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_2030.vcf.gz --id UKB-b:10169 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2030.txt.gz --cohort_cases 129383 --cohort_controls 321321 --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-10169/UKB-b-10169_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10169/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10169/UKB-b-10169_data.vcf.gz ...
Read summary statistics for 9208717 SNPs.
Dropped 9758 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, 1287720 SNPs remain.
After merging with regression SNP LD, 1287720 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0491 (0.0021)
Lambda GC: 1.371
Mean Chi^2: 1.46
Intercept: 1.0257 (0.0087)
Ratio: 0.056 (0.0189)
Analysis finished at Thu Oct 17 14:41:56 2019
Total time elapsed: 1.0m:38.82s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9483,
    "inflation_factor": 1.2544,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 29,
    "n_p_sig": 4305,
    "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": 105115,
    "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": 1287720,
    "ldsc_nsnp_merge_regression_ld": 1287720,
    "ldsc_observed_scale_h2_beta": 0.0491,
    "ldsc_observed_scale_h2_se": 0.0021,
    "ldsc_intercept_beta": 1.0257,
    "ldsc_intercept_se": 0.0087,
    "ldsc_lambda_gc": 1.371,
    "ldsc_mean_chisq": 1.46,
    "ldsc_ratio": 0.0559
}

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 1.0000000 3 58 0 9199008 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 9208717 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 8.637353e+00 5.755043e+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 7.880488e+07 5.631617e+07 828.0000000 3.248659e+07 6.938378e+07 1.145351e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.830000e-05 3.516200e-03 -0.0537440 -1.395600e-03 1.830000e-05 1.441100e-03 4.210160e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.636700e-03 2.029100e-03 0.0009087 1.089700e-03 1.703800e-03 3.617900e-03 2.215480e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.609413e-01 2.996689e-01 0.0000000 1.900002e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.609432e-01 2.996442e-01 0.0000000 1.915939e-01 4.471551e-01 7.207026e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.157879e-01 2.580454e-01 0.0027060 1.840300e-02 9.530800e-02 3.394820e-01 9.972940e-01 ▇▂▁▁▁
numeric AF_reference 105115 0.9885853 NA NA NA NA NA NA NA 2.165116e-01 2.498817e-01 0.0000000 1.557510e-02 1.134190e-01 3.386580e-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 0.0021067 0.0016713 0.2099999 0.2074931 0.623760 0.7821490 NA
1 54676 rs2462492 C T -0.0028737 0.0016558 0.0830004 0.0826555 0.400421 NA NA
1 86028 rs114608975 T C 0.0038428 0.0026470 0.1499999 0.1465764 0.103561 0.0277556 NA
1 91536 rs6702460 G T 0.0006943 0.0016302 0.6700003 0.6701835 0.456801 0.4207270 NA
1 234313 rs8179466 C T -0.0036730 0.0032178 0.2500000 0.2536833 0.074489 NA NA
1 534192 rs6680723 C T -0.0011317 0.0018620 0.5400003 0.5433149 0.240963 NA NA
1 546697 rs12025928 A G -0.0020698 0.0023228 0.3700002 0.3728892 0.913460 NA NA
1 693731 rs12238997 A G 0.0004318 0.0015610 0.7800007 0.7820637 0.116267 0.1417730 NA
1 705882 rs72631875 G A 0.0019286 0.0022867 0.4000000 0.3990026 0.067292 0.0315495 NA
1 706368 rs55727773 A G -0.0018229 0.0011559 0.1100001 0.1147950 0.515612 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0000941 0.0013957 0.9500000 0.9462557 0.137948 0.2052720 NA
22 51219387 rs9616832 T C -0.0018565 0.0018120 0.3100002 0.3055689 0.073705 0.0654952 NA
22 51219704 rs147475742 G A -0.0031504 0.0024276 0.1900002 0.1943850 0.041940 0.0473243 NA
22 51221190 rs369304721 G A -0.0026238 0.0024243 0.2800000 0.2791166 0.049694 NA NA
22 51221731 rs115055839 T C -0.0016499 0.0018132 0.3599996 0.3628440 0.073193 0.0625000 NA
22 51222100 rs114553188 G T 0.0035862 0.0021332 0.0929994 0.0927377 0.054495 0.0880591 NA
22 51223637 rs375798137 G A 0.0033608 0.0021436 0.1199999 0.1169201 0.054124 0.0788738 NA
22 51229805 rs9616985 T C -0.0015417 0.0018197 0.4000000 0.3968888 0.073024 0.0730831 NA
22 51232488 rs376461333 A G 0.0068831 0.0042844 0.1100001 0.1081541 0.020052 NA NA
22 51237063 rs3896457 T C -0.0006664 0.0011125 0.5500004 0.5491924 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62376  ES:SE:LP:AF:ID  0.00210668:0.00167132:0.677781:0.62376:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400421 ES:SE:LP:AF:ID  -0.00287368:0.00165584:1.08092:0.400421:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103561 ES:SE:LP:AF:ID  0.00384279:0.00264704:0.823909:0.103561:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456801 ES:SE:LP:AF:ID  0.000694313:0.00163024:0.173925:0.456801:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074489 ES:SE:LP:AF:ID  -0.00367301:0.00321785:0.60206:0.074489:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240963 ES:SE:LP:AF:ID  -0.00113172:0.00186197:0.267606:0.240963:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91346  ES:SE:LP:AF:ID  -0.00206977:0.00232278:0.431798:0.91346:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116267 ES:SE:LP:AF:ID  0.000431826:0.00156102:0.107905:0.116267:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067292 ES:SE:LP:AF:ID  0.00192862:0.00228671:0.39794:0.067292:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515612 ES:SE:LP:AF:ID  -0.00182292:0.00115594:0.958607:0.515612:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033034 ES:SE:LP:AF:ID  0.00607715:0.00291341:1.4318:0.033034:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036658 ES:SE:LP:AF:ID  0.00547747:0.00264627:1.42022:0.036658:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036769 ES:SE:LP:AF:ID  0.00554015:0.00263646:1.4437:0.036769:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036466 ES:SE:LP:AF:ID  0.00539553:0.00265551:1.37675:0.036466:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01641  ES:SE:LP:AF:ID  0.000176737:0.00408942:0.0132283:0.01641:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037008 ES:SE:LP:AF:ID  0.00563039:0.002626:1.49485:0.037008:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037103 ES:SE:LP:AF:ID  0.00556897:0.00261708:1.48149:0.037103:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101202 ES:SE:LP:AF:ID  0.00128687:0.00190742:0.30103:0.101202:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959081 ES:SE:LP:AF:ID  -0.0041157:0.00252462:1:0.959081:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031461 ES:SE:LP:AF:ID  0.00441321:0.00458069:0.468521:0.031461:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053279 ES:SE:LP:AF:ID  -0.00446857:0.00364421:0.657577:0.053279:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036623 ES:SE:LP:AF:ID  0.00532053:0.00263388:1.36653:0.036623:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036934 ES:SE:LP:AF:ID  0.00514056:0.0026101:1.3098:0.036934:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843262 ES:SE:LP:AF:ID  -0.00125387:0.00135287:0.455932:0.843262:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055906 ES:SE:LP:AF:ID  0.00188917:0.00219023:0.408935:0.055906:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122261 ES:SE:LP:AF:ID  0.000530116:0.00148078:0.142668:0.122261:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025725 ES:SE:LP:AF:ID  -0.000340244:0.00364028:0.0315171:0.025725:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121504 ES:SE:LP:AF:ID  0.000674865:0.00148142:0.187087:0.121504:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132301 ES:SE:LP:AF:ID  0.000960664:0.00145995:0.29243:0.132301:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011139 ES:SE:LP:AF:ID  -0.00049692:0.00530564:0.0315171:0.011139:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005708 ES:SE:LP:AF:ID  0.000607183:0.00684544:0.0315171:0.005708:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036848 ES:SE:LP:AF:ID  0.00543142:0.00258381:1.4437:0.036848:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838977 ES:SE:LP:AF:ID  -0.00127984:0.00131018:0.481486:0.838977:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838606 ES:SE:LP:AF:ID  -0.00114758:0.00130874:0.420216:0.838606:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86983  ES:SE:LP:AF:ID  -0.000458455:0.00140447:0.130768:0.86983:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129819 ES:SE:LP:AF:ID  0.000449922:0.00140733:0.124939:0.129819:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037356 ES:SE:LP:AF:ID  0.00546332:0.00254019:1.50864:0.037356:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.0376   ES:SE:LP:AF:ID  0.00549217:0.00252417:1.52288:0.0376:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869176 ES:SE:LP:AF:ID  -0.00038347:0.00140171:0.107905:0.869176:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869275 ES:SE:LP:AF:ID  -0.000368025:0.00140227:0.102373:0.869275:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037556 ES:SE:LP:AF:ID  0.00547909:0.00253514:1.50864:0.037556:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869179 ES:SE:LP:AF:ID  -0.000387032:0.00140168:0.107905:0.869179:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005141 ES:SE:LP:AF:ID  -0.00545481:0.00717959:0.346787:0.005141:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005106 ES:SE:LP:AF:ID  -0.00525418:0.00719866:0.327902:0.005106:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838066 ES:SE:LP:AF:ID  -0.00109222:0.00130516:0.39794:0.838066:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037567 ES:SE:LP:AF:ID  0.00550102:0.00253874:1.52288:0.037567:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838697 ES:SE:LP:AF:ID  -0.00105494:0.00130883:0.376751:0.838697:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013788 ES:SE:LP:AF:ID  -0.0020566:0.00456496:0.187087:0.013788:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005552 ES:SE:LP:AF:ID  -0.00347678:0.00704416:0.207608:0.005552:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839825 ES:SE:LP:AF:ID  -0.00136856:0.00132656:0.522879:0.839825:rs3131965