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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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_3486.vcf.gz --id UKB-b:13936 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3486.txt.gz --cohort_controls 35910 --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-13936/UKB-b-13936_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13936/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:04 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13936/UKB-b-13936_data.vcf.gz ...
Read summary statistics for 7772000 SNPs.
Dropped 5682 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, 1280242 SNPs remain.
After merging with regression SNP LD, 1280242 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0376 (0.0137)
Lambda GC: 1.0481
Mean Chi^2: 1.0402
Intercept: 1.014 (0.0066)
Ratio: 0.3475 (0.1648)
Analysis finished at Thu Oct 17 14:44:36 2019
Total time elapsed: 1.0m:32.31s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9418,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0.0001,
    "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": 72417,
    "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": 1280242,
    "ldsc_nsnp_merge_regression_ld": 1280242,
    "ldsc_observed_scale_h2_beta": 0.0376,
    "ldsc_observed_scale_h2_se": 0.0137,
    "ldsc_intercept_beta": 1.014,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0481,
    "ldsc_mean_chisq": 1.0402,
    "ldsc_ratio": 0.3483
}

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 7766344 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 7772000 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 8.661895e+00 5.764280e+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 7.868784e+07 5.643378e+07 828.0000000 3.221924e+07 6.914463e+07 1.145571e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -6.400000e-05 1.228170e-02 -0.1206330 -5.967600e-03 -5.900000e-06 5.900300e-03 1.254200e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.059170e-02 6.050100e-03 0.0050196 5.814900e-03 7.942800e-03 1.379780e-02 6.195460e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.935803e-01 2.897666e-01 0.0000001 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.935804e-01 2.897404e-01 0.0000001 2.413168e-01 4.906045e-01 7.441509e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.520074e-01 2.608112e-01 0.0097470 4.000800e-02 1.458450e-01 3.986400e-01 9.902530e-01 ▇▂▂▁▁
numeric AF_reference 72417 0.9906823 NA NA NA NA NA NA NA 2.511168e-01 2.526773e-01 0.0000000 4.353040e-02 1.605430e-01 3.933710e-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 0.0088542 0.0093170 0.3400001 0.3419502 0.622758 0.7821490 NA
1 54676 rs2462492 C T 0.0070551 0.0092080 0.4400003 0.4435643 0.399989 NA NA
1 86028 rs114608975 T C -0.0085221 0.0148382 0.5700002 0.5657415 0.103209 0.0277556 NA
1 91536 rs6702460 G T 0.0137180 0.0090988 0.1299999 0.1316378 0.456432 0.4207270 NA
1 234313 rs8179466 C T -0.0053585 0.0179745 0.7700005 0.7656132 0.074085 NA NA
1 534192 rs6680723 C T -0.0141504 0.0102525 0.1700000 0.1675281 0.243806 NA NA
1 546697 rs12025928 A G -0.0185689 0.0131100 0.1600000 0.1566607 0.914684 NA NA
1 693731 rs12238997 A G -0.0109404 0.0086571 0.2099999 0.2063218 0.116303 0.1417730 NA
1 705882 rs72631875 G A 0.0030394 0.0128250 0.8100000 0.8126656 0.066509 0.0315495 NA
1 706368 rs55727773 A G 0.0094958 0.0064336 0.1400000 0.1399493 0.513340 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0020491 0.0078423 0.7899998 0.7938693 0.135231 0.2052720 NA
22 51219387 rs9616832 T C 0.0120513 0.0101840 0.2399999 0.2366680 0.071735 0.0654952 NA
22 51219704 rs147475742 G A 0.0062258 0.0136163 0.6499995 0.6475031 0.041003 0.0473243 NA
22 51221190 rs369304721 G A 0.0051384 0.0136453 0.7099994 0.7064959 0.048194 NA NA
22 51221731 rs115055839 T C 0.0115752 0.0101876 0.2599998 0.2558709 0.071327 0.0625000 NA
22 51222100 rs114553188 G T -0.0096661 0.0119864 0.4199997 0.4199989 0.053770 0.0880591 NA
22 51223637 rs375798137 G A -0.0088181 0.0120402 0.4600002 0.4639337 0.053398 0.0788738 NA
22 51229805 rs9616985 T C 0.0113326 0.0102149 0.2700001 0.2672496 0.071202 0.0730831 NA
22 51232488 rs376461333 A G -0.0123280 0.0245356 0.6200004 0.6153485 0.019225 NA NA
22 51237063 rs3896457 T C -0.0014072 0.0061815 0.8200001 0.8199263 0.297213 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622758 ES:SE:LP:AF:ID  0.00885417:0.00931705:0.468521:0.622758:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399989 ES:SE:LP:AF:ID  0.0070551:0.00920805:0.356547:0.399989:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103209 ES:SE:LP:AF:ID  -0.00852209:0.0148382:0.244125:0.103209:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456432 ES:SE:LP:AF:ID  0.013718:0.00909878:0.886057:0.456432:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074085 ES:SE:LP:AF:ID  -0.00535852:0.0179745:0.113509:0.074085:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.243806 ES:SE:LP:AF:ID  -0.0141504:0.0102525:0.769551:0.243806:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914684 