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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}

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-5620/UKB-b-5620_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5620/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:31 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5620/UKB-b-5620_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0178 (0.0019)
Lambda GC: 1.3482
Mean Chi^2: 1.3668
Intercept: 1.2061 (0.009)
Ratio: 0.5618 (0.0245)
Analysis finished at Thu Oct 17 14:46:59 2019
Total time elapsed: 1.0m:27.55s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.3107,
    "mean_EFFECT": 0.0004,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 7,
    "n_p_sig": 106,
    "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": 184849,
    "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": 1289166,
    "ldsc_nsnp_merge_regression_ld": 1289166,
    "ldsc_observed_scale_h2_beta": 0.0178,
    "ldsc_observed_scale_h2_se": 0.0019,
    "ldsc_intercept_beta": 1.2061,
    "ldsc_intercept_se": 0.009,
    "ldsc_lambda_gc": 1.3482,
    "ldsc_mean_chisq": 1.3668,
    "ldsc_ratio": 0.5619
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
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 FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio TRUE
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 9837196 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 9851866 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 8.622825e+00 5.748290e+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 7.886027e+07 5.628334e+07 828.0000000 3.259061e+07 6.948835e+07 1.145912e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.904000e-04 1.077350e-02 -0.1497650 -3.135900e-03 8.630000e-05 3.440600e-03 1.773410e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.993800e-03 6.620600e-03 0.0019573 2.395600e-03 4.017100e-03 9.269000e-03 1.029150e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.575007e-01 2.987550e-01 0.0000000 1.900002e-01 4.400003e-01 7.199992e-01 1.000000e+00 ▇▆▆▅▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.575035e-01 2.987303e-01 0.0000000 1.887419e-01 4.407693e-01 7.161502e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035075e-01 2.568621e-01 0.0009790 1.316800e-02 7.791200e-02 3.164550e-01 9.990120e-01 ▇▂▁▁▁
numeric AF_reference 184849 0.9812372 NA NA NA NA NA NA NA 2.068392e-01 2.482924e-01 0.0000000 1.198080e-02 9.984030e-02 3.202880e-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 -0.0029614 0.0035991 0.4100001 0.4106093 0.623756 0.7821490 NA
1 54676 rs2462492 C T 0.0040265 0.0035664 0.2599998 0.2588861 0.400407 NA NA
1 86028 rs114608975 T C 0.0037867 0.0057022 0.5099998 0.5066414 0.103555 0.0277556 NA
1 91536 rs6702460 G T 0.0009843 0.0035108 0.7800007 0.7792095 0.456805 0.4207270 NA
1 234313 rs8179466 C T 0.0008736 0.0069201 0.9000000 0.8995462 0.074538 NA NA
1 534192 rs6680723 C T -0.0002663 0.0040100 0.9500000 0.9470531 0.240997 NA NA
1 546697 rs12025928 A G 0.0066816 0.0050052 0.1800002 0.1818991 0.913520 NA NA
1 693731 rs12238997 A G 0.0052461 0.0033598 0.1199999 0.1184287 0.116381 0.1417730 NA
1 705882 rs72631875 G A 0.0011146 0.0049278 0.8200001 0.8210619 0.067232 0.0315495 NA
1 706368 rs55727773 A G -0.0051727 0.0024901 0.0379997 0.0377775 0.515569 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0030156 0.0052318 0.5600000 0.5643463 0.041920 0.0473243 NA
22 51219766 rs182321900 C T 0.0036908 0.0243925 0.8800001 0.8797324 0.001934 NA NA
22 51220146 rs868950473 C T 0.0041586 0.0241683 0.8600001 0.8633835 0.001981 NA NA
22 51221190 rs369304721 G A 0.0018643 0.0052240 0.7199992 0.7211881 0.049688 NA NA
22 51221731 rs115055839 T C 0.0025378 0.0039058 0.5199996 0.5158577 0.073200 0.0625000 NA
22 51222100 rs114553188 G T 0.0004477 0.0045972 0.9199999 0.9224228 0.054461 0.0880591 NA
22 51223637 rs375798137 G A 0.0002726 0.0046194 0.9500000 0.9529436 0.054091 0.0788738 NA
22 51229805 rs9616985 T C 0.0024089 0.0039199 0.5400003 0.5388727 0.073035 0.0730831 NA
22 51232488 rs376461333 A G -0.0050390 0.0092323 0.5900000 0.5851980 0.020045 NA NA
22 51237063 rs3896457 T C 0.0005619 0.0023967 0.8100000 0.8146479 0.297983 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623756 ES:SE:LP:AF:ID  -0.00296141:0.00359909:0.387216:0.623756:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400407 ES:SE:LP:AF:ID  0.00402652:0.00356636:0.585027:0.400407:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103555 ES:SE:LP:AF:ID  0.00378671:0.00570222:0.29243:0.103555:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456805 ES:SE:LP:AF:ID  0.000984259:0.00351083:0.107905:0.456805:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074538 ES:SE:LP:AF:ID  0.000873561:0.00692014:0.0457575:0.074538:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240997 ES:SE:LP:AF:ID  -0.000266298:0.00401004:0.0222764:0.240997:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91352  ES:SE:LP:AF:ID  