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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_102220.vcf.gz --id UKB-b:1814 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102220.txt.gz --cohort_controls 64947 --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-1814/UKB-b-1814_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1814/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1814/UKB-b-1814_data.vcf.gz ...
Read summary statistics for 8624950 SNPs.
Dropped 7371 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, 1285748 SNPs remain.
After merging with regression SNP LD, 1285748 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0001 (0.0062)
Lambda GC: 0.9957
Mean Chi^2: 0.9991
Intercept: 0.9989 (0.0058)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:41:59 2019
Total time elapsed: 1.0m:41.35s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -8.448e-06,
    "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": 83093,
    "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": 1285748,
    "ldsc_nsnp_merge_regression_ld": 1285748,
    "ldsc_observed_scale_h2_beta": 0.0001,
    "ldsc_observed_scale_h2_se": 0.0062,
    "ldsc_intercept_beta": 0.9989,
    "ldsc_intercept_se": 0.0058,
    "ldsc_lambda_gc": 0.9957,
    "ldsc_mean_chisq": 0.9991,
    "ldsc_ratio": 1.2222
}

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 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 1.000000 3 58 0 8617613 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 8624950 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.650353e+00 5.760993e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.877008e+07 5.637172e+07 828.0000000 3.238534e+07 6.929204e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -8.400000e-06 4.553500e-03 -0.0451196 -1.920700e-03 -3.490000e-05 1.838000e-03 5.333560e-02 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 3.764500e-03 2.569600e-03 0.0014951 1.760700e-03 2.596000e-03 5.088800e-03 2.577340e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 5.007501e-01 2.884646e-01 0.0000001 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 5.007493e-01 2.884377e-01 0.0000001 2.511408e-01 5.011280e-01 7.506456e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.291180e-01 2.594882e-01 0.0053900 2.519000e-02 1.138980e-01 3.626188e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83093 0.990366 NA NA NA NA NA NA NA 2.288669e-01 2.514590e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 0.0003125 0.0027554 0.9100000 0.9096955 0.623810 0.7821490 NA
1 54676 rs2462492 C T -0.0009062 0.0027475 0.7400005 0.7415407 0.399142 NA NA
1 86028 rs114608975 T C -0.0061718 0.0043734 0.1600000 0.1581847 0.103538 0.0277556 NA
1 91536 rs6702460 G T 0.0009658 0.0027023 0.7199992 0.7207990 0.455919 0.4207270 NA
1 234313 rs8179466 C T 0.0104784 0.0053440 0.0500000 0.0499066 0.074456 NA NA
1 534192 rs6680723 C T 0.0006539 0.0030777 0.8300000 0.8317569 0.242058 NA NA
1 546697 rs12025928 A G 0.0021796 0.0038188 0.5700002 0.5681707 0.912860 NA NA
1 693731 rs12238997 A G -0.0005347 0.0025663 0.8300000 0.8349451 0.117310 0.1417730 NA
1 705882 rs72631875 G A -0.0022840 0.0037406 0.5400003 0.5414770 0.067700 0.0315495 NA
1 706368 rs55727773 A G -0.0014060 0.0019050 0.4600002 0.4604540 0.513305 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0029712 0.0023169 0.2000000 0.1997101 0.136311 0.2052720 NA
22 51219387 rs9616832 T C 0.0034259 0.0030198 0.2599998 0.2565877 0.071799 0.0654952 NA
22 51219704 rs147475742 G A 0.0044707 0.0040171 0.2700001 0.2657453 0.041192 0.0473243 NA
22 51221190 rs369304721 G A 0.0048149 0.0040456 0.2300001 0.2339897 0.048374 NA NA
22 51221731 rs115055839 T C 0.0032142 0.0030204 0.2900000 0.2872610 0.071350 0.0625000 NA
22 51222100 rs114553188 G T 0.0026076 0.0035002 0.4600002 0.4562914 0.054845 0.0880591 NA
22 51223637 rs375798137 G A 0.0022615 0.0035185 0.5199996 0.5203819 0.054465 0.0788738 NA
22 51229805 rs9616985 T C 0.0033067 0.0030298 0.2800000 0.2750898 0.071255 0.0730831 NA
22 51232488 rs376461333 A G -0.0028669 0.0069783 0.6800001 0.6811935 0.020458 NA NA
22 51237063 rs3896457 T C -0.0018813 0.0018291 0.2999998 0.3036968 0.298382 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62381  ES:SE:LP:AF:ID  0.000312527:0.00275542:0.0409586:0.62381:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399142 ES:SE:LP:AF:ID  -0.00090615:0.00274746:0.130768:0.399142:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103538 ES:SE:LP:AF:ID  -0.00617177:0.00437341:0.79588:0.103538:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455919 ES:SE:LP:AF:ID  0.000965771:0.00270228:0.142668:0.455919:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074456 ES:SE:LP:AF:ID  0.0104784:0.00534404:1.30103:0.074456:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242058 ES:SE:LP:AF:ID  0.000653856:0.00307771:0.0809219:0.242058:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91286  