ES:SE:LP:AF:ID  -0.0185689:0.01311:0.79588:0.914684:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116303 ES:SE:LP:AF:ID  -0.0109404:0.00865713:0.677781:0.116303:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066509 ES:SE:LP:AF:ID  0.00303938:0.012825:0.091515:0.066509:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51334  ES:SE:LP:AF:ID  0.00949582:0.00643357:0.853872:0.51334:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033613 ES:SE:LP:AF:ID  -0.0122196:0.0160715:0.346787:0.033613:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037272 ES:SE:LP:AF:ID  -0.00977475:0.0145897:0.30103:0.037272:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037333 ES:SE:LP:AF:ID  -0.0102442:0.0145511:0.318759:0.037333:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037064 ES:SE:LP:AF:ID  -0.00867869:0.0146443:0.259637:0.037064:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016607 ES:SE:LP:AF:ID  0.0175052:0.0225634:0.356547:0.016607:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037634 ES:SE:LP:AF:ID  -0.0108721:0.0144781:0.346787:0.037634:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037747 ES:SE:LP:AF:ID  -0.00998143:0.0144191:0.309804:0.037747:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099706 ES:SE:LP:AF:ID  -0.0179317:0.0106757:1.03152:0.099706:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958188 ES:SE:LP:AF:ID  0.00483603:0.0138538:0.136677:0.958188:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03081  ES:SE:LP:AF:ID  0.0738349:0.0261717:2.31876:0.03081:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053293 ES:SE:LP:AF:ID  0.0159149:0.020355:0.366532:0.053293:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037316 ES:SE:LP:AF:ID  -0.0112544:0.0145131:0.356547:0.037316:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037547 ES:SE:LP:AF:ID  -0.0108802:0.0144002:0.346787:0.037547:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842017 ES:SE:LP:AF:ID  0.010534:0.00746952:0.79588:0.842017:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.057159 ES:SE:LP:AF:ID  -0.00631335:0.0120603:0.221849:0.057159:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12312  ES:SE:LP:AF:ID  -0.0121052:0.00817924:0.853872:0.12312:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025837 ES:SE:LP:AF:ID  -0.01154:0.020313:0.244125:0.025837:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122318 ES:SE:LP:AF:ID  -0.0117664:0.0081838:0.823909:0.122318:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132962 ES:SE:LP:AF:ID  -0.0158693:0.0080908:1.30103:0.132962:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011665 ES:SE:LP:AF:ID  0.0448039:0.0289883:0.920819:0.011665:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037592 ES:SE:LP:AF:ID  -0.0105203:0.01423:0.337242:0.037592:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837934 ES:SE:LP:AF:ID  0.0108909:0.00724845:0.886057:0.837934:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837529 ES:SE:LP:AF:ID  0.0103024:0.00723764:0.823909:0.837529:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869018 ES:SE:LP:AF:ID  0.0108619:0.00777439:0.79588:0.869018:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130834 ES:SE:LP:AF:ID  -0.0108193:0.00778237:0.79588:0.130834:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038035 ES:SE:LP:AF:ID  -0.0101338:0.0140037:0.327902:0.038035:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038278 ES:SE:LP:AF:ID  -0.00935917:0.0139221:0.30103:0.038278:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868396 ES:SE:LP:AF:ID  0.0103155:0.00775806:0.744727:0.868396:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868485 ES:SE:LP:AF:ID  0.0104583:0.00776008:0.744727:0.868485:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038245 ES:SE:LP:AF:ID  -0.0101673:0.0139704:0.327902:0.038245:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868409 ES:SE:LP:AF:ID  0.0102679:0.00775769:0.721246:0.868409:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837016 ES:SE:LP:AF:ID  0.0104655:0.00722134:0.823909:0.837016:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038252 ES:SE:LP:AF:ID  -0.0101246:0.0139916:0.327902:0.038252:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837676 ES:SE:LP:AF:ID  0.0102293:0.0072423:0.79588:0.837676:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013834 ES:SE:LP:AF:ID  0.00121297:0.0254593:0.0177288:0.013834:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.838884 ES:SE:LP:AF:ID  0.00985893:0.00734093:0.744727:0.838884:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868628 ES:SE:LP:AF:ID  0.0107231:0.00774893:0.769551:0.868628:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868163 ES:SE:LP:AF:ID  0.0108138:0.0077305:0.79588:0.868163:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867111 ES:SE:LP:AF:ID  0.00997708:0.0077168:0.69897:0.867111:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868368 ES:SE:LP:AF:ID  0.0106317:0.00773774:0.769551:0.868368:rs4951929