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_100890.vcf.gz --id UKB-b:228 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_100890.txt.gz --cohort_controls 64949 --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-228/UKB-b-228_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-228/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:23 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-228/UKB-b-228_data.vcf.gz ...
Read summary statistics for 8625255 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, 1285751 SNPs remain.
After merging with regression SNP LD, 1285751 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0258 (0.0075)
Lambda GC: 1.0718
Mean Chi^2: 1.0713
Intercept: 1.0382 (0.006)
Ratio: 0.5355 (0.0836)
Analysis finished at Thu Oct 17 14:44:06 2019
Total time elapsed: 1.0m:43.15s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0.0001,
    "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": 83101,
    "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": 1285751,
    "ldsc_nsnp_merge_regression_ld": 1285751,
    "ldsc_observed_scale_h2_beta": 0.0258,
    "ldsc_observed_scale_h2_se": 0.0075,
    "ldsc_intercept_beta": 1.0382,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0718,
    "ldsc_mean_chisq": 1.0713,
    "ldsc_ratio": 0.5358
}

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 8617917 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 8625255 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 8.650335e+00 5.760993e+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 7.877026e+07 5.637169e+07 828.0000000 3.238547e+07 6.929218e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.326000e-04 1.653650e-02 -0.1871160 -7.001700e-03 -7.700000e-05 6.824200e-03 1.730990e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.336580e-02 9.124000e-03 0.0053079 6.250900e-03 9.216800e-03 1.806800e-02 9.150380e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.906376e-01 2.910840e-01 0.0000001 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.906385e-01 2.910576e-01 0.0000001 2.358936e-01 4.875585e-01 7.425320e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291107e-01 2.594876e-01 0.0053890 2.518600e-02 1.138890e-01 3.626125e-01 9.946110e-01 ▇▂▁▁▁
numeric AF_reference 83101 0.9903654 NA NA NA NA NA NA NA 2.288600e-01 2.514584e-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 -0.0069114 0.0097814 0.4799997 0.4798251 0.623812 0.7821490 NA
1 54676 rs2462492 C T 0.0093256 0.0097530 0.3400001 0.3389805 0.399144 NA NA
1 86028 rs114608975 T C 0.0170450 0.0155252 0.2700001 0.2722515 0.103536 0.0277556 NA
1 91536 rs6702460 G T -0.0142157 0.0095927 0.1400000 0.1383600 0.455916 0.4207270 NA
1 234313 rs8179466 C T 0.0242359 0.0189709 0.2000000 0.2014151 0.074455 NA NA
1 534192 rs6680723 C T 0.0010263 0.0109256 0.9299999 0.9251576 0.242057 NA NA
1 546697 rs12025928 A G 0.0039352 0.0135563 0.7700005 0.7715977 0.912862 NA NA
1 693731 rs12238997 A G 0.0173743 0.0091101 0.0569994 0.0565016 0.117313 0.1417730 NA
1 705882 rs72631875 G A -0.0192407 0.0132789 0.1499999 0.1473465 0.067698 0.0315495 NA
1 706368 rs55727773 A G -0.0090604 0.0067622 0.1800002 0.1802896 0.513304 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0028834 0.0082264 0.7300002 0.7259608 0.136315 0.2052720 NA
22 51219387 rs9616832 T C -0.0058587 0.0107221 0.5800000 0.5847843 0.071797 0.0654952 NA
22 51219704 rs147475742 G A -0.0047981 0.0142633 0.7400005 0.7365732 0.041190 0.0473243 NA
22 51221190 rs369304721 G A 0.0014149 0.0143644 0.9199999 0.9215338 0.048372 NA NA
22 51221731 rs115055839 T C -0.0064945 0.0107245 0.5400003 0.5447957 0.071348 0.0625000 NA
22 51222100 rs114553188 G T 0.0046723 0.0124273 0.7099994 0.7069357 0.054850 0.0880591 NA
22 51223637 rs375798137 G A 0.0041406 0.0124922 0.7400005 0.7403004 0.054470 0.0788738 NA
22 51229805 rs9616985 T C -0.0071260 0.0107576 0.5099998 0.5077038 0.071253 0.0730831 NA
22 51232488 rs376461333 A G 0.0196476 0.0247767 0.4299995 0.4277854 0.020460 NA NA
22 51237063 rs3896457 T C 0.0015758 0.0064944 0.8100000 0.8082806 0.298393 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623812 ES:SE:LP:AF:ID  -0.00691138:0.0097814:0.318759:0.623812:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399144 ES:SE:LP:AF:ID  0.0093256:0.00975296:0.468521:0.399144:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  0.017045:0.0155252:0.568636:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455916 ES:SE:LP:AF:ID  -0.0142157:0.00959272:0.853872:0.455916:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074455 ES:SE:LP:AF:ID  0.0242359:0.0189709:0.69897:0.074455:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242057 ES:SE:LP:AF:ID  0.00102634:0.0109256:0.0315171:0.242057:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912862 ES:SE:LP:AF:ID  