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_22166.vcf.gz --id UKB-b:12753 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_22166.txt.gz --cohort_cases 12212 --cohort_controls 13274 --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-12753/UKB-b-12753_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12753/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:06 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12753/UKB-b-12753_data.vcf.gz ...
Read summary statistics for 6237640 SNPs.
Dropped 3077 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, 1229525 SNPs remain.
After merging with regression SNP LD, 1229525 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0386 (0.0165)
Lambda GC: 1.0209
Mean Chi^2: 1.0135
Intercept: 0.9938 (0.0064)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:43:19 2019
Total time elapsed: 1.0m:13.04s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9277,
    "inflation_factor": 1,
    "mean_EFFECT": -1.0582e-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": 56999,
    "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": 1229525,
    "ldsc_nsnp_merge_regression_ld": 1229525,
    "ldsc_observed_scale_h2_beta": 0.0386,
    "ldsc_observed_scale_h2_se": 0.0165,
    "ldsc_intercept_beta": 0.9938,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0209,
    "ldsc_mean_chisq": 1.0135,
    "ldsc_ratio": -0.4593
}

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 6234583 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 6237640 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 8.668590e+00 5.762909e+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 7.859527e+07 5.651980e+07 828.0000000 3.200485e+07 6.902068e+07 1.145276e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.100000e-06 7.223800e-03 -0.0596727 -4.173000e-03 7.500000e-06 4.225700e-03 7.485650e-02 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.759100e-03 2.469400e-03 0.0042591 4.739700e-03 5.778400e-03 8.178300e-03 2.669540e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.979023e-01 2.889625e-01 0.0000003 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.979039e-01 2.889357e-01 0.0000003 2.470964e-01 4.969233e-01 7.479640e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.026487e-01 2.554564e-01 0.0286610 8.665900e-02 2.173380e-01 4.663832e-01 9.713380e-01 ▇▃▂▂▁
numeric AF_reference 56999 0.9908621 NA NA NA NA NA NA NA 2.997013e-01 2.482097e-01 0.0000000 9.604630e-02 2.256390e-01 4.576680e-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 0.0104733 0.0078551 0.1800002 0.1824294 0.624922 0.7821490 NA
1 54676 rs2462492 C T -0.0168245 0.0077816 0.0309999 0.0306108 0.397912 NA NA
1 86028 rs114608975 T C -0.0002033 0.0123098 0.9900000 0.9868255 0.103681 0.0277556 NA
1 91536 rs6702460 G T -0.0045539 0.0076168 0.5500004 0.5499246 0.457884 0.4207270 NA
1 234313 rs8179466 C T 0.0328861 0.0148281 0.0269998 0.0265669 0.075119 NA NA
1 534192 rs6680723 C T -0.0031068 0.0087451 0.7199992 0.7223910 0.240099 NA NA
1 546697 rs12025928 A G -0.0154522 0.0109373 0.1600000 0.1577150 0.912738 NA NA
1 693731 rs12238997 A G 0.0092999 0.0073257 0.2000000 0.2042627 0.116766 0.1417730 NA
1 705882 rs72631875 G A 0.0040351 0.0105985 0.6999999 0.7034083 0.068727 0.0315495 NA
1 706368 rs55727773 A G -0.0113478 0.0054393 0.0369999 0.0369539 0.518329 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0064454 0.0085922 0.4500005 0.4531615 0.072356 0.0826677 NA
22 51219006 rs28729663 G A -0.0030763 0.0066458 0.6400000 0.6434368 0.134145 0.2052720 NA
22 51219387 rs9616832 T C 0.0057938 0.0086116 0.5000000 0.5010825 0.072476 0.0654952 NA
22 51219704 rs147475742 G A 0.0051840 0.0115805 0.6499995 0.6544077 0.041277 0.0473243 NA
22 51221190 rs369304721 G A 0.0103658 0.0115794 0.3700002 0.3706838 0.048656 NA NA
22 51221731 rs115055839 T C 0.0064612 0.0086147 0.4500005 0.4532432 0.072038 0.0625000 NA
22 51222100 rs114553188 G T -0.0134351 0.0103038 0.1900002 0.1922685 0.051948 0.0880591 NA
22 51223637 rs375798137 G A -0.0127409 0.0103471 0.2200002 0.2181920 0.051649 0.0788738 NA
22 51229805 rs9616985 T C 0.0066589 0.0086559 0.4400003 0.4417219 0.071810 0.0730831 NA
22 51237063 rs3896457 T C -0.0003670 0.0052210 0.9400001 0.9439557 0.296779 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624922 ES:SE:LP:AF:ID  0.0104733:0.0078551:0.744727:0.624922:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.397912 ES:SE:LP:AF:ID  -0.0168245:0.00778157:1.50864:0.397912:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103681 ES:SE:LP:AF:ID  -0.000203266:0.0123098:0.00436481:0.103681:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457884 ES:SE:LP:AF:ID  -0.00455391:0.00761685:0.259637:0.457884:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.075119 ES:SE:LP:AF:ID  0.0328861:0.0148281:1.56864:0.075119:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240099 ES:SE:LP:AF:ID  -0.00310684:0.00874513:0.142668:0.240099:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912738 ES:SE:LP:AF:ID  -0.0154522:0.0109373:0.79588:0.912738:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116766 ES:SE:LP:AF:ID  0.00929994:0.00732567:0.69897:0.116766:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068727 ES:SE:LP:AF:ID  0.0040351:0.0105985:0.154902:0.068727:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.518329 ES:SE:LP:AF:ID  -0.0113478:0.00543927:1.4318:0.518329:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032479 ES:SE:LP:AF:ID  -0.012676:0.0137797:0.443698:0.032479:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035835 ES:SE:LP:AF:ID  -0.0143011:0.0125871:0.585027:0.035835:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035947 ES:SE:LP:AF:ID  -0.0145848:0.012537:0.619789:0.035947:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035701 ES:SE:LP:AF:ID  -0.0142278:0.0126201:0.585027:0.035701:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036274 ES:SE:LP:AF:ID  -0.013969:0.0124717:0.585027:0.036274:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036296 ES:SE:LP:AF:ID  -0.0138027:0.0124396:0.568636:0.036296:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099758 ES:SE:LP:AF:ID  0.0183761:0.00900303:1.38722:0.099758:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.960017 ES:SE:LP:AF:ID  0.0179414:0.0120138:0.853872:0.960017:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031384 ES:SE:LP:AF:ID  0.0158745:0.0216022:0.337242:0.031384:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053356 ES:SE:LP:AF:ID  0.0132158:0.0171411:0.356547:0.053356:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03593  ES:SE:LP:AF:ID  -0.0150854:0.012488:0.638272:0.03593:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036111 ES:SE:LP:AF:ID  -0.0166522:0.0124105:0.744727:0.036111:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843563 ES:SE:LP:AF:ID  -0.000874959:0.00637866:0.05061:0.843563:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055985 ES:SE:LP:AF:ID  0.0103059:0.01028:0.49485:0.055985:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122511 ES:SE:LP:AF:ID  0.00829959:0.00696672:0.638272:0.122511:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121739 ES:SE:LP:AF:ID  0.00813535:0.00697155:0.619789:0.121739:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132214 ES:SE:LP:AF:ID  0.000796426:0.00686058:0.0409586:0.132214:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.035856 ES:SE:LP:AF:ID  -0.0163572:0.0123065:0.744727:0.035856:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839132 ES:SE:LP:AF:ID  -0.00278713:0.00617113:0.187087:0.839132:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83899  ES:SE:LP:AF:ID  -0.00301125:0.0061672:0.200659:0.83899:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869302 ES:SE:LP:AF:ID  -0.00828162:0.00659841:0.677781:0.869302:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130191 ES:SE:LP:AF:ID  0.00926914:0.00661492:0.79588:0.130191:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036466 ES:SE:LP:AF:ID  -0.0156492:0.0120688:0.721246:0.036466:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036706 ES:SE:LP:AF:ID  -0.015577:0.0119933:0.721246:0.036706:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86876  ES:SE:LP:AF:ID  -0.00811713:0.00658653:0.657577:0.86876:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868864 ES:SE:LP:AF:ID  -0.00855217:0.00658857:0.721246:0.868864:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036716 ES:SE:LP:AF:ID  -0.0156139:0.012045:0.721246:0.036716:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868762 ES:SE:LP:AF:ID  -0.00815881:0.00658623:0.657577:0.868762:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838288 ES:SE:LP:AF:ID  -0.00319351:0.00614755:0.221849:0.838288:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036762 ES:SE:LP:AF:ID  -0.0145629:0.0120583:0.638272:0.036762:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838795 ES:SE:LP:AF:ID  -0.00340148:0.00616234:0.236572:0.838795:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839943 ES:SE:LP:AF:ID  -0.00289455:0.006244:0.19382:0.839943:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868914 ES:SE:LP:AF:ID  -0.0077564:0.00657614:0.619789:0.868914:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868457 ES:SE:LP:AF:ID  -0.00745772:0.00655853:0.585027:0.868457:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.86747  ES:SE:LP:AF:ID  -0.00672995:0.00654819:0.522879:0.86747:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868542 ES:SE:LP:AF:ID  -0.00743806:0.00656445:0.585027:0.868542:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868548 ES:SE:LP:AF:ID  -0.00745058:0.00656486:0.585027:0.868548:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.86855  ES:SE:LP:AF:ID  -0.00743559:0.00656491:0.585027:0.86855:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869086 ES:SE:LP:AF:ID  -0.00786957:0.006584:0.638272:0.869086:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.037118 ES:SE:LP:AF:ID  -0.014719:0.0119322:0.657577:0.037118:rs114525117