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_5254.vcf.gz --id UKB-b:19277 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5254.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-19277/UKB-b-19277_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19277/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:08 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19277/UKB-b-19277_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: 0.1495 (0.009)
Lambda GC: 1.237
Mean Chi^2: 1.3232
Intercept: 1.0379 (0.0096)
Ratio: 0.1171 (0.0299)
Analysis finished at Thu Oct 17 14:42:45 2019
Total time elapsed: 1.0m:36.97s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9479,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 53,
    "n_p_sig": 2363,
    "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": 0.1495,
    "ldsc_observed_scale_h2_se": 0.009,
    "ldsc_intercept_beta": 1.0379,
    "ldsc_intercept_se": 0.0096,
    "ldsc_lambda_gc": 1.237,
    "ldsc_mean_chisq": 1.3232,
    "ldsc_ratio": 0.1173
}

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 -7.340000e-05 1.533570e-02 -0.1647700 -6.352700e-03 -7.370000e-05 6.180300e-03 1.879340e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.188580e-02 8.709100e-03 0.0043187 5.149600e-03 7.885400e-03 1.630370e-02 1.005560e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.733686e-01 2.960877e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.733686e-01 2.960632e-01 0.0000000 2.109484e-01 4.644206e-01 7.296178e-01 9.999999e-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 -0.0244464 0.0079716 0.0022000 0.0021646 0.623639 0.7821490 NA
1 54676 rs2462492 C T 0.0144062 0.0079208 0.0690001 0.0689463 0.398765 NA NA
1 86028 rs114608975 T C -0.0088733 0.0125628 0.4799997 0.4799900 0.104042 0.0277556 NA
1 91536 rs6702460 G T -0.0013041 0.0077846 0.8700001 0.8669561 0.455650 0.4207270 NA
1 234313 rs8179466 C T -0.0069912 0.0152411 0.6499995 0.6464427 0.074856 NA NA
1 534192 rs6680723 C T 0.0070594 0.0089146 0.4299995 0.4284208 0.240319 NA NA
1 546697 rs12025928 A G 0.0029508 0.0110367 0.7899998 0.7891907 0.912775 NA NA
1 693731 rs12238997 A G -0.0015179 0.0074100 0.8400000 0.8376972 0.117798 0.1417730 NA
1 705882 rs72631875 G A 0.0096661 0.0108409 0.3700002 0.3725889 0.067670 0.0315495 NA
1 706368 rs55727773 A G 0.0027284 0.0054969 0.6200004 0.6196400 0.514152 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0099976 0.0066550 0.1299999 0.1330296 0.137058 0.2052720 NA
22 51219387 rs9616832 T C 0.0104425 0.0086722 0.2300001 0.2285390 0.072551 0.0654952 NA
22 51219704 rs147475742 G A 0.0000110 0.0115469 1.0000000 0.9992396 0.041724 0.0473243 NA
22 51221190 rs369304721 G A 0.0132206 0.0116016 0.2500000 0.2544739 0.049105 NA NA
22 51221731 rs115055839 T C 0.0103876 0.0086741 0.2300001 0.2310969 0.072094 0.0625000 NA
22 51222100 rs114553188 G T 0.0085454 0.0101518 0.4000000 0.3999223 0.054410 0.0880591 NA
22 51223637 rs375798137 G A 0.0093269 0.0102066 0.3599996 0.3608143 0.054005 0.0788738 NA
22 51229805 rs9616985 T C 0.0113411 0.0087051 0.1900002 0.1926388 0.071952 0.0730831 NA
22 51232488 rs376461333 A G 0.0087266 0.0205061 0.6700003 0.6704262 0.020047 NA NA
22 51237063 rs3896457 T C 0.0012108 0.0052939 0.8200001 0.8190924 0.298272 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623639 ES:SE:LP:AF:ID  -0.0244464:0.00797164:2.65758:0.623639:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398765 ES:SE:LP:AF:ID  0.0144062:0.00792084:1.16115:0.398765:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104042 ES:SE:LP:AF:ID  -0.00887334:0.0125628:0.318759:0.104042:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45565  ES:SE:LP:AF:ID  -0.00130412:0.00778458:0.0604807:0.45565:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074856 ES:SE:LP:AF:ID  -0.00699124:0.0152411:0.187087:0.074856:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240319 ES:SE:LP:AF:ID  0.00705942:0.00891457:0.366532:0.240319:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912775 ES:SE:LP:AF:ID  0.00295079:0.0110367:0.102373:0.912775:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117798 ES:SE:LP:AF:ID  -0.00151787:0.00741003:0.0757207:0.117798:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06767  ES:SE:LP:AF:ID  0.00966612:0.0108409:0.431798:0.06767:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514152 ES:SE:LP:AF:ID  0.00272844:0.00549689:0.207608:0.514152:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033619 ES:SE:LP:AF:ID  