0.0039352:0.0135563:0.113509:0.912862:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117313 ES:SE:LP:AF:ID  0.0173743:0.0091101:1.24413:0.117313:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067698 ES:SE:LP:AF:ID  -0.0192407:0.0132789:0.823909:0.067698:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  -0.00906036:0.00676215:0.744727:0.513304:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033677 ES:SE:LP:AF:ID  -0.0388943:0.016855:1.67778:0.033677:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037457 ES:SE:LP:AF:ID  -0.0331942:0.0152866:1.52288:0.037457:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037644 ES:SE:LP:AF:ID  -0.0317867:0.0152066:1.4318:0.037644:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03722  ES:SE:LP:AF:ID  -0.0336348:0.0153456:1.55284:0.03722:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016282 ES:SE:LP:AF:ID  0.0396162:0.0240705:1:0.016282:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03786  ES:SE:LP:AF:ID  -0.0311297:0.0151546:1.39794:0.03786:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037955 ES:SE:LP:AF:ID  -0.0318544:0.0151066:1.45593:0.037955:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102736 ES:SE:LP:AF:ID  0.0100935:0.011035:0.443698:0.102736:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95809  ES:SE:LP:AF:ID  0.024404:0.0145907:1.02687:0.95809:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03169  ES:SE:LP:AF:ID  0.00639637:0.0267179:0.091515:0.03169:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  -0.00823205:0.0215205:0.154902:0.052723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037449 ES:SE:LP:AF:ID  -0.0317385:0.0152058:1.4318:0.037449:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037717 ES:SE:LP:AF:ID  -0.0297692:0.0150798:1.31876:0.037717:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841441 ES:SE:LP:AF:ID  -0.00548136:0.00787855:0.309804:0.841441:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056334 ES:SE:LP:AF:ID  0.0189127:0.0127983:0.853872:0.056334:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123078 ES:SE:LP:AF:ID  0.0168071:0.00865354:1.284:0.123078:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02513  ES:SE:LP:AF:ID  -0.0143943:0.0215502:0.30103:0.02513:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12233  ES:SE:LP:AF:ID  0.0167474:0.00865633:1.27572:0.12233:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134139 ES:SE:LP:AF:ID  -0.000186818:0.0084976:0.00877392:0.134139:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  -0.0461315:0.0302858:0.886057:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.0061   ES:SE:LP:AF:ID  0.00902718:0.0384593:0.091515:0.0061:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.0376   ES:SE:LP:AF:ID  -0.0294747:0.0149401:1.3098:0.0376:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837029 ES:SE:LP:AF:ID  -0.00239196:0.00762154:0.124939:0.837029:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836733 ES:SE:LP:AF:ID  -0.00257532:0.00761595:0.130768:0.836733:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868562 ES:SE:LP:AF:ID  -0.0125828:0.00818373:0.920819:0.868562:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131004 ES:SE:LP:AF:ID  0.0121055:0.00820475:0.853872:0.131004:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038045 ES:SE:LP:AF:ID  -0.0269173:0.0147057:1.17393:0.038045:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038292 ES:SE:LP:AF:ID  -0.0272627:0.0146146:1.20761:0.038292:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867976 ES:SE:LP:AF:ID  -0.0127826:0.00817057:0.920819:0.867976:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86805  ES:SE:LP:AF:ID  -0.0125674:0.00817391:0.920819:0.86805:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038199 ES:SE:LP:AF:ID  -0.0286583:0.0146821:1.29243:0.038199:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867987 ES:SE:LP:AF:ID  -0.0129446:0.00817037:0.958607:0.867987:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005396 ES:SE:LP:AF:ID  -0.0465751:0.0409495:0.585027:0.005396:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836159 ES:SE:LP:AF:ID  -0.00259129:0.00759367:0.136677:0.836159:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038203 ES:SE:LP:AF:ID  -0.0294847:0.0147035:1.34679:0.038203:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836793 ES:SE:LP:AF:ID  -0.00288815:0.00761447:0.154902:0.836793:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.0237188:0.0275483:0.408935:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.017197:0.0408959:0.173925:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838109 ES:SE:LP:AF:ID  -0.00368335:0.00772166:0.200659:0.838109:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868228 ES:SE:LP:AF:ID  -0.0127825:0.00815985:0.920819:0.868228:rs3115858