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_41249_2002.vcf.gz --id UKB-b:12332 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41249_2002.txt.gz --cohort_cases 38496 --cohort_controls 424021 --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-12332/UKB-b-12332_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12332/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:36 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12332/UKB-b-12332_data.vcf.gz ...
Read summary statistics for 7868537 SNPs.
Dropped 5877 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, 1281070 SNPs remain.
After merging with regression SNP LD, 1281070 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0127 (0.0012)
Lambda GC: 1.1654
Mean Chi^2: 1.168
Intercept: 1.0534 (0.0071)
Ratio: 0.3177 (0.0422)
Analysis finished at Thu Oct 17 14:43:02 2019
Total time elapsed: 1.0m:25.35s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9422,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 1.5753e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 2,
    "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": 73362,
    "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": 1281070,
    "ldsc_nsnp_merge_regression_ld": 1281070,
    "ldsc_observed_scale_h2_beta": 0.0127,
    "ldsc_observed_scale_h2_se": 0.0012,
    "ldsc_intercept_beta": 1.0534,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.1654,
    "ldsc_mean_chisq": 1.168,
    "ldsc_ratio": 0.3179
}

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 7862687 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 7868537 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 8.661316e+00 5.763753e+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 7.870381e+07 5.642664e+07 828.0000000 3.224218e+07 6.917205e+07 1.145592e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.600000e-06 1.431300e-03 -0.0135861 -6.878000e-04 2.000000e-07 6.904000e-04 1.465070e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.185300e-03 6.926000e-04 0.0005539 6.393000e-04 8.805000e-04 1.551200e-03 6.817100e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.794359e-01 2.943279e-01 0.0000000 2.200002e-01 4.700002e-01 7.400005e-01 1.000000e+00 ▇▆▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.794383e-01 2.943028e-01 0.0000000 2.196515e-01 4.710923e-01 7.350075e-01 9.999994e-01 ▇▇▇▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.492088e-01 2.608073e-01 0.0090920 3.795800e-02 1.417180e-01 3.944210e-01 9.909080e-01 ▇▂▂▁▁
numeric AF_reference 73362 0.9906765 NA NA NA NA NA NA NA 2.483786e-01 2.526435e-01 0.0000000 4.073480e-02 1.567490e-01 3.893770e-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 -0.0005122 0.0010192 0.6200004 0.6152883 0.623760 0.7821490 NA
1 54676 rs2462492 C T 0.0000383 0.0010098 0.9699999 0.9697393 0.400405 NA NA
1 86028 rs114608975 T C 0.0023402 0.0016145 0.1499999 0.1471910 0.103553 0.0277556 NA
1 91536 rs6702460 G T -0.0005588 0.0009942 0.5700002 0.5741006 0.456864 0.4207270 NA
1 234313 rs8179466 C T 0.0039632 0.0019602 0.0430002 0.0431871 0.074511 NA NA
1 534192 rs6680723 C T -0.0003621 0.0011357 0.7499995 0.7498199 0.240945 NA NA
1 546697 rs12025928 A G -0.0001616 0.0014168 0.9100000 0.9092110 0.913474 NA NA
1 693731 rs12238997 A G -0.0009470 0.0009517 0.3200000 0.3197124 0.116326 0.1417730 NA
1 705882 rs72631875 G A -0.0003993 0.0013946 0.7700005 0.7746202 0.067299 0.0315495 NA
1 706368 rs55727773 A G 0.0000580 0.0007050 0.9299999 0.9343913 0.515656 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0001202 0.0008506 0.8900000 0.8876518 0.137944 0.2052720 NA
22 51219387 rs9616832 T C -0.0004852 0.0011042 0.6600001 0.6603617 0.073741 0.0654952 NA
22 51219704 rs147475742 G A -0.0011974 0.0014795 0.4199997 0.4183198 0.041960 0.0473243 NA
22 51221190 rs369304721 G A -0.0010760 0.0014771 0.4700002 0.4663468 0.049732 NA NA
22 51221731 rs115055839 T C -0.0003809 0.0011049 0.7300002 0.7303023 0.073231 0.0625000 NA
22 51222100 rs114553188 G T 0.0002745 0.0013007 0.8300000 0.8328552 0.054461 0.0880591 NA
22 51223637 rs375798137 G A 0.0001581 0.0013070 0.9000000 0.9037394 0.054089 0.0788738 NA
22 51229805 rs9616985 T C -0.0003957 0.0011089 0.7199992 0.7211777 0.073066 0.0730831 NA
22 51232488 rs376461333 A G -0.0022306 0.0026120 0.3900004 0.3931294 0.020044 NA NA
22 51237063 rs3896457 T C 0.0008122 0.0006782 0.2300001 0.2310631 0.297996 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62376  ES:SE:LP:AF:ID  -0.000512193:0.00101921:0.207608:0.62376:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400405 ES:SE:LP:AF:ID  3.83051e-05:0.00100975:0.0132283:0.400405:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103553 ES:SE:LP:AF:ID  0.0023402:0.00161446:0.823909:0.103553:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456864 ES:SE:LP:AF:ID  -0.000558782:0.000994234:0.244125:0.456864:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074511 ES:SE:LP:AF:ID  0.00396324:0.00196016:1.36653:0.074511:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240945 ES:SE:LP:AF:ID  -0.000362142:0.00113568:0.124939:0.240945:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913474 