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_103050.vcf.gz --id UKB-b:39 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_103050.txt.gz --cohort_controls 64946 --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-39/UKB-b-39_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-39/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-39/UKB-b-39_data.vcf.gz ...
Read summary statistics for 8624970 SNPs.
Dropped 7371 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, 1285748 SNPs remain.
After merging with regression SNP LD, 1285748 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0027 (0.0072)
Lambda GC: 1.006
Mean Chi^2: 1.0085
Intercept: 1.005 (0.0064)
Ratio: 0.5911 (0.7597)
Analysis finished at Thu Oct 17 14:45:31 2019
Total time elapsed: 1.0m:40.81s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 83095,
    "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": 1285748,
    "ldsc_nsnp_merge_regression_ld": 1285748,
    "ldsc_observed_scale_h2_beta": 0.0027,
    "ldsc_observed_scale_h2_se": 0.0072,
    "ldsc_intercept_beta": 1.005,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.006,
    "ldsc_mean_chisq": 1.0085,
    "ldsc_ratio": 0.5882
}

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 8617633 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 8624970 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 8.650336e+00 5.760982e+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 7.877011e+07 5.637172e+07 828.0000000 3.238533e+07 6.929178e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.140000e-05 6.476000e-03 -0.0655410 -2.716000e-03 -1.580000e-05 2.665900e-03 8.603080e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.357900e-03 3.657300e-03 0.0021279 2.505900e-03 3.694800e-03 7.242700e-03 3.668290e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.994712e-01 2.888521e-01 0.0000001 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 4.994723e-01 2.888247e-01 0.0000001 2.498872e-01 4.989498e-01 7.495960e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291177e-01 2.594884e-01 0.0053900 2.519000e-02 1.138960e-01 3.626170e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83095 0.9903658 NA NA NA NA NA NA NA 2.288669e-01 2.514591e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 -0.0026100 0.0039222 0.5099998 0.5057651 0.623816 0.7821490 NA
1 54676 rs2462492 C T -0.0051562 0.0039108 0.1900002 0.1873503 0.399138 NA NA
1 86028 rs114608975 T C 0.0013363 0.0062252 0.8300000 0.8300321 0.103537 0.0277556 NA
1 91536 rs6702460 G T -0.0103865 0.0038465 0.0069000 0.0069282 0.455921 0.4207270 NA
1 234313 rs8179466 C T 0.0105759 0.0076070 0.1600000 0.1644417 0.074453 NA NA
1 534192 rs6680723 C T -0.0040718 0.0043809 0.3500000 0.3526640 0.242060 NA NA
1 546697 rs12025928 A G -0.0046302 0.0054358 0.3900004 0.3943260 0.912864 NA NA
1 693731 rs12238997 A G 0.0000232 0.0036530 0.9900000 0.9949257 0.117311 0.1417730 NA
1 705882 rs72631875 G A 0.0035022 0.0053246 0.5099998 0.5106999 0.067698 0.0315495 NA
1 706368 rs55727773 A G -0.0028032 0.0027115 0.2999998 0.3012262 0.513304 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0011686 0.0032976 0.7199992 0.7230543 0.136313 0.2052720 NA
22 51219387 rs9616832 T C 0.0028763 0.0042981 0.5000000 0.5033721 0.071793 0.0654952 NA
22 51219704 rs147475742 G A 0.0019129 0.0057174 0.7400005 0.7379482 0.041192 0.0473243 NA
22 51221190 rs369304721 G A 0.0055239 0.0057581 0.3400001 0.3373932 0.048370 NA NA
22 51221731 rs115055839 T C 0.0034774 0.0042991 0.4199997 0.4185922 0.071344 0.0625000 NA
22 51222100 rs114553188 G T -0.0012666 0.0049815 0.8000000 0.7992906 0.054853 0.0880591 NA
22 51223637 rs375798137 G A -0.0012921 0.0050075 0.8000000 0.7963784 0.054473 0.0788738 NA
22 51229805 rs9616985 T C 0.0032224 0.0043124 0.4500005 0.4549188 0.071248 0.0730831 NA
22 51232488 rs376461333 A G -0.0049844 0.0099317 0.6200004 0.6157623 0.020461 NA NA
22 51237063 rs3896457 T C 0.0037715 0.0026033 0.1499999 0.1474102 0.298392 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623816 ES:SE:LP:AF:ID  -0.00260998:0.00392215:0.29243:0.623816:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399138 ES:SE:LP:AF:ID  -0.00515616:0.00391075:0.721246:0.399138:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103537 ES:SE:LP:AF:ID  0.0013363:0.00622518:0.0809219:0.103537:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455921 ES:SE:LP:AF:ID  -0.0103865:0.00384646:2.16115:0.455921:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074453 ES:SE:LP:AF:ID  0.0105759:0.00760699:0.79588:0.074453:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24206  ES:SE:LP:AF:ID  -0.00407175:0.00438089:0.455932:0.24206:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912864 ES:SE:LP:AF:ID  -0.00463018:0.00543577:0.408935:0.912864:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117311 ES:SE:LP:AF:ID  2.32321e-05:0.003653:0.00436481:0.117311:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067698 ES:SE:LP:AF:ID  0.00350222:0.00532456:0.29243:0.067698:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  -0.00280321:0.00271154:0.522879:0.513304:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033679 ES:SE:LP:AF:ID  0.0156472:0.0067584:1.67778:0.033679:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037459 ES:SE:LP:AF:ID  0.0154192:0.00612953:1.92082:0.037459:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037646 ES:SE:LP:AF:ID  0.0151595:0.00609746:1.88606:0.037646:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037222 ES:SE:LP:AF:ID  0.015708:0.00615318:1.95861:0.037222:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  0.00482248:0.00965165:0.207608:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037862 ES:SE:LP:AF:ID  0.0148642:0.00607661:1.85387:0.037862:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037956 ES:SE:LP:AF:ID  0.0149018:0.00605734:1.85387:0.037956:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10274  ES:SE:LP:AF:ID  -0.0045757:0.00442475:0.522879:0.10274:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958095 ES:SE:LP:AF:ID  -0.0144288:0.00585101:1.85387:0.958095:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031691 ES:SE:LP:AF:ID  0.00489092:0.0107132:0.187087:0.031691:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052724 ES:SE:LP:AF:ID  -0.00204761:0.00862918:0.091515:0.052724:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03745  ES:SE:LP:AF:ID  0.0155473:0.00609712:1.95861:0.03745:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037719 ES:SE:LP:AF:ID  0.0152125:0.00604661:1.92082:0.037719:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841449 ES:SE:LP:AF:ID  -0.00380428:0.00315938:0.638272:0.841449:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056336 ES:SE:LP:AF:ID  -0.0035755:0.00513177:0.309804:0.056336:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123075 ES:SE:LP:AF:ID  6.61966e-05:0.00346992:0.00877392:0.123075:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025131 ES:SE:LP:AF:ID  -0.00233997:0.00864108:0.102373:0.025131:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122328 ES:SE:LP:AF:ID  0.000218782:0.00347104:0.0222764:0.122328:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134138 ES:SE:LP:AF:ID  0.00305919:0.00340739:0.431798:0.134138:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  -0.00480111:0.0121438:0.161151:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  -0.0117848:0.0154211:0.356547:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037601 ES:SE:LP:AF:ID  0.0150278:0.00599058:1.92082:0.037601:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83703  ES:SE:LP:AF:ID  -0.0033569:0.00305609:0.568636:0.83703:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836741 ES:SE:LP:AF:ID  -0.00360185:0.00305406:0.619789:0.836741:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868564 ES:SE:LP:AF:ID  0.000602049:0.00328153:0.0705811:0.868564:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130995 ES:SE:LP:AF:ID  -0.000417322:0.00329024:0.0457575:0.130995:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038047 ES:SE:LP:AF:ID  0.0146348:0.00589661:1.88606:0.038047:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038293 ES:SE:LP:AF:ID  0.0146276:0.00586006:1.88606:0.038293:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867985 ES:SE:LP:AF:ID  0.000346972:0.00327653:0.0362122:0.867985:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868059 ES:SE:LP:AF:ID  0.000446901:0.00327787:0.05061:0.868059:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038201 ES:SE:LP:AF:ID  0.0145019:0.00588712:1.85387:0.038201:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867996 ES:SE:LP:AF:ID  0.000349515:0.00327645:0.0362122:0.867996:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.0258575:0.0164197:0.920819:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836167 ES:SE:LP:AF:ID  -0.00308798:0.00304512:0.508638:0.836167:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038204 ES:SE:LP:AF:ID  0.0144945:0.00589572:1.85387:0.038204:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836801 ES:SE:LP:AF:ID  -0.00298621:0.00305347:0.481486:0.836801:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.0247154:0.0110461:1.60206:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.0171682:0.0163981:0.522879:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.83811  ES:SE:LP:AF:ID  -0.00283588:0.00309624:0.443698:0.83811:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868229 ES:SE:LP:AF:ID  0.000569924:0.00327196:0.0655015:0.868229:rs3115858