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-3855/UKB-b-3855_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3855/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3855/UKB-b-3855_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: 0.031 (0.0018)
Lambda GC: 1.2762
Mean Chi^2: 1.3135
Intercept: 1.0293 (0.0082)
Ratio: 0.0934 (0.0261)
Analysis finished at Thu Oct 17 14:45:38 2019
Total time elapsed: 1.0m:50.52s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 9,
    "n_p_sig": 268,
    "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": 0.031,
    "ldsc_observed_scale_h2_se": 0.0018,
    "ldsc_intercept_beta": 1.0293,
    "ldsc_intercept_se": 0.0082,
    "ldsc_lambda_gc": 1.2762,
    "ldsc_mean_chisq": 1.3135,
    "ldsc_ratio": 0.0935
}

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 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 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 -2.960000e-05 2.400100e-03 -0.0363936 -7.763000e-04 -5.800000e-06 7.563000e-04 5.096580e-02 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.690100e-03 1.600100e-03 0.0004729 5.789000e-04 9.707000e-04 2.240000e-03 2.483960e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.716044e-01 2.962431e-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 4.716042e-01 2.962178e-01 0.0000000 2.078929e-01 4.611533e-01 7.282908e-01 1.000000e+00 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035073e-01 2.568631e-01 0.0009870 1.316900e-02 7.791200e-02 3.164580e-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 -0.0007263 0.0008699 0.4000000 0.4037276 0.623753 0.7821490 NA
1 54676 rs2462492 C T -0.0009088 0.0008618 0.2900000 0.2916128 0.400435 NA NA
1 86028 rs114608975 T C -0.0009331 0.0013780 0.5000000 0.4983155 0.103548 0.0277556 NA
1 91536 rs6702460 G T -0.0011119 0.0008486 0.1900002 0.1901290 0.456835 0.4207270 NA
1 234313 rs8179466 C T -0.0026423 0.0016738 0.1100001 0.1144240 0.074495 NA NA
1 534192 rs6680723 C T -0.0011955 0.0009693 0.2200002 0.2174392 0.240974 NA NA
1 546697 rs12025928 A G -0.0012648 0.0012093 0.2999998 0.2956373 0.913470 NA NA
1 693731 rs12238997 A G -0.0005727 0.0008123 0.4799997 0.4807821 0.116360 0.1417730 NA
1 705882 rs72631875 G A 0.0008041 0.0011903 0.5000000 0.4993460 0.067283 0.0315495 NA
1 706368 rs55727773 A G -0.0005505 0.0006018 0.3599996 0.3603519 0.515638 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0013887 0.0012634 0.2700001 0.2717054 0.041940 0.0473243 NA
22 51219766 rs182321900 C T 0.0003529 0.0058996 0.9500000 0.9522950 0.001928 NA NA
22 51220146 rs868950473 C T -0.0013706 0.0058426 0.8100000 0.8145323 0.001977 NA NA
22 51221190 rs369304721 G A -0.0000101 0.0012611 0.9900000 0.9936000 0.049736 NA NA
22 51221731 rs115055839 T C -0.0000603 0.0009433 0.9500000 0.9489898 0.073230 0.0625000 NA
22 51222100 rs114553188 G T 0.0007593 0.0011103 0.4899999 0.4940733 0.054467 0.0880591 NA
22 51223637 rs375798137 G A 0.0008653 0.0011157 0.4400003 0.4379735 0.054097 0.0788738 NA
22 51229805 rs9616985 T C -0.0001682 0.0009467 0.8600001 0.8589606 0.073065 0.0730831 NA
22 51232488 rs376461333 A G 0.0020512 0.0022311 0.3599996 0.3579128 0.020028 NA NA
22 51237063 rs3896457 T C -0.0009842 0.0005790 0.0890000 0.0891567 0.297970 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623753 ES:SE:LP:AF:ID  -0.000726332:0.000869877:0.39794:0.623753:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400435 ES:SE:LP:AF:ID  -0.000908793:0.00086175:0.537602:0.400435:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103548 ES:SE:LP:AF:ID  -0.000933089:0.00137798:0.30103:0.103548:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456835 ES:SE:LP:AF:ID  -0.00111189:0.000848643:0.721246:0.456835:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074495 ES:SE:LP:AF:ID  -0.00264232:0.00167382:0.958607:0.074495:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240974 ES:SE:LP:AF:ID  -0.00119554:0.000969331:0.657577:0.240974:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91347  ES:SE:LP:AF:ID  -0.00126477:0.00120934:0.522879:0.91347:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11636  ES:SE:LP:AF:ID  -0.000572704:0.000812294:0.318759:0.11636:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067283 ES:SE:LP:AF:ID  0.000804084:0.00119032:0.30103:0.067283:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515638 ES:SE:LP:AF:ID  -0.000550466:0.000601803:0.443698:0.515638:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033022 ES:SE:LP:AF:ID  -0.000375508:0.0015167:0.09691:0.033022:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036635 