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_3506.vcf.gz --id UKB-b:17820 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3506.txt.gz --cohort_controls 36019 --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-17820/UKB-b-17820_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17820/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17820/UKB-b-17820_data.vcf.gz ...
Read summary statistics for 7776394 SNPs.
Dropped 5694 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, 1280292 SNPs remain.
After merging with regression SNP LD, 1280292 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0277 (0.0119)
Lambda GC: 1.0425
Mean Chi^2: 1.0335
Intercept: 1.0141 (0.0058)
Ratio: 0.4205 (0.1724)
Analysis finished at Thu Oct 17 14:42:02 2019
Total time elapsed: 1.0m:33.37s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9418,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -8.7679e-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": 72473,
    "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": 1280292,
    "ldsc_nsnp_merge_regression_ld": 1280292,
    "ldsc_observed_scale_h2_beta": 0.0277,
    "ldsc_observed_scale_h2_se": 0.0119,
    "ldsc_intercept_beta": 1.0141,
    "ldsc_intercept_se": 0.0058,
    "ldsc_lambda_gc": 1.0425,
    "ldsc_mean_chisq": 1.0335,
    "ldsc_ratio": 0.4209
}

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 7770726 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 7776394 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 8.662043e+00 5.764341e+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 7.868702e+07 5.643350e+07 828.0000000 3.221944e+07 6.914365e+07 1.145540e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -8.800000e-06 1.265940e-02 -0.1398910 -6.062300e-03 3.200000e-06 6.069000e-03 1.167920e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.089740e-02 6.231100e-03 0.0051599 5.978100e-03 8.168900e-03 1.419880e-02 6.367210e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.949529e-01 2.898171e-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 4.949525e-01 2.897931e-01 0.0000001 2.430441e-01 4.927050e-01 7.461099e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.518852e-01 2.608164e-01 0.0097180 3.991600e-02 1.456670e-01 3.984710e-01 9.902820e-01 ▇▂▂▁▁
numeric AF_reference 72473 0.9906804 NA NA NA NA NA NA NA 2.509972e-01 2.526789e-01 0.0000000 4.333070e-02 1.603430e-01 3.931710e-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 -0.0017474 0.0095743 0.8600001 0.8551810 0.622840 0.7821490 NA
1 54676 rs2462492 C T -0.0034465 0.0094615 0.7199992 0.7156597 0.399870 NA NA
1 86028 rs114608975 T C -0.0175432 0.0152620 0.2500000 0.2503625 0.103129 0.0277556 NA
1 91536 rs6702460 G T -0.0072320 0.0093538 0.4400003 0.4394283 0.456455 0.4207270 NA
1 234313 rs8179466 C T -0.0014514 0.0184640 0.9400001 0.9373431 0.074124 NA NA
1 534192 rs6680723 C T -0.0025129 0.0105378 0.8100000 0.8115217 0.243770 NA NA
1 546697 rs12025928 A G 0.0063383 0.0134801 0.6400000 0.6382123 0.914721 NA NA
1 693731 rs12238997 A G -0.0043274 0.0088978 0.6300007 0.6267234 0.116318 0.1417730 NA
1 705882 rs72631875 G A -0.0150120 0.0132007 0.2599998 0.2554495 0.066382 0.0315495 NA
1 706368 rs55727773 A G 0.0017932 0.0066140 0.7899998 0.7863014 0.513243 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0025208 0.0080583 0.7499995 0.7544222 0.135366 0.2052720 NA
22 51219387 rs9616832 T C 0.0120467 0.0104665 0.2500000 0.2497417 0.071768 0.0654952 NA
22 51219704 rs147475742 G A 0.0136598 0.0140012 0.3300000 0.3292546 0.040972 0.0473243 NA
22 51221190 rs369304721 G A 0.0101244 0.0140213 0.4700002 0.4702497 0.048230 NA NA
22 51221731 rs115055839 T C 0.0114295 0.0104703 0.2700001 0.2750039 0.071361 0.0625000 NA
22 51222100 rs114553188 G T 0.0003564 0.0123126 0.9800000 0.9769061 0.053851 0.0880591 NA
22 51223637 rs375798137 G A 0.0007473 0.0123696 0.9500000 0.9518276 0.053469 0.0788738 NA
22 51229805 rs9616985 T C 0.0103466 0.0104984 0.3200000 0.3243586 0.071234 0.0730831 NA
22 51232488 rs376461333 A G 0.0132368 0.0252005 0.5999997 0.5994029 0.019256 NA NA
22 51237063 rs3896457 T C 0.0163486 0.0063519 0.0100000 0.0100585 0.297364 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62284  ES:SE:LP:AF:ID  -0.00174742:0.00957428:0.0655015:0.62284:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39987  ES:SE:LP:AF:ID  -0.00344651:0.00946153:0.142668:0.39987:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103129 ES:SE:LP:AF:ID  -0.0175432:0.015262:0.60206:0.103129:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456455 ES:SE:LP:AF:ID  -0.00723199:0.00935382:0.356547:0.456455:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074124 ES:SE:LP:AF:ID  -0.00145145:0.018464:0.0268721:0.074124:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24377  ES:SE:LP:AF:ID  -0.00251288:0.0105378:0.091515:0.24377:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914721 ES:SE:LP:AF:ID  