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_5255.vcf.gz --id UKB-b:14146 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5255.txt.gz --cohort_controls 97653 --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-14146/UKB-b-14146_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14146/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14146/UKB-b-14146_data.vcf.gz ...
Read summary statistics for 9007739 SNPs.
Dropped 8738 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, 1287226 SNPs remain.
After merging with regression SNP LD, 1287226 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2092 (0.0107)
Lambda GC: 1.3127
Mean Chi^2: 1.4569
Intercept: 1.0564 (0.0106)
Ratio: 0.1234 (0.0233)
Analysis finished at Thu Oct 17 14:44:52 2019
Total time elapsed: 1.0m:36.68s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9479,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 93,
    "n_p_sig": 7286,
    "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": 93467,
    "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": 1287226,
    "ldsc_nsnp_merge_regression_ld": 1287226,
    "ldsc_observed_scale_h2_beta": 0.2092,
    "ldsc_observed_scale_h2_se": 0.0107,
    "ldsc_intercept_beta": 1.0564,
    "ldsc_intercept_se": 0.0106,
    "ldsc_lambda_gc": 1.3127,
    "ldsc_mean_chisq": 1.4569,
    "ldsc_ratio": 0.1234
}

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 TRUE
n_p_sig TRUE
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 8999041 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 9007739 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 8.643121e+00 5.758235e+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 7.878827e+07 5.634077e+07 828.0000000 3.243030e+07 6.934949e+07 1.145443e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.029000e-04 1.540970e-02 -0.2261750 -6.474900e-03 -9.260000e-05 6.275400e-03 1.835810e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.180160e-02 8.647400e-03 0.0042854 5.113200e-03 7.829600e-03 1.618850e-02 9.982490e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.668422e-01 2.978713e-01 0.0000000 2.000000e-01 4.600002e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.668433e-01 2.978485e-01 0.0000000 2.010616e-01 4.551727e-01 7.247012e-01 9.999996e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.202167e-01 2.585375e-01 0.0035850 2.051700e-02 1.015160e-01 3.472660e-01 9.964150e-01 ▇▂▁▁▁
numeric AF_reference 93467 0.9896237 NA NA NA NA NA NA NA 2.205272e-01 2.504438e-01 0.0000000 1.777160e-02 1.190100e-01 3.456470e-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 -0.0147923 0.0079114 0.0619998 0.0615206 0.623639 0.7821490 NA
1 54676 rs2462492 C T 0.0128864 0.0078610 0.1000000 0.1011557 0.398765 NA NA
1 86028 rs114608975 T C 0.0029547 0.0124679 0.8100000 0.8126694 0.104042 0.0277556 NA
1 91536 rs6702460 G T 0.0037978 0.0077258 0.6200004 0.6230180 0.455650 0.4207270 NA
1 234313 rs8179466 C T -0.0130181 0.0151261 0.3900004 0.3894373 0.074856 NA NA
1 534192 rs6680723 C T 0.0035738 0.0088472 0.6899999 0.6862559 0.240319 NA NA
1 546697 rs12025928 A G 0.0016939 0.0109534 0.8800001 0.8771025 0.912775 NA NA
1 693731 rs12238997 A G 0.0012552 0.0073541 0.8600001 0.8644716 0.117798 0.1417730 NA
1 705882 rs72631875 G A 0.0002647 0.0107590 0.9800000 0.9803700 0.067670 0.0315495 NA
1 706368 rs55727773 A G 0.0013682 0.0054554 0.8000000 0.8019672 0.514152 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0076835 0.0066120 0.2500000 0.2452107 0.137058 0.2052720 NA
22 51219387 rs9616832 T C 0.0056846 0.0086161 0.5099998 0.5094062 0.072551 0.0654952 NA
22 51219704 rs147475742 G A -0.0093782 0.0114722 0.4100001 0.4136564 0.041724 0.0473243 NA
22 51221190 rs369304721 G A 0.0045732 0.0115265 0.6899999 0.6915498 0.049105 NA NA
22 51221731 rs115055839 T C 0.0058204 0.0086180 0.5000000 0.4994337 0.072094 0.0625000 NA
22 51222100 rs114553188 G T 0.0105426 0.0100861 0.2999998 0.2959027 0.054410 0.0880591 NA
22 51223637 rs375798137 G A 0.0112174 0.0101405 0.2700001 0.2686409 0.054005 0.0788738 NA
22 51229805 rs9616985 T C 0.0063772 0.0086487 0.4600002 0.4609045 0.071952 0.0730831 NA
22 51232488 rs376461333 A G 0.0004826 0.0203733 0.9800000 0.9811017 0.020047 NA NA
22 51237063 rs3896457 T C 0.0003965 0.0052597 0.9400001 0.9399042 0.298272 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623639 ES:SE:LP:AF:ID  -0.0147923:0.00791144:1.20761:0.623639:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398765 ES:SE:LP:AF:ID  0.0128864:0.00786103:1:0.398765:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104042 ES:SE:LP:AF:ID  0.00295469:0.0124679:0.091515:0.104042:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45565  ES:SE:LP:AF:ID  0.00379783:0.0077258:0.207608:0.45565:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074856 ES:SE:LP:AF:ID  -0.0130181:0.0151261:0.408935:0.074856:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240319 ES:SE:LP:AF:ID  0.00357377:0.00884725:0.161151:0.240319:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912775 