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_20003_1140864752.vcf.gz --id UKB-b:19156 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140864752.txt.gz --cohort_cases 16448 --cohort_controls 446485 --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-19156/UKB-b-19156_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19156/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:01 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19156/UKB-b-19156_data.vcf.gz ...
Read summary statistics for 6655368 SNPs.
Dropped 3779 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, 1250817 SNPs remain.
After merging with regression SNP LD, 1250817 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0109 (0.0012)
Lambda GC: 1.1236
Mean Chi^2: 1.1293
Intercept: 1.0305 (0.0069)
Ratio: 0.2361 (0.0535)
Analysis finished at Thu Oct 17 14:42:18 2019
Total time elapsed: 1.0m:17.29s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9325,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 2.5252e-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": 61109,
    "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": 1250817,
    "ldsc_nsnp_merge_regression_ld": 1250817,
    "ldsc_observed_scale_h2_beta": 0.0109,
    "ldsc_observed_scale_h2_se": 0.0012,
    "ldsc_intercept_beta": 1.0305,
    "ldsc_intercept_se": 0.0069,
    "ldsc_lambda_gc": 1.1236,
    "ldsc_mean_chisq": 1.1293,
    "ldsc_ratio": 0.2359
}

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 6651611 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 6655368 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 8.664150e+00 5.763800e+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 7.859961e+07 5.648079e+07 828.0000000 3.206582e+07 6.904866e+07 1.144971e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.500000e-06 7.149000e-04 -0.0061693 -3.994000e-04 2.500000e-06 4.047000e-04 6.470900e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.298000e-04 2.643000e-04 0.0003712 4.159000e-04 5.215000e-04 7.787000e-04 3.217900e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.816342e-01 2.936102e-01 0.0000002 2.200002e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.816356e-01 2.935854e-01 0.0000002 2.227347e-01 4.753946e-01 7.353948e-01 9.999999e-01 ▇▇▇▇▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.875935e-01 2.582740e-01 0.0212800 7.099200e-02 1.960820e-01 4.479400e-01 9.787190e-01 ▇▃▂▂▁
numeric AF_reference 61109 0.9908181 NA NA NA NA NA NA NA 2.853816e-01 2.505188e-01 0.0000000 8.007190e-02 2.064700e-01 4.400960e-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 -0.0011139 0.0006831 0.1000000 0.1029590 0.623763 0.7821490 NA
1 54676 rs2462492 C T -0.0001752 0.0006767 0.8000000 0.7957028 0.400401 NA NA
1 86028 rs114608975 T C 0.0027132 0.0010820 0.0120000 0.0121562 0.103556 0.0277556 NA
1 91536 rs6702460 G T -0.0004274 0.0006664 0.5199996 0.5212867 0.456851 0.4207270 NA
1 234313 rs8179466 C T -0.0001497 0.0013139 0.9100000 0.9092988 0.074508 NA NA
1 534192 rs6680723 C T -0.0004239 0.0007611 0.5800000 0.5775780 0.240960 NA NA
1 546697 rs12025928 A G 0.0010035 0.0009496 0.2900000 0.2906010 0.913473 NA NA
1 693731 rs12238997 A G -0.0000906 0.0006379 0.8900000 0.8870600 0.116325 0.1417730 NA
1 705882 rs72631875 G A -0.0020135 0.0009347 0.0309999 0.0312321 0.067285 0.0315495 NA
1 706368 rs55727773 A G -0.0000480 0.0004725 0.9199999 0.9191535 0.515650 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0008124 0.0007384 0.2700001 0.2712607 0.073625 0.0826677 NA
22 51219006 rs28729663 G A 0.0002641 0.0005700 0.6400000 0.6431688 0.137953 0.2052720 NA
22 51219387 rs9616832 T C 0.0008523 0.0007399 0.2500000 0.2493725 0.073747 0.0654952 NA
22 51219704 rs147475742 G A 0.0016907 0.0009915 0.0879995 0.0881675 0.041955 0.0473243 NA
22 51221190 rs369304721 G A 0.0013314 0.0009899 0.1800002 0.1786332 0.049731 NA NA
22 51221731 rs115055839 T C 0.0008440 0.0007404 0.2500000 0.2543128 0.073238 0.0625000 NA
22 51222100 rs114553188 G T -0.0006061 0.0008717 0.4899999 0.4868308 0.054459 0.0880591 NA
22 51223637 rs375798137 G A -0.0006142 0.0008759 0.4799997 0.4831301 0.054088 0.0788738 NA
22 51229805 rs9616985 T C 0.0008502 0.0007431 0.2500000 0.2525476 0.073073 0.0730831 NA
22 51237063 rs3896457 T C 0.0003947 0.0004545 0.3900004 0.3851154 0.297971 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  -0.00111393:0.00068311:1:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  -0.000175218:0.000676746:0.09691:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  0.00271315:0.00108198:1.92082:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  -0.000427373:0.000666351:0.283997:0.456851:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074508 ES:SE:LP:AF:ID  -0.000149679:0.00131386:0.0409586:0.074508:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  -0.000423897:0.000761137:0.236572:0.24096:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913473 ES:SE:LP:AF:ID  0.0010035:0.000949561:0.537602:0.913473:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116325 