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_102520.vcf.gz --id UKB-b:19073 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102520.txt.gz --cohort_cases 8783 --cohort_controls 56166 --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-19073/UKB-b-19073_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19073/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:57 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19073/UKB-b-19073_data.vcf.gz ...
Read summary statistics for 5761522 SNPs.
Dropped 2423 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, 1196842 SNPs remain.
After merging with regression SNP LD, 1196842 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0163 (0.0074)
Lambda GC: 1.0352
Mean Chi^2: 1.0329
Intercept: 1.011 (0.0071)
Ratio: 0.3358 (0.2154)
Analysis finished at Thu Oct 17 14:42:01 2019
Total time elapsed: 1.0m:3.97s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9211,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 9.2348e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": 51417,
    "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": 1196842,
    "ldsc_nsnp_merge_regression_ld": 1196842,
    "ldsc_observed_scale_h2_beta": 0.0163,
    "ldsc_observed_scale_h2_se": 0.0074,
    "ldsc_intercept_beta": 1.011,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.0352,
    "ldsc_mean_chisq": 1.0329,
    "ldsc_ratio": 0.3343
}

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 5759115 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 5761522 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 8.670510e+00 5.762390e+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 7.860432e+07 5.655109e+07 828.0000000 3.199876e+07 6.904396e+07 1.145408e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 9.200000e-06 2.870400e-03 -0.0262739 -1.742500e-03 5.400000e-06 1.752900e-03 2.350580e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.699300e-03 8.276000e-04 0.0018320 2.013400e-03 2.383100e-03 3.191000e-03 8.913400e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.939883e-01 2.901383e-01 0.0000000 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.939890e-01 2.901138e-01 0.0000000 2.413803e-01 4.914061e-01 7.454951e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.209360e-01 2.506038e-01 0.0398500 1.076230e-01 2.427670e-01 4.872630e-01 9.601500e-01 ▇▃▂▂▁
numeric AF_reference 51417 0.9910758 NA NA NA NA NA NA NA 3.168277e-01 2.443530e-01 0.0000000 1.158150e-01 2.492010e-01 4.772360e-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 0.0010363 0.0033766 0.7600007 0.7589157 0.623812 0.7821490 NA
1 54676 rs2462492 C T 0.0000892 0.0033667 0.9800000 0.9788631 0.399144 NA NA
1 86028 rs114608975 T C 0.0051577 0.0053593 0.3400001 0.3358591 0.103536 0.0277556 NA
1 91536 rs6702460 G T -0.0020748 0.0033114 0.5300002 0.5309432 0.455916 0.4207270 NA
1 234313 rs8179466 C T -0.0068719 0.0065488 0.2900000 0.2940192 0.074455 NA NA
1 534192 rs6680723 C T -0.0029206 0.0037715 0.4400003 0.4386982 0.242057 NA NA
1 546697 rs12025928 A G -0.0002332 0.0046796 0.9599999 0.9602619 0.912862 NA NA
1 693731 rs12238997 A G 0.0039143 0.0031448 0.2099999 0.2132450 0.117313 0.1417730 NA
1 705882 rs72631875 G A 0.0010632 0.0045839 0.8200001 0.8165863 0.067698 0.0315495 NA
1 706368 rs55727773 A G -0.0023894 0.0023343 0.3100002 0.3060221 0.513304 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0049497 0.0036906 0.1800002 0.1798628 0.071792 0.0826677 NA
22 51219006 rs28729663 G A -0.0000094 0.0028391 1.0000000 0.9973498 0.136315 0.2052720 NA
22 51219387 rs9616832 T C 0.0051087 0.0037004 0.1700000 0.1674098 0.071797 0.0654952 NA
22 51219704 rs147475742 G A -0.0015470 0.0049226 0.7499995 0.7533195 0.041190 0.0473243 NA
22 51221190 rs369304721 G A 0.0035135 0.0049575 0.4799997 0.4784893 0.048372 NA NA
22 51221731 rs115055839 T C 0.0048162 0.0037012 0.1900002 0.1931765 0.071348 0.0625000 NA
22 51222100 rs114553188 G T -0.0052270 0.0042889 0.2200002 0.2229538 0.054850 0.0880591 NA
22 51223637 rs375798137 G A -0.0051993 0.0043113 0.2300001 0.2278348 0.054470 0.0788738 NA
22 51229805 rs9616985 T C 0.0045510 0.0037127 0.2200002 0.2202718 0.071253 0.0730831 NA
22 51237063 rs3896457 T C -0.0005213 0.0022414 0.8200001 0.8160745 0.298393 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623812 ES:SE:LP:AF:ID  0.00103628:0.00337655:0.119186:0.623812:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399144 ES:SE:LP:AF:ID  8.91991e-05:0.00336673:0.00877392:0.399144:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  0.00515767:0.00535929:0.468521:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455916 ES:SE:LP:AF:ID  -0.00207482:0.00331141:0.275724:0.455916:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074455 ES:SE:LP:AF:ID  -0.0068719:0.00654875:0.537602:0.074455:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242057 ES:SE:LP:AF:ID  -0.00292064:0.00377152:0.356547:0.242057:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912862 ES:SE:LP:AF:ID  -0.000233163:0.00467964:0.0177288:0.912862:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117313 ES:SE:LP:AF:ID  0.00391432:0.00314481:0.677781:0.117313:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067698 ES:SE:LP:AF:ID  0.00106318:0.0045839:0.0861861:0.067698:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  -0.0023894:0.0023343:0.508638:0.513304:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.102736 ES:SE:LP:AF:ID  0.00299959:0.00380928:0.366532:0.102736:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95809  ES:SE:LP:AF:ID  -0.0041698:0.00503671:0.387216:0.95809:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  -0.00125479:0.00742889:0.0604807:0.052723:rs865924913