ES:SE:LP:AF:ID  -2.61038e-05:0.00137778:0.00877392:0.036635:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03675  ES:SE:LP:AF:ID  4.88869e-05:0.00137259:0.0132283:0.03675:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03645  ES:SE:LP:AF:ID  2.02125e-05:0.00138248:0.00436481:0.03645:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016402 ES:SE:LP:AF:ID  0.00357015:0.00212959:1.02687:0.016402:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036993 ES:SE:LP:AF:ID  -0.000133848:0.0013671:0.0362122:0.036993:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037089 ES:SE:LP:AF:ID  -9.92126e-05:0.00136244:0.0268721:0.037089:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101215 ES:SE:LP:AF:ID  0.00172889:0.000992685:1.08619:0.101215:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959096 ES:SE:LP:AF:ID  -0.000120488:0.00131427:0.0315171:0.959096:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031455 ES:SE:LP:AF:ID  0.00118221:0.00238551:0.207608:0.031455:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053246 ES:SE:LP:AF:ID  0.00304948:0.00189815:0.958607:0.053246:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036606 ES:SE:LP:AF:ID  4.53967e-05:0.00137128:0.0132283:0.036606:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036923 ES:SE:LP:AF:ID  -1.79395e-06:0.00135876:-0:0.036923:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843184 ES:SE:LP:AF:ID  0.000483673:0.000703995:0.309804:0.843184:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055962 ES:SE:LP:AF:ID  -0.00136361:0.00113945:0.638272:0.055962:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122347 ES:SE:LP:AF:ID  -0.000730431:0.000770524:0.468521:0.122347:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025694 ES:SE:LP:AF:ID  -0.00674296:0.00189615:3.42022:0.025694:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12159  ES:SE:LP:AF:ID  -0.000719422:0.000770846:0.455932:0.12159:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132378 ES:SE:LP:AF:ID  -0.00108064:0.000759745:0.823909:0.132378:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011124 ES:SE:LP:AF:ID  0.00109607:0.00276416:0.161151:0.011124:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005703 ES:SE:LP:AF:ID  -0.00394687:0.00356525:0.568636:0.005703:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002255 ES:SE:LP:AF:ID  0.00963963:0.00601815:0.958607:0.002255:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001029 ES:SE:LP:AF:ID  0.00874201:0.00981628:0.431798:0.001029:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036835 ES:SE:LP:AF:ID  5.06519e-06:0.00134511:-0:0.036835:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838931 ES:SE:LP:AF:ID  0.000157225:0.000681833:0.0861861:0.838931:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838562 ES:SE:LP:AF:ID  0.00013409:0.000681093:0.0757207:0.838562:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869765 ES:SE:LP:AF:ID  7.74156e-05:0.000730865:0.0362122:0.869765:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129888 ES:SE:LP:AF:ID  -7.60085e-05:0.000732345:0.0362122:0.129888:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037344 ES:SE:LP:AF:ID  2.23954e-05:0.00132233:0.00436481:0.037344:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037587 ES:SE:LP:AF:ID  9.10045e-05:0.00131398:0.0268721:0.037587:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86911  ES:SE:LP:AF:ID  2.26553e-05:0.000729433:0.00877392:0.86911:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869209 ES:SE:LP:AF:ID  -1.47968e-05:0.000729725:0.00877392:0.869209:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037545 ES:SE:LP:AF:ID  4.67086e-05:0.00131967:0.0132283:0.037545:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  3.65001e-05:0.000729419:0.0177288:0.869113:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005112 ES:SE:LP:AF:ID  0.00943259:0.0037491:1.92082:0.005112:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005078 ES:SE:LP:AF:ID  0.00949166:0.00375893:1.92082:0.005078:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838015 ES:SE:LP:AF:ID  8.73231e-05:0.000679207:0.0457575:0.838015:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037558 ES:SE:LP:AF:ID  5.25849e-05:0.00132153:0.0132283:0.037558:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838644 ES:SE:LP:AF:ID  8.36314e-05:0.000681116:0.0457575:0.838644:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013791 ES:SE:LP:AF:ID  -0.00283186:0.00237577:0.638272:0.013791:rs181660517