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_2149.vcf.gz --id UKB-b:4256 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2149.txt.gz --cohort_controls 378882 --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-4256/UKB-b-4256_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4256/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4256/UKB-b-4256_data.vcf.gz ...
Read summary statistics for 9851864 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: 0.0757 (0.0029)
Lambda GC: 1.4867
Mean Chi^2: 1.6154
Intercept: 1.0542 (0.0087)
Ratio: 0.0881 (0.0141)
Analysis finished at Thu Oct 17 14:45:55 2019
Total time elapsed: 1.0m:49.05s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.369,
    "mean_EFFECT": 0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 63,
    "n_p_sig": 6336,
    "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": 0.0757,
    "ldsc_observed_scale_h2_se": 0.0029,
    "ldsc_intercept_beta": 1.0542,
    "ldsc_intercept_se": 0.0087,
    "ldsc_lambda_gc": 1.4867,
    "ldsc_mean_chisq": 1.6154,
    "ldsc_ratio": 0.0881
}

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 9837194 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 9851864 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 8.622824e+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 1.541000e-04 9.293300e-03 -0.1595200 -3.049700e-03 4.610000e-05 3.166700e-03 1.483440e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.365900e-03 6.038500e-03 0.0017792 2.178500e-03 3.653300e-03 8.429900e-03 9.292460e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.522712e-01 3.011626e-01 0.0000000 1.800002e-01 4.299995e-01 7.099994e-01 1.000000e+00 ▇▆▆▅▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.522738e-01 3.011383e-01 0.0000000 1.792351e-01 4.348929e-01 7.130260e-01 9.999999e-01 ▇▆▆▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035057e-01 2.568728e-01 0.0009250 1.316500e-02 7.789600e-02 3.164340e-01 9.990700e-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 0.0030990 0.0032745 0.3400001 0.3439518 0.623829 0.7821490 NA
1 54676 rs2462492 C T 0.0001006 0.0032417 0.9800000 0.9752464 0.400668 NA NA
1 86028 rs114608975 T C -0.0022096 0.0051847 0.6700003 0.6699837 0.103523 0.0277556 NA
1 91536 rs6702460 G T 0.0023457 0.0031929 0.4600002 0.4625402 0.457176 0.4207270 NA
1 234313 rs8179466 C T 0.0154433 0.0062976 0.0140001 0.0141968 0.074542 NA NA
1 534192 rs6680723 C T 0.0033833 0.0036473 0.3500000 0.3536042 0.241073 NA NA
1 546697 rs12025928 A G 0.0056127 0.0045473 0.2200002 0.2170950 0.913372 NA NA
1 693731 rs12238997 A G 0.0006803 0.0030536 0.8200001 0.8237061 0.116471 0.1417730 NA
1 705882 rs72631875 G A -0.0039128 0.0044795 0.3800004 0.3823880 0.067260 0.0315495 NA
1 706368 rs55727773 A G -0.0044455 0.0022645 0.0500000 0.0496314 0.515944 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0027130 0.0047577 0.5700002 0.5685181 0.041934 0.0473243 NA
22 51219766 rs182321900 C T -0.0165646 0.0223027 0.4600002 0.4576529 0.001915 NA NA
22 51220146 rs868950473 C T -0.0187441 0.0221251 0.4000000 0.3968908 0.001958 NA NA
22 51221190 rs369304721 G A -0.0028327 0.0047517 0.5500004 0.5510795 0.049668 NA NA
22 51221731 rs115055839 T C -0.0011381 0.0035516 0.7499995 0.7486350 0.073234 0.0625000 NA
22 51222100 rs114553188 G T 0.0034791 0.0041778 0.4000000 0.4049747 0.054492 0.0880591 NA
22 51223637 rs375798137 G A 0.0032623 0.0041976 0.4400003 0.4370591 0.054126 0.0788738 NA
22 51229805 rs9616985 T C -0.0008499 0.0035635 0.8100000 0.8114877 0.073099 0.0730831 NA
22 51232488 rs376461333 A G 0.0045742 0.0083929 0.5900000 0.5857471 0.020042 NA NA
22 51237063 rs3896457 T C -0.0058888 0.0021809 0.0069000 0.0069310 0.297991 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623829 ES:SE:LP:AF:ID  0.00309895:0.00327451:0.468521:0.623829:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400668 ES:SE:LP:AF:ID  0.000100588:0.00324174:0.00877392:0.400668:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103523 ES:SE:LP:AF:ID  -0.00220957:0.00518471:0.173925:0.103523:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457176 ES:SE:LP:AF:ID  0.0023457:0.00319286:0.337242:0.457176:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074542 ES:SE:LP:AF:ID  0.0154433:0.00629762:1.85387:0.074542:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241073 ES:SE:LP:AF:ID  0.00338334:0.00364733:0.455932:0.241073:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913372 ES:SE:LP:AF:ID  0.00561271:0.00454732:0.657577:0.913372:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116471 ES:SE:LP:AF:ID  0.00068029:0.00305363:0.0861861:0.116471:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06726  ES:SE:LP:AF:ID  -0.00391283:0.00447946:0.420216:0.06726:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515944 