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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_23098.vcf.gz --id UKB-b:12039 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_23098.txt.gz --cohort_controls 454893 --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-12039/UKB-b-12039_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12039/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12039/UKB-b-12039_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: 0.2518 (0.0077)
Lambda GC: 2.5399
Mean Chi^2: 3.5597
Intercept: 1.2408 (0.0198)
Ratio: 0.0941 (0.0077)
Analysis finished at Thu Oct 17 14:42:55 2019
Total time elapsed: 1.0m:40.26s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.92,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 490,
    "n_p_sig": 83099,
    "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": 0.2518,
    "ldsc_observed_scale_h2_se": 0.0077,
    "ldsc_intercept_beta": 1.2408,
    "ldsc_intercept_se": 0.0198,
    "ldsc_lambda_gc": 2.5399,
    "ldsc_mean_chisq": 3.5597,
    "ldsc_ratio": 0.0941
}
 

Flags

name value
af_correlation FALSE
inflation_factor TRUE
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 1.000000 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+07 828.000000 3.259061e+07 6.948835e+07 1.145912e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.740000e-05 9.650300e-03 -0.255406 -3.736100e-03 -7.500000e-06 3.732800e-03 1.621800e-01 ▁▁▂▇▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.020400e-03 5.698100e-03 0.001682 2.062500e-03 3.458500e-03 7.979700e-03 8.841240e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 3.926359e-01 3.122721e-01 0.000000 9.599970e-02 3.500000e-01 6.600001e-01 1.000000e+00 ▇▃▃▃▃
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 3.926381e-01 3.122513e-01 0.000000 9.555670e-02 3.456129e-01 6.594552e-01 9.999999e-01 ▇▃▃▃▃
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035077e-01 2.568614e-01 0.000985 1.316900e-02 7.791400e-02 3.164540e-01 9.990060e-01 ▇▂▁▁▁
numeric AF_reference 184849 0.9812372 NA NA NA NA NA NA NA 2.068392e-01 2.482924e-01 0.000000 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 0.0009258 0.0030905 0.7600007 0.7645185 0.623738 0.7821490 NA
1 54676 rs2462492 C T 0.0023036 0.0030623 0.4500005 0.4519003 0.400349 NA NA
1 86028 rs114608975 T C 0.0048863 0.0048953 0.3200000 0.3181991 0.103559 0.0277556 NA
1 91536 rs6702460 G T 0.0039795 0.0030146 0.1900002 0.1868087 0.456839 0.4207270 NA
1 234313 rs8179466 C T -0.0127749 0.0059467 0.0320000 0.0316946 0.074495 NA NA
1 534192 rs6680723 C T -0.0021984 0.0034439 0.5199996 0.5232615 0.240938 NA NA
1 546697 rs12025928 A G 0.0039892 0.0042963 0.3500000 0.3531299 0.913460 NA NA
1 693731 rs12238997 A G -0.0022388 0.0028876 0.4400003 0.4381462 0.116215 0.1417730 NA
1 705882 rs72631875 G A -0.0033107 0.0042282 0.4299995 0.4336209 0.067322 0.0315495 NA
1 706368 rs55727773 A G -0.0009270 0.0021381 0.6600001 0.6646063 0.515783 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0019812 0.0045180 0.6600001 0.6610140 0.041967 0.0473243 NA
22 51219766 rs182321900 C T -0.0221196 0.0210294 0.2900000 0.2928722 0.001942 NA NA
22 51220146 rs868950473 C T -0.0249990 0.0208291 0.2300001 0.2300633 0.001991 NA NA
22 51221190 rs369304721 G A 0.0026083 0.0045098 0.5600000 0.5630107 0.049763 NA NA
22 51221731 rs115055839 T C 0.0037728 0.0033733 0.2599998 0.2633913 0.073283 0.0625000 NA
22 51222100 rs114553188 G T 0.0007766 0.0039717 0.8400000 0.8449657 0.054489 0.0880591 NA
22 51223637 rs375798137 G A 0.0008666 0.0039909 0.8300000 0.8280943 0.054119 0.0788738 NA
22 51229805 rs9616985 T C 0.0036939 0.0033856 0.2800000 0.2752333 0.073116 0.0730831 NA
22 51232488 rs376461333 A G 0.0025633 0.0079743 0.7499995 0.7478753 0.020056 NA NA
22 51237063 rs3896457 T C 0.0025146 0.0020718 0.2200002 0.2248468 0.297887 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623738 ES:SE:LP:AF:ID  0.000925758:0.00309047:0.119186:0.623738:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400349 ES:SE:LP:AF:ID  0.00230364:0.00306233:0.346787:0.400349:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103559 ES:SE:LP:AF:ID  0.00488629:0.00489527:0.49485:0.103559:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456839 ES:SE:LP:AF:ID  0.00397951:0.0030146:0.721246:0.456839:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074495 ES:SE:LP:AF:ID  -0.0127749:0.00594668:1.49485:0.074495:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240938 ES:SE:LP:AF:ID  -0.00219835:0.00344393:0.283997:0.240938:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91346  ES:SE:LP:AF:ID  0.00398923:0.00429626:0.455932:0.91346:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116215 ES:SE:LP:AF:ID  -0.00223882:0.00288758:0.356547:0.116215:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067322 