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_20107_9.vcf.gz --id UKB-b:20211 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20107_9.txt.gz --cohort_cases 38850 --cohort_controls 361837 --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-20211/UKB-b-20211_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20211/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20211/UKB-b-20211_data.vcf.gz ...
Read summary statistics for 7880914 SNPs.
Dropped 5900 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, 1281152 SNPs remain.
After merging with regression SNP LD, 1281152 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0198 (0.002)
Lambda GC: 1.1948
Mean Chi^2: 1.2339
Intercept: 1.0767 (0.0076)
Ratio: 0.3279 (0.0324)
Analysis finished at Thu Oct 17 14:43:35 2019
Total time elapsed: 1.0m:28.18s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9422,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 26,
    "n_p_sig": 1035,
    "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": 73491,
    "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": 1281152,
    "ldsc_nsnp_merge_regression_ld": 1281152,
    "ldsc_observed_scale_h2_beta": 0.0198,
    "ldsc_observed_scale_h2_se": 0.002,
    "ldsc_intercept_beta": 1.0767,
    "ldsc_intercept_se": 0.0076,
    "ldsc_lambda_gc": 1.1948,
    "ldsc_mean_chisq": 1.2339,
    "ldsc_ratio": 0.3279
}
 

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 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 7875041 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 7880914 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 8.661094e+00 5.763697e+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 7.870569e+07 5.642567e+07 828.0000000 3.224675e+07 6.917336e+07 1.145607e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.360000e-05 1.680100e-03 -0.0185597 -8.042000e-04 2.400000e-06 8.127000e-04 2.240080e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.366200e-03 8.006000e-04 0.0006365 7.352000e-04 1.013600e-03 1.789000e-03 7.857800e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.742790e-01 2.956388e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.742811e-01 2.956150e-01 0.0000000 2.120319e-01 4.650958e-01 7.304220e-01 1.000000e+00 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.488467e-01 2.608021e-01 0.0090100 3.768800e-02 1.411960e-01 3.938850e-01 9.909900e-01 ▇▂▂▁▁
numeric AF_reference 73491 0.9906748 NA NA NA NA NA NA NA 2.480206e-01 2.526351e-01 0.0000000 4.033550e-02 1.563500e-01 3.889780e-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 0.0008335 0.0011719 0.4799997 0.4769282 0.623696 0.7821490 NA
1 54676 rs2462492 C T 0.0002775 0.0011606 0.8100000 0.8110019 0.400449 NA NA
1 86028 rs114608975 T C 0.0000633 0.0018564 0.9699999 0.9728168 0.103515 0.0277556 NA
1 91536 rs6702460 G T 0.0003941 0.0011438 0.7300002 0.7304242 0.457020 0.4207270 NA
1 234313 rs8179466 C T -0.0001580 0.0022571 0.9400001 0.9441936 0.074447 NA NA
1 534192 rs6680723 C T -0.0009590 0.0013058 0.4600002 0.4627091 0.241061 NA NA
1 546697 rs12025928 A G -0.0018884 0.0016287 0.2500000 0.2462678 0.913473 NA NA
1 693731 rs12238997 A G 0.0000867 0.0010934 0.9400001 0.9367873 0.116328 0.1417730 NA
1 705882 rs72631875 G A 0.0005778 0.0016022 0.7199992 0.7183803 0.067402 0.0315495 NA
1 706368 rs55727773 A G 0.0008893 0.0008103 0.2700001 0.2724381 0.515668 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0010579 0.0009787 0.2800000 0.2796903 0.137834 0.2052720 NA
22 51219387 rs9616832 T C -0.0001837 0.0012702 0.8800001 0.8849749 0.073730 0.0654952 NA
22 51219704 rs147475742 G A -0.0002434 0.0017033 0.8900000 0.8863917 0.041900 0.0473243 NA
22 51221190 rs369304721 G A -0.0013108 0.0016988 0.4400003 0.4403292 0.049691 NA NA
22 51221731 rs115055839 T C -0.0002980 0.0012709 0.8100000 0.8146058 0.073216 0.0625000 NA
22 51222100 rs114553188 G T -0.0011782 0.0014964 0.4299995 0.4310920 0.054388 0.0880591 NA
22 51223637 rs375798137 G A -0.0010756 0.0015036 0.4700002 0.4744000 0.054022 0.0788738 NA
22 51229805 rs9616985 T C -0.0003644 0.0012754 0.7800007 0.7751173 0.073051 0.0730831 NA
22 51232488 rs376461333 A G -0.0003881 0.0030077 0.9000000 0.8973261 0.019981 NA NA
22 51237063 rs3896457 T C 0.0013841 0.0007796 0.0759994 0.0758388 0.297982 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623696 ES:SE:LP:AF:ID  0.000833516:0.0011719:0.318759:0.623696:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400449 ES:SE:LP:AF:ID  0.000277541:0.00116061:0.091515:0.400449:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103515 ES:SE:LP:AF:ID  6.32563e-05:0.00185635:0.0132283:0.103515:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45702  ES:SE:LP:AF:ID  0.000394092:0.00114375:0.136677:0.45702:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074447 ES:SE:LP:AF:ID  -0.000157995:0.00225707:0.0268721:0.074447:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241061 ES:SE:LP:AF:ID  -0.000959:0.00130584:0.337242:0.241061:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913473 ES:SE:LP:AF:ID  -0.00188837:0.00162866:0.60206:0.913473:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116328 ES:SE:LP:AF:ID  8.6715e-05:0.00109339:0.0268721:0.116328:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067402 ES:SE:LP:AF:ID  0.000577785:0.00160218:0.142668:0.067402:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515668 ES:SE:LP:AF:ID  0.00088931:0.000810331:0.568636:0.515668:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033063 ES:SE:LP:AF:ID  -0.00194932:0.00204157:0.468521:0.033063:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036695 ES:SE:LP:AF:ID  -0.00140495:0.00185409:0.346787:0.036695:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03681  ES:SE:LP:AF:ID  -0.00154389:0.00184719:0.39794:0.03681:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036507 ES:SE:LP:AF:ID  -0.00148026:0.00186051:0.366532:0.036507:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01645  ES:SE:LP:AF:ID  0.00092343:0.00286196:0.124939:0.01645:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03705  ES:SE:LP:AF:ID  -0.00139839:0.00183988:0.346787:0.03705:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037144 ES:SE:LP:AF:ID  -0.00151623:0.00183366:0.387216:0.037144:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101363 ES:SE:LP:AF:ID  -0.00104193:0.00133558:0.356547:0.101363:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959106 ES:SE:LP:AF:ID  0.000518109:0.00177041:0.113509:0.959106:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031527 ES:SE:LP:AF:ID  0.00285652:0.00320254:0.431798:0.031527:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053255 ES:SE:LP:AF:ID  -0.00152848:0.00255612:0.259637:0.053255:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036652 ES:SE:LP:AF:ID  -0.00112891:0.00184555:0.267606:0.036652:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036976 ES:SE:LP:AF:ID  -0.00150327:0.00182862:0.387216:0.036976:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843197 ES:SE:LP:AF:ID  -2.91565e-05:0.000947643:0.00877392:0.843197:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055911 ES:SE:LP:AF:ID  0.000249935:0.00153454:0.0604807:0.055911:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122359 ES:SE:LP:AF:ID  0.000152914:0.00103688:0.0555173:0.122359:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025695 ES:SE:LP:AF:ID  -0.00226064:0.00255326:0.420216:0.025695:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121594 ES:SE:LP:AF:ID  0.000147666:0.00103733:0.05061:0.121594:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132419 ES:SE:LP:AF:ID  -0.000608039:0.00102257:0.259637:0.132419:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011046 ES:SE:LP:AF:ID  0.00268827:0.00373943:0.327902:0.011046:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036894 ES:SE:LP:AF:ID  -0.00166134:0.00180998:0.443698:0.036894:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838841 ES:SE:LP:AF:ID  -0.000379068:0.000917614:0.167491:0.838841:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838495 ES:SE:LP:AF:ID  -0.000481647:0.000916671:0.221849:0.838495:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869732 ES:SE:LP:AF:ID  -0.000719765:0.000983678:0.337242:0.869732:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129896 ES:SE:LP:AF:ID  0.000623029:0.000985709:0.275724:0.129896:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037415 ES:SE:LP:AF:ID  -0.00176141:0.00177904:0.49485:0.037415:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037651 ES:SE:LP:AF:ID  -0.00174801:0.0017681:0.49485:0.037651:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869101 ES:SE:LP:AF:ID  -0.000816442:0.00098181:0.387216:0.869101:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869205 ES:SE:LP:AF:ID  -0.000794775:0.000982197:0.376751:0.869205:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037614 ES:SE:LP:AF:ID  -0.00177568:0.00177554:0.49485:0.037614:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869102 ES:SE:LP:AF:ID  -0.000833265:0.000981791:0.39794:0.869102:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837942 ES:SE:LP:AF:ID  -0.000361721:0.000914101:0.161151:0.837942:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037627 ES:SE:LP:AF:ID  -0.00184734:0.00177809:0.522879:0.037627:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838576 ES:SE:LP:AF:ID  -0.000353323:0.000916668:0.154902:0.838576:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013804 ES:SE:LP:AF:ID  -0.00280854:0.0031998:0.420216:0.013804:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839653 ES:SE:LP:AF:ID  -0.00018874:0.000928924:0.0757207:0.839653:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869356 ES:SE:LP:AF:ID  -0.000684732:0.000980587:0.318759:0.869356:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.8689   ES:SE:LP:AF:ID  -0.000624972:0.000978096:0.283997:0.8689:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867876 ES:SE:LP:AF:ID  -0.000983096:0.000976261:0.508638:0.867876:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869039 ES:SE:LP:AF:ID  -0.000654505:0.000978888:0.30103:0.869039:rs4951929