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_2907.vcf.gz --id UKB-b:11355 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2907.txt.gz --cohort_cases 48981 --cohort_controls 64249 --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-11355/UKB-b-11355_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11355/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-11355/UKB-b-11355_data.vcf.gz ...
Read summary statistics for 8210439 SNPs.
Dropped 6507 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, 1283903 SNPs remain.
After merging with regression SNP LD, 1283903 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0174 (0.0043)
Lambda GC: 1.0349
Mean Chi^2: 1.0451
Intercept: 1.0062 (0.0062)
Ratio: 0.1366 (0.1383)
Analysis finished at Thu Oct 17 14:41:54 2019
Total time elapsed: 1.0m:36.59s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9441,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 140,
    "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": 76995,
    "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": 1283903,
    "ldsc_nsnp_merge_regression_ld": 1283903,
    "ldsc_observed_scale_h2_beta": 0.0174,
    "ldsc_observed_scale_h2_se": 0.0043,
    "ldsc_intercept_beta": 1.0062,
    "ldsc_intercept_se": 0.0062,
    "ldsc_lambda_gc": 1.0349,
    "ldsc_mean_chisq": 1.0451,
    "ldsc_ratio": 0.1375
}

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 8203961 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 8210439 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 8.656323e+00 5.762608e+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 7.872845e+07 5.639182e+07 828.0000000 3.231545e+07 6.921232e+07 1.145467e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 6.460000e-05 5.493900e-03 -0.0608544 -2.416900e-03 3.410000e-05 2.507300e-03 5.970130e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.612700e-03 2.904100e-03 0.0020034 2.335500e-03 3.313600e-03 6.122600e-03 3.126440e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.953345e-01 2.903473e-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 4.953370e-01 2.903229e-01 0.0000000 2.423390e-01 4.945503e-01 7.465354e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.397780e-01 2.604205e-01 0.0071460 3.161300e-02 1.285910e-01 3.799890e-01 9.928530e-01 ▇▂▂▁▁
numeric AF_reference 76995 0.9906223 NA NA NA NA NA NA NA 2.392035e-01 2.522910e-01 0.0000000 3.214860e-02 1.443690e-01 3.755990e-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 0.0049010 0.0036960 0.1800002 0.1848277 0.624926 0.7821490 NA
1 54676 rs2462492 C T 0.0015517 0.0036513 0.6700003 0.6708478 0.400595 NA NA
1 86028 rs114608975 T C -0.0099577 0.0058250 0.0870001 0.0873631 0.103742 0.0277556 NA
1 91536 rs6702460 G T -0.0010834 0.0035930 0.7600007 0.7630051 0.457868 0.4207270 NA
1 234313 rs8179466 C T -0.0019148 0.0070610 0.7899998 0.7862580 0.074784 NA NA
1 534192 rs6680723 C T 0.0048951 0.0041257 0.2399999 0.2354207 0.240559 NA NA
1 546697 rs12025928 A G -0.0025453 0.0051282 0.6200004 0.6196615 0.913517 NA NA
1 693731 rs12238997 A G 0.0011128 0.0034580 0.7499995 0.7475951 0.115624 0.1417730 NA
1 705882 rs72631875 G A -0.0042535 0.0050599 0.4000000 0.4005529 0.067031 0.0315495 NA
1 706368 rs55727773 A G -0.0011705 0.0025550 0.6499995 0.6468568 0.515541 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0003026 0.0030831 0.9199999 0.9218071 0.138092 0.2052720 NA
22 51219387 rs9616832 T C -0.0004329 0.0039957 0.9100000 0.9137316 0.073750 0.0654952 NA
22 51219704 rs147475742 G A -0.0053978 0.0053550 0.3100002 0.3134544 0.041956 0.0473243 NA
22 51221190 rs369304721 G A -0.0022478 0.0053596 0.6700003 0.6749272 0.049567 NA NA
22 51221731 rs115055839 T C -0.0004077 0.0039984 0.9199999 0.9187834 0.073215 0.0625000 NA
22 51222100 rs114553188 G T -0.0018886 0.0047042 0.6899999 0.6880727 0.054478 0.0880591 NA
22 51223637 rs375798137 G A -0.0024332 0.0047271 0.6100002 0.6067351 0.054102 0.0788738 NA
22 51229805 rs9616985 T C -0.0001927 0.0040127 0.9599999 0.9616988 0.073073 0.0730831 NA
22 51232488 rs376461333 A G 0.0038997 0.0094969 0.6800001 0.6813432 0.020023 NA NA
22 51237063 rs3896457 T C 0.0005797 0.0024529 0.8100000 0.8131825 0.299001 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624926 ES:SE:LP:AF:ID  0.00490099:0.00369597:0.744727:0.624926:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400595 ES:SE:LP:AF:ID  0.00155173:0.00365126:0.173925:0.400595:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103742 ES:SE:LP:AF:ID  -0.00995769:0.005825:1.06048:0.103742:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457868 ES:SE:LP:AF:ID  -0.00108341:0.00359296:0.119186:0.457868:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074784 ES:SE:LP:AF:ID  -0.00191475:0.00706099:0.102373:0.074784:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240559 ES:SE:LP:AF:ID  0.00489514:0.00412566:0.619789:0.240559:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913517 ES:SE:LP:AF:ID  -0.00254529:0.00512822:0.207608:0.913517:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.115624 ES:SE:LP:AF:ID  0.00111281:0.00345795:0.124939:0.115624:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067031 ES:SE:LP:AF:ID  -0.0042535:0.00505987:0.39794:0.067031:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515541 ES:SE:LP:AF:ID  -0.00117053:0.002555:0.187087:0.515541:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032929 ES:SE:LP:AF:ID  0.00450489:0.00644105:0.318759:0.032929:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036545 ES:SE:LP:AF:ID  0.00350964:0.00585144:0.259637:0.036545:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03667  ES:SE:LP:AF:ID  0.0033315:0.00582807:0.244125:0.03667:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036348 ES:SE:LP:AF:ID  0.00346143:0.00587174:0.251812:0.036348:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016464 ES:SE:LP:AF:ID  -0.0106636:0.00897257:0.638272:0.016464:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036901 ES:SE:LP:AF:ID  0.00247453:0.00580469:0.173925:0.036901:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036997 ES:SE:LP:AF:ID  0.00344824:0.00578561:0.259637:0.036997:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10126  ES:SE:LP:AF:ID  -0.00530083:0.0042093:0.677781:0.10126:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959105 ES:SE:LP:AF:ID  -0.00232302:0.00557963:0.167491:0.959105:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031587 ES:SE:LP:AF:ID  -0.0150422:0.0100433:0.886057:0.031587:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053198 ES:SE:LP:AF:ID  -0.0104777:0.00806623:0.721246:0.053198:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036524 ES:SE:LP:AF:ID  0.00388888:0.00582311:0.30103:0.036524:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036774 ES:SE:LP:AF:ID  0.00402133:0.00577579:0.309804:0.036774:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843781 ES:SE:LP:AF:ID  -0.00257785:0.00298987:0.408935:0.843781:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05522  ES:SE:LP:AF:ID  0.00647566:0.00487403:0.744727:0.05522:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12189  ES:SE:LP:AF:ID  0.00277003:0.00327195:0.39794:0.12189:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025735 ES:SE:LP:AF:ID  -0.00106437:0.00803781:0.05061:0.025735:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121141 ES:SE:LP:AF:ID  0.00247204:0.00327309:0.346787:0.121141:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.131759 ES:SE:LP:AF:ID  0.00407298:0.00323231:0.677781:0.131759:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010975 ES:SE:LP:AF:ID  0.00740336:0.0118194:0.275724:0.010975:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036753 ES:SE:LP:AF:ID  0.00363096:0.00571055:0.283997:0.036753:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839533 ES:SE:LP:AF:ID  -0.00377731:0.00289827:0.721246:0.839533:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.839172 ES:SE:LP:AF:ID  -0.00339348:0.00289503:0.619789:0.839172:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.87009  ES:SE:LP:AF:ID  -0.00376448:0.00310371:0.638272:0.87009:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129575 ES:SE:LP:AF:ID  0.00354521:0.00310982:0.60206:0.129575:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037226 ES:SE:LP:AF:ID  0.00223013:0.00561635:0.161151:0.037226:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037486 ES:SE:LP:AF:ID  0.00259581:0.00557874:0.19382:0.037486:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869408 ES:SE:LP:AF:ID  -0.00349192:0.00309727:0.585027:0.869408:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869513 ES:SE:LP:AF:ID  -0.00357186:0.00309841:0.60206:0.869513:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037438 ES:SE:LP:AF:ID  0.0018215:0.00560408:0.124939:0.037438:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869416 ES:SE:LP:AF:ID  -0.0034766:0.0030974:0.585027:0.869416:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838603 ES:SE:LP:AF:ID  -0.00327257:0.00288699:0.585027:0.838603:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037448 ES:SE:LP:AF:ID  0.00128667:0.00561267:0.0861861:0.037448:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.8392   ES:SE:LP:AF:ID  -0.00313408:0.00289478:0.552842:0.8392:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013859 ES:SE:LP:AF:ID  0.00261987:0.0100434:0.102373:0.013859:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.840286 ES:SE:LP:AF:ID  -0.00328593:0.00293347:0.585027:0.840286:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869735 ES:SE:LP:AF:ID  -0.00375924:0.00309393:0.657577:0.869735:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.869284 ES:SE:LP:AF:ID  -0.00390652:0.00308528:0.677781:0.869284:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868251 ES:SE:LP:AF:ID  -0.00372114:0.00308043:0.638272:0.868251:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869432 ES:SE:LP:AF:ID  -0.00396891:0.00308854:0.69897:0.869432:rs4951929