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_4407.vcf.gz --id UKB-b:17300 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4407.txt.gz --cohort_controls 34179 --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-17300/UKB-b-17300_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17300/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-17300/UKB-b-17300_data.vcf.gz ...
Read summary statistics for 7704269 SNPs.
Dropped 5549 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, 1279552 SNPs remain.
After merging with regression SNP LD, 1279552 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0402 (0.013)
Lambda GC: 1.0474
Mean Chi^2: 1.054
Intercept: 1.0273 (0.006)
Ratio: 0.5049 (0.1104)
Analysis finished at Thu Oct 17 14:41:48 2019
Total time elapsed: 1.0m:29.82s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9415,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 71712,
    "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": 1279552,
    "ldsc_nsnp_merge_regression_ld": 1279552,
    "ldsc_observed_scale_h2_beta": 0.0402,
    "ldsc_observed_scale_h2_se": 0.013,
    "ldsc_intercept_beta": 1.0273,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0474,
    "ldsc_mean_chisq": 1.054,
    "ldsc_ratio": 0.5056
}

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 7698744 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 7704269 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 8.661712e+00 5.763890e+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 7.868736e+07 5.643712e+07 828.0000000 3.220385e+07 6.914515e+07 1.145702e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 4.540000e-05 1.497060e-02 -0.1495030 -7.224000e-03 -8.600000e-06 7.250600e-03 1.595270e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.281150e-02 7.200800e-03 0.0061896 7.120400e-03 9.672500e-03 1.663940e-02 7.418780e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.931099e-01 2.906360e-01 0.0000005 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 4.931108e-01 2.906113e-01 0.0000005 2.395049e-01 4.913023e-01 7.450894e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.539890e-01 2.607891e-01 0.0102410 4.148900e-02 1.486140e-01 4.014700e-01 9.897590e-01 ▇▂▂▁▁
numeric AF_reference 71712 0.9906919 NA NA NA NA NA NA NA 2.530573e-01 2.526667e-01 0.0000000 4.552720e-02 1.631390e-01 3.961660e-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 0.0108007 0.0114228 0.3400001 0.3443839 0.623483 0.7821490 NA
1 54676 rs2462492 C T -0.0127839 0.0113170 0.2599998 0.2586367 0.398326 NA NA
1 86028 rs114608975 T C -0.0092871 0.0179419 0.5999997 0.6047229 0.104100 0.0277556 NA
1 91536 rs6702460 G T 0.0010989 0.0111351 0.9199999 0.9213889 0.455524 0.4207270 NA
1 234313 rs8179466 C T -0.0002037 0.0220135 0.9900000 0.9926186 0.074680 NA NA
1 534192 rs6680723 C T 0.0291006 0.0127728 0.0230001 0.0227072 0.240570 NA NA
1 546697 rs12025928 A G 0.0256980 0.0156190 0.1000000 0.0999072 0.911553 NA NA
1 693731 rs12238997 A G 0.0124729 0.0105166 0.2399999 0.2356143 0.118331 0.1417730 NA
1 705882 rs72631875 G A 0.0141752 0.0155145 0.3599996 0.3608880 0.068213 0.0315495 NA
1 706368 rs55727773 A G 0.0009394 0.0078434 0.9000000 0.9046710 0.514092 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0013426 0.0095779 0.8900000 0.8885186 0.137148 0.2052720 NA
22 51219387 rs9616832 T C 0.0029141 0.0124050 0.8100000 0.8142779 0.073516 0.0654952 NA
22 51219704 rs147475742 G A 0.0005385 0.0165246 0.9699999 0.9740028 0.042204 0.0473243 NA
22 51221190 rs369304721 G A 0.0034676 0.0166084 0.8300000 0.8346135 0.049689 NA NA
22 51221731 rs115055839 T C 0.0038239 0.0124067 0.7600007 0.7579188 0.073122 0.0625000 NA
22 51222100 rs114553188 G T -0.0147348 0.0147257 0.3200000 0.3170115 0.053392 0.0880591 NA
22 51223637 rs375798137 G A -0.0148894 0.0148092 0.3100002 0.3146968 0.052990 0.0788738 NA
22 51229805 rs9616985 T C 0.0020147 0.0124545 0.8700001 0.8714904 0.072964 0.0730831 NA
