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_20245.vcf.gz --id UKB-b:3841 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20245.txt.gz --cohort_controls 114724 --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-3841/UKB-b-3841_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3841/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3841/UKB-b-3841_data.vcf.gz ...
Read summary statistics for 9123792 SNPs.
Dropped 9277 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, 1287497 SNPs remain.
After merging with regression SNP LD, 1287497 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0034 (0.0037)
Lambda GC: 1.0188
Mean Chi^2: 1.0201
Intercept: 1.0124 (0.0063)
Ratio: 0.6189 (0.3114)
Analysis finished at Thu Oct 17 14:45:27 2019
Total time elapsed: 1.0m:36.55s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9481,
    "inflation_factor": 1,
    "mean_EFFECT": -8.8034e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 2,
    "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": 99462,
    "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": 1287497,
    "ldsc_nsnp_merge_regression_ld": 1287497,
    "ldsc_observed_scale_h2_beta": 0.0034,
    "ldsc_observed_scale_h2_se": 0.0037,
    "ldsc_intercept_beta": 1.0124,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0188,
    "ldsc_mean_chisq": 1.0201,
    "ldsc_ratio": 0.6169
}

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 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 9114559 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 9123792 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 8.640077e+00 5.756612e+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 7.879225e+07 5.632420e+07 828.0000000 3.245725e+07 6.936353e+07 1.145240e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -8.800000e-06 4.459800e-03 -0.0644170 -1.669400e-03 7.700000e-06 1.705100e-03 4.785390e-02 ▁▁▇▂▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.543000e-03 2.668700e-03 0.0012502 1.494300e-03 2.316100e-03 4.862800e-03 2.834180e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.968962e-01 2.894559e-01 0.0000000 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.968952e-01 2.894307e-01 0.0000000 2.450282e-01 4.959788e-01 7.478236e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.175944e-01 2.582445e-01 0.0030510 1.924000e-02 9.786200e-02 3.427480e-01 9.969490e-01 ▇▂▁▁▁
numeric AF_reference 99462 0.9890986 NA NA NA NA NA NA NA 2.181224e-01 2.501077e-01 0.0000000 1.637380e-02 1.156150e-01 3.414540e-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 0.0022586 0.0022943 0.3200000 0.3248930 0.623797 0.7821490 NA
1 54676 rs2462492 C T -0.0013778 0.0022757 0.5400003 0.5448802 0.399424 NA NA
1 86028 rs114608975 T C 0.0010310 0.0036492 0.7800007 0.7775458 0.103370 0.0277556 NA
1 91536 rs6702460 G T -0.0005550 0.0022406 0.8000000 0.8043778 0.456357 0.4207270 NA
1 234313 rs8179466 C T 0.0014316 0.0044527 0.7499995 0.7478269 0.074090 NA NA
1 534192 rs6680723 C T 0.0001522 0.0025560 0.9500000 0.9525175 0.241047 NA NA
1 546697 rs12025928 A G -0.0002868 0.0031827 0.9299999 0.9282013 0.912962 NA NA
1 693731 rs12238997 A G 0.0032746 0.0021437 0.1299999 0.1266207 0.116996 0.1417730 NA
1 705882 rs72631875 G A -0.0015676 0.0031229 0.6200004 0.6156970 0.067837 0.0315495 NA
1 706368 rs55727773 A G -0.0006462 0.0015892 0.6800001 0.6842660 0.515954 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0004687 0.0019274 0.8100000 0.8078868 0.136475 0.2052720 NA
22 51219387 rs9616832 T C 0.0012480 0.0025042 0.6200004 0.6182214 0.072728 0.0654952 NA
22 51219704 rs147475742 G A 0.0067643 0.0033697 0.0449997 0.0447060 0.041130 0.0473243 NA
22 51221190 rs369304721 G A 0.0041774 0.0033503 0.2099999 0.2124330 0.049147 NA NA
22 51221731 rs115055839 T C 0.0008223 0.0025062 0.7400005 0.7428302 0.072178 0.0625000 NA
22 51222100 rs114553188 G T -0.0003380 0.0029445 0.9100000 0.9086242 0.054161 0.0880591 NA
22 51223637 rs375798137 G A -0.0003653 0.0029596 0.9000000 0.9017711 0.053794 0.0788738 NA
22 51229805 rs9616985 T C 0.0008646 0.0025154 0.7300002 0.7310573 0.072001 0.0730831 NA
22 51232488 rs376461333 A G 0.0028790 0.0059511 0.6300007 0.6285429 0.019748 NA NA
22 51237063 rs3896457 T C -0.0012327 0.0015234 0.4199997 0.4184082 0.298521 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623797 ES:SE:LP:AF:ID  0.0022586:0.00229427:0.49485:0.623797:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399424 ES:SE:LP:AF:ID  -0.00137781:0.00227568:0.267606:0.399424:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10337  ES:SE:LP:AF:ID  0.00103097:0.00364921:0.107905:0.10337:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456357 ES:SE:LP:AF:ID  -0.000554974:0.00224064:0.09691:0.456357:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07409  ES:SE:LP:AF:ID  0.00143158:0.00445273:0.124939:0.07409:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241047 ES:SE:LP:AF:ID  0.000152197:0.00255597:0.0222764:0.241047:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912962 ES:SE:LP:AF:ID  -0.000286788:0.00318271:0.0315171:0.912962:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116996 ES:SE:LP:AF:ID  0.00327464:0.0021437:0.886057:0.116996:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067837 ES:SE:LP:AF:ID  -0.00156758:0.00312293:0.207608:0.067837:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515954 ES:SE:LP:AF:ID  -0.000646242:0.00158919:0.167491:0.515954:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032829 ES:SE:LP:AF:ID  -0.000818841:0.00402189:0.0757207:0.032829:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036426 ES:SE:LP:AF:ID  -0.000936278:0.00365227:0.09691:0.036426:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03654  ES:SE:LP:AF:ID  -0.000431638:0.00363814:0.0409586:0.03654:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03623  ES:SE:LP:AF:ID  -0.000203119:0.00366543:0.0177288:0.03623:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016632 ES:SE:LP:AF:ID  0.0113779:0.00558386:1.37675:0.016632:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036771 ES:SE:LP:AF:ID  -0.00063577:0.00362397:0.0655015:0.036771:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036884 ES:SE:LP:AF:ID  -0.000336789:0.00361145:0.0315171:0.036884:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101781 ES:SE:LP:AF:ID  -0.00327549:0.00261596:0.677781:0.101781:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959461 ES:SE:LP:AF:ID  -0.000245524:0.00348897:0.0268721:0.959461:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031203 ES:SE:LP:AF:ID  0.00146289:0.00638618:0.0861861:0.031203:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053314 ES:SE:LP:AF:ID  0.0040907:0.00501023:0.387216:0.053314:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036372 ES:SE:LP:AF:ID  -0.000438942:0.00363469:0.0457575:0.036372:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036678 ES:SE:LP:AF:ID  -7.18767e-05:0.00360234:0.00877392:0.036678:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843094 ES:SE:LP:AF:ID  -0.00306463:0.00186231:1:0.843094:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055914 ES:SE:LP:AF:ID  0.000547172:0.00301601:0.0655015:0.055914:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122931 ES:SE:LP:AF:ID  0.00270641:0.00203549:0.744727:0.122931:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024841 ES:SE:LP:AF:ID  -0.0102379:0.00509216:1.35655:0.024841:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122159 ES:SE:LP:AF:ID  0.00271958:0.00203663:0.744727:0.122159:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13246  ES:SE:LP:AF:ID  0.00152186:0.00200819:0.346787:0.13246:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011059 ES:SE:LP:AF:ID  0.00906495:0.00734699:0.657577:0.011059:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005666 ES:SE:LP:AF:ID  -0.0018896:0.00948195:0.0757207:0.005666:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.03659  ES:SE:LP:AF:ID  -2.07076e-05:0.0035657:-0:0.03659:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838764 ES:SE:LP:AF:ID  -0.00162297:0.00180341:0.431798:0.838764:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838412 ES:SE:LP:AF:ID  -0.00161623:0.00180154:0.431798:0.838412:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869193 ES:SE:LP:AF:ID  -0.00127208:0.00193057:0.29243:0.869193:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130462 ES:SE:LP:AF:ID  0.00129285:0.00193468:0.30103:0.130462:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037103 ES:SE:LP:AF:ID  -0.000717752:0.00350477:0.0757207:0.037103:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037351 ES:SE:LP:AF:ID  -0.000937325:0.00348171:0.102373:0.037351:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868535 ES:SE:LP:AF:ID  -0.0013349:0.00192691:0.309804:0.868535:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868635 ES:SE:LP:AF:ID  -0.00139608:0.00192784:0.327902:0.868635:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037307 ES:SE:LP:AF:ID  -0.000841733:0.00349782:0.091515:0.037307:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868543 ES:SE:LP:AF:ID  -0.00132405:0.00192687:0.309804:0.868543:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005182 ES:SE:LP:AF:ID  -0.0223252:0.00983595:1.63827:0.005182:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005153 ES:SE:LP:AF:ID  -0.0223384:0.00985306:1.63827:0.005153:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837829 ES:SE:LP:AF:ID  -0.00149823:0.0017964:0.39794:0.837829:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037342 ES:SE:LP:AF:ID  -0.000728869:0.00350197:0.0757207:0.037342:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838423 ES:SE:LP:AF:ID  -0.00143737:0.00180121:0.376751:0.838423:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01362  ES:SE:LP:AF:ID  -0.00751619:0.00631397:0.638272:0.01362:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005505 ES:SE:LP:AF:ID  -0.0112375:0.00970161:0.60206:0.005505:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839567 ES:SE:LP:AF:ID  -0.00175203:0.00182523:0.468521:0.839567:rs3131965