ES:SE:LP:AF:ID  -9.0594e-05:0.000637872:0.05061:0.116325:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067285 ES:SE:LP:AF:ID  -0.0020135:0.000934728:1.50864:0.067285:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  -4.79593e-05:0.000472505:0.0362122:0.51565:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033004 ES:SE:LP:AF:ID  -0.000213782:0.00119122:0.0655015:0.033004:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036621 ES:SE:LP:AF:ID  -0.000295934:0.00108201:0.107905:0.036621:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036737 ES:SE:LP:AF:ID  -0.000209904:0.00107791:0.0705811:0.036737:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036437 ES:SE:LP:AF:ID  -0.000311022:0.00108568:0.113509:0.036437:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036976 ES:SE:LP:AF:ID  -0.000308477:0.00107366:0.113509:0.036976:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037072 ES:SE:LP:AF:ID  -0.000306274:0.00106998:0.113509:0.037072:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101199 ES:SE:LP:AF:ID  0.000694258:0.000779603:0.431798:0.101199:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959096 ES:SE:LP:AF:ID  0.000391794:0.00103197:0.154902:0.959096:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031451 ES:SE:LP:AF:ID  0.000934568:0.00187333:0.207608:0.031451:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053254 ES:SE:LP:AF:ID  -0.000468827:0.00149032:0.124939:0.053254:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03659  ES:SE:LP:AF:ID  -0.000270836:0.0010769:0.09691:0.03659:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036907 ES:SE:LP:AF:ID  -0.000454768:0.0010671:0.173925:0.036907:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843212 ES:SE:LP:AF:ID  4.92706e-05:0.000552797:0.0315171:0.843212:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055911 ES:SE:LP:AF:ID  -0.000792918:0.000895065:0.420216:0.055911:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122307 ES:SE:LP:AF:ID  3.95472e-05:0.000605085:0.0222764:0.122307:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025713 ES:SE:LP:AF:ID  -0.00205292:0.00148838:0.769551:0.025713:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121549 ES:SE:LP:AF:ID  7.80235e-06:0.000605339:0.00436481:0.121549:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13233  ES:SE:LP:AF:ID  -0.000419456:0.000596621:0.318759:0.13233:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036821 ES:SE:LP:AF:ID  -0.000492799:0.00105631:0.19382:0.036821:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838951 ES:SE:LP:AF:ID  5.49369e-05:0.000535345:0.0362122:0.838951:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83858  ES:SE:LP:AF:ID  5.88755e-05:0.000534769:0.0409586:0.83858:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869781 ES:SE:LP:AF:ID  -8.79253e-05:0.000573828:0.0555173:0.869781:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129871 ES:SE:LP:AF:ID  0.000139708:0.000575003:0.091515:0.129871:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037332 ES:SE:LP:AF:ID  -0.000415802:0.0010384:0.161151:0.037332:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037576 ES:SE:LP:AF:ID  -0.000391391:0.00103184:0.154902:0.037576:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869123 ES:SE:LP:AF:ID  -7.81944e-05:0.000572705:0.05061:0.869123:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869221 ES:SE:LP:AF:ID  -8.50861e-05:0.000572932:0.0555173:0.869221:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037534 ES:SE:LP:AF:ID  -0.00042344:0.0010363:0.167491:0.037534:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869126 ES:SE:LP:AF:ID  -0.000101275:0.000572693:0.0655015:0.869126:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838033 ES:SE:LP:AF:ID  5.81806e-05:0.000533285:0.0409586:0.838033:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037547 ES:SE:LP:AF:ID  -0.000457762:0.00103777:0.180456:0.037547:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838664 ES:SE:LP:AF:ID  5.9835e-05:0.000534783:0.0409586:0.838664:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839777 ES:SE:LP:AF:ID  6.32428e-05:0.000542016:0.0409586:0.839777:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869405 ES:SE:LP:AF:ID  -0.000110212:0.000572031:0.0705811:0.869405:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868952 ES:SE:LP:AF:ID  -0.0001295:0.000570592:0.0861861:0.868952:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867905 ES:SE:LP:AF:ID  -0.000113643:0.000569498:0.0757207:0.867905:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869095 ES:SE:LP:AF:ID  -0.000111258:0.000571059:0.0705811:0.869095:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869104 ES:SE:LP:AF:ID  -0.00010935:0.000571103:0.0705811:0.869104:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869112 ES:SE:LP:AF:ID  -0.000111307:0.000571116:0.0705811:0.869112:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869589 ES:SE:LP:AF:ID  -0.00011669:0.000572684:0.0757207:0.869589:rs3131954