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_20004_1297.vcf.gz --id UKB-b:1021 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20004_1297.txt.gz --cohort_cases 13359 --cohort_controls 449574 --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-1021/UKB-b-1021_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1021/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-1021/UKB-b-1021_data.vcf.gz ...
Read summary statistics for 6362186 SNPs.
Dropped 3306 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, 1236392 SNPs remain.
After merging with regression SNP LD, 1236392 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.002 (0.0011)
Lambda GC: 1.0289
Mean Chi^2: 1.0279
Intercept: 1.0095 (0.0065)
Ratio: 0.3395 (0.2327)
Analysis finished at Thu Oct 17 14:41:31 2019
Total time elapsed: 1.0m:13.46s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9293,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 2.2687e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 8,
    "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": 58281,
    "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": 1236392,
    "ldsc_nsnp_merge_regression_ld": 1236392,
    "ldsc_observed_scale_h2_beta": 0.002,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0095,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0289,
    "ldsc_mean_chisq": 1.0279,
    "ldsc_ratio": 0.3405
}

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 6358901 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 6362186 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 8.667663e+00 5.763487e+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 7.857755e+07 5.649893e+07 828.0000000 3.201264e+07 6.900989e+07 1.144947e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.300000e-06 5.880000e-04 -0.0049262 -3.365000e-04 4.000000e-07 3.384000e-04 5.310200e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.419000e-04 2.066000e-04 0.0003355 3.735000e-04 4.590000e-04 6.601000e-04 2.908000e-03 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.949651e-01 2.901991e-01 0.0000000 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 4.949656e-01 2.901745e-01 0.0000000 2.418590e-01 4.927845e-01 7.469319e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.981179e-01 2.564610e-01 0.0262000 8.183000e-02 2.108000e-01 4.608410e-01 9.738000e-01 ▇▃▂▂▁
numeric AF_reference 58281 0.9908395 NA NA NA NA NA NA NA 2.953839e-01 2.490364e-01 0.0000000 9.105430e-02 2.198480e-01 4.524760e-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 -0.0011506 0.0006174 0.0619998 0.0623641 0.623763 0.7821490 NA
1 54676 rs2462492 C T 0.0009338 0.0006116 0.1299999 0.1268068 0.400401 NA NA
1 86028 rs114608975 T C -0.0010356 0.0009778 0.2900000 0.2895956 0.103556 0.0277556 NA
1 91536 rs6702460 G T -0.0002196 0.0006022 0.7199992 0.7153112 0.456851 0.4207270 NA
1 234313 rs8179466 C T 0.0007481 0.0011874 0.5300002 0.5286505 0.074508 NA NA
1 534192 rs6680723 C T -0.0003133 0.0006879 0.6499995 0.6487797 0.240960 NA NA
1 546697 rs12025928 A G -0.0012167 0.0008582 0.1600000 0.1562537 0.913473 NA NA
1 693731 rs12238997 A G 0.0007966 0.0005765 0.1700000 0.1669975 0.116325 0.1417730 NA
1 705882 rs72631875 G A 0.0008014 0.0008448 0.3400001 0.3427946 0.067285 0.0315495 NA
1 706368 rs55727773 A G -0.0002523 0.0004270 0.5500004 0.5547017 0.515650 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0002254 0.0006673 0.7400005 0.7354942 0.073625 0.0826677 NA
22 51219006 rs28729663 G A -0.0008309 0.0005151 0.1100001 0.1067189 0.137953 0.2052720 NA
22 51219387 rs9616832 T C -0.0002231 0.0006686 0.7400005 0.7386701 0.073747 0.0654952 NA
22 51219704 rs147475742 G A -0.0005875 0.0008960 0.5099998 0.5120448 0.041955 0.0473243 NA
22 51221190 rs369304721 G A -0.0001577 0.0008946 0.8600001 0.8601121 0.049731 NA NA
22 51221731 rs115055839 T C -0.0002693 0.0006691 0.6899999 0.6872726 0.073238 0.0625000 NA
22 51222100 rs114553188 G T -0.0012592 0.0007877 0.1100001 0.1099275 0.054459 0.0880591 NA
22 51223637 rs375798137 G A -0.0012547 0.0007915 0.1100001 0.1129126 0.054088 0.0788738 NA
22 51229805 rs9616985 T C -0.0002825 0.0006715 0.6700003 0.6739938 0.073073 0.0730831 NA
22 51237063 rs3896457 T C 0.0003106 0.0004107 0.4500005 0.4494129 0.297971 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  -0.00115058:0.000617364:1.20761:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000933819:0.000611612:0.886057:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  -0.00103555:0.000977848:0.537602:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  -0.000219648:0.000602217:0.142668:0.456851:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074508 ES:SE:LP:AF:ID  0.000748142:0.0011874:0.275724:0.074508:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  -0.000313301:0.000687881:0.187087:0.24096:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913473 ES:SE:LP:AF:ID  -0.0012167:0.00085817:0.79588:0.913473:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116325 