ES:SE:LP:AF:ID  0.00217956:0.00381878:0.244125:0.91286:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11731  ES:SE:LP:AF:ID  -0.000534734:0.00256635:0.0809219:0.11731:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.0677   ES:SE:LP:AF:ID  -0.00228397:0.00374065:0.267606:0.0677:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513305 ES:SE:LP:AF:ID  -0.00140605:0.00190496:0.337242:0.513305:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033678 ES:SE:LP:AF:ID  -0.00201876:0.00474799:0.173925:0.033678:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037458 ES:SE:LP:AF:ID  -0.00151027:0.0043062:0.136677:0.037458:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037645 ES:SE:LP:AF:ID  -0.00148518:0.00428367:0.136677:0.037645:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037221 ES:SE:LP:AF:ID  -0.00102489:0.00432281:0.091515:0.037221:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016282 ES:SE:LP:AF:ID  0.00900388:0.0067806:0.744727:0.016282:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037862 ES:SE:LP:AF:ID  -0.00149274:0.00426902:0.136677:0.037862:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037956 ES:SE:LP:AF:ID  -0.00115436:0.00425548:0.102373:0.037956:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102739 ES:SE:LP:AF:ID  0.00226675:0.00310853:0.327902:0.102739:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958088 ES:SE:LP:AF:ID  0.00196041:0.00411015:0.200659:0.958088:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031691 ES:SE:LP:AF:ID  -0.00690435:0.00752636:0.443698:0.031691:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052724 ES:SE:LP:AF:ID  0.0119645:0.00606228:1.31876:0.052724:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03745  ES:SE:LP:AF:ID  -0.00158173:0.00428342:0.148742:0.03745:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037719 ES:SE:LP:AF:ID  -0.000953233:0.00424794:0.0861861:0.037719:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841444 ES:SE:LP:AF:ID  0.000166623:0.0022194:0.0268721:0.841444:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056336 ES:SE:LP:AF:ID  -0.00306416:0.00360524:0.39794:0.056336:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123074 ES:SE:LP:AF:ID  -0.000367311:0.00243773:0.0555173:0.123074:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025131 ES:SE:LP:AF:ID  -0.0028287:0.00607064:0.19382:0.025131:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122326 ES:SE:LP:AF:ID  -0.000424755:0.00243852:0.0655015:0.122326:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134136 ES:SE:LP:AF:ID  -0.00131065:0.0023938:0.236572:0.134136:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  0.00134251:0.00853142:0.0604807:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.0061   ES:SE:LP:AF:ID  0.0220061:0.0108339:1.37675:0.0061:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037601 ES:SE:LP:AF:ID  -0.00158089:0.00420858:0.148742:0.037601:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837032 ES:SE:LP:AF:ID  0.000619212:0.002147:0.113509:0.837032:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836736 ES:SE:LP:AF:ID  0.000497346:0.00214543:0.0861861:0.836736:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868566 ES:SE:LP:AF:ID  0.00091112:0.00230538:0.161151:0.868566:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131001 ES:SE:LP:AF:ID  -0.000594522:0.0023113:0.09691:0.131001:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038046 ES:SE:LP:AF:ID  -0.00176245:0.00414256:0.173925:0.038046:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038293 ES:SE:LP:AF:ID  -0.00192036:0.00411689:0.19382:0.038293:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867979 ES:SE:LP:AF:ID  0.000758098:0.00230167:0.130768:0.867979:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868053 ES:SE:LP:AF:ID  0.000724949:0.00230261:0.124939:0.868053:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038201 ES:SE:LP:AF:ID  -0.00174098:0.0041359:0.173925:0.038201:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86799  ES:SE:LP:AF:ID  0.00074823:0.00230162:0.124939:0.86799:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.00306704:0.0115354:0.102373:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836162 ES:SE:LP:AF:ID  0.000337686:0.00213915:0.0604807:0.836162:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038204 ES:SE:LP:AF:ID  -0.00165491:0.00414194:0.161151:0.038204:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836796 ES:SE:LP:AF:ID  0.000311375:0.00214501:0.0555173:0.836796:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  0.00188437:0.00776028:0.091515:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  -0.00874212:0.0115202:0.346787:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838112 ES:SE:LP:AF:ID  0.000232413:0.0021752:0.0409586:0.838112:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868231 ES:SE:LP:AF:ID  0.000965107:0.00229865:0.173925:0.868231:rs3115858