22 51232488 rs376461333 A G -0.0106258 0.0302393 0.7300002 0.7252955 0.019262 NA NA
22 51237063 rs3896457 T C -0.0036252 0.0076028 0.6300007 0.6334822 0.298760 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623483 ES:SE:LP:AF:ID  0.0108007:0.0114228:0.468521:0.623483:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398326 ES:SE:LP:AF:ID  -0.0127839:0.011317:0.585027:0.398326:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.1041   ES:SE:LP:AF:ID  -0.0092871:0.0179419:0.221849:0.1041:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455524 ES:SE:LP:AF:ID  0.00109886:0.0111351:0.0362122:0.455524:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07468  ES:SE:LP:AF:ID  -0.000203655:0.0220135:0.00436481:0.07468:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24057  ES:SE:LP:AF:ID  0.0291006:0.0127728:1.63827:0.24057:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.911553 ES:SE:LP:AF:ID  0.025698:0.015619:1:0.911553:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.118331 ES:SE:LP:AF:ID  0.0124729:0.0105166:0.619789:0.118331:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068213 ES:SE:LP:AF:ID  0.0141752:0.0155145:0.443698:0.068213:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514092 ES:SE:LP:AF:ID  0.000939353:0.00784344:0.0457575:0.514092:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033854 ES:SE:LP:AF:ID  0.00840938:0.0196329:0.173925:0.033854:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037674 ES:SE:LP:AF:ID  0.00533274:0.0178007:0.119186:0.037674:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037858 ES:SE:LP:AF:ID  0.00743234:0.0177148:0.173925:0.037858:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037505 ES:SE:LP:AF:ID  0.00585314:0.0178617:0.130768:0.037505:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016541 ES:SE:LP:AF:ID  0.00768852:0.0278531:0.107905:0.016541:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.038118 ES:SE:LP:AF:ID  0.00529216:0.0176463:0.119186:0.038118:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.038171 ES:SE:LP:AF:ID  0.00653766:0.0176031:0.148742:0.038171:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10129  ES:SE:LP:AF:ID  -0.0143576:0.0129705:0.568636:0.10129:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957834 ES:SE:LP:AF:ID  -0.00796229:0.0169912:0.19382:0.957834:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031316 ES:SE:LP:AF:ID  0.0409838:0.0315153:0.721246:0.031316:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052501 ES:SE:LP:AF:ID  -0.00765732:0.0251154:0.119186:0.052501:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037544 ES:SE:LP:AF:ID  0.00666651:0.0177467:0.148742:0.037544:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037877 ES:SE:LP:AF:ID  0.00571788:0.0175825:0.124939:0.037877:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840301 ES:SE:LP:AF:ID  -0.0150347:0.00915208:1:0.840301:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056115 ES:SE:LP:AF:ID  0.0218872:0.0149288:0.853872:0.056115:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123985 ES:SE:LP:AF:ID  0.0122742:0.0100048:0.657577:0.123985:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024926 ES:SE:LP:AF:ID  -0.00466687:0.0254713:0.0705811:0.024926:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.123214 ES:SE:LP:AF:ID  0.0122311:0.0100126:0.657577:0.123214:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134266 ES:SE:LP:AF:ID  0.0188191:0.00989164:1.24413:0.134266:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011386 ES:SE:LP:AF:ID  -0.0524791:0.0355565:0.853872:0.011386:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.03794  ES:SE:LP:AF:ID  0.00536611:0.0173832:0.119186:0.03794:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836292 ES:SE:LP:AF:ID  -0.0139468:0.00887705:0.920819:0.836292:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.835859 ES:SE:LP:AF:ID  -0.0137257:0.00886539:0.920819:