ES:SE:LP:AF:ID  0.000796647:0.00057648:0.769551:0.116325:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067285 ES:SE:LP:AF:ID  0.000801393:0.000844765:0.468521:0.067285:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  -0.000252258:0.000427028:0.259637:0.51565:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033004 ES:SE:LP:AF:ID  0.000168494:0.00107657:0.0555173:0.033004:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036621 ES:SE:LP:AF:ID  0.00027234:0.00097787:0.107905:0.036621:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036737 ES:SE:LP:AF:ID  0.000268584:0.00097417:0.107905:0.036737:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036437 ES:SE:LP:AF:ID  0.000185165:0.00098119:0.0705811:0.036437:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036976 ES:SE:LP:AF:ID  0.000178373:0.000970322:0.0705811:0.036976:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037072 ES:SE:LP:AF:ID  0.000138903:0.000966998:0.05061:0.037072:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101199 ES:SE:LP:AF:ID  -0.000906219:0.000704569:0.69897:0.101199:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959096 ES:SE:LP:AF:ID  -1.6284e-06:0.00093265:-0:0.959096:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031451 ES:SE:LP:AF:ID  0.00133235:0.00169303:0.366532:0.031451:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053254 ES:SE:LP:AF:ID  -0.00131298:0.00134688:0.481486:0.053254:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03659  ES:SE:LP:AF:ID  0.000197839:0.000973257:0.0757207:0.03659:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036907 ES:SE:LP:AF:ID  9.27037e-05:0.000964398:0.0362122:0.036907:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843212 ES:SE:LP:AF:ID  -0.000416709:0.000499593:0.39794:0.843212:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055911 ES:SE:LP:AF:ID  0.000513416:0.000808919:0.275724:0.055911:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122307 ES:SE:LP:AF:ID  0.000672202:0.000546848:0.657577:0.122307:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121549 ES:SE:LP:AF:ID  0.000651059:0.000547078:0.638272:0.121549:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13233  ES:SE:LP:AF:ID  4.63251e-05:0.000539199:0.0315171:0.13233:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036821 ES:SE:LP:AF:ID  0.000162914:0.000954644:0.0655015:0.036821:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838951 ES:SE:LP:AF:ID  -0.000422947:0.00048382:0.420216:0.838951:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83858  ES:SE:LP:AF:ID  -0.000422611:0.0004833:0.420216:0.83858:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869781 ES:SE:LP:AF:ID  -0.000559692:0.0005186:0.552842:0.869781:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129871 ES:SE:LP:AF:ID  0.000531613:0.000519662:0.508638:0.129871:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037332 ES:SE:LP:AF:ID  0.000417333:0.000938457:0.180456:0.037332:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037576 ES:SE:LP:AF:ID  0.000405673:0.000932527:0.180456:0.037576:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869123 ES:SE:LP:AF:ID  -0.000565286:0.000517584:0.568636:0.869123:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869221 ES:SE:LP:AF:ID  -0.000572226:0.00051779:0.568636:0.869221:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037534 ES:SE:LP:AF:ID  0.000408782:0.000936561:0.180456:0.037534:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869126 ES:SE:LP:AF:ID  -0.000563231:0.000517574:0.552842:0.869126:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838033 ES:SE:LP:AF:ID  -0.000452229:0.000481958:0.455932:0.838033:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037547 ES:SE:LP:AF:ID  0.000401905:0.000937885:0.173925:0.037547:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838664 ES:SE:LP:AF:ID  -0.000378681:0.000483313:0.366532:0.838664:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839777 ES:SE:LP:AF:ID  -0.000451833:0.00048985:0.443698:0.839777:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869405 ES:SE:LP:AF:ID  -0.000558445:0.000516976:0.552842:0.869405:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868952 ES:SE:LP:AF:ID  -0.000560141:0.000515675:0.552842:0.868952:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867905 ES:SE:LP:AF:ID  -0.000481976:0.000514687:0.455932:0.867905:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869095 ES:SE:LP:AF:ID  -0.00056197:0.000516097:0.552842:0.869095:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869104 ES:SE:LP:AF:ID  -0.000561857:0.000516137:0.552842:0.869104:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869112 ES:SE:LP:AF:ID  -0.000563786:0.000516149:0.568636:0.869112:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869589 ES:SE:LP:AF:ID  -0.000575055:0.000517565:0.568636:0.869589:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.037596 ES:SE:LP:AF:ID  0.00047436:0.000932392:0.21467:0.037596:rs114525117