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_3839.vcf.gz --id UKB-b:419 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3839.txt.gz --cohort_controls 78700 --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-419/UKB-b-419_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-419/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:03 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-419/UKB-b-419_data.vcf.gz ...
Read summary statistics for 8827200 SNPs.
Dropped 8006 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, 1286581 SNPs remain.
After merging with regression SNP LD, 1286581 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.026 (0.0063)
Lambda GC: 1.051
Mean Chi^2: 1.0519
Intercept: 1.0113 (0.0065)
Ratio: 0.2185 (0.1257)
Analysis finished at Thu Oct 17 14:45:41 2019
Total time elapsed: 1.0m:38.38s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9471,
    "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": 87458,
    "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": 1286581,
    "ldsc_nsnp_merge_regression_ld": 1286581,
    "ldsc_observed_scale_h2_beta": 0.026,
    "ldsc_observed_scale_h2_se": 0.0063,
    "ldsc_intercept_beta": 1.0113,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.051,
    "ldsc_mean_chisq": 1.0519,
    "ldsc_ratio": 0.2177
}

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 8819231 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 8827200 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 8.648333e+00 5.760304e+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 7.877997e+07 5.635022e+07 828.0000000 3.241013e+07 6.933153e+07 1.145590e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -6.270000e-05 1.125680e-02 -0.1349490 -4.571300e-03 -1.610000e-05 4.536500e-03 1.197960e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.091700e-03 6.429200e-03 0.0034466 4.088400e-03 6.148500e-03 1.240220e-02 5.989720e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.932091e-01 2.905638e-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 4.932082e-01 2.905381e-01 0.0000002 2.398972e-01 4.906497e-01 7.450138e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.243041e-01 2.589907e-01 0.0044480 2.262000e-02 1.072610e-01 3.543330e-01 9.955520e-01 ▇▂▁▁▁
numeric AF_reference 87458 0.9900922 NA NA NA NA NA NA NA 2.243077e-01 2.509414e-01 0.0000000 2.016770e-02 1.242010e-01 3.520370e-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 -0.0022943 0.0063607 0.7199992 0.7183249 0.623069 0.7821490 NA
1 54676 rs2462492 C T 0.0065090 0.0062804 0.2999998 0.3000137 0.400432 NA NA
1 86028 rs114608975 T C 0.0025177 0.0100590 0.8000000 0.8023575 0.103822 0.0277556 NA
1 91536 rs6702460 G T -0.0046587 0.0061917 0.4500005 0.4518057 0.455881 0.4207270 NA
1 234313 rs8179466 C T -0.0152988 0.0122342 0.2099999 0.2111190 0.074391 NA NA
1 534192 rs6680723 C T 0.0056133 0.0070875 0.4299995 0.4283620 0.241205 NA NA
1 546697 rs12025928 A G 0.0056902 0.0088382 0.5199996 0.5196955 0.913342 NA NA
1 693731 rs12238997 A G 0.0022600 0.0058894 0.6999999 0.7011729 0.117594 0.1417730 NA
1 705882 rs72631875 G A -0.0078365 0.0087017 0.3700002 0.3678147 0.067189 0.0315495 NA
1 706368 rs55727773 A G 0.0040103 0.0043827 0.3599996 0.3601725 0.513249 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0016894 0.0053167 0.7499995 0.7506719 0.137476 0.2052720 NA
22 51219387 rs9616832 T C 0.0006217 0.0068940 0.9299999 0.9281435 0.073484 0.0654952 NA
22 51219704 rs147475742 G A 0.0017048 0.0092474 0.8499999 0.8537354 0.041648 0.0473243 NA
22 51221190 rs369304721 G A 0.0021784 0.0092628 0.8100000 0.8140676 0.049188 NA NA
22 51221731 rs115055839 T C 0.0003794 0.0068976 0.9599999 0.9561320 0.072996 0.0625000 NA
22 51222100 rs114553188 G T 0.0033954 0.0081184 0.6800001 0.6757788 0.054342 0.0880591 NA
22 51223637 rs375798137 G A 0.0037314 0.0081559 0.6499995 0.6473077 0.053971 0.0788738 NA
22 51229805 rs9616985 T C 0.0002980 0.0069204 0.9699999 0.9656507 0.072913 0.0730831 NA
22 51232488 rs376461333 A G 0.0057206 0.0162235 0.7199992 0.7243812 0.020188 NA NA
22 51237063 rs3896457 T C -0.0012530 0.0042202 0.7700005 0.7665437 0.296595 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623069 ES:SE:LP:AF:ID  -0.00229428:0.00636066:0.142668:0.623069:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400432 ES:SE:LP:AF:ID  0.00650898:0.00628035:0.522879:0.400432:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103822 ES:SE:LP:AF:ID  0.00251774:0.010059:0.09691:0.103822:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455881 ES:SE:LP:AF:ID  -0.00465867:0.00619168:0.346787:0.455881:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074391 ES:SE:LP:AF:ID  -0.0152988:0.0122342:0.677781:0.074391:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241205 ES:SE:LP:AF:ID  0.00561326:0.00708747:0.366532:0.241205:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913342 ES:SE:LP:AF:ID  0.00569019:0.00883824:0.283997:0.913342:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117594 