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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}

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-4710/UKB-b-4710_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4710/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:38 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4710/UKB-b-4710_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: 0.0401 (0.0021)
Lambda GC: 1.3135
Mean Chi^2: 1.3709
Intercept: 1.0228 (0.0079)
Ratio: 0.0614 (0.0213)
Analysis finished at Thu Oct 17 14:46:30 2019
Total time elapsed: 1.0m:52.77s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 18,
    "n_p_sig": 1794,
    "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": 0.0401,
    "ldsc_observed_scale_h2_se": 0.0021,
    "ldsc_intercept_beta": 1.0228,
    "ldsc_intercept_se": 0.0079,
    "ldsc_lambda_gc": 1.3135,
    "ldsc_mean_chisq": 1.3709,
    "ldsc_ratio": 0.0615
}

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 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 -2.490000e-05 2.429130e-02 -0.3776390 -7.858800e-03 2.120000e-05 7.890100e-03 3.820680e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.714100e-02 1.623080e-02 0.0047951 5.870600e-03 9.844400e-03 2.271500e-02 2.521880e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.686224e-01 2.972398e-01 0.0000000 2.000000e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.686247e-01 2.972159e-01 0.0000000 2.037261e-01 4.574078e-01 7.261276e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035070e-01 2.568637e-01 0.0009650 1.316800e-02 7.791000e-02 3.164490e-01 9.990290e-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 -0.0084856 0.0088252 0.3400001 0.3362889 0.623821 0.7821490 NA
1 54676 rs2462492 C T 0.0203680 0.0087412 0.0200000 0.0198005 0.400346 NA NA
1 86028 rs114608975 T C 0.0168030 0.0139784 0.2300001 0.2293368 0.103593 0.0277556 NA
1 91536 rs6702460 G T 0.0132332 0.0086066 0.1199999 0.1241565 0.456835 0.4207270 NA
1 234313 rs8179466 C T -0.0066368 0.0169711 0.6999999 0.6957472 0.074499 NA NA
1 534192 rs6680723 C T -0.0022683 0.0098345 0.8200001 0.8175897 0.241019 NA NA
1 546697 rs12025928 A G -0.0241921 0.0122581 0.0479999 0.0484318 0.913436 NA NA
1 693731 rs12238997 A G 0.0057339 0.0082364 0.4899999 0.4863254 0.116400 0.1417730 NA
1 705882 rs72631875 G A 0.0209409 0.0120711 0.0830004 0.0827769 0.067267 0.0315495 NA
1 706368 rs55727773 A G 0.0030806 0.0061023 0.6100002 0.6136845 0.515732 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0362902 0.0128156 0.0046000 0.0046298 0.041907 0.0473243 NA
22 51219766 rs182321900 C T 0.0006841 0.0596586 0.9900000 0.9908510 0.001937 NA NA
22 51220146 rs868950473 C T -0.0160143 0.0591150 0.7899998 0.7864673 0.001985 NA NA
22 51221190 rs369304721 G A -0.0347830 0.0127958 0.0065999 0.0065616 0.049652 NA NA
22 51221731 rs115055839 T C -0.0306050 0.0095722 0.0014000 0.0013874 0.073118 0.0625000 NA
22 51222100 rs114553188 G T -0.0080844 0.0112619 0.4700002 0.4728451 0.054446 0.0880591 NA
22 51223637 rs375798137 G A -0.0082294 0.0113164 0.4700002 0.4670978 0.054075 0.0788738 NA
22 51229805 rs9616985 T C -0.0300108 0.0096064 0.0018000 0.0017839 0.072958 0.0730831 NA
22 51232488 rs376461333 A G 0.0044804 0.0226143 0.8400000 0.8429496 0.020042 NA NA
22 51237063 rs3896457 T C 0.0049443 0.0058724 0.4000000 0.3998127 0.298016 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623821 ES:SE:LP:AF:ID  -0.00848564:0.0088252:0.468521:0.623821:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400346 ES:SE:LP:AF:ID  0.020368:0.00874123:1.69897:0.400346:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103593 ES:SE:LP:AF:ID  0.016803:0.0139784:0.638272:0.103593:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456835 ES:SE:LP:AF:ID  0.0132332:0.00860663:0.920819:0.456835:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074499 ES:SE:LP:AF:ID  -0.00663685:0.0169711:0.154902:0.074499:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241019 ES:SE:LP:AF:ID  -0.00226829:0.0098345:0.0861861:0.241019:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913436 ES:SE:LP:AF:ID  -0.0241921:0.0122581:1.31876:0.913436:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.1164   ES:SE:LP:AF:ID  0.00573393:0.00823645:0.309804:0.1164:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067267 ES:SE:LP:AF:ID  0.0209409:0.0120711:1.08092:0.067267:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515732 ES:SE:LP:AF:ID  0.00308059:0.00610234:0.21467:0.515732:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03297  