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_20016.vcf.gz --id UKB-b:5238 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20016.txt.gz --cohort_controls 149051 --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-5238/UKB-b-5238_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5238/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5238/UKB-b-5238_data.vcf.gz ...
Read summary statistics for 9313047 SNPs.
Dropped 10362 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, 1287969 SNPs remain.
After merging with regression SNP LD, 1287969 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2182 (0.0091)
Lambda GC: 1.5766
Mean Chi^2: 1.736
Intercept: 1.0835 (0.0107)
Ratio: 0.1135 (0.0145)
Analysis finished at Thu Oct 17 14:46:41 2019
Total time elapsed: 1.0m:36.24s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9487,
    "inflation_factor": 1.4295,
    "mean_EFFECT": -0.0006,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 79,
    "n_p_sig": 6054,
    "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": 112781,
    "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": 1287969,
    "ldsc_nsnp_merge_regression_ld": 1287969,
    "ldsc_observed_scale_h2_beta": 0.2182,
    "ldsc_observed_scale_h2_se": 0.0091,
    "ldsc_intercept_beta": 1.0835,
    "ldsc_intercept_se": 0.0107,
    "ldsc_lambda_gc": 1.5766,
    "ldsc_mean_chisq": 1.736,
    "ldsc_ratio": 0.1135
}

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 1.00000 3 58 0 9302737 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.00000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.00000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 9313047 0.00000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.00000 NA NA NA NA NA NA NA 8.634787e+00 5.753971e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.00000 NA NA NA NA NA NA NA 7.881279e+07 5.630865e+07 828.0000000 3.250298e+07 6.939589e+07 1.145430e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.00000 NA NA NA NA NA NA NA -5.505000e-04 3.041900e-02 -0.3538060 -1.265150e-02 -3.089000e-04 1.177190e-02 3.488880e-01 ▁▁▇▁▁
numeric SE 0 1.00000 NA NA NA NA NA NA NA 2.182230e-02 1.726030e-02 0.0072901 8.791500e-03 1.389860e-02 2.991140e-02 2.528540e-01 ▇▁▁▁▁
numeric PVAL 0 1.00000 NA NA NA NA NA NA NA 4.447484e-01 3.029934e-01 0.0000000 1.700000e-01 4.199997e-01 7.099994e-01 1.000000e+00 ▇▆▆▅▅
numeric PVAL_ztest 0 1.00000 NA NA NA NA NA NA NA 4.447489e-01 3.029674e-01 0.0000000 1.681446e-01 4.239609e-01 7.069554e-01 9.999999e-01 ▇▆▅▅▅
numeric AF 0 1.00000 NA NA NA NA NA NA NA 2.136377e-01 2.577973e-01 0.0023490 1.743000e-02 9.231400e-02 3.354130e-01 9.976510e-01 ▇▂▁▁▁
numeric AF_reference 112781 0.98789 NA NA NA NA NA NA NA 2.146635e-01 2.495870e-01 0.0000000 1.477640e-02 1.108230e-01 3.352640e-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 0.0168891 0.0134437 0.2099999 0.2090132 0.623797 0.7821490 NA
1 54676 rs2462492 C T -0.0247100 0.0133616 0.0640000 0.0644103 0.399246 NA NA
1 86028 rs114608975 T C -0.0137045 0.0212874 0.5199996 0.5197151 0.103783 0.0277556 NA
1 91536 rs6702460 G T -0.0171895 0.0131639 0.1900002 0.1916185 0.456196 0.4207270 NA
1 234313 rs8179466 C T 0.0318972 0.0259584 0.2200002 0.2191538 0.074589 NA NA
1 534192 rs6680723 C T 0.0096471 0.0150419 0.5199996 0.5212946 0.241309 NA NA
1 546697 rs12025928 A G 0.0137466 0.0186733 0.4600002 0.4616313 0.913100 NA NA
1 693731 rs12238997 A G 0.0005384 0.0125240 0.9699999 0.9657099 0.117215 0.1417730 NA
1 705882 rs72631875 G A -0.0108154 0.0183394 0.5600000 0.5553678 0.067617 0.0315495 NA
1 706368 rs55727773 A G -0.0071614 0.0092833 0.4400003 0.4404491 0.514688 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0006523 0.0112898 0.9500000 0.9539280 0.136982 0.2052720 NA
22 51219387 rs9616832 T C 0.0029798 0.0146807 0.8400000 0.8391538 0.072723 0.0654952 NA
22 51219704 rs147475742 G A -0.0071437 0.0195938 0.7199992 0.7154165 0.041651 0.0473243 NA
22 51221190 rs369304721 G A 0.0073970 0.0196527 0.7099994 0.7066281 0.049069 NA NA
22 51221731 rs115055839 T C 0.0002145 0.0146912 0.9900000 0.9883493 0.072202 0.0625000 NA
22 51222100 rs114553188 G T 0.0037447 0.0172096 0.8300000 0.8277462 0.054402 0.0880591 NA
22 51223637 rs375798137 G A 0.0052850 0.0172966 0.7600007 0.7599445 0.054017 0.0788738 NA
22 51229805 rs9616985 T C 0.0021947 0.0147451 0.8800001 0.8816772 0.072056 0.0730831 NA
22 51232488 rs376461333 A G 0.0179199 0.0345469 0.5999997 0.6039614 0.020142 NA NA
