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_20153.vcf.gz --id UKB-b:8428 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20153.txt.gz --cohort_controls 148653 --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-8428/UKB-b-8428_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8428/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:36 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8428/UKB-b-8428_data.vcf.gz ...
Read summary statistics for 9294495 SNPs.
Dropped 10292 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, 1287895 SNPs remain.
After merging with regression SNP LD, 1287895 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1613 (0.0096)
Lambda GC: 1.3245
Mean Chi^2: 1.557
Intercept: 1.0716 (0.011)
Ratio: 0.1286 (0.0197)
Analysis finished at Thu Oct 17 14:43:19 2019
Total time elapsed: 1.0m:42.57s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9484,
    "inflation_factor": 1.2544,
    "mean_EFFECT": -5.5697e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 123,
    "n_p_sig": 10643,
    "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": 112794,
    "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": 1287895,
    "ldsc_nsnp_merge_regression_ld": 1287895,
    "ldsc_observed_scale_h2_beta": 0.1613,
    "ldsc_observed_scale_h2_se": 0.0096,
    "ldsc_intercept_beta": 1.0716,
    "ldsc_intercept_se": 0.011,
    "ldsc_lambda_gc": 1.3245,
    "ldsc_mean_chisq": 1.557,
    "ldsc_ratio": 0.1285
}

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 9284254 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 9294495 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 8.634415e+00 5.753956e+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 7.881294e+07 5.630963e+07 828.0000000 3.250548e+07 6.939352e+07 1.145423e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -5.600000e-06 9.056900e-03 -0.1117320 -3.495600e-03 -1.150000e-05 3.474000e-03 1.074220e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.671500e-03 5.266500e-03 0.0022407 2.695600e-03 4.253800e-03 9.140200e-03 7.717230e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.642559e-01 2.993039e-01 0.0000000 2.000000e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.642562e-01 2.992799e-01 0.0000000 1.966578e-01 4.526985e-01 7.235909e-01 9.999998e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.139035e-01 2.577953e-01 0.0023550 1.755200e-02 9.266600e-02 3.360455e-01 9.976450e-01 ▇▂▁▁▁
numeric AF_reference 112794 0.9878644 NA NA NA NA NA NA NA 2.148413e-01 2.496088e-01 0.0000000 1.477640e-02 1.110220e-01 3.358630e-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 0.0042190 0.0041212 0.3100002 0.3059659 0.624076 0.7821490 NA
1 54676 rs2462492 C T -0.0056302 0.0040879 0.1700000 0.1684221 0.400879 NA NA
1 86028 rs114608975 T C 0.0040152 0.0065410 0.5400003 0.5393127 0.103399 0.0277556 NA
1 91536 rs6702460 G T 0.0001425 0.0040232 0.9699999 0.9717437 0.456786 0.4207270 NA
1 234313 rs8179466 C T -0.0042734 0.0079716 0.5900000 0.5919069 0.074382 NA NA
1 534192 rs6680723 C T -0.0042480 0.0045885 0.3500000 0.3545480 0.241045 NA NA
1 546697 rs12025928 A G 0.0004449 0.0057514 0.9400001 0.9383384 0.914079 NA NA
1 693731 rs12238997 A G -0.0068462 0.0038310 0.0739997 0.0739340 0.117190 0.1417730 NA
1 705882 rs72631875 G A -0.0001503 0.0056759 0.9800000 0.9788789 0.066512 0.0315495 NA
1 706368 rs55727773 A G 0.0059522 0.0028465 0.0369999 0.0365199 0.515316 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0001237 0.0034460 0.9699999 0.9713706 0.137964 0.2052720 NA
22 51219387 rs9616832 T C 0.0001273 0.0044852 0.9800000 0.9773540 0.073364 0.0654952 NA
22 51219704 rs147475742 G A -0.0080855 0.0060307 0.1800002 0.1800125 0.041407 0.0473243 NA
22 51221190 rs369304721 G A -0.0028232 0.0060158 0.6400000 0.6388534 0.049310 NA NA
22 51221731 rs115055839 T C -0.0000351 0.0044895 0.9900000 0.9937538 0.072842 0.0625000 NA
22 51222100 rs114553188 G T 0.0003445 0.0052560 0.9500000 0.9477352 0.054804 0.0880591 NA
22 51223637 rs375798137 G A 0.0010537 0.0052809 0.8400000 0.8418505 0.054445 0.0788738 NA
22 51229805 rs9616985 T C -0.0002716 0.0045061 0.9500000 0.9519444 0.072700 0.0730831 NA
22 51232488 rs376461333 A G -0.0061247 0.0105221 0.5600000 0.5605143 0.020229 NA NA
22 51237063 rs3896457 T C 0.0008312 0.0027420 0.7600007 0.7617907 0.297765 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624076 ES:SE:LP:AF:ID  0.004219:0.00412123:0.508638:0.624076:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400879 ES:SE:LP:AF:ID  -0.00563023:0.0040879:0.769551:0.400879:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103399 ES:SE:LP:AF:ID  0.00401518:0.00654095:0.267606:0.103399:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456786 ES:SE:LP:AF:ID  0.000142509:0.00402325:0.0132283:0.456786:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074382 ES:SE:LP:AF:ID  -0.0042734:0.00797165:0.229148:0.074382:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241045 ES:SE:LP:AF:ID  -0.00424805:0.0045885:0.455932:0.241045:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914079 