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-11535/UKB-b-11535_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11535/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11535/UKB-b-11535_data.vcf.gz ...
Read summary statistics for 8928504 SNPs.
Dropped 8408 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, 1286894 SNPs remain.
After merging with regression SNP LD, 1286894 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0229 (0.0015)
Lambda GC: 1.2071
Mean Chi^2: 1.2366
Intercept: 1.0336 (0.0071)
Ratio: 0.142 (0.0298)
Analysis finished at Thu Oct 17 14:42:31 2019
Total time elapsed: 1.0m:41.14s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9474,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 309,
    "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": 90721,
    "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": 1286894,
    "ldsc_nsnp_merge_regression_ld": 1286894,
    "ldsc_observed_scale_h2_beta": 0.0229,
    "ldsc_observed_scale_h2_se": 0.0015,
    "ldsc_intercept_beta": 1.0336,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.2071,
    "ldsc_mean_chisq": 1.2366,
    "ldsc_ratio": 0.142
}

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 8920135 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 8928504 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 8.645380e+00 5.758995e+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 7.879073e+07 5.634320e+07 828.0000000 3.242598e+07 6.935255e+07 1.145610e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.700000e-05 2.732500e-03 -0.0297547 -1.141000e-03 -9.400000e-06 1.112300e-03 3.234570e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.135400e-03 1.540300e-03 0.0007907 9.402000e-04 1.428600e-03 2.923200e-03 1.833530e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.759804e-01 2.950912e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.759809e-01 2.950654e-01 0.0000000 2.143673e-01 4.677884e-01 7.315535e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.219373e-01 2.587395e-01 0.0039180 2.137600e-02 1.039400e-01 3.503040e-01 9.960820e-01 ▇▂▁▁▁
numeric AF_reference 90721 0.9898392 NA NA NA NA NA NA NA 2.220817e-01 2.506596e-01 0.0000000 1.877000e-02 1.212060e-01 3.484420e-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 0.0010907 0.0014548 0.4500005 0.4534245 0.623762 0.7821490 NA
1 54676 rs2462492 C T -0.0000860 0.0014412 0.9500000 0.9524141 0.400415 NA NA
1 86028 rs114608975 T C -0.0025713 0.0023043 0.2599998 0.2644869 0.103553 0.0277556 NA
1 91536 rs6702460 G T 0.0002378 0.0014192 0.8700001 0.8669157 0.456847 0.4207270 NA
1 234313 rs8179466 C T 0.0018038 0.0027978 0.5199996 0.5191133 0.074513 NA NA
1 534192 rs6680723 C T 0.0007047 0.0016211 0.6600001 0.6637591 0.240955 NA NA
1 546697 rs12025928 A G -0.0000965 0.0020223 0.9599999 0.9619263 0.913469 NA NA
1 693731 rs12238997 A G -0.0012720 0.0013584 0.3500000 0.3490683 0.116331 0.1417730 NA
1 705882 rs72631875 G A 0.0020036 0.0019907 0.3100002 0.3141823 0.067287 0.0315495 NA
1 706368 rs55727773 A G -0.0013550 0.0010064 0.1800002 0.1781500 0.515637 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0026852 0.0012142 0.0269998 0.0270062 0.137942 0.2052720 NA
22 51219387 rs9616832 T C 0.0034921 0.0015762 0.0269998 0.0267207 0.073734 0.0654952 NA
22 51219704 rs147475742 G A 0.0022437 0.0021123 0.2900000 0.2881508 0.041939 0.0473243 NA
22 51221190 rs369304721 G A 0.0032054 0.0021086 0.1299999 0.1284623 0.049727 NA NA
22 51221731 rs115055839 T C 0.0033953 0.0015772 0.0309999 0.0313333 0.073225 0.0625000 NA
22 51222100 rs114553188 G T 0.0021194 0.0018566 0.2500000 0.2536537 0.054460 0.0880591 NA
22 51223637 rs375798137 G A 0.0021628 0.0018656 0.2500000 0.2463459 0.054089 0.0788738 NA
22 51229805 rs9616985 T C 0.0035968 0.0015828 0.0230001 0.0230646 0.073060 0.0730831 NA
22 51232488 rs376461333 A G 0.0014974 0.0037283 0.6899999 0.6879562 0.020046 NA NA
22 51237063 rs3896457 T C -0.0006611 0.0009680 0.4899999 0.4946380 0.297990 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623762 ES:SE:LP:AF:ID  0.00109072:0.00145484:0.346787:0.623762:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400415 ES:SE:LP:AF:ID  -8.60072e-05:0.00144125:0.0222764:0.400415:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103553 ES:SE:LP:AF:ID  -0.00257125:0.0023043:0.585027:0.103553:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456847 ES:SE:LP:AF:ID  0.000237821:0.00141917:0.0604807:0.456847:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074513 ES:SE:LP:AF:ID  0.00180381:0.00279785:0.283997:0.074513:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240955 ES:SE:LP:AF:ID  0.000704748:0.00162112:0.180456:0.240955:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913469 ES:SE:LP:AF:ID  -9.65378e-05:0.00202231:0.0177288:0.913469:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116331 ES:SE:LP:AF:ID  -0.00127205:0.00135845:0.455932:0.116331:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067287 ES:SE:LP:AF:ID  0.00200364:0.00199073:0.508638:0.067287:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515637 ES:SE:LP:AF:ID  -0.00135505:0.00100637:0.744727:0.515637:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032998 ES:SE:LP:AF:ID  0.000353323:0.00253724:0.05061:0.032998:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036614 ES:SE:LP:AF:ID  -1.00669e-05:0.00230465:-0:0.036614:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03673  ES:SE:LP:AF:ID  7.64184e-05:0.00229595:0.0132283:0.03673:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03643  ES:SE:LP:AF:ID  -0.000123247:0.00231243:0.0177288:0.03643:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016403 ES:SE:LP:AF:ID  -0.00309062:0.00356089:0.408935:0.016403:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036969 ES:SE:LP:AF:ID  -4.21525e-05:0.00228683:0.00436481:0.036969:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037065 ES:SE:LP:AF:ID  -0.000146455:0.002279:0.0222764:0.037065:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10123  ES:SE:LP:AF:ID  0.00449775:0.00166002:2.17393:0.10123:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959105 ES:SE:LP:AF:ID  -0.000814494:0.00219808:0.148742:0.959105:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03145  ES:SE:LP:AF:ID  -0.00867177:0.00398928:1.52288:0.03145:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053265 ES:SE:LP:AF:ID  -0.00031897:0.00317325:0.0362122:0.053265:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036585 ES:SE:LP:AF:ID  -6.61817e-05:0.0022937:0.00877392:0.036585:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036901 ES:SE:LP:AF:ID  3.95624e-05:0.0022728:0.00436481:0.036901:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843218 ES:SE:LP:AF:ID  0.000665639:0.00117734:0.244125:0.843218:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05592  ES:SE:LP:AF:ID  -0.00177188:0.00190612:0.455932:0.05592:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12231  ES:SE:LP:AF:ID  -0.000887865:0.00128864:0.309804:0.12231:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025712 ES:SE:LP:AF:ID  0.000457342:0.00316949:0.05061:0.025712:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121555 ES:SE:LP:AF:ID  -0.00104823:0.00128917:0.376751:0.121555:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132327 ES:SE:LP:AF:ID  -0.00109146:0.00127068:0.408935:0.132327:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011139 ES:SE:LP:AF:ID  -0.0016985:0.00461835:0.148742:0.011139:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005706 ES:SE:LP:AF:ID  0.00262598:0.00596021:0.180456:0.005706:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036814 ES:SE:LP:AF:ID  -0.000524392:0.00224991:0.0861861:0.036814:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  0.000752957:0.00114018:0.29243:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  0.000675922:0.00113895:0.259637:0.838573:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869766 ES:SE:LP:AF:ID  0.000538474:0.00122212:0.180456:0.869766:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129884 ES:SE:LP:AF:ID  -0.000291254:0.0012246:0.091515:0.129884:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037325 ES:SE:LP:AF:ID  -0.000435398:0.00221175:0.0757207:0.037325:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037569 ES:SE:LP:AF:ID  -0.000473306:0.00219775:0.0809219:0.037569:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869108 ES:SE:LP:AF:ID  0.000391931:0.00121972:0.124939:0.869108:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869206 ES:SE:LP:AF:ID  0.000394984:0.0012202:0.124939:0.869206:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037527 ES:SE:LP:AF:ID  -0.000343901:0.00220727:0.0555173:0.037527:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869111 ES:SE:LP:AF:ID  0.000410601:0.00121969:0.130768:0.869111:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005132 ES:SE:LP:AF:ID  0.0138634:0.00625532:1.56864:0.005132:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005098 ES:SE:LP:AF:ID  0.0133241:0.00627153:1.46852:0.005098:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  0.000693934:0.00113578:0.267606:0.838026:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03754  ES:SE:LP:AF:ID  -0.000372178:0.00221039:0.0604807:0.03754:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  0.000720883:0.00113898:0.275724:0.838657:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013783 ES:SE:LP:AF:ID  -0.00158082:0.00397405:0.161151:0.013783:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005545 ES:SE:LP:AF:ID  0.00445389:0.00613533:0.327902:0.005545:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839766 ES:SE:LP:AF:ID  0.00091337:0.00115438:0.366532:0.839766:rs3131965