22 51237063 rs3896457 T C -0.0066881 0.0089614 0.4600002 0.4554680 0.297740 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623797 ES:SE:LP:AF:ID  0.0168891:0.0134437:0.677781:0.623797:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399246 ES:SE:LP:AF:ID  -0.02471:0.0133616:1.19382:0.399246:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103783 ES:SE:LP:AF:ID  -0.0137045:0.0212874:0.283997:0.103783:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456196 ES:SE:LP:AF:ID  -0.0171895:0.0131639:0.721246:0.456196:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074589 ES:SE:LP:AF:ID  0.0318972:0.0259584:0.657577:0.074589:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241309 ES:SE:LP:AF:ID  0.00964714:0.0150419:0.283997:0.241309:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.9131   ES:SE:LP:AF:ID  0.0137466:0.0186733:0.337242:0.9131:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117215 ES:SE:LP:AF:ID  0.0005384:0.012524:0.0132283:0.117215:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067617 ES:SE:LP:AF:ID  -0.0108154:0.0183394:0.251812:0.067617:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514688 ES:SE:LP:AF:ID  -0.00716144:0.00928327:0.356547:0.514688:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03346  ES:SE:LP:AF:ID  0.0117392:0.0232401:0.21467:0.03346:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037142 ES:SE:LP:AF:ID  0.0070363:0.0211058:0.130768:0.037142:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037273 ES:SE:LP:AF:ID  0.0065404:0.0210216:0.119186:0.037273:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036928 ES:SE:LP:AF:ID  0.00805943:0.0211821:0.154902:0.036928:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016415 ES:SE:LP:AF:ID  -0.00259057:0.0329593:0.0268721:0.016415:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03751  ES:SE:LP:AF:ID  0.00511197:0.0209389:0.091515:0.03751:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037606 ES:SE:LP:AF:ID  0.00467799:0.020871:0.0861861:0.037606:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101484 ES:SE:LP:AF:ID  0.00955199:0.0152879:0.275724:0.101484:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958471 ES:SE:LP:AF:ID  -0.012764:0.0201467:0.275724:0.958471:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031664 ES:SE:LP:AF:ID  -0.0534582:0.0367917:0.823909:0.031664:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052699 ES:SE:LP:AF:ID  0.00873702:0.0296267:0.113509:0.052699:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037087 ES:SE:LP:AF:ID  0.00292316:0.0210133:0.05061:0.037087:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037426 ES:SE:LP:AF:ID  0.00116895:0.020823:0.0177288:0.037426:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841813 ES:SE:LP:AF:ID  -0.00226865:0.0108477:0.0809219:0.841813:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056281 ES:SE:LP:AF:ID  0.0022228:0.0176002:0.0457575:0.056281:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123156 ES:SE:LP:AF:ID  -0.00272365:0.0118872:0.0861861:0.123156:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025766 ES:SE:LP:AF:ID  -0.0169004:0.0292408:0.251812:0.025766:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12237  ES:SE:LP:AF:ID  -0.00324727:0.0118931:0.107905:0.12237:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133512 ES:SE:LP:AF:ID  -0.00229095:0.0117053:0.0757207:0.133512:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01123  ES:SE:LP:AF:ID  0.00728974:0.0425067:0.0655015:0.01123:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.00586  ES:SE:LP:AF:ID  -0.0400778:0.0542031:0.337242:0.00586:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037354 ES:SE:LP:AF:ID  -0.00150518:0.0206127:0.0268721:0.037354:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837543 ES:SE:LP:AF:ID  -0.00461911:0.0105036:0.180456:0.837543:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837172 ES:SE:LP:AF:ID  -0.00557541:0.0104925:0.221849:0.837172:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868739 ES:SE:LP:AF:ID  -0.00477945:0.0112564:0.173925:0.868739:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130956 ES:SE:LP:AF:ID  0.00567894:0.0112797:0.21467:0.130956:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037851 ES:SE:LP:AF:ID  -0.00151766:0.0202737:0.0268721:0.037851:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038101 ES:SE:LP:AF:ID  -0.00198557:0.020145:0.0362122:0.038101:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868073 ES:SE:LP:AF:ID  -0.00630922:0.0112347:0.244125:0.868073:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868179 ES:SE:LP:AF:ID  -0.00587372:0.0112394:0.221849:0.868179:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038032 ES:SE:LP:AF:ID  -0.000803602:0.0202334:0.0132283:0.038032:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868075 ES:SE:LP:AF:ID  -0.0059857:0.0112341:0.229148:0.868075:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005148 ES:SE:LP:AF:ID  0.123015:0.0577884:1.48149:0.005148:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005118 ES:SE:LP:AF:ID  0.122446:0.0579165:1.46852:0.005118:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836656 ES:SE:LP:AF:ID  -0.00569318:0.010465:0.229148:0.836656:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038037 ES:SE:LP:AF:ID  -0.0019657:0.0202627:0.0362122:0.038037:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837289 ES:SE:LP:AF:ID  -0.00550811:0.0104939:0.221849:0.837289:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013291 ES:SE:LP:AF:ID  -0.0154348:0.0375148:0.167491:0.013291:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005446 ES:SE:LP:AF:ID  -0.0710109:0.0570812:0.677781:0.005446:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838475 ES:SE:LP:AF:ID  -0.00383644:0.0106352:0.142668:0.838475:rs3131965