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_102980.vcf.gz --id UKB-b:8266 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102980.txt.gz --cohort_controls 64942 --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-8266/UKB-b-8266_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8266/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8266/UKB-b-8266_data.vcf.gz ...
Read summary statistics for 8624954 SNPs.
Dropped 7372 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, 1285749 SNPs remain.
After merging with regression SNP LD, 1285749 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0062 (0.0062)
Lambda GC: 1.0002
Mean Chi^2: 1.0032
Intercept: 1.0111 (0.006)
Ratio: 3.4909 (1.8819)
Analysis finished at Thu Oct 17 14:43:00 2019
Total time elapsed: 1.0m:33.77s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -5.467e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 8,
    "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": 1285749,
    "ldsc_nsnp_merge_regression_ld": 1285749,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0111,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0002,
    "ldsc_mean_chisq": 1.0032,
    "ldsc_ratio": 3.4687
}

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 8617616 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 8624954 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 8.650343e+00 5.760992e+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 7.877021e+07 5.637177e+07 828.0000000 3.238540e+07 6.929182e+07 1.145691e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -5.500000e-06 3.480100e-03 -0.0340191 -1.482200e-03 -3.660000e-05 1.391900e-03 5.021980e-02 ▁▆▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.872500e-03 1.960800e-03 0.0011408 1.343500e-03 1.980900e-03 3.883000e-03 1.966580e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.999496e-01 2.885599e-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 4.999502e-01 2.885323e-01 0.0000000 2.497242e-01 5.002887e-01 7.494861e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291182e-01 2.594884e-01 0.0053900 2.519100e-02 1.138990e-01 3.626200e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83095 0.9903657 NA NA NA NA NA NA NA 2.288672e-01 2.514593e-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 0.0020792 0.0021025 0.3200000 0.3227057 0.623816 0.7821490 NA
1 54676 rs2462492 C T -0.0015092 0.0020965 0.4700002 0.4715891 0.399137 NA NA
1 86028 rs114608975 T C -0.0023887 0.0033373 0.4700002 0.4741404 0.103531 0.0277556 NA
1 91536 rs6702460 G T 0.0015682 0.0020621 0.4500005 0.4469454 0.455922 0.4207270 NA
1 234313 rs8179466 C T -0.0018741 0.0040777 0.6499995 0.6458053 0.074459 NA NA
1 534192 rs6680723 C T -0.0031231 0.0023486 0.1800002 0.1835868 0.242060 NA NA
1 546697 rs12025928 A G 0.0033548 0.0029143 0.2500000 0.2496660 0.912872 NA NA
1 693731 rs12238997 A G 0.0047977 0.0019583 0.0140001 0.0142900 0.117303 0.1417730 NA
1 705882 rs72631875 G A -0.0007065 0.0028545 0.8000000 0.8045080 0.067691 0.0315495 NA
1 706368 rs55727773 A G -0.0026335 0.0014536 0.0700003 0.0700248 0.513290 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0004277 0.0017679 0.8100000 0.8088165 0.136329 0.2052720 NA
22 51219387 rs9616832 T C -0.0012318 0.0023042 0.5900000 0.5929267 0.071805 0.0654952 NA
22 51219704 rs147475742 G A -0.0044707 0.0030652 0.1400000 0.1446910 0.041195 0.0473243 NA
22 51221190 rs369304721 G A -0.0032296 0.0030870 0.2999998 0.2954728 0.048378 NA NA
22 51221731 rs115055839 T C -0.0011196 0.0023047 0.6300007 0.6271191 0.071356 0.0625000 NA
22 51222100 rs114553188 G T 0.0007630 0.0026707 0.7800007 0.7751043 0.054856 0.0880591 NA
22 51223637 rs375798137 G A 0.0008178 0.0026846 0.7600007 0.7606629 0.054476 0.0788738 NA
22 51229805 rs9616985 T C -0.0010716 0.0023118 0.6400000 0.6429884 0.071260 0.0730831 NA
22 51232488 rs376461333 A G -0.0039337 0.0053246 0.4600002 0.4600435 0.020462 NA NA
22 51237063 rs3896457 T C 0.0019940 0.0013958 0.1499999 0.1531169 0.298387 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623816 ES:SE:LP:AF:ID  0.00207916:0.00210247:0.49485:0.623816:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399137 ES:SE:LP:AF:ID  -0.00150924:0.00209647:0.327902:0.399137:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103531 ES:SE:LP:AF:ID  -0.00238868:0.00333727:0.327902:0.103531:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455922 ES:SE:LP:AF:ID  0.00156824:0.00206207:0.346787:0.455922:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074459 ES:SE:LP:AF:ID  -0.0018741:0.0040777:0.187087:0.074459:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24206  ES:SE:LP:AF:ID  -0.00312311:0.00234857:0.744727:0.24206:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912872 ES:SE:LP:AF:ID  0.00335484:0.00291431:0.60206:0.912872:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117303 