0.0121576:0.0137182:0.420216:0.033619:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037258 ES:SE:LP:AF:ID  0.0143107:0.01248:0.60206:0.037258:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03734  ES:SE:LP:AF:ID  0.0138193:0.0124388:0.568636:0.03734:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037031 ES:SE:LP:AF:ID  0.013607:0.012526:0.552842:0.037031:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016817 ES:SE:LP:AF:ID  0.00282305:0.0192411:0.0555173:0.016817:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037595 ES:SE:LP:AF:ID  0.0138015:0.0123859:0.568636:0.037595:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037684 ES:SE:LP:AF:ID  0.0126641:0.0123471:0.508638:0.037684:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101333 ES:SE:LP:AF:ID  0.0234903:0.00908826:2.01323:0.101333:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958113 ES:SE:LP:AF:ID  -0.0130339:0.0118796:0.568636:0.958113:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031747 ES:SE:LP:AF:ID  -0.051684:0.0217736:1.74473:0.031747:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052584 ES:SE:LP:AF:ID  0.00366874:0.0175326:0.0809219:0.052584:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037155 ES:SE:LP:AF:ID  0.0159006:0.0124327:0.69897:0.037155:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03747  ES:SE:LP:AF:ID  0.0149396:0.0123271:0.638272:0.03747:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841023 ES:SE:LP:AF:ID  -0.000176039:0.00642007:0.00877392:0.841023:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056091 ES:SE:LP:AF:ID  -0.00217669:0.0104532:0.0757207:0.056091:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123639 ES:SE:LP:AF:ID  -0.00284589:0.00703707:0.161151:0.123639:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025832 ES:SE:LP:AF:ID  0.0181295:0.0172928:0.537602:0.025832:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122835 ES:SE:LP:AF:ID  -0.00242209:0.00704072:0.136677:0.122835:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.1335   ES:SE:LP:AF:ID  0.00371778:0.00694235:0.229148:0.1335:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011218 ES:SE:LP:AF:ID  0.00203587:0.0251498:0.0268721:0.011218:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006027 ES:SE:LP:AF:ID  -0.0111538:0.0315826:0.142668:0.006027:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037422 ES:SE:LP:AF:ID  0.0130293:0.0121944:0.537602:0.037422:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836824 ES:SE:LP:AF:ID  -0.00211709:0.00621081:0.136677:0.836824:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83637  ES:SE:LP:AF:ID  -0.00188748:0.00620331:0.119186:0.83637:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86807  ES:SE:LP:AF:ID  -0.000129166:0.00665488:0.00877392:0.86807:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131612 ES:SE:LP:AF:ID  -0.00102794:0.00666889:0.0555173:0.131612:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037855 ES:SE:LP:AF:ID  0.0147484:0.0120032:0.657577:0.037855:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038104 ES:SE:LP:AF:ID  0.0148091:0.0119282:0.677781:0.038104:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867362 ES:SE:LP:AF:ID  0.000192742:0.00664117:0.00877392:0.867362:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86747  ES:SE:LP:AF:ID  0.000347569:0.00664443:0.0177288:0.86747:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038052 ES:SE:LP:AF:ID  0.013985:0.011977:0.619789:0.038052:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86736  ES:SE:LP:AF:ID  -3.00685e-05:0.00664078:-0:0.86736:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005081 ES:SE:LP:AF:ID  0.0225383:0.0344556:0.29243:0.005081:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005048 ES:SE:LP:AF:ID  0.023691:0.0345492:0.309804:0.005048:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835916 ES:SE:LP:AF:ID  -0.00232206:0.00619038:0.148742:0.835916:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038072 ES:SE:LP:AF:ID  0.0133457:0.0119927:0.568636:0.038072:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836522 ES:SE:LP:AF:ID  -0.00212629:0.00620708:0.136677:0.836522:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013098 ES:SE:LP:AF:ID  -0.0122497:0.0223416:0.236572:0.013098:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005542 ES:SE:LP:AF:ID  -0.00484628:0.0335186:0.05061:0.005542:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837811 ES:SE:LP:AF:ID  -0.00224589:0.00629243:0.142668:0.837811:rs3131965