ES:SE:LP:AF:ID  -0.00444554:0.00226452:1.30103:0.515944:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032969 ES:SE:LP:AF:ID  0.00458564:0.0057093:0.376751:0.032969:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036584 ES:SE:LP:AF:ID  0.00389056:0.00518526:0.346787:0.036584:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036699 ES:SE:LP:AF:ID  0.00418797:0.00516615:0.376751:0.036699:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036395 ES:SE:LP:AF:ID  0.00381625:0.00520351:0.337242:0.036395:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016401 ES:SE:LP:AF:ID  0.00431703:0.00800701:0.229148:0.016401:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036941 ES:SE:LP:AF:ID  0.00427374:0.00514519:0.387216:0.036941:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037032 ES:SE:LP:AF:ID  0.00446487:0.00512812:0.420216:0.037032:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101186 ES:SE:LP:AF:ID  -0.00197318:0.00373395:0.221849:0.101186:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959222 ES:SE:LP:AF:ID  -0.00227525:0.00495145:0.187087:0.959222:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031492 ES:SE:LP:AF:ID  -0.0124952:0.00897267:0.79588:0.031492:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053308 ES:SE:LP:AF:ID  0.0107995:0.00713744:0.886057:0.053308:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036542 ES:SE:LP:AF:ID  0.00399936:0.00516187:0.356547:0.036542:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036856 ES:SE:LP:AF:ID  0.00409237:0.00511466:0.376751:0.036856:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843174 ES:SE:LP:AF:ID  -0.00174811:0.0026483:0.29243:0.843174:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056014 ES:SE:LP:AF:ID  0.000554601:0.00428318:0.0457575:0.056014:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122472 ES:SE:LP:AF:ID  0.00110969:0.00289727:0.154902:0.122472:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025723 ES:SE:LP:AF:ID  0.00394738:0.00713289:0.236572:0.025723:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121695 ES:SE:LP:AF:ID  0.00113734:0.00289858:0.161151:0.121695:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132465 ES:SE:LP:AF:ID  0.00232981:0.00285669:0.387216:0.132465:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011059 ES:SE:LP:AF:ID  0.00208409:0.0104312:0.0757207:0.011059:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005707 ES:SE:LP:AF:ID  0.0143163:0.0134021:0.537602:0.005707:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002215 ES:SE:LP:AF:ID  -0.0249096:0.022934:0.552842:0.002215:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001017 ES:SE:LP:AF:ID  0.0219718:0.0372252:0.251812:0.001017:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036776 ES:SE:LP:AF:ID  0.00565731:0.00506264:0.585027:0.036776:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838893 ES:SE:LP:AF:ID  -0.00187769:0.00256372:0.337242:0.838893:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838528 ES:SE:LP:AF:ID  -0.00170218:0.00256109:0.29243:0.838528:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869684 ES:SE:LP:AF:ID  -0.000857763:0.00274794:0.124939:0.869684:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129982 ES:SE:LP:AF:ID  0.00103343:0.00275374:0.148742:0.129982:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037286 ES:SE:LP:AF:ID  0.00458692:0.00497749:0.443698:0.037286:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037529 ES:SE:LP:AF:ID  0.00447492:0.00494616:0.431798:0.037529:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869035 ES:SE:LP:AF:ID  -0.00076855:0.00274279:0.107905:0.869035:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869138 ES:SE:LP:AF:ID  -0.000751676:0.00274394:0.107905:0.869138:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037484 ES:SE:LP:AF:ID  0.00476329:0.00496757:0.468521:0.037484:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869038 ES:SE:LP:AF:ID  -0.000747944:0.00274275:0.102373:0.869038:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005102 ES:SE:LP:AF:ID  -0.000395656:0.0141118:0.00877392:0.005102:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005067 ES:SE:LP:AF:ID  -0.00125899:0.0141512:0.0315171:0.005067:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837989 ES:SE:LP:AF:ID  -0.00187532:0.00255413:0.337242:0.837989:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037491 ES:SE:LP:AF:ID  0.00493195:0.00497482:0.49485:0.037491:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838615 ES:SE:LP:AF:ID  -0.00198509:0.00256128:0.356547:0.838615:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013868 ES:SE:LP:AF:ID  -0.00976387:0.00890464:0.568636:0.013868:rs181660517