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_22507.vcf.gz --id UKB-b:4509 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_22507.txt.gz --cohort_controls 41215 --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-4509/UKB-b-4509_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4509/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:24 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4509/UKB-b-4509_data.vcf.gz ...
Read summary statistics for 7965821 SNPs.
Dropped 6070 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, 1282025 SNPs remain.
After merging with regression SNP LD, 1282025 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0367 (0.011)
Lambda GC: 1.0257
Mean Chi^2: 1.036
Intercept: 1.0062 (0.0062)
Ratio: 0.173 (0.1731)
Analysis finished at Thu Oct 17 14:45:57 2019
Total time elapsed: 1.0m:33.04s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9427,
    "inflation_factor": 1.0475,
    "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": 74411,
    "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": 1282025,
    "ldsc_nsnp_merge_regression_ld": 1282025,
    "ldsc_observed_scale_h2_beta": 0.0367,
    "ldsc_observed_scale_h2_se": 0.011,
    "ldsc_intercept_beta": 1.0062,
    "ldsc_intercept_se": 0.0062,
    "ldsc_lambda_gc": 1.0257,
    "ldsc_mean_chisq": 1.036,
    "ldsc_ratio": 0.1722
}

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 7959778 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 7965821 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 8.662123e+00 5.763672e+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 7.869544e+07 5.640971e+07 828.0000000 3.225626e+07 6.916062e+07 1.145314e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.010000e-05 1.674030e-02 -0.1641650 -7.731800e-03 4.390000e-05 7.789900e-03 1.659530e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.421350e-02 8.489400e-03 0.0065167 7.531800e-03 1.046140e-02 1.866940e-02 7.950570e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.951281e-01 2.899250e-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 4.951299e-01 2.899004e-01 0.0000001 2.426721e-01 4.940311e-01 7.462956e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.464725e-01 2.607569e-01 0.0084930 3.603100e-02 1.378550e-01 3.903780e-01 9.915070e-01 ▇▂▂▁▁
numeric AF_reference 74411 0.9906587 NA NA NA NA NA NA NA 2.457181e-01 2.525865e-01 0.0000000 3.813900e-02 1.531550e-01 3.855830e-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 -0.0071890 0.0119838 0.5500004 0.5485774 0.625255 0.7821490 NA
1 54676 rs2462492 C T 0.0125578 0.0118191 0.2900000 0.2880085 0.400821 NA NA
1 86028 rs114608975 T C -0.0065482 0.0191170 0.7300002 0.7319507 0.102430 0.0277556 NA
1 91536 rs6702460 G T 0.0132515 0.0116795 0.2599998 0.2565451 0.458718 0.4207270 NA
1 234313 rs8179466 C T 0.0280222 0.0228093 0.2200002 0.2192433 0.074669 NA NA
1 534192 rs6680723 C T -0.0127950 0.0132953 0.3400001 0.3358637 0.242202 NA NA
1 546697 rs12025928 A G -0.0683636 0.0165881 0.0000380 0.0000377 0.913362 NA NA
1 693731 rs12238997 A G -0.0107025 0.0111741 0.3400001 0.3381660 0.117170 0.1417730 NA
1 705882 rs72631875 G A 0.0367333 0.0163801 0.0250000 0.0249254 0.066913 0.0315495 NA
1 706368 rs55727773 A G 0.0163438 0.0082798 0.0479999 0.0483892 0.513948 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0030787 0.0100493 0.7600007 0.7593301 0.135577 0.2052720 NA
22 51219387 rs9616832 T C -0.0131162 0.0130584 0.3200000 0.3151732 0.072282 0.0654952 NA
22 51219704 rs147475742 G A -0.0194649 0.0177065 0.2700001 0.2716337 0.040233 0.0473243 NA
22 51221190 rs369304721 G A -0.0370518 0.0175784 0.0350002 0.0350481 0.048372 NA NA
22 51221731 rs115055839 T C -0.0130775 0.0130755 0.3200000 0.3172365 0.071668 0.0625000 NA
22 51222100 rs114553188 G T 0.0048954 0.0153693 0.7499995 0.7500910 0.053854 0.0880591 NA
22 51223637 rs375798137 G A 0.0032658 0.0154499 0.8300000 0.8325902 0.053454 0.0788738 NA
22 51229805 rs9616985 T C -0.0132387 0.0131234 0.3100002 0.3130774 0.071425 0.0730831 NA
22 51232488 rs376461333 A G 0.0145983 0.0314857 0.6400000 0.6428995 0.019378 NA NA
22 51237063 rs3896457 T C 0.0059099 0.0079520 0.4600002 0.4573622 0.297909 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.625255 ES:SE:LP:AF:ID  -0.007189:0.0119838:0.259637:0.625255:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400821 ES:SE:LP:AF:ID  0.0125578:0.0118191:0.537602:0.400821:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10243  ES:SE:LP:AF:ID  -0.00654818:0.019117:0.136677:0.10243:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.458718 ES:SE:LP:AF:ID  0.0132515:0.0116795:0.585027:0.458718:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074669 ES:SE:LP:AF:ID  0.0280222:0.0228093:0.657577:0.074669:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242202 ES:SE:LP:AF:ID  -0.012795:0.0132953:0.468521:0.242202:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913362 ES:SE:LP:AF:ID  -0.0683636:0.0165881:4.42022:0.913362:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11717  ES:SE:LP:AF:ID  -0.0107025:0.0111741:0.468521:0.11717:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066913 ES:SE:LP:AF:ID  0.0367333:0.0163801:1.60206:0.066913:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513948 ES:SE:LP:AF:ID  0.0163438:0.00827981:1.31876:0.513948:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.031987 ES:SE:LP:AF:ID  -0.0164834:0.0212467:0.356547:0.031987:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035811 ES:SE:LP:AF:ID  -0.0125978:0.0191907:0.29243:0.035811:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035929 ES:SE:LP:AF:ID  -0.0125672:0.0191151:0.29243:0.035929:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035623 ES:SE:LP:AF:ID  -0.0131732:0.0192574:0.309804:0.035623:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01719  ES:SE:LP:AF:ID  -0.0138733:0.0285839:0.200659:0.01719:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036209 ES:SE:LP:AF:ID  -0.0134883:0.0190166:0.318759:0.036209:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036252 ES:SE:LP:AF:ID  -0.01393:0.0189698:0.337242:0.036252:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102017 ES:SE:LP:AF:ID  -0.0189936:0.0136216:0.79588:0.102017:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.960364 ES:SE:LP:AF:ID  0.00751003:0.0183586:0.167491:0.960364:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031779 ES:SE:LP:AF:ID  0.0388179:0.0325079:0.638272:0.031779:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053427 ES:SE:LP:AF:ID  -0.0057105:0.026058:0.0809219:0.053427:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.035822 ES:SE:LP:AF:ID  -0.0118705:0.0190791:0.275724:0.035822:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036009 ES:SE:LP:AF:ID  -0.0146566:0.0189417:0.356547:0.036009:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843803 ES:SE:LP:AF:ID  0.0127599:0.00972534:0.721246:0.843803:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055582 ES:SE:LP:AF:ID  -0.0194971:0.0157904:0.657577:0.055582:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122862 ES:SE:LP:AF:ID  -0.0118474:0.0106154:0.585027:0.122862:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024755 ES:SE:LP:AF:ID  -0.00863067:0.0266984:0.124939:0.024755:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122132 ES:SE:LP:AF:ID  -0.0108329:0.010622:0.508638:0.122132:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.131287 ES:SE:LP:AF:ID  -0.0182522:0.0105177:1.08092:0.131287:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010569 ES:SE:LP:AF:ID  0.0170221:0.0396163:0.173925:0.010569:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036033 ES:SE:LP:AF:ID  -0.0125293:0.0187154:0.30103:0.036033:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839344 ES:SE:LP:AF:ID  0.0116734:0.0094206:0.657577:0.839344:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.839023 ES:SE:LP:AF:ID  0.0116276:0.00941039:0.657577:0.839023:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869111 ES:SE:LP:AF:ID  0.00859839:0.0100706:0.408935:0.869111:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130795 ES:SE:LP:AF:ID  -0.0087581:0.0100827:0.408935:0.130795:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036547 ES:SE:LP:AF:ID  -0.0138106:0.0183864:0.346787:0.036547:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036844 ES:SE:LP:AF:ID  -0.013752:0.0182477:0.346787:0.036844:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868427 ES:SE:LP:AF:ID  0.00802518:0.0100499:0.376751:0.868427:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868501 ES:SE:LP:AF:ID  0.00809473:0.0100541:0.376751:0.868501:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036743 ES:SE:LP:AF:ID  -0.0146859:0.0183537:0.376751:0.036743:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868438 ES:SE:LP:AF:ID  0.00806837:0.0100507:0.376751:0.868438:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838345 ES:SE:LP:AF:ID  0.011148:0.0093813:0.638272:0.838345:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036769 ES:SE:LP:AF:ID  -0.0163762:0.0183785:0.431798:0.036769:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838923 ES:SE:LP:AF:ID  0.0108524:0.00940486:0.60206:0.838923:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01357  ES:SE:LP:AF:ID  -0.0321209:0.0330094:0.481486:0.01357:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.840228 ES:SE:LP:AF:ID  0.0113854:0.00953299:0.638272:0.840228:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868645 ES:SE:LP:AF:ID  0.00737508:0.0100382:0.337242:0.868645:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.8682   ES:SE:LP:AF:ID  0.00711466:0.0100095:0.318759:0.8682:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867237 ES:SE:LP:AF:ID  0.00738427:0.00999663:0.337242:0.867237:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868345 ES:SE:LP:AF:ID  0.00727644:0.0100198:0.327902:0.868345:rs4951929