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_4230.vcf.gz --id UKB-b:43 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4230.txt.gz --cohort_controls 148728 --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-43/UKB-b-43_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-43/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:08 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-43/UKB-b-43_data.vcf.gz ...
Read summary statistics for 9312752 SNPs.
Dropped 10353 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, 1287972 SNPs remain.
After merging with regression SNP LD, 1287972 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0202 (0.0036)
Lambda GC: 1.1102
Mean Chi^2: 1.1169
Intercept: 1.0578 (0.007)
Ratio: 0.4941 (0.0597)
Analysis finished at Thu Oct 17 14:45:50 2019
Total time elapsed: 1.0m:42.14s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9487,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "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": 112618,
    "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": 1287972,
    "ldsc_nsnp_merge_regression_ld": 1287972,
    "ldsc_observed_scale_h2_beta": 0.0202,
    "ldsc_observed_scale_h2_se": 0.0036,
    "ldsc_intercept_beta": 1.0578,
    "ldsc_intercept_se": 0.007,
    "ldsc_lambda_gc": 1.1102,
    "ldsc_mean_chisq": 1.1169,
    "ldsc_ratio": 0.4944
}

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 9302451 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 9312752 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 8.634860e+00 5.754010e+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 7.881181e+07 5.630722e+07 828.0000000 3.250351e+07 6.939512e+07 1.145416e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.264000e-04 1.371760e-02 -0.1596630 -4.897700e-03 9.380000e-05 5.154900e-03 1.720490e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.042080e-02 8.240100e-03 0.0034902 4.199000e-03 6.636700e-03 1.428410e-02 1.208630e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.853008e-01 2.927013e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.853009e-01 2.926764e-01 0.0000000 2.282472e-01 4.806584e-01 7.386157e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.136503e-01 2.578000e-01 0.0023540 1.743800e-02 9.234550e-02 3.354320e-01 9.976460e-01 ▇▂▁▁▁
numeric AF_reference 112618 0.9879071 NA NA NA NA NA NA NA 2.146773e-01 2.495890e-01 0.0000000 1.477640e-02 1.108230e-01 3.354630e-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 0.0020521 0.0064313 0.7499995 0.7496618 0.623817 0.7821490 NA
1 54676 rs2462492 C T -0.0080429 0.0063897 0.2099999 0.2081294 0.399249 NA NA
1 86028 rs114608975 T C 0.0030356 0.0101908 0.7700005 0.7657974 0.103696 0.0277556 NA
1 91536 rs6702460 G T 0.0038860 0.0062958 0.5400003 0.5370732 0.456324 0.4207270 NA
1 234313 rs8179466 C T 0.0018463 0.0124146 0.8800001 0.8817752 0.074565 NA NA
1 534192 rs6680723 C T 0.0027580 0.0071985 0.6999999 0.7016210 0.241171 NA NA
1 546697 rs12025928 A G 0.0142039 0.0089312 0.1100001 0.1117529 0.913117 NA NA
1 693731 rs12238997 A G 0.0061872 0.0059956 0.2999998 0.3020899 0.117038 0.1417730 NA
1 705882 rs72631875 G A -0.0086707 0.0087775 0.3200000 0.3232358 0.067511 0.0315495 NA
1 706368 rs55727773 A G -0.0056088 0.0044409 0.2099999 0.2065925 0.514781 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0023661 0.0053796 0.6600001 0.6600672 0.137133 0.2052720 NA
22 51219387 rs9616832 T C 0.0091599 0.0069984 0.1900002 0.1905847 0.072756 0.0654952 NA
22 51219704 rs147475742 G A 0.0058388 0.0093329 0.5300002 0.5315686 0.041733 0.0473243 NA
22 51221190 rs369304721 G A 0.0051126 0.0093610 0.5800000 0.5849537 0.049150 NA NA
22 51221731 rs115055839 T C 0.0090907 0.0070035 0.1900002 0.1942797 0.072229 0.0625000 NA
22 51222100 rs114553188 G T -0.0077522 0.0081939 0.3400001 0.3440963 0.054487 0.0880591 NA
22 51223637 rs375798137 G A -0.0084146 0.0082347 0.3100002 0.3068536 0.054106 0.0788738 NA
22 51229805 rs9616985 T C 0.0095487 0.0070291 0.1700000 0.1743205 0.072082 0.0730831 NA
22 51232488 rs376461333 A G -0.0231037 0.0164435 0.1600000 0.1600107 0.020184 NA NA
22 51237063 rs3896457 T C -0.0000036 0.0042747 1.0000000 0.9993203 0.297511 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623817 ES:SE:LP:AF:ID  0.00205213:0.00643129:0.124939:0.623817:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399249 ES:SE:LP:AF:ID  -0.00804293:0.00638974:0.677781:0.399249:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103696 ES:SE:LP:AF:ID  0.0030356:0.0101908:0.113509:0.103696:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456324 ES:SE:LP:AF:ID  0.00388602:0.00629575:0.267606:0.456324:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074565 ES:SE:LP:AF:ID  0.00184629:0.0124146:0.0555173:0.074565:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241171 ES:SE:LP:AF:ID  0.002758:0.00719854:0.154902:0.241171:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913117 ES:SE:LP:AF:ID  