ES:SE:LP:AF:ID  -0.000161563:0.0014168:0.0409586:0.913474:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116326 ES:SE:LP:AF:ID  -0.000947013:0.000951725:0.49485:0.116326:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067299 ES:SE:LP:AF:ID  -0.000399311:0.00139455:0.113509:0.067299:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515656 ES:SE:LP:AF:ID  5.8036e-05:0.000704995:0.0315171:0.515656:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033005 ES:SE:LP:AF:ID  -0.000402678:0.00177732:0.0861861:0.033005:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03662  ES:SE:LP:AF:ID  3.40081e-05:0.00161443:0.00877392:0.03662:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036736 ES:SE:LP:AF:ID  -0.000200776:0.00160832:0.0457575:0.036736:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036436 ES:SE:LP:AF:ID  -1.71774e-05:0.0016199:0.00436481:0.036436:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016408 ES:SE:LP:AF:ID  -0.00212568:0.00249418:0.408935:0.016408:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036976 ES:SE:LP:AF:ID  -1.28323e-05:0.00160193:0.00436481:0.036976:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037073 ES:SE:LP:AF:ID  -0.000262675:0.00159645:0.0604807:0.037073:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101214 ES:SE:LP:AF:ID  0.00200034:0.00116313:1.07058:0.101214:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959097 ES:SE:LP:AF:ID  0.00082762:0.00153975:0.229148:0.959097:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031447 ES:SE:LP:AF:ID  0.00161825:0.00279544:0.251812:0.031447:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053252 ES:SE:LP:AF:ID  -0.00381684:0.0022239:1.0655:0.053252:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03659  ES:SE:LP:AF:ID  0.000118346:0.0016068:0.0268721:0.03659:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036907 ES:SE:LP:AF:ID  1.7884e-05:0.00159218:0.00436481:0.036907:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843214 ES:SE:LP:AF:ID  0.00106784:0.000824794:0.69897:0.843214:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055921 ES:SE:LP:AF:ID  -0.00116716:0.00133531:0.420216:0.055921:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122308 ES:SE:LP:AF:ID  -0.000983188:0.000902811:0.552842:0.122308:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025714 ES:SE:LP:AF:ID  0.00334526:0.00222056:0.886057:0.025714:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121551 ES:SE:LP:AF:ID  -0.0010134:0.000903188:0.585027:0.121551:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132331 ES:SE:LP:AF:ID  -0.000798314:0.000890161:0.431798:0.132331:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011137 ES:SE:LP:AF:ID  -0.00260614:0.00323615:0.376751:0.011137:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036821 ES:SE:LP:AF:ID  8.35224e-05:0.00157608:0.0177288:0.036821:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838956 ES:SE:LP:AF:ID  0.00101369:0.000798758:0.69897:0.838956:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838584 ES:SE:LP:AF:ID  0.000966594:0.000797898:0.638272:0.838584:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869782 ES:SE:LP:AF:ID  0.00127088:0.000856176:0.853872:0.869782:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129871 ES:SE:LP:AF:ID  -0.00123069:0.000857927:0.823909:0.129871:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037333 ES:SE:LP:AF:ID  -2.05179e-05:0.00154934:0.00436481:0.037333:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037576 ES:SE:LP:AF:ID  4.06046e-05:0.00153955:0.00877392:0.037576:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869123 ES:SE:LP:AF:ID  0.00131579:0.000854496:0.920819:0.869123:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869221 ES:SE:LP:AF:ID  0.00135458:0.000854836:0.958607:0.869221:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037534 ES:SE:LP:AF:ID  -0.00012953:0.00154621:0.0315171:0.037534:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869127 ES:SE:LP:AF:ID  0.00130331:0.000854481:0.886057:0.869127:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838037 ES:SE:LP:AF:ID  0.00106797:0.000795683:0.744727:0.838037:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037547 ES:SE:LP:AF:ID  -9.42694e-05:0.00154839:0.0222764:0.037547:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838668 ES:SE:LP:AF:ID  0.00106199:0.00079792:0.744727:0.838668:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013772 ES:SE:LP:AF:ID  -0.00295481:0.00278525:0.537602:0.013772:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839779 ES:SE:LP:AF:ID  0.00104779:0.000808709:0.69897:0.839779:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869406 ES:SE:LP:AF:ID  0.00129066:0.000853493:0.886057:0.869406:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868953 ES:SE:LP:AF:ID  0.00125952:0.000851345:0.853872:0.868953:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867906 ES:SE:LP:AF:ID  0.00125346:0.000849713:0.853872:0.867906:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869096 ES:SE:LP:AF:ID  0.0012561:0.000852042:0.853872:0.869096:rs4951929