1   729679  rs4951859   C   G   .   PASS    AF=0.841441 ES:SE:LP:AF:ID  -0.00242233:0.00271968:0.431798:0.841441:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056334 ES:SE:LP:AF:ID  0.00513146:0.00441797:0.60206:0.056334:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123078 ES:SE:LP:AF:ID  0.00253686:0.00298721:0.39794:0.123078:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.12233  ES:SE:LP:AF:ID  0.00264724:0.00298817:0.420216:0.12233:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134139 ES:SE:LP:AF:ID  0.00164526:0.00293337:0.244125:0.134139:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.837029 ES:SE:LP:AF:ID  -0.00291444:0.00263096:0.568636:0.837029:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836733 ES:SE:LP:AF:ID  -0.00283342:0.00262903:0.552842:0.836733:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868562 ES:SE:LP:AF:ID  -0.00346991:0.00282503:0.657577:0.868562:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131004 ES:SE:LP:AF:ID  0.00339691:0.00283228:0.638272:0.131004:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.867976 ES:SE:LP:AF:ID  -0.00340486:0.00282049:0.638272:0.867976:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86805  ES:SE:LP:AF:ID  -0.00331949:0.00282164:0.619789:0.86805:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.867987 ES:SE:LP:AF:ID  -0.0034175:0.00282042:0.638272:0.867987:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836159 ES:SE:LP:AF:ID  -0.00296362:0.00262134:0.585027:0.836159:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.836793 ES:SE:LP:AF:ID  -0.00313886:0.00262852:0.638272:0.836793:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.838109 ES:SE:LP:AF:ID  -0.00273954:0.00266552:0.522879:0.838109:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868228 ES:SE:LP:AF:ID  -0.00323356:0.00281678:0.60206:0.868228:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867742 ES:SE:LP:AF:ID  -0.00349776:0.00280932:0.677781:0.867742:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866644 ES:SE:LP:AF:ID  -0.00349014:0.00280417:0.677781:0.866644:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867911 ES:SE:LP:AF:ID  -0.00348923:0.00281217:0.677781:0.867911:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.867923 ES:SE:LP:AF:ID  -0.00346402:0.00281236:0.657577:0.867923:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.867932 ES:SE:LP:AF:ID  -0.00341979:0.00281247:0.657577:0.867932:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.868398 ES:SE:LP:AF:ID  -0.00328212:0.00281987:0.619789:0.868398:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.836369 ES:SE:LP:AF:ID  -0.00313602:0.0026148:0.638272:0.836369:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.836495 ES:SE:LP:AF:ID  -0.00315666:0.00261658:0.638272:0.836495:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.860851 ES:SE:LP:AF:ID  -0.00358237:0.0028025:0.69897:0.860851:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.705804 ES:SE:LP:AF:ID  2.86953e-06:0.00273475:-0:0.705804:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.106094 ES:SE:LP:AF:ID  0.00108238:0.00314685:0.136677:0.106094:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.758252 ES:SE:LP:AF:ID  -0.00194814:0.00221653:0.420216:0.758252:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.108262 ES:SE:LP:AF:ID  -0.000186298:0.00305076:0.0222764:0.108262:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.13066  ES:SE:LP:AF:ID  0.00342906:0.0028312:0.638272:0.13066:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.867852 ES:SE:LP:AF:ID  -0.0032811:0.00281574:0.619789:0.867852:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.130756 ES:SE:LP:AF:ID  0.00319902:0.00282931:0.585027:0.130756:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.867878 ES:SE:LP:AF:ID  -0.00324462:0.0028159:0.60206:0.867878:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.263729 ES:SE:LP:AF:ID  -0.00662445:0.0025046:2.08619:0.263729:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.869067 ES:SE:LP:AF:ID  -0.00277639:0.00282332:0.481486:0.869067:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.096639 ES:SE:LP:AF:ID  -0.00209117:0.00325202:0.283997:0.096639:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.129594 ES:SE:LP:AF:ID  0.00277713:0.00283408:0.481486:0.129594:rs1055606