0.00633834:0.0134801:0.19382:0.914721:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116318 ES:SE:LP:AF:ID  -0.00432739:0.0088978:0.200659:0.116318:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066382 ES:SE:LP:AF:ID  -0.015012:0.0132007:0.585027:0.066382:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513243 ES:SE:LP:AF:ID  0.00179317:0.00661402:0.102373:0.513243:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03359  ES:SE:LP:AF:ID  0.0161722:0.0165246:0.481486:0.03359:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037255 ES:SE:LP:AF:ID  0.0107864:0.0149975:0.327902:0.037255:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037316 ES:SE:LP:AF:ID  0.0115733:0.0149576:0.356547:0.037316:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037047 ES:SE:LP:AF:ID  0.0116326:0.0150537:0.356547:0.037047:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016589 ES:SE:LP:AF:ID  0.00285968:0.0232043:0.0457575:0.016589:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037617 ES:SE:LP:AF:ID  0.0125013:0.0148826:0.39794:0.037617:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037729 ES:SE:LP:AF:ID  0.011527:0.0148221:0.356547:0.037729:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.09968  ES:SE:LP:AF:ID  0.000134477:0.0109719:0.00436481:0.09968:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95822  ES:SE:LP:AF:ID  -0.00883494:0.0142426:0.267606:0.95822:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03083  ES:SE:LP:AF:ID  0.00203205:0.0268971:0.0268721:0.03083:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053284 ES:SE:LP:AF:ID  0.0185699:0.0209232:0.431798:0.053284:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037297 ES:SE:LP:AF:ID  0.0117534:0.0149194:0.366532:0.037297:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037531 ES:SE:LP:AF:ID  0.0134131:0.0148023:0.443698:0.037531:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842055 ES:SE:LP:AF:ID  0.000777098:0.00767769:0.0362122:0.842055:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.057092 ES:SE:LP:AF:ID  0.00490413:0.0124047:0.161151:0.057092:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123117 ES:SE:LP:AF:ID  -0.00169711:0.00840688:0.0757207:0.123117:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025831 ES:SE:LP:AF:ID  -0.0218378:0.0208842:0.522879:0.025831:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.1223   ES:SE:LP:AF:ID  -0.00237638:0.00841197:0.107905:0.1223:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132955 ES:SE:LP:AF:ID  -0.000613948:0.0083152:0.0268721:0.132955:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011672 ES:SE:LP:AF:ID  -0.00624223:0.0297771:0.0809219:0.011672:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037575 ES:SE:LP:AF:ID  0.0112002:0.014628:0.356547:0.037575:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83798  ES:SE:LP:AF:ID  -0.000756332:0.00745058:0.0362122:0.83798:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837577 ES:SE:LP:AF:ID  -0.000768531:0.0074395:0.0362122:0.837577:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869063 ES:SE:LP:AF:ID  0.00105844:0.00799194:0.05061:0.869063:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130791 ES:SE:LP:AF:ID  -0.000403613:0.00800011:0.0177288:0.130791:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038017 ES:SE:LP:AF:ID  0.0114464:0.014396:0.366532:0.038017:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03826  ES:SE:LP:AF:ID  0.0129073:0.0143123:0.431798:0.03826:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868443 ES:SE:LP:AF:ID  0.000767116:0.00797517:0.0362122:0.868443:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86853  ES:SE:LP:AF:ID  0.000966908:0.00797721:0.0457575:0.86853:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038224 ES:SE:LP:AF:ID  0.011459:0.0143625:0.376751:0.038224:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868456 ES:SE:LP:AF:ID  0.000798554:0.00797479:0.0362122:0.868456:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837065 ES:SE:LP:AF:ID  -0.00125893:0.00742279:0.0604807:0.837065:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038233 ES:SE:LP:AF:ID  0.0112968:0.0143835:0.366532:0.038233:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837728 ES:SE:LP:AF:ID  -0.00097737:0.00744445:0.0457575:0.837728:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013822 ES:SE:LP:AF:ID  0.0136942:0.0261855:0.221849:0.013822:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.83893  ES:SE:LP:AF:ID  -0.000839193:0.00754576:0.0409586:0.83893:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868677 ES:SE:LP:AF:ID  0.000771279:0.00796588:0.0362122:0.868677:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868201 ES:SE:LP:AF:ID  0.00101802:0.0079468:0.0457575:0.868201:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867151 ES:SE:LP:AF:ID  0.000406603:0.00793272:0.0177288:0.867151:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868405 ES:SE:LP:AF:ID  0.000992192:0.0079542:0.0457575:0.868405:rs4951929