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_4451.vcf.gz --id UKB-b:2603 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4451.txt.gz --cohort_controls 34270 --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-2603/UKB-b-2603_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2603/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2603/UKB-b-2603_data.vcf.gz ...
Read summary statistics for 7707916 SNPs.
Dropped 5555 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, 1279591 SNPs remain.
After merging with regression SNP LD, 1279591 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0261 (0.0133)
Lambda GC: 1.0107
Mean Chi^2: 1.0149
Intercept: 0.9974 (0.0061)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:44:20 2019
Total time elapsed: 1.0m:29.54s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9415,
    "inflation_factor": 1,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 2,
    "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": 71755,
    "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": 1279591,
    "ldsc_nsnp_merge_regression_ld": 1279591,
    "ldsc_observed_scale_h2_beta": 0.0261,
    "ldsc_observed_scale_h2_se": 0.0133,
    "ldsc_intercept_beta": 0.9974,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0107,
    "ldsc_mean_chisq": 1.0149,
    "ldsc_ratio": -0.1745
}

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 7702385 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 7707916 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 8.661764e+00 5.763969e+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 7.868840e+07 5.643743e+07 828.0000000 3.220398e+07 6.914617e+07 1.145715e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.330000e-05 1.144540e-02 -0.1033630 -5.619800e-03 -8.420000e-05 5.420300e-03 1.418810e-01 ▁▃▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.969300e-03 5.608400e-03 0.0048136 5.537100e-03 7.524100e-03 1.295060e-02 5.741590e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.989963e-01 2.893076e-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 4.989971e-01 2.892825e-01 0.0000000 2.476084e-01 4.987744e-01 7.498516e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.538808e-01 2.607884e-01 0.0102140 4.140600e-02 1.484690e-01 4.013080e-01 9.897860e-01 ▇▂▂▁▁
numeric AF_reference 71755 0.9906907 NA NA NA NA NA NA NA 2.529514e-01 2.526650e-01 0.0000000 4.532750e-02 1.629390e-01 3.959660e-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 0.0000210 0.0088864 1.0000000 0.9981126 0.623604 0.7821490 NA
1 54676 rs2462492 C T -0.0022328 0.0088012 0.8000000 0.7997314 0.398361 NA NA
1 86028 rs114608975 T C -0.0083933 0.0139615 0.5500004 0.5477256 0.104072 0.0277556 NA
1 91536 rs6702460 G T 0.0001530 0.0086617 0.9900000 0.9859038 0.455424 0.4207270 NA
1 234313 rs8179466 C T 0.0096521 0.0171182 0.5700002 0.5728553 0.074699 NA NA
1 534192 rs6680723 C T 0.0016676 0.0099345 0.8700001 0.8666981 0.240574 NA NA
1 546697 rs12025928 A G -0.0013528 0.0121480 0.9100000 0.9113329 0.911512 NA NA
1 693731 rs12238997 A G 0.0033700 0.0081789 0.6800001 0.6803169 0.118287 0.1417730 NA
1 705882 rs72631875 G A -0.0044777 0.0120680 0.7099994 0.7106084 0.068184 0.0315495 NA
1 706368 rs55727773 A G 0.0042410 0.0061000 0.4899999 0.4868988 0.514274 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0068501 0.0074440 0.3599996 0.3574617 0.137095 0.2052720 NA
22 51219387 rs9616832 T C -0.0122921 0.0096461 0.2000000 0.2025553 0.073408 0.0654952 NA
22 51219704 rs147475742 G A -0.0118242 0.0128588 0.3599996 0.3578124 0.042152 0.0473243 NA
22 51221190 rs369304721 G A -0.0037529 0.0129246 0.7700005 0.7715353 0.049588 NA NA
22 51221731 rs115055839 T C -0.0117872 0.0096475 0.2200002 0.2217888 0.073014 0.0625000 NA
22 51222100 rs114553188 G T -0.0022991 0.0114425 0.8400000 0.8407541 0.053461 0.0880591 NA
22 51223637 rs375798137 G A -0.0020325 0.0115075 0.8600001 0.8598017 0.053058 0.0788738 NA
22 51229805 rs9616985 T C -0.0125995 0.0096851 0.1900002 0.1932886 0.072855 0.0730831 NA
22 51232488 rs376461333 A G -0.0193477 0.0235042 0.4100001 0.4104174 0.019292 NA NA
22 51237063 rs3896457 T C 0.0036693 0.0059092 0.5300002 0.5346370 0.298852 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623604 ES:SE:LP:AF:ID  2.10207e-05:0.00888642:-0:0.623604:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398361 ES:SE:LP:AF:ID  -0.00223281:0.00880117:0.09691:0.398361:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104072 ES:SE:LP:AF:ID  -0.00839326:0.0139615:0.259637:0.104072:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455424 ES:SE:LP:AF:ID  0.000153033:0.00866166:0.00436481:0.455424:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074699 ES:SE:LP:AF:ID  0.00965212:0.0171182:0.244125:0.074699:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240574 ES:SE:LP:AF:ID  0.00166755:0.00993452:0.0604807:0.240574:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.911512 ES:SE:LP:AF:ID  -0.00135277:0.012148:0.0409586:0.911512:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.118287 ES:SE:LP:AF:ID  0.00336997:0.00817893:0.167491:0.118287:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068184 ES:SE:LP:AF:ID  -0.0044777:0.012068:0.148742:0.068184:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514274 ES:SE:LP:AF:ID  0.00424103:0.00610001:0.309804:0.514274:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033933 ES:SE:LP:AF:ID  0.000663091:0.0152497:0.0132283:0.033933:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037753 ES:SE:LP:AF:ID  0.000254133:0.0138293:0.00436481:0.037753:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037942 ES:SE:LP:AF:ID  0.000898284:0.0137616:0.0222764:0.037942:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037586 ES:SE:LP:AF:ID  0.00075327:0.0138761:0.0177288:0.037586:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016532 ES:SE:LP:AF:ID  0.00147148:0.0216547:0.0222764:0.016532:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.038205 ES:SE:LP:AF:ID  0.000980391:0.0137071:0.0268721:0.038205:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.038255 ES:SE:LP:AF:ID  0.00110516:0.0136745:0.0268721:0.038255:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101242 ES:SE:LP:AF:ID  -0.0202344:0.0100864:1.34679:0.101242:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957775 ES:SE:LP:AF:ID  0.00119036:0.0132092:0.0315171:0.957775:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031344 ES:SE:LP:AF:ID  -0.00216216:0.0244802:0.0315171:0.031344:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052435 ES:SE:LP:AF:ID  -0.0246382:0.0195569:0.677781:0.052435:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037642 ES:SE:LP:AF:ID  0.00117489:0.0137829:0.0315171:0.037642:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037963 ES:SE:LP:AF:ID  0.000935235:0.0136577:0.0222764:0.037963:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840312 ES:SE:LP:AF:ID  -0.0017856:0.0071165:0.09691:0.840312:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056064 ES:SE:LP:AF:ID  0.0060634:0.0116142:0.221849:0.056064:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123933 ES:SE:LP:AF:ID  0.0027192:0.00778024:0.136677:0.123933:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024941 ES:SE:LP:AF:ID  0.0220416:0.019783:0.568636:0.024941:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.123164 ES:SE:LP:AF:ID  0.00281066:0.0077863:0.142668:0.123164:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134314 ES:SE:LP:AF:ID  0.00370512:0.00769078:0.200659:0.134314:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011384 ES:SE:LP:AF:ID  0.0143263:0.0276426:0.221849:0.011384:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.038037 ES:SE:LP:AF:ID  0.00029965:0.0135016:0.00877392:0.038037:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836289 ES:SE:LP:AF:ID  0.00176175:0.00690193:0.09691:0.836289:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.835872 ES:SE:LP:AF:ID  0.00229487:0.00689356:0.130768:0.835872:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868177 ES:SE:LP:AF:ID  0.00263213:0.00738587:0.142668:0.868177:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131443 ES:SE:LP:AF:ID  -0.00398229:0.00740233:0.229148:0.131443:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038439 ES:SE:LP:AF:ID  -0.00100128:0.0132992:0.0268721:0.038439:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038723 ES:SE:LP:AF:ID  -0.00144747:0.0132114:0.0409586:0.038723:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867507 ES:SE:LP:AF:ID  0.00348679:0.00737269:0.19382:0.867507:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867647 ES:SE:LP:AF:ID  0.00333197:0.00737718:0.187087:0.867647:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038662 ES:SE:LP:AF:ID  -0.000925515:0.0132578:0.0268721:0.038662:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86752  ES:SE:LP:AF:ID  0.00347074:0.00737244:0.19382:0.86752:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.835409 ES:SE:LP:AF:ID  0.00241059:0.00687685:0.136677:0.835409:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038603 ES:SE:LP:AF:ID  -0.00139085:0.0132889:0.0362122:0.038603:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836093 ES:SE:LP:AF:ID  0.00248756:0.00689754:0.142668:0.836093:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013071 ES:SE:LP:AF:ID  -0.0367735:0.0250382:0.853872:0.013071:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.837505 ES:SE:LP:AF:ID  0.00257727:0.00698958:0.148742:0.837505:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.867738 ES:SE:LP:AF:ID  0.00293837:0.00736378:0.161151:0.867738:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867314 ES:SE:LP:AF:ID  0.00223328:0.00734612:0.119186:0.867314:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866097 ES:SE:LP:AF:ID  0.00399831:0.00733052:0.229148:0.866097:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867397 ES:SE:LP:AF:ID  0.00319578:0.00735085:0.180456:0.867397:rs4951929