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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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_3829.vcf.gz --id UKB-b:6412 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3829.txt.gz --cohort_controls 78879 --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-6412/UKB-b-6412_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6412/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6412/UKB-b-6412_data.vcf.gz ...
Read summary statistics for 8829003 SNPs.
Dropped 8011 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, 1286590 SNPs remain.
After merging with regression SNP LD, 1286590 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.007 (0.0066)
Lambda GC: 1.027
Mean Chi^2: 1.0321
Intercept: 1.0209 (0.0063)
Ratio: 0.6527 (0.1973)
Analysis finished at Thu Oct 17 14:42:02 2019
Total time elapsed: 1.0m:43.53s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9471,
    "inflation_factor": 1,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 24,
    "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": 87500,
    "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": 1286590,
    "ldsc_nsnp_merge_regression_ld": 1286590,
    "ldsc_observed_scale_h2_beta": 0.007,
    "ldsc_observed_scale_h2_se": 0.0066,
    "ldsc_intercept_beta": 1.0209,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.027,
    "ldsc_mean_chisq": 1.0321,
    "ldsc_ratio": 0.6511
}

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 TRUE
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 8821029 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 8829003 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 8.648304e+00 5.760291e+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 7.878034e+07 5.635016e+07 828.0000000 3.241047e+07 6.933165e+07 1.145594e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 4.260000e-05 5.357700e-03 -0.0637929 -2.160000e-03 -2.050000e-05 2.145200e-03 6.370860e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.344300e-03 3.073100e-03 0.0016464 1.952800e-03 2.937400e-03 5.926500e-03 2.851130e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.965293e-01 2.897398e-01 0.0000000 2.399999e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.965301e-01 2.897133e-01 0.0000000 2.447888e-01 4.952857e-01 7.475263e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.242624e-01 2.589877e-01 0.0044380 2.259600e-02 1.072020e-01 3.542610e-01 9.955620e-01 ▇▂▁▁▁
numeric AF_reference 87500 0.9900895 NA NA NA NA NA NA NA 2.242677e-01 2.509373e-01 0.0000000 2.016770e-02 1.242010e-01 3.520370e-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 -0.0044528 0.0030381 0.1400000 0.1427349 0.623045 0.7821490 NA
1 54676 rs2462492 C T 0.0071227 0.0030000 0.0179999 0.0175840 0.400454 NA NA
1 86028 rs114608975 T C 0.0023984 0.0048048 0.6200004 0.6176585 0.103829 0.0277556 NA
1 91536 rs6702460 G T 0.0011820 0.0029574 0.6899999 0.6893916 0.455966 0.4207270 NA
1 234313 rs8179466 C T -0.0017358 0.0058412 0.7700005 0.7663352 0.074428 NA NA
1 534192 rs6680723 C T 0.0028906 0.0033868 0.3900004 0.3933876 0.241154 NA NA
1 546697 rs12025928 A G 0.0009729 0.0042223 0.8200001 0.8177561 0.913336 NA NA
1 693731 rs12238997 A G -0.0001517 0.0028138 0.9599999 0.9569911 0.117512 0.1417730 NA
1 705882 rs72631875 G A -0.0093596 0.0041579 0.0239999 0.0243825 0.067171 0.0315495 NA
1 706368 rs55727773 A G -0.0067220 0.0020932 0.0013000 0.0013212 0.513221 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0028991 0.0025397 0.2500000 0.2536547 0.137413 0.2052720 NA
22 51219387 rs9616832 T C -0.0012724 0.0032923 0.6999999 0.6991354 0.073476 0.0654952 NA
22 51219704 rs147475742 G A -0.0041986 0.0044172 0.3400001 0.3418568 0.041618 0.0473243 NA
22 51221190 rs369304721 G A -0.0006833 0.0044243 0.8800001 0.8772675 0.049172 NA NA
22 51221731 rs115055839 T C -0.0011941 0.0032941 0.7199992 0.7169931 0.072983 0.0625000 NA
22 51222100 rs114553188 G T -0.0048104 0.0038795 0.2099999 0.2149871 0.054282 0.0880591 NA
22 51223637 rs375798137 G A -0.0049671 0.0038972 0.2000000 0.2024700 0.053914 0.0788738 NA
22 51229805 rs9616985 T C -0.0010528 0.0033050 0.7499995 0.7500620 0.072900 0.0730831 NA
22 51232488 rs376461333 A G -0.0086719 0.0077565 0.2599998 0.2635632 0.020159 NA NA
22 51237063 rs3896457 T C 0.0022227 0.0020149 0.2700001 0.2699707 0.296691 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623045 ES:SE:LP:AF:ID  -0.00445283:0.00303806:0.853872:0.623045:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400454 ES:SE:LP:AF:ID  0.00712274:0.00299998:1.74473:0.400454:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103829 ES:SE:LP:AF:ID  0.00239841:0.00480478:0.207608:0.103829:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455966 ES:SE:LP:AF:ID  0.001182:0.00295736:0.161151:0.455966:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074428 ES:SE:LP:AF:ID  -0.00173585:0.00584123:0.113509:0.074428:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241154 ES:SE:LP:AF:ID  0.0028906:0.0033868:0.408935:0.241154:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913336 