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_3536.vcf.gz --id UKB-b:9487 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3536.txt.gz --cohort_controls 87834 --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-9487/UKB-b-9487_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9487/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:43 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9487/UKB-b-9487_data.vcf.gz ...
Read summary statistics for 8911560 SNPs.
Dropped 8341 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, 1286787 SNPs remain.
After merging with regression SNP LD, 1286787 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1028 (0.0088)
Lambda GC: 1.1462
Mean Chi^2: 1.192
Intercept: 1.0039 (0.0087)
Ratio: 0.0203 (0.0451)
Analysis finished at Thu Oct 17 14:44:12 2019
Total time elapsed: 1.0m:29.14s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9472,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 24,
    "n_p_sig": 1094,
    "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": 90294,
    "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": 1286787,
    "ldsc_nsnp_merge_regression_ld": 1286787,
    "ldsc_observed_scale_h2_beta": 0.1028,
    "ldsc_observed_scale_h2_se": 0.0088,
    "ldsc_intercept_beta": 1.0039,
    "ldsc_intercept_se": 0.0087,
    "ldsc_lambda_gc": 1.1462,
    "ldsc_mean_chisq": 1.192,
    "ldsc_ratio": 0.0203
}

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 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 8903258 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 8911560 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 8.645898e+00 5.759474e+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 7.878665e+07 5.634269e+07 828.0000000 3.242219e+07 6.934451e+07 1.145574e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.660000e-05 1.556530e-02 -0.1745440 -6.395200e-03 -1.820000e-05 6.356100e-03 1.700770e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.231020e-02 8.853700e-03 0.0045672 5.437900e-03 8.249600e-03 1.684230e-02 1.092480e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.821501e-01 2.941125e-01 0.0000000 2.200002e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.821519e-01 2.940867e-01 0.0000000 2.228604e-01 4.765098e-01 7.370668e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.223040e-01 2.587926e-01 0.0039850 2.155800e-02 1.044540e-01 3.509400e-01 9.960150e-01 ▇▂▁▁▁
numeric AF_reference 90294 0.9898678 NA NA NA NA NA NA NA 2.224084e-01 2.507068e-01 0.0000000 1.896960e-02 1.216050e-01 3.490420e-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 0.0077161 0.0084328 0.3599996 0.3601880 0.623682 0.7821490 NA
1 54676 rs2462492 C T 0.0029242 0.0083243 0.7300002 0.7253778 0.400667 NA NA
1 86028 rs114608975 T C -0.0272488 0.0133474 0.0409996 0.0412005 0.103428 0.0277556 NA
1 91536 rs6702460 G T 0.0118570 0.0082058 0.1499999 0.1484714 0.457913 0.4207270 NA
1 234313 rs8179466 C T 0.0188318 0.0162444 0.2500000 0.2463423 0.074128 NA NA
1 534192 rs6680723 C T 0.0123928 0.0093624 0.1900002 0.1856125 0.242004 NA NA
1 546697 rs12025928 A G -0.0002978 0.0117267 0.9800000 0.9797372 0.913914 NA NA
1 693731 rs12238997 A G 0.0052477 0.0078443 0.5000000 0.5035088 0.116590 0.1417730 NA
1 705882 rs72631875 G A 0.0011821 0.0115391 0.9199999 0.9184057 0.067093 0.0315495 NA
1 706368 rs55727773 A G -0.0028695 0.0058374 0.6200004 0.6230186 0.514008 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0013158 0.0070344 0.8499999 0.8516226 0.137926 0.2052720 NA
22 51219387 rs9616832 T C 0.0056943 0.0091142 0.5300002 0.5321218 0.073851 0.0654952 NA
22 51219704 rs147475742 G A 0.0012572 0.0121838 0.9199999 0.9178165 0.042161 0.0473243 NA
22 51221190 rs369304721 G A 0.0078309 0.0121672 0.5199996 0.5198279 0.049892 NA NA
22 51221731 rs115055839 T C 0.0042547 0.0091169 0.6400000 0.6407226 0.073414 0.0625000 NA
22 51222100 rs114553188 G T 0.0017659 0.0107534 0.8700001 0.8695575 0.054447 0.0880591 NA
22 51223637 rs375798137 G A 0.0019218 0.0108070 0.8600001 0.8588571 0.054068 0.0788738 NA
22 51229805 rs9616985 T C 0.0043410 0.0091448 0.6400000 0.6350067 0.073287 0.0730831 NA
22 51232488 rs376461333 A G 0.0074878 0.0217412 0.7300002 0.7305424 0.019970 NA NA
22 51237063 rs3896457 T C 0.0015529 0.0055934 0.7800007 0.7812903 0.298543 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623682 ES:SE:LP:AF:ID  0.00771607:0.0084328:0.443698:0.623682:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400667 ES:SE:LP:AF:ID  0.00292418:0.00832434:0.136677:0.400667:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103428 ES:SE:LP:AF:ID  -0.0272488:0.0133474:1.38722:0.103428:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457913 ES:SE:LP:AF:ID  0.011857:0.00820581:0.823909:0.457913:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074128 ES:SE:LP:AF:ID  0.0188318:0.0162444:0.60206:0.074128:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242004 ES:SE:LP:AF:ID  0.0123928:0.00936245:0.721246:0.242004:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913914 