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_102260.vcf.gz --id UKB-b:117 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102260.txt.gz --cohort_controls 64944 --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-117/UKB-b-117_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-117/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:00 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-117/UKB-b-117_data.vcf.gz ...
Read summary statistics for 8624987 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: 0.011 (0.0068)
Lambda GC: 1.0291
Mean Chi^2: 1.0212
Intercept: 1.0071 (0.006)
Ratio: 0.3335 (0.2838)
Analysis finished at Thu Oct 17 14:42:39 2019
Total time elapsed: 1.0m:39.02s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": 0.011,
    "ldsc_observed_scale_h2_se": 0.0068,
    "ldsc_intercept_beta": 1.0071,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0291,
    "ldsc_mean_chisq": 1.0212,
    "ldsc_ratio": 0.3349
}

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 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 8617650 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 8624987 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 8.650346e+00 5.760989e+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 7.877017e+07 5.637173e+07 828.0000000 3.238532e+07 6.929207e+07 1.145691e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.750000e-05 1.325970e-02 -0.1340740 -5.606400e-03 -5.450000e-05 5.488600e-03 1.470370e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.093790e-02 7.466300e-03 0.0043441 5.115700e-03 7.542800e-03 1.478560e-02 7.488550e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.971612e-01 2.892476e-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 4.971608e-01 2.892213e-01 0.0000000 2.461940e-01 4.960100e-01 7.473739e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291173e-01 2.594883e-01 0.0053900 2.519000e-02 1.138960e-01 3.626230e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83095 0.9903658 NA NA NA NA NA NA NA 2.288665e-01 2.514590e-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 0.0013303 0.0080062 0.8700001 0.8680334 0.623818 0.7821490 NA
1 54676 rs2462492 C T -0.0055871 0.0079827 0.4799997 0.4839865 0.399145 NA NA
1 86028 rs114608975 T C 0.0085115 0.0127067 0.5000000 0.5029571 0.103536 0.0277556 NA
1 91536 rs6702460 G T -0.0015521 0.0078514 0.8400000 0.8432913 0.455907 0.4207270 NA
1 234313 rs8179466 C T 0.0184575 0.0155271 0.2300001 0.2345467 0.074454 NA NA
1 534192 rs6680723 C T -0.0074278 0.0089423 0.4100001 0.4061738 0.242064 NA NA
1 546697 rs12025928 A G 0.0016022 0.0110955 0.8900000 0.8851861 0.912866 NA NA
1 693731 rs12238997 A G 0.0007429 0.0074562 0.9199999 0.9206383 0.117317 0.1417730 NA
1 705882 rs72631875 G A -0.0137887 0.0108683 0.2000000 0.2045452 0.067699 0.0315495 NA
1 706368 rs55727773 A G -0.0012897 0.0055345 0.8200001 0.8157436 0.513310 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0016624 0.0067319 0.8000000 0.8049561 0.136310 0.2052720 NA
22 51219387 rs9616832 T C -0.0047208 0.0087743 0.5900000 0.5905548 0.071795 0.0654952 NA
22 51219704 rs147475742 G A 0.0078110 0.0116716 0.5000000 0.5033452 0.041193 0.0473243 NA
22 51221190 rs369304721 G A -0.0089000 0.0117547 0.4500005 0.4489641 0.048372 NA NA
22 51221731 rs115055839 T C -0.0048408 0.0087762 0.5800000 0.5812351 0.071346 0.0625000 NA
22 51222100 rs114553188 G T 0.0004326 0.0101699 0.9699999 0.9660721 0.054847 0.0880591 NA
22 51223637 rs375798137 G A 0.0003724 0.0102230 0.9699999 0.9709417 0.054467 0.0788738 NA
22 51229805 rs9616985 T C -0.0055535 0.0088033 0.5300002 0.5281420 0.071251 0.0730831 NA
22 51232488 rs376461333 A G -0.0102141 0.0202761 0.6100002 0.6144366 0.020458 NA NA
22 51237063 rs3896457 T C -0.0038918 0.0053144 0.4600002 0.4639779 0.298397 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623818 ES:SE:LP:AF:ID  0.00133028:0.00800616:0.0604807:0.623818:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399145 ES:SE:LP:AF:ID  -0.00558712:0.00798268:0.318759:0.399145:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  0.00851151:0.0127067:0.30103:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455907 ES:SE:LP:AF:ID  -0.00155211:0.00785142:0.0757207:0.455907:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074454 ES:SE:LP:AF:ID  0.0184575:0.0155271:0.638272:0.074454:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242064 ES:SE:LP:AF:ID  -0.00742785:0.00894226:0.387216:0.242064:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912866 ES:SE:LP:AF:ID  0.00160217:0.0110955:0.05061:0.912866:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117317 ES:SE:LP:AF:ID  