ES:SE:LP:AF:ID  -0.00711173:0.0153929:0.19382:0.03297:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036585 ES:SE:LP:AF:ID  -0.00317944:0.0139807:0.0861861:0.036585:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036703 ES:SE:LP:AF:ID  -0.00390347:0.0139274:0.107905:0.036703:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036399 ES:SE:LP:AF:ID  -0.00414174:0.0140287:0.113509:0.036399:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016462 ES:SE:LP:AF:ID  -0.00159656:0.0215496:0.0268721:0.016462:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036939 ES:SE:LP:AF:ID  -0.00522364:0.0138734:0.148742:0.036939:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037037 ES:SE:LP:AF:ID  -0.00421212:0.0138255:0.119186:0.037037:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101275 ES:SE:LP:AF:ID  0.00595089:0.0100662:0.259637:0.101275:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959138 ES:SE:LP:AF:ID  0.000538056:0.0133359:0.0132283:0.959138:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031402 ES:SE:LP:AF:ID  0.0255914:0.0242397:0.537602:0.031402:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053308 ES:SE:LP:AF:ID  -0.027897:0.0192294:0.823909:0.053308:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036558 ES:SE:LP:AF:ID  -0.00034724:0.0139139:0.00877392:0.036558:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036874 ES:SE:LP:AF:ID  -0.00073177:0.0137873:0.0177288:0.036874:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843192 ES:SE:LP:AF:ID  -0.00330167:0.00713852:0.19382:0.843192:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055945 ES:SE:LP:AF:ID  0.00834334:0.0115535:0.327902:0.055945:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122373 ES:SE:LP:AF:ID  0.00377111:0.00781328:0.200659:0.122373:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025625 ES:SE:LP:AF:ID  -0.0319407:0.0192624:1.01323:0.025625:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121614 ES:SE:LP:AF:ID  0.00391658:0.00781649:0.207608:0.121614:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132305 ES:SE:LP:AF:ID  0.00488619:0.00770507:0.275724:0.132305:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011126 ES:SE:LP:AF:ID  -0.0313932:0.0280376:0.585027:0.011126:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005701 ES:SE:LP:AF:ID  0.0121051:0.0361639:0.130768:0.005701:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002254 ES:SE:LP:AF:ID  0.0616556:0.0610406:0.508638:0.002254:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001024 ES:SE:LP:AF:ID  -0.147259:0.0998427:0.853872:0.001024:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036778 ES:SE:LP:AF:ID  -0.0010686:0.0136511:0.0268721:0.036778:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838927 ES:SE:LP:AF:ID  -0.00610772:0.00691316:0.420216:0.838927:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83856  ES:SE:LP:AF:ID  -0.00603259:0.006906:0.420216:0.83856:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869717 ES:SE:LP:AF:ID  -0.00920935:0.00740976:0.677781:0.869717:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129935 ES:SE:LP:AF:ID  0.00829748:0.00742496:0.585027:0.129935:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037281 ES:SE:LP:AF:ID  0.00159582:0.0134209:0.0409586:0.037281:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037529 ES:SE:LP:AF:ID  0.00187147:0.0133348:0.05061:0.037529:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86906  ES:SE:LP:AF:ID  -0.00894429:0.00739545:0.638272:0.86906:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86916  ES:SE:LP:AF:ID  -0.00906811:0.00739839:0.657577:0.86916:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037486 ES:SE:LP:AF:ID  0.0012775:0.0133931:0.0362122:0.037486:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869064 ES:SE:LP:AF:ID  -0.00897387:0.00739534:0.657577:0.869064:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005141 ES:SE:LP:AF:ID  0.0853351:0.037898:1.61979:0.005141:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005107 ES:SE:LP:AF:ID  0.0874727:0.0379964:1.67778:0.005107:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838006 ES:SE:LP:AF:ID  -0.00621758:0.00688668:0.431798:0.838006:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.0375   ES:SE:LP:AF:ID  0.00207063:0.0134118:0.0555173:0.0375:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838638 ES:SE:LP:AF:ID  -0.00638949:0.00690609:0.455932:0.838638:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013756 ES:SE:LP:AF:ID  -0.0269378:0.0241265:0.585027:0.013756:rs181660517