ES:SE:LP:AF:ID  0.00226:0.00588944:0.154902:0.117594:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067189 ES:SE:LP:AF:ID  -0.00783651:0.00870168:0.431798:0.067189:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513249 ES:SE:LP:AF:ID  0.00401033:0.0043827:0.443698:0.513249:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033104 ES:SE:LP:AF:ID  0.00490945:0.0110377:0.180456:0.033104:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036703 ES:SE:LP:AF:ID  0.00552061:0.0100379:0.236572:0.036703:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036764 ES:SE:LP:AF:ID  0.00619362:0.0100089:0.267606:0.036764:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036445 ES:SE:LP:AF:ID  0.00481728:0.0100842:0.200659:0.036445:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016447 ES:SE:LP:AF:ID  -0.0335859:0.0155137:1.52288:0.016447:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036979 ES:SE:LP:AF:ID  0.00600941:0.00997519:0.259637:0.036979:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037059 ES:SE:LP:AF:ID  0.00520996:0.00994524:0.221849:0.037059:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100825 ES:SE:LP:AF:ID  0.0047923:0.00725638:0.29243:0.100825:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958838 ES:SE:LP:AF:ID  -0.00534781:0.0095495:0.236572:0.958838:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03125  ES:SE:LP:AF:ID  0.00202241:0.017403:0.0409586:0.03125:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053403 ES:SE:LP:AF:ID  0.00364629:0.0138244:0.102373:0.053403:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036568 ES:SE:LP:AF:ID  0.00547127:0.0100064:0.236572:0.036568:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03694  ES:SE:LP:AF:ID  0.00577813:0.00991292:0.251812:0.03694:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841727 ES:SE:LP:AF:ID  -0.00342057:0.00511512:0.30103:0.841727:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055923 ES:SE:LP:AF:ID  0.00925216:0.00829593:0.585027:0.055923:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123605 ES:SE:LP:AF:ID  0.00120053:0.00558842:0.0809219:0.123605:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025329 ES:SE:LP:AF:ID  0.00210914:0.0139427:0.0555173:0.025329:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122789 ES:SE:LP:AF:ID  0.0014175:0.00559212:0.09691:0.122789:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133462 ES:SE:LP:AF:ID  0.00786338:0.00551844:0.823909:0.133462:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010896 ES:SE:LP:AF:ID  0.0177558:0.0204729:0.408935:0.010896:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006021 ES:SE:LP:AF:ID  -0.0365855:0.0250262:0.853872:0.006021:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036879 ES:SE:LP:AF:ID  0.00570662:0.00980554:0.251812:0.036879:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837463 ES:SE:LP:AF:ID  -0.00243294:0.00494785:0.207608:0.837463:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836959 ES:SE:LP:AF:ID  -0.00260492:0.00494051:0.221849:0.836959:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868264 ES:SE:LP:AF:ID  0.000356603:0.00529532:0.0222764:0.868264:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131449 ES:SE:LP:AF:ID  -7.49525e-05:0.00530509:0.00436481:0.131449:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037262 ES:SE:LP:AF:ID  0.00521576:0.00965533:0.229148:0.037262:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037504 ES:SE:LP:AF:ID  0.00483218:0.00959589:0.21467:0.037504:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867489 ES:SE:LP:AF:ID  0.000165133:0.00528287:0.00877392:0.867489:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86758  ES:SE:LP:AF:ID  0.000365035:0.00528559:0.0268721:0.86758:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037451 ES:SE:LP:AF:ID  0.00583745:0.00963712:0.267606:0.037451:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867491 ES:SE:LP:AF:ID  0.000145132:0.00528292:0.00877392:0.867491:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005179 ES:SE:LP:AF:ID  -0.0241231:0.0272674:0.420216:0.005179:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005143 ES:SE:LP:AF:ID  -0.0245195:0.0273401:0.431798:0.005143:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836451 ES:SE:LP:AF:ID  -0.00281532:0.00492768:0.244125:0.836451:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03747  ES:SE:LP:AF:ID  0.00630742:0.00964926:0.29243:0.03747:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837077 ES:SE:LP:AF:ID  -0.00276398:0.00494134:0.236572:0.837077:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013598 ES:SE:LP:AF:ID  -0.00234292:0.0173987:0.05061:0.013598:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005878 ES:SE:LP:AF:ID  0.0120037:0.0260983:0.187087:0.005878:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838293 ES:SE:LP:AF:ID  -0.00138775:0.00500757:0.107905:0.838293:rs3131965