0.000742858:0.00745619:0.0362122:0.117317:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067699 ES:SE:LP:AF:ID  -0.0137887:0.0108683:0.69897:0.067699:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51331  ES:SE:LP:AF:ID  -0.00128967:0.00553452:0.0861861:0.51331:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033673 ES:SE:LP:AF:ID  0.0303784:0.013796:1.55284:0.033673:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037454 ES:SE:LP:AF:ID  0.0302784:0.012512:1.79588:0.037454:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037641 ES:SE:LP:AF:ID  0.0314475:0.0124465:1.92082:0.037641:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037217 ES:SE:LP:AF:ID  0.0313183:0.0125603:1.88606:0.037217:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016282 ES:SE:LP:AF:ID  -0.00651814:0.0197005:0.130768:0.016282:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037857 ES:SE:LP:AF:ID  0.0304466:0.0124041:1.85387:0.037857:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037951 ES:SE:LP:AF:ID  0.0301598:0.0123647:1.82391:0.037951:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102731 ES:SE:LP:AF:ID  0.0106561:0.00903202:0.619789:0.102731:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958093 ES:SE:LP:AF:ID  -0.0327194:0.0119424:2.21467:0.958093:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031692 ES:SE:LP:AF:ID  -0.0326612:0.0218673:0.853872:0.031692:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052717 ES:SE:LP:AF:ID  0.00548047:0.0176167:0.119186:0.052717:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037445 ES:SE:LP:AF:ID  0.0332915:0.012446:2.12494:0.037445:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037714 ES:SE:LP:AF:ID  0.0316255:0.0123429:2:0.037714:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841437 ES:SE:LP:AF:ID  -0.00842076:0.00644828:0.721246:0.841437:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056338 ES:SE:LP:AF:ID  0.000936505:0.0104748:0.0315171:0.056338:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12308  ES:SE:LP:AF:ID  -0.00119045:0.00708253:0.0604807:0.12308:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025132 ES:SE:LP:AF:ID  -0.00533142:0.0176378:0.119186:0.025132:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122333 ES:SE:LP:AF:ID  -0.00136755:0.00708482:0.0705811:0.122333:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134144 ES:SE:LP:AF:ID  0.00901728:0.00695498:0.721246:0.134144:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011552 ES:SE:LP:AF:ID  0.00318613:0.0247956:0.0457575:0.011552:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.0392078:0.0314769:0.677781:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037595 ES:SE:LP:AF:ID  0.0328843:0.0122288:2.14267:0.037595:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837025 ES:SE:LP:AF:ID  -0.00583734:0.00623799:0.455932:0.837025:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836729 ES:SE:LP:AF:ID  -0.00627134:0.00623341:0.508638:0.836729:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86856  ES:SE:LP:AF:ID  0.00204022:0.00669813:0.119186:0.86856:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131007 ES:SE:LP:AF:ID  -0.00132385:0.00671533:0.0757207:0.131007:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03804  ES:SE:LP:AF:ID  0.0293121:0.012037:1.82391:0.03804:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038287 ES:SE:LP:AF:ID  0.0294243:0.0119624:1.85387:0.038287:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867973 ES:SE:LP:AF:ID  0.00171749:0.00668736:0.09691:0.867973:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868047 ES:SE:LP:AF:ID  0.00200002:0.00669009:0.119186:0.868047:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038195 ES:SE:LP:AF:ID  0.0295264:0.0120176:1.85387:0.038195:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867984 ES:SE:LP:AF:ID  0.00171299:0.00668719:0.09691:0.867984:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.0281358:0.033515:0.39794:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836154 ES:SE:LP:AF:ID  -0.00511003:0.00621517:0.387216:0.836154:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038198 ES:SE:LP:AF:ID  0.0291404:0.0120352:1.82391:0.038198:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836788 ES:SE:LP:AF:ID  -0.00500881:0.0062322:0.376751:0.836788:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  0.0146808:0.0225469:0.29243:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.0170258:0.0334711:0.21467:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838105 ES:SE:LP:AF:ID  -0.00332212:0.00631992:0.221849:0.838105:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868225 ES:SE:LP:AF:ID  0.00282592:0.00667859:0.173925:0.868225:rs3115858