ES:SE:LP:AF:ID  0.00169387:0.0109534:0.0555173:0.912775:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117798 ES:SE:LP:AF:ID  0.00125523:0.00735408:0.0655015:0.117798:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06767  ES:SE:LP:AF:ID  0.000264726:0.010759:0.00877392:0.06767:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514152 ES:SE:LP:AF:ID  0.00136822:0.00545538:0.09691:0.514152:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033619 ES:SE:LP:AF:ID  0.0357513:0.0136146:2.0655:0.033619:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037258 ES:SE:LP:AF:ID  0.0342715:0.0123858:2.24413:0.037258:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03734  ES:SE:LP:AF:ID  0.0342599:0.0123448:2.25964:0.03734:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037031 ES:SE:LP:AF:ID  0.0337038:0.0124314:2.17393:0.037031:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016817 ES:SE:LP:AF:ID  -0.012124:0.0190958:0.275724:0.016817:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037595 ES:SE:LP:AF:ID  0.0345248:0.0122924:2.30103:0.037595:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037684 ES:SE:LP:AF:ID  0.033306:0.0122538:2.18046:0.037684:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101333 ES:SE:LP:AF:ID  0.0166941:0.00901964:1.19382:0.101333:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958113 ES:SE:LP:AF:ID  -0.0298318:0.0117899:1.95861:0.958113:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031747 ES:SE:LP:AF:ID  -0.0426066:0.0216091:1.3098:0.031747:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052584 ES:SE:LP:AF:ID  -0.00126826:0.0174002:0.0268721:0.052584:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037155 ES:SE:LP:AF:ID  0.0353088:0.0123388:2.37675:0.037155:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03747  ES:SE:LP:AF:ID  0.0335483:0.012234:2.21467:0.03747:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841023 ES:SE:LP:AF:ID  -0.00701437:0.00637159:0.568636:0.841023:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056091 ES:SE:LP:AF:ID  -0.0022958:0.0103742:0.0861861:0.056091:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123639 ES:SE:LP:AF:ID  0.000829024:0.00698393:0.0409586:0.123639:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025832 ES:SE:LP:AF:ID  0.00484409:0.0171622:0.107905:0.025832:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122835 ES:SE:LP:AF:ID  0.00130082:0.00698755:0.0705811:0.122835:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.1335   ES:SE:LP:AF:ID  0.0136331:0.00688993:1.31876:0.1335:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011218 ES:SE:LP:AF:ID  -0.00607978:0.0249599:0.091515:0.011218:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006027 ES:SE:LP:AF:ID  0.0292737:0.0313441:0.455932:0.006027:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037422 ES:SE:LP:AF:ID  0.0331403:0.0121023:2.20761:0.037422:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836824 ES:SE:LP:AF:ID  -0.00740862:0.00616391:0.638272:0.836824:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83637  ES:SE:LP:AF:ID  -0.00692806:0.00615647:0.585027:0.83637:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86807  ES:SE:LP:AF:ID  -0.00215727:0.00660463:0.130768:0.86807:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131612 ES:SE:LP:AF:ID  0.00167108:0.00661854:0.09691:0.131612:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037855 ES:SE:LP:AF:ID  0.033547:0.0119126:2.3098:0.037855:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038104 ES:SE:LP:AF:ID  0.0339207:0.0118382:2.37675:0.038104:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867362 ES:SE:LP:AF:ID  -0.00171484:0.00659103:0.102373:0.867362:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86747  ES:SE:LP:AF:ID  -0.00155827:0.00659426:0.091515:0.86747:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038052 ES:SE:LP:AF:ID  0.0328125:0.0118865:2.23657:0.038052:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86736  ES:SE:LP:AF:ID  -0.00186477:0.00659064:0.107905:0.86736:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005081 ES:SE:LP:AF:ID  -0.0016319:0.0341954:0.0177288:0.005081:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005048 ES:SE:LP:AF:ID  -0.00147676:0.0342883:0.0132283:0.005048:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835916 ES:SE:LP:AF:ID  -0.00754379:0.00614364:0.657577:0.835916:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038072 ES:SE:LP:AF:ID  0.0323896:0.0119021:2.18709:0.038072:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836522 ES:SE:LP:AF:ID  -0.00710326:0.00616022:0.60206:0.836522:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013098 ES:SE:LP:AF:ID  0.006782:0.0221729:0.119186:0.013098:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005542 ES:SE:LP:AF:ID  0.0334545:0.0332655:0.508638:0.005542:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837811 ES:SE:LP:AF:ID  -0.00733542:0.00624492:0.619789:0.837811:rs3131965