0.0142039:0.00893123:0.958607:0.913117:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117038 ES:SE:LP:AF:ID  0.00618718:0.00599555:0.522879:0.117038:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067511 ES:SE:LP:AF:ID  -0.0086707:0.00877752:0.49485:0.067511:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514781 ES:SE:LP:AF:ID  -0.00560879:0.00444088:0.677781:0.514781:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033502 ES:SE:LP:AF:ID  -0.00461991:0.0111106:0.167491:0.033502:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037192 ES:SE:LP:AF:ID  -0.00411295:0.0100916:0.167491:0.037192:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037324 ES:SE:LP:AF:ID  -0.00464775:0.0100513:0.19382:0.037324:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036978 ES:SE:LP:AF:ID  -0.0042943:0.0101278:0.173925:0.036978:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016422 ES:SE:LP:AF:ID  -0.00238652:0.0157614:0.0555173:0.016422:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03756  ES:SE:LP:AF:ID  -0.00359995:0.0100122:0.142668:0.03756:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037656 ES:SE:LP:AF:ID  -0.00510308:0.00997955:0.21467:0.037656:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101588 ES:SE:LP:AF:ID  -0.00232276:0.00731024:0.124939:0.101588:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958408 ES:SE:LP:AF:ID  0.00123979:0.00962919:0.0457575:0.958408:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031713 ES:SE:LP:AF:ID  -0.00656267:0.0175796:0.148742:0.031713:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052618 ES:SE:LP:AF:ID  -0.0292774:0.0141938:1.40894:0.052618:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037129 ES:SE:LP:AF:ID  -0.00447837:0.0100488:0.180456:0.037129:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037457 ES:SE:LP:AF:ID  -0.00518887:0.00995913:0.221849:0.037457:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841916 ES:SE:LP:AF:ID  -0.00379578:0.00519083:0.337242:0.841916:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056151 ES:SE:LP:AF:ID  -0.00120159:0.00842942:0.05061:0.056151:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122979 ES:SE:LP:AF:ID  0.00588322:0.00569032:0.522879:0.122979:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02581  ES:SE:LP:AF:ID  0.00506117:0.0139789:0.142668:0.02581:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122204 ES:SE:LP:AF:ID  0.00581697:0.00569258:0.508638:0.122204:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133381 ES:SE:LP:AF:ID  0.00346188:0.00560328:0.267606:0.133381:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011253 ES:SE:LP:AF:ID  0.0161531:0.0203052:0.366532:0.011253:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005876 ES:SE:LP:AF:ID  0.0246135:0.0258849:0.468521:0.005876:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037409 ES:SE:LP:AF:ID  -0.00358286:0.00985504:0.142668:0.037409:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837583 ES:SE:LP:AF:ID  -0.00306372:0.00502538:0.267606:0.837583:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837202 ES:SE:LP:AF:ID  -0.00331257:0.00502007:0.29243:0.837202:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86884  ES:SE:LP:AF:ID  -0.00527237:0.00538671:0.481486:0.86884:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130834 ES:SE:LP:AF:ID  0.00511553:0.00539814:0.468521:0.130834:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037898 ES:SE:LP:AF:ID  -0.00405449:0.00969394:0.167491:0.037898:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038157 ES:SE:LP:AF:ID  -0.00432132:0.0096312:0.187087:0.038157:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868171 ES:SE:LP:AF:ID  -0.00547713:0.00537633:0.508638:0.868171:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868277 ES:SE:LP:AF:ID  -0.00539293:0.00537875:0.49485:0.868277:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038088 ES:SE:LP:AF:ID  -0.00435512:0.00967347:0.187087:0.038088:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868167 ES:SE:LP:AF:ID  -0.00545921:0.00537596:0.508638:0.868167:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005181 ES:SE:LP:AF:ID  0.00891475:0.0275728:0.124939:0.005181:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005151 ES:SE:LP:AF:ID  0.00851234:0.0276331:0.119186:0.005151:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836688 ES:SE:LP:AF:ID  -0.0027403:0.0050071:0.236572:0.836688:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038102 ES:SE:LP:AF:ID  -0.00417257:0.00968645:0.173925:0.038102:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837318 ES:SE:LP:AF:ID  -0.00284751:0.00502092:0.244125:0.837318:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01328  ES:SE:LP:AF:ID  -0.0136899:0.0179515:0.346787:0.01328:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005464 ES:SE:LP:AF:ID  0.0121733:0.0272772:0.180456:0.005464:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838526 ES:SE:LP:AF:ID  -0.0023729:0.00508899:0.19382:0.838526:rs3131965