ES:SE:LP:AF:ID  -0.000297839:0.0117267:0.00877392:0.913914:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11659  ES:SE:LP:AF:ID  0.00524766:0.00784428:0.30103:0.11659:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067093 ES:SE:LP:AF:ID  0.00118209:0.0115391:0.0362122:0.067093:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514008 ES:SE:LP:AF:ID  -0.00286953:0.0058374:0.207608:0.514008:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032292 ES:SE:LP:AF:ID  -0.0116885:0.0148258:0.366532:0.032292:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035758 ES:SE:LP:AF:ID  -0.00841666:0.0134875:0.275724:0.035758:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035881 ES:SE:LP:AF:ID  -0.00879706:0.0134324:0.29243:0.035881:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035531 ES:SE:LP:AF:ID  -0.00897764:0.0135436:0.29243:0.035531:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016508 ES:SE:LP:AF:ID  -0.008596:0.020478:0.173925:0.016508:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03609  ES:SE:LP:AF:ID  -0.00886733:0.0133807:0.29243:0.03609:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036136 ES:SE:LP:AF:ID  -0.00961997:0.0133466:0.327902:0.036136:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101442 ES:SE:LP:AF:ID  0.00404193:0.00959745:0.173925:0.101442:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.960265 ES:SE:LP:AF:ID  0.0137326:0.0128984:0.537602:0.960265:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.030867 ES:SE:LP:AF:ID  -0.0187495:0.0235828:0.366532:0.030867:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053505 ES:SE:LP:AF:ID  -0.00788384:0.0182865:0.173925:0.053505:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.035727 ES:SE:LP:AF:ID  -0.00937267:0.0134183:0.318759:0.035727:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036087 ES:SE:LP:AF:ID  -0.00939183:0.0132932:0.318759:0.036087:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843774 ES:SE:LP:AF:ID  -0.00231641:0.00682082:0.136677:0.843774:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056038 ES:SE:LP:AF:ID  0.000929728:0.011016:0.0315171:0.056038:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12274  ES:SE:LP:AF:ID  0.0052702:0.00744017:0.318759:0.12274:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02607  ES:SE:LP:AF:ID  -0.0344896:0.0181529:1.24413:0.02607:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121938 ES:SE:LP:AF:ID  0.0055823:0.00744401:0.346787:0.121938:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.131863 ES:SE:LP:AF:ID  0.00260382:0.0073643:0.142668:0.131863:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011316 ES:SE:LP:AF:ID  0.0153436:0.0263457:0.251812:0.011316:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005723 ES:SE:LP:AF:ID  0.0262095:0.0345568:0.346787:0.005723:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.035997 ES:SE:LP:AF:ID  -0.00859231:0.0131564:0.29243:0.035997:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839501 ES:SE:LP:AF:ID  -0.00112596:0.00660767:0.0655015:0.839501:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.839121 ES:SE:LP:AF:ID  -0.000882311:0.00660166:0.05061:0.839121:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869612 ES:SE:LP:AF:ID  -0.00344372:0.00707144:0.200659:0.869612:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130151 ES:SE:LP:AF:ID  0.00424634:0.00708274:0.259637:0.130151:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036514 ES:SE:LP:AF:ID  -0.0100827:0.0129301:0.356547:0.036514:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036803 ES:SE:LP:AF:ID  -0.00957304:0.0128377:0.337242:0.036803:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868957 ES:SE:LP:AF:ID  -0.003118:0.00705786:0.180456:0.868957:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869003 ES:SE:LP:AF:ID  -0.00305366:0.00706004:0.173925:0.869003:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036729 ES:SE:LP:AF:ID  -0.010759:0.0128998:0.39794:0.036729:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868948 ES:SE:LP:AF:ID  -0.00332094:0.00705742:0.19382:0.868948:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.004903 ES:SE:LP:AF:ID  -0.019845:0.0371383:0.229148:0.004903:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.004867 ES:SE:LP:AF:ID  -0.0205077:0.0372501:0.236572:0.004867:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838596 ES:SE:LP:AF:ID  -0.000328431:0.00658303:0.0177288:0.838596:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036732 ES:SE:LP:AF:ID  -0.0101193:0.0129198:0.366532:0.036732:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.839239 ES:SE:LP:AF:ID  -7.99204e-05:0.00660225:0.00436481:0.839239:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013701 ES:SE:LP:AF:ID  -0.0586133:0.023049:1.95861:0.013701:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005694 ES:SE:LP:AF:ID  -0.0101749:0.0347532:0.113509:0.005694:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.840326 ES:SE:LP:AF:ID  -0.00111412:0.0066907:0.0604807:0.840326:rs3131965