00469908:0.00884155:0.221849:0.123846:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024951 ES:SE:LP:AF:ID  -0.0102765:0.0224639:0.187087:0.024951:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.123075 ES:SE:LP:AF:ID  0.00490436:0.00884846:0.236572:0.123075:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134219 ES:SE:LP:AF:ID  0.00265843:0.0087395:0.119186:0.134219:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011413 ES:SE:LP:AF:ID  0.0115382:0.031362:0.148742:0.011413:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037995 ES:SE:LP:AF:ID  -0.00122944:0.0153464:0.0268721:0.037995:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836378 ES:SE:LP:AF:ID  -0.0064856:0.00784302:0.387216:0.836378:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.835962 ES:SE:LP:AF:ID  -0.00644862:0.00783349:0.387216:0.835962:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868217 ES:SE:LP:AF:ID  -0.00836707:0.00839202:0.49485:0.868217:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131405 ES:SE:LP:AF:ID  0.0084872:0.00841063:0.508638:0.131405:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038398 ES:SE:LP:AF:ID  -0.00061794:0.0151159:0.0132283:0.038398:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038681 ES:SE:LP:AF:ID  -0.00172803:0.0150161:0.0409586:0.038681:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867546 ES:SE:LP:AF:ID  -0.00829207:0.00837704:0.49485:0.867546:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867685 ES:SE:LP:AF:ID  -0.00863168:0.00838214:0.522879:0.867685:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038639 ES:SE:LP:AF:ID  -0.000877036:0.0150661:0.0222764:0.038639:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867559 ES:SE:LP:AF:ID  -0.00833593:0.00837676:0.49485:0.867559:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.835498 ES:SE:LP:AF:ID  -0.00628117:0.00781463:0.376751:0.835498:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03858  ES:SE:LP:AF:ID  -0.00178228:0.015102:0.0409586:0.03858:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83618  ES:SE:LP:AF:ID  -0.00610845:0.00783808:0.356547:0.83618:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013067 ES:SE:LP:AF:ID  -0.00568153:0.0284397:0.0757207:0.013067:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.837585 ES:SE:LP:AF:ID  -0.00580454:0.00794327:0.337242:0.837585:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.867781 ES:SE:LP:AF:ID  -0.00870829:0.00836685:0.522879:0.867781:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867356 ES:SE:LP:AF:ID  -0.00812926:0.00834669:0.481486:0.867356:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866135 ES:SE:LP:AF:ID  -0.00834717:0.00832896:0.49485:0.866135:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867438 ES:SE:LP:AF:ID  -0.00841692:0.00835204:0.508638:0.867438:rs4951929