ES:SE:LP:AF:ID  0.000972946:0.00422227:0.0861861:0.913336:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117512 ES:SE:LP:AF:ID  -0.000151746:0.00281377:0.0177288:0.117512:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067171 ES:SE:LP:AF:ID  -0.00935963:0.0041579:1.61979:0.067171:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513221 ES:SE:LP:AF:ID  -0.00672204:0.00209322:2.88606:0.513221:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033116 ES:SE:LP:AF:ID  0.00311547:0.00527069:0.259637:0.033116:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036708 ES:SE:LP:AF:ID  0.00352263:0.00479398:0.337242:0.036708:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03677  ES:SE:LP:AF:ID  0.00320306:0.00478006:0.30103:0.03677:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036451 ES:SE:LP:AF:ID  0.00377835:0.00481597:0.366532:0.036451:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016449 ES:SE:LP:AF:ID  0.00391325:0.00740955:0.221849:0.016449:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036985 ES:SE:LP:AF:ID  0.00410353:0.00476398:0.408935:0.036985:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037064 ES:SE:LP:AF:ID  0.00340971:0.00474966:0.327902:0.037064:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100844 ES:SE:LP:AF:ID  0.00772777:0.00346486:1.58503:0.100844:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958846 ES:SE:LP:AF:ID  -0.00375775:0.00456152:0.387216:0.958846:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031274 ES:SE:LP:AF:ID  -0.00593461:0.00830638:0.327902:0.031274:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053404 ES:SE:LP:AF:ID  -0.0117856:0.00660375:1.13077:0.053404:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036575 ES:SE:LP:AF:ID  0.00319236:0.00477881:0.30103:0.036575:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036942 ES:SE:LP:AF:ID  0.00384652:0.00473449:0.376751:0.036942:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841817 ES:SE:LP:AF:ID  -0.000787178:0.00244356:0.124939:0.841817:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055914 ES:SE:LP:AF:ID  0.000419804:0.00396282:0.0362122:0.055914:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123534 ES:SE:LP:AF:ID  -6.18899e-05:0.00266973:0.00877392:0.123534:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025301 ES:SE:LP:AF:ID  -0.00141949:0.00666354:0.0809219:0.025301:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122718 ES:SE:LP:AF:ID  -1.8164e-05:0.00267152:0.00436481:0.122718:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133399 ES:SE:LP:AF:ID  0.000837068:0.00263622:0.124939:0.133399:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010912 ES:SE:LP:AF:ID  0.0133742:0.00976975:0.769551:0.010912:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006021 ES:SE:LP:AF:ID  0.00749581:0.0119532:0.275724:0.006021:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036884 ES:SE:LP:AF:ID  0.00403842:0.0046829:0.408935:0.036884:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837559 ES:SE:LP:AF:ID  -0.00110789:0.00236366:0.19382:0.837559:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83705  ES:SE:LP:AF:ID  -0.000951692:0.00236013:0.161151:0.83705:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868365 ES:SE:LP:AF:ID  -0.000438664:0.00252982:0.0655015:0.868365:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131359 ES:SE:LP:AF:ID  0.000350127:0.00253445:0.05061:0.131359:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037266 ES:SE:LP:AF:ID  0.00395045:0.00461128:0.408935:0.037266:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037508 ES:SE:LP:AF:ID  0.00380362:0.00458291:0.387216:0.037508:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867586 ES:SE:LP:AF:ID  -0.000330563:0.00252386:0.0457575:0.867586:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867677 ES:SE:LP:AF:ID  -0.000393042:0.00252516:0.0555173:0.867677:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037456 ES:SE:LP:AF:ID  0.00395152:0.00460259:0.408935:0.037456:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867588 ES:SE:LP:AF:ID  -0.000339125:0.00252389:0.05061:0.867588:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005179 ES:SE:LP:AF:ID  -0.0110126:0.0130234:0.39794:0.005179:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005144 ES:SE:LP:AF:ID  -0.0109462:0.0130581:0.39794:0.005144:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836541 ES:SE:LP:AF:ID  -0.000966057:0.00235402:0.167491:0.836541:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037476 ES:SE:LP:AF:ID  0.00386853:0.00460838:0.39794:0.037476:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837169 ES:SE:LP:AF:ID  -0.000861772:0.00236057:0.142668:0.837169:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013602 ES:SE:LP:AF:ID  0.0136385:0.00830751:1:0.013602:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005877 ES:SE:LP:AF:ID  0.0199452:0.0124608:0.958607:0.005877:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838402 ES:SE:LP:AF:ID  -0.00105754:0.00239228:0.180456:0.838402:rs3131965