ES:SE:LP:AF:ID  -0.00331071:0.00422818:0.366532:0.067322:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515783 ES:SE:LP:AF:ID  -0.000927017:0.00213814:0.180456:0.515783:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032986 ES:SE:LP:AF:ID  -0.00207727:0.00539097:0.154902:0.032986:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036597 ES:SE:LP:AF:ID  -0.00208588:0.00489725:0.173925:0.036597:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036716 ES:SE:LP:AF:ID  -0.00188134:0.00487836:0.154902:0.036716:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036413 ES:SE:LP:AF:ID  -0.00309743:0.00491381:0.275724:0.036413:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016391 ES:SE:LP:AF:ID  -0.00306988:0.00756766:0.167491:0.016391:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036953 ES:SE:LP:AF:ID  -0.00257737:0.00485933:0.221849:0.036953:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037051 ES:SE:LP:AF:ID  -0.0026453:0.00484255:0.236572:0.037051:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101236 ES:SE:LP:AF:ID  0.00245242:0.00352706:0.309804:0.101236:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959099 ES:SE:LP:AF:ID  0.00361429:0.00466941:0.356547:0.959099:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031463 ES:SE:LP:AF:ID  0.00414268:0.00847109:0.207608:0.031463:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053257 ES:SE:LP:AF:ID  0.00150308:0.006738:0.0861861:0.053257:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036572 ES:SE:LP:AF:ID  -0.00344456:0.00487366:0.318759:0.036572:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03689  ES:SE:LP:AF:ID  -0.00429454:0.00482928:0.431798:0.03689:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843325 ES:SE:LP:AF:ID  0.00311181:0.00250188:0.677781:0.843325:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055856 ES:SE:LP:AF:ID  -0.00660201:0.00405172:1:0.055856:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12219  ES:SE:LP:AF:ID  -0.00261247:0.00273911:0.468521:0.12219:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025731 ES:SE:LP:AF:ID  -0.00480005:0.00673058:0.318759:0.025731:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12143  ES:SE:LP:AF:ID  -0.00236728:0.00274027:0.408935:0.12143:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132216 ES:SE:LP:AF:ID  -0.00321349:0.00270041:0.638272:0.132216:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011122 ES:SE:LP:AF:ID  0.00275598:0.00982456:0.107905:0.011122:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005694 ES:SE:LP:AF:ID  0.0165222:0.0126734:0.721246:0.005694:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002287 ES:SE:LP:AF:ID  0.00662901:0.0212195:0.124939:0.002287:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001037 ES:SE:LP:AF:ID  0.00652155:0.0347103:0.0705811:0.001037:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036804 ES:SE:LP:AF:ID  -0.00339848:0.00478047:0.318759:0.036804:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839054 ES:SE:LP:AF:ID  0.00283994:0.00242273:0.619789:0.839054:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838682 ES:SE:LP:AF:ID  0.00318807:0.00242008:0.721246:0.838682:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869876 ES:SE:LP:AF:ID  0.00226385:0.00259718:0.420216:0.869876:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129764 ES:SE:LP:AF:ID  -0.00292735:0.0026026:0.585027:0.129764:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037316 ES:SE:LP:AF:ID  -0.00512905:0.00469946:0.552842:0.037316:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037561 ES:SE:LP:AF:ID  -0.00500963:0.00466969:0.552842:0.037561:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86922  ES:SE:LP:AF:ID  0.00257144:0.00259207:0.49485:0.86922:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869318 ES:SE:LP:AF:ID  0.00254637:0.0025931:0.481486:0.869318:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037514 ES:SE:LP:AF:ID  -0.00517224:0.00469016:0.568636:0.037514:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869223 ES:SE:LP:AF:ID  0.00261537:0.00259202:0.508638:0.869223:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005131 ES:SE:LP:AF:ID  -0.00791145:0.0132886:0.259637:0.005131:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005097 ES:SE:LP:AF:ID  -0.00823298:0.013323:0.267606:0.005097:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838138 ES:SE:LP:AF:ID  0.00329527:0.00241341:0.769551:0.838138:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037526 ES:SE:LP:AF:ID  -0.00540741:0.00469679:0.60206:0.037526:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838771 ES:SE:LP:AF:ID  0.00329076:0.00242023:0.769551:0.838771:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013776 ES:SE:LP:AF:ID  0.0170463:0.00844412:1.35655:0.013776:rs181660517