ES:SE:LP:AF:ID  0.00479767:0.00195832:1.85387:0.117303:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067691 ES:SE:LP:AF:ID  -0.000706528:0.00285446:0.09691:0.067691:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51329  ES:SE:LP:AF:ID  -0.00263347:0.00145355:1.1549:0.51329:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033681 ES:SE:LP:AF:ID  -0.000236162:0.00362287:0.0222764:0.033681:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037461 ES:SE:LP:AF:ID  0.000610545:0.00328577:0.0705811:0.037461:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037648 ES:SE:LP:AF:ID  0.000375929:0.00326857:0.0409586:0.037648:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037224 ES:SE:LP:AF:ID  0.000610525:0.00329844:0.0705811:0.037224:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016276 ES:SE:LP:AF:ID  0.00288946:0.00517523:0.236572:0.016276:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037864 ES:SE:LP:AF:ID  0.000460733:0.0032574:0.05061:0.037864:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037959 ES:SE:LP:AF:ID  0.000685393:0.00324707:0.0809219:0.037959:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102745 ES:SE:LP:AF:ID  1.07238e-06:0.00237192:-0:0.102745:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958085 ES:SE:LP:AF:ID  -0.000784054:0.00313618:0.09691:0.958085:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031685 ES:SE:LP:AF:ID  0.00862672:0.00574342:0.886057:0.031685:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052718 ES:SE:LP:AF:ID  -0.00549343:0.00462652:0.619789:0.052718:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037453 ES:SE:LP:AF:ID  0.000889466:0.00326839:0.102373:0.037453:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037721 ES:SE:LP:AF:ID  0.00124717:0.00324131:0.154902:0.037721:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841447 ES:SE:LP:AF:ID  -0.00412122:0.00169353:1.82391:0.841447:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056325 ES:SE:LP:AF:ID  0.00934128:0.00275127:3.16115:0.056325:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123067 ES:SE:LP:AF:ID  0.00500827:0.00186015:2.14874:0.123067:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025128 ES:SE:LP:AF:ID  0.00132418:0.00463248:0.113509:0.025128:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12232  ES:SE:LP:AF:ID  0.00485802:0.00186076:2.04576:0.12232:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134138 ES:SE:LP:AF:ID  0.00471294:0.0018266:2.00436:0.134138:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  0.0038897:0.00650973:0.259637:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.00597811:0.00826655:0.327902:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037604 ES:SE:LP:AF:ID  0.000592819:0.00321128:0.0705811:0.037604:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837035 ES:SE:LP:AF:ID  -0.00355516:0.00163828:1.52288:0.837035:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836739 ES:SE:LP:AF:ID  -0.0038153:0.00163708:1.69897:0.836739:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868571 ES:SE:LP:AF:ID  -0.00416309:0.00175915:1.74473:0.868571:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130995 ES:SE:LP:AF:ID  0.00459404:0.00176367:2.03621:0.130995:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038049 ES:SE:LP:AF:ID  0.000224421:0.0031609:0.0268721:0.038049:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038296 ES:SE:LP:AF:ID  0.00017461:0.00314132:0.0177288:0.038296:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867985 ES:SE:LP:AF:ID  -0.00452478:0.00175632:2:0.867985:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868059 ES:SE:LP:AF:ID  -0.00454639:0.00175704:2.01323:0.868059:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038204 ES:SE:LP:AF:ID  0.000314529:0.00315582:0.0362122:0.038204:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867996 ES:SE:LP:AF:ID  -0.00453457:0.00175628:2.00877:0.867996:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.00166224:0.00880182:0.0705811:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836165 ES:SE:LP:AF:ID  -0.00385327:0.00163229:1.74473:0.836165:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038207 ES:SE:LP:AF:ID  0.000337476:0.00316043:0.0409586:0.038207:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836799 ES:SE:LP:AF:ID  -0.00386731:0.00163676:1.74473:0.836799:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  -0.00915394:0.00592133:0.920819:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.0183413:0.00879029:1.4318:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838115 ES:SE:LP:AF:ID  -0.00379795:0.00165981:1.65758:0.838115:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868237 ES:SE:LP:AF:ID  -0.00429967:0.00175402:1.85387:0.868237:rs3115858