0.00668162:0.00500522:0.744727:0.91352:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116381 ES:SE:LP:AF:ID  0.00524608:0.00335985:0.920819:0.116381:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067232 ES:SE:LP:AF:ID  0.00111456:0.00492777:0.0861861:0.067232:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515569 ES:SE:LP:AF:ID  -0.00517267:0.00249014:1.42022:0.515569:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033    ES:SE:LP:AF:ID  0.00333845:0.00627691:0.229148:0.033:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036611 ES:SE:LP:AF:ID  0.00187661:0.00570179:0.130768:0.036611:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036726 ES:SE:LP:AF:ID  0.00159345:0.00568027:0.107905:0.036726:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036422 ES:SE:LP:AF:ID  0.000876985:0.00572149:0.0555173:0.036422:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01643  ES:SE:LP:AF:ID  -0.00157827:0.00880029:0.0655015:0.01643:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036964 ES:SE:LP:AF:ID  0.00117193:0.00565784:0.0757207:0.036964:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037061 ES:SE:LP:AF:ID  0.00106598:0.00563847:0.0705811:0.037061:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101221 ES:SE:LP:AF:ID  -0.00531576:0.00410753:0.69897:0.101221:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959116 ES:SE:LP:AF:ID  -0.00469432:0.0054387:0.408935:0.959116:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031451 ES:SE:LP:AF:ID  0.000874511:0.00986787:0.0315171:0.031451:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053252 ES:SE:LP:AF:ID  -0.00909257:0.00785364:0.60206:0.053252:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036579 ES:SE:LP:AF:ID  0.000909872:0.00567506:0.0604807:0.036579:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.0369   ES:SE:LP:AF:ID  0.00124519:0.00562305:0.0861861:0.0369:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843173 ES:SE:LP:AF:ID  -0.00420258:0.00291208:0.823909:0.843173:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055948 ES:SE:LP:AF:ID  0.00305446:0.00471414:0.283997:0.055948:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12237  ES:SE:LP:AF:ID  0.00355713:0.00318709:0.585027:0.12237:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025756 ES:SE:LP:AF:ID  0.000664017:0.00783537:0.0315171:0.025756:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121613 ES:SE:LP:AF:ID  0.00356884:0.00318839:0.585027:0.121613:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132352 ES:SE:LP:AF:ID  0.00431939:0.00314291:0.769551:0.132352:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011107 ES:SE:LP:AF:ID  0.0122011:0.011447:0.537602:0.011107:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005704 ES:SE:LP:AF:ID  -0.000770594:0.0147469:0.0177288:0.005704:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002262 ES:SE:LP:AF:ID  0.0349333:0.0248434:0.79588:0.002262:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001028 ES:SE:LP:AF:ID  0.064713:0.0406728:0.958607:0.001028:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036814 ES:SE:LP:AF:ID  0.00150205:0.0055662:0.102373:0.036814:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838895 ES:SE:LP:AF:ID  -0.00561144:0.00282001:1.3279:0.838895:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838528 ES:SE:LP:AF:ID  -0.00569818:0.00281701:1.36653:0.838528:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869711 ES:SE:LP:AF:ID  -0.00572569:0.00302245:1.23657:0.869711:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129936 ES:SE:LP:AF:ID  0.00567547:0.00302869:1.21467:0.129936:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037328 ES:SE:LP:AF:ID  0.000609354:0.00547147:0.0409586:0.037328:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037572 ES:SE:LP:AF:ID  0.000588446:0.00543682:0.0409586:0.037572:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869056 ES:SE:LP:AF:ID  -0.00580874:0.00301659:1.26761:0.869056:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869154 ES:SE:LP:AF:ID  -0.00584857:0.00301779:1.27572:0.869154:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037531 ES:SE:LP:AF:ID  0.0007863:0.00546033:0.05061:0.037531:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86906  ES:SE:LP:AF:ID  -0.00580867:0.00301655:1.26761:0.86906:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005128 ES:SE:LP:AF:ID  0.0109009:0.0154819:0.318759:0.005128:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005094 ES:SE:LP:AF:ID  0.00930541:0.0155228:0.259637:0.005094:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837976 ES:SE:LP:AF:ID  -0.00541394:0.0028091:1.26761:0.837976:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037544 ES:SE:LP:AF:ID  0.000674835:0.00546806:0.0457575:0.037544:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838606 ES:SE:LP:AF:ID  -0.00552228:0.00281699:1.30103:0.838606:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013775 ES:SE:LP:AF:ID  -0.0135248:0.00983339:0.769551:0.013775:rs181660517