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_102460.vcf.gz --id UKB-b:12602 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102460.txt.gz --cohort_controls 64949 --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-12602/UKB-b-12602_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12602/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:04 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12602/UKB-b-12602_data.vcf.gz ...
Read summary statistics for 8625255 SNPs.
Dropped 7372 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, 1285751 SNPs remain.
After merging with regression SNP LD, 1285751 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0154 (0.0076)
Lambda GC: 1.0402
Mean Chi^2: 1.0371
Intercept: 1.0168 (0.0064)
Ratio: 0.4531 (0.1715)
Analysis finished at Thu Oct 17 14:43:35 2019
Total time elapsed: 1.0m:31.4s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "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": 83101,
    "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": 1285751,
    "ldsc_nsnp_merge_regression_ld": 1285751,
    "ldsc_observed_scale_h2_beta": 0.0154,
    "ldsc_observed_scale_h2_se": 0.0076,
    "ldsc_intercept_beta": 1.0168,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0402,
    "ldsc_mean_chisq": 1.0371,
    "ldsc_ratio": 0.4528
}

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 8617917 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 8625255 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 8.650335e+00 5.760993e+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 7.877026e+07 5.637169e+07 828.0000000 3.238547e+07 6.929218e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.180000e-05 1.307160e-02 -0.1249740 -5.523500e-03 -5.800000e-06 5.452700e-03 1.438560e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.074550e-02 7.335300e-03 0.0042675 5.025500e-03 7.409900e-03 1.452590e-02 7.356620e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.945624e-01 2.901537e-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 4.945616e-01 2.901289e-01 0.0000001 2.421752e-01 4.926362e-01 7.456247e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291107e-01 2.594876e-01 0.0053890 2.518600e-02 1.138890e-01 3.626125e-01 9.946110e-01 ▇▂▁▁▁
numeric AF_reference 83101 0.9903654 NA NA NA NA NA NA NA 2.288600e-01 2.514584e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 0.0007929 0.0078647 0.9199999 0.9196937 0.623812 0.7821490 NA
1 54676 rs2462492 C T -0.0022712 0.0078419 0.7700005 0.7721023 0.399144 NA NA
1 86028 rs114608975 T C 0.0032159 0.0124830 0.8000000 0.7966990 0.103536 0.0277556 NA
1 91536 rs6702460 G T -0.0022862 0.0077130 0.7700005 0.7669219 0.455916 0.4207270 NA
1 234313 rs8179466 C T 0.0271698 0.0152535 0.0749998 0.0748769 0.074455 NA NA
1 534192 rs6680723 C T -0.0048853 0.0087847 0.5800000 0.5781311 0.242057 NA NA
1 546697 rs12025928 A G -0.0015072 0.0108999 0.8900000 0.8900204 0.912862 NA NA
1 693731 rs12238997 A G -0.0121003 0.0073250 0.0990011 0.0985503 0.117313 0.1417730 NA
1 705882 rs72631875 G A 0.0103512 0.0106769 0.3300000 0.3322983 0.067698 0.0315495 NA
1 706368 rs55727773 A G 0.0103792 0.0054371 0.0560003 0.0562679 0.513304 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0079462 0.0066133 0.2300001 0.2295387 0.136315 0.2052720 NA
22 51219387 rs9616832 T C 0.0105437 0.0086196 0.2200002 0.2212436 0.071797 0.0654952 NA
22 51219704 rs147475742 G A -0.0002971 0.0114663 0.9800000 0.9793293 0.041190 0.0473243 NA
22 51221190 rs369304721 G A 0.0138038 0.0115477 0.2300001 0.2319416 0.048372 NA NA
22 51221731 rs115055839 T C 0.0111672 0.0086215 0.2000000 0.1952238 0.071348 0.0625000 NA
22 51222100 rs114553188 G T 0.0060559 0.0099904 0.5400003 0.5444037 0.054850 0.0880591 NA
22 51223637 rs375798137 G A 0.0062094 0.0100426 0.5400003 0.5363730 0.054470 0.0788738 NA
22 51229805 rs9616985 T C 0.0111491 0.0086481 0.2000000 0.1973282 0.071253 0.0730831 NA
22 51232488 rs376461333 A G -0.0055849 0.0199182 0.7800007 0.7791773 0.020460 NA NA
22 51237063 rs3896457 T C -0.0026826 0.0052209 0.6100002 0.6073762 0.298393 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623812 ES:SE:LP:AF:ID  0.00079292:0.00786474:0.0362122:0.623812:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399144 ES:SE:LP:AF:ID  -0.00227121:0.00784187:0.113509:0.399144:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  0.00321589:0.012483:0.09691:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455916 ES:SE:LP:AF:ID  -0.00228617:0.00771303:0.113509:0.455916:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074455 ES:SE:LP:AF:ID  0.0271698:0.0152535:1.12494:0.074455:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242057 ES:SE:LP:AF:ID  -0.00488534:0.00878474:0.236572:0.242057:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912862 ES:SE:LP:AF:ID  -0.00150722:0.0108999:0.05061:0.912862:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117313 ES:SE:LP:AF:ID  -0.0121003:0.00732498:1.00436:0.117313:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067698 ES:SE:LP:AF:ID  0.0103512:0.0106769:0.481486:0.067698:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  0.0103792:0.00543711:1.25181:0.513304:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033677 ES:SE:LP:AF:ID  0.0111637:0.0135522:0.387216:0.033677:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037457 ES:SE:LP:AF:ID  0.0106372:0.0122912:0.408935:0.037457:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037644 ES:SE:LP:AF:ID  0.0103332:0.0122269:0.39794:0.037644:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03722  ES:SE:LP:AF:ID  0.0114914:0.0123386:0.455932:0.03722:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016282 ES:SE:LP:AF:ID  0.00834744:0.0193539:0.173925:0.016282:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03786  ES:SE:LP:AF:ID  0.0104168:0.0121851:0.408935:0.03786:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037955 ES:SE:LP:AF:ID  0.0091889:0.0121464:0.346787:0.037955:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102736 ES:SE:LP:AF:ID  0.00354578:0.00887268:0.161151:0.102736:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95809  ES:SE:LP:AF:ID  -0.00756867:0.0117316:0.283997:0.95809:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03169  ES:SE:LP:AF:ID  0.0138868:0.0214825:0.283997:0.03169:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  -0.0240916:0.0173036:0.79588:0.052723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037449 ES:SE:LP:AF:ID  0.0105802:0.0122262:0.408935:0.037449:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037717 ES:SE:LP:AF:ID  0.0114659:0.0121249:0.468521:0.037717:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841441 ES:SE:LP:AF:ID  0.005932:0.00633475:0.455932:0.841441:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056334 ES:SE:LP:AF:ID  -0.00342726:0.0102905:0.130768:0.056334:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123078 ES:SE:LP:AF:ID  -0.0102761:0.00695788:0.853872:0.123078:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02513  ES:SE:LP:AF:ID  0.0251101:0.0173275:0.823909:0.02513:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12233  ES:SE:LP:AF:ID  -0.0105894:0.00696013:0.886057:0.12233:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134139 ES:SE:LP:AF:ID  -0.00915286:0.0068325:0.744727:0.134139:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  -0.0032576:0.0243513:0.05061:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.0061   ES:SE:LP:AF:ID  -0.0243837:0.0309232:0.366532:0.0061:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.0376   ES:SE:LP:AF:ID  0.0129849:0.0120126:0.552842:0.0376:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837029 ES:SE:LP:AF:ID  0.0053573:0.0061281:0.420216:0.837029:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836733 ES:SE:LP:AF:ID  0.00545257:0.00612361:0.431798:0.836733:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868562 ES:SE:LP:AF:ID  0.0109611:0.00658013:1.01773:0.868562:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131004 ES:SE:LP:AF:ID  -0.0114891:0.00659703:1.08619:0.131004:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038045 ES:SE:LP:AF:ID  0.0126183:0.0118241:0.537602:0.038045:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038292 ES:SE:LP:AF:ID  0.0118634:0.0117509:0.508638:0.038292:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867976 ES:SE:LP:AF:ID  0.0110513:0.00656955:1.03152:0.867976:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86805  ES:SE:LP:AF:ID  0.010976:0.00657224:1.02228:0.86805:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038199 ES:SE:LP:AF:ID  0.0121031:0.0118051:0.508638:0.038199:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867987 ES:SE:LP:AF:ID  0.0110128:0.00656939:1.02687:0.867987:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005396 ES:SE:LP:AF:ID  0.0335654:0.0329255:0.508638:0.005396:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836159 ES:SE:LP:AF:ID  0.0062996:0.00610569:0.522879:0.836159:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038203 ES:SE:LP:AF:ID  0.01202:0.0118224:0.508638:0.038203:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836793 ES:SE:LP:AF:ID  0.00637135:0.00612242:0.522879:0.836793:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.0278304:0.0221502:0.677781:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  -0.0371227:0.0328824:0.585027:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838109 ES:SE:LP:AF:ID  0.00550611:0.0062086:0.420216:0.838109:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868228 ES:SE:LP:AF:ID  0.011477:0.00656093:1.09691:0.868228:rs3115858