ES:SE:LP:AF:ID  0.000444919:0.0057514:0.0268721:0.914079:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11719  ES:SE:LP:AF:ID  -0.00684617:0.00383105:1.13077:0.11719:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066512 ES:SE:LP:AF:ID  -0.000150267:0.00567592:0.00877392:0.066512:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515316 ES:SE:LP:AF:ID  0.00595225:0.00284648:1.4318:0.515316:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032412 ES:SE:LP:AF:ID  -0.00643847:0.00725118:0.431798:0.032412:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03601  ES:SE:LP:AF:ID  -0.0067026:0.00657791:0.508638:0.03601:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036115 ES:SE:LP:AF:ID  -0.0067946:0.00655579:0.522879:0.036115:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035849 ES:SE:LP:AF:ID  -0.00713349:0.00659889:0.552842:0.035849:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016325 ES:SE:LP:AF:ID  0.00241896:0.0100962:0.091515:0.016325:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03634  ES:SE:LP:AF:ID  -0.00612252:0.00653132:0.455932:0.03634:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036458 ES:SE:LP:AF:ID  -0.00597066:0.00650602:0.443698:0.036458:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101473 ES:SE:LP:AF:ID  -0.00179906:0.00469011:0.154902:0.101473:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.960027 ES:SE:LP:AF:ID  0.00751973:0.00629561:0.638272:0.960027:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031305 ES:SE:LP:AF:ID  -0.0193773:0.0113411:1.05552:0.031305:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053038 ES:SE:LP:AF:ID  -0.00132231:0.00900254:0.0555173:0.053038:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036011 ES:SE:LP:AF:ID  -0.00664148:0.0065456:0.508638:0.036011:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036296 ES:SE:LP:AF:ID  -0.00718501:0.00648706:0.568636:0.036296:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843279 ES:SE:LP:AF:ID  0.00748002:0.00333292:1.60206:0.843279:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056515 ES:SE:LP:AF:ID  -0.00279846:0.00537524:0.221849:0.056515:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.1232   ES:SE:LP:AF:ID  -0.00689922:0.003636:1.23657:0.1232:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02605  ES:SE:LP:AF:ID  0.0210561:0.00889474:1.74473:0.02605:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122477 ES:SE:LP:AF:ID  -0.00682698:0.00363667:1.22185:0.122477:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132727 ES:SE:LP:AF:ID  -0.00929028:0.00359051:2.01323:0.132727:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011138 ES:SE:LP:AF:ID  0.021988:0.0130707:1.03152:0.011138:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005704 ES:SE:LP:AF:ID  -0.012288:0.0169112:0.327902:0.005704:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036239 ES:SE:LP:AF:ID  -0.00858572:0.00641799:0.744727:0.036239:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839121 ES:SE:LP:AF:ID  0.00679216:0.00322876:1.45593:0.839121:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838841 ES:SE:LP:AF:ID  0.0065792:0.003226:1.38722:0.838841:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869241 ES:SE:LP:AF:ID  0.00593185:0.00345594:1.0655:0.869241:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130397 ES:SE:LP:AF:ID  -0.00512925:0.00346246:0.853872:0.130397:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036689 ES:SE:LP:AF:ID  -0.010455:0.00631309:1.00877:0.036689:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036929 ES:SE:LP:AF:ID  -0.0103828:0.00627247:1.00877:0.036929:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868651 ES:SE:LP:AF:ID  0.00572457:0.00344963:1.01323:0.868651:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868742 ES:SE:LP:AF:ID  0.00579824:0.00345068:1.03152:0.868742:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036886 ES:SE:LP:AF:ID  -0.0106934:0.00630075:1.04576:0.036886:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868653 ES:SE:LP:AF:ID  0.00576238:0.00344944:1.02228:0.868653:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005054 ES:SE:LP:AF:ID  0.0016914:0.0179114:0.0362122:0.005054:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00502  ES:SE:LP:AF:ID  0.00213028:0.0179575:0.0409586:0.00502:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838256 ES:SE:LP:AF:ID  0.00664512:0.00321787:1.40894:0.838256:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036917 ES:SE:LP:AF:ID  -0.0103414:0.00630769:1:0.036917:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838878 ES:SE:LP:AF:ID  0.00658192:0.00322666:1.38722:0.838878:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013787 ES:SE:LP:AF:ID  0.00141655:0.0112812:0.0457575:0.013787:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005476 ES:SE:LP:AF:ID  -0.026874:0.0174852:0.920819:0.005476:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839787 ES:SE:LP:AF:ID  0.00642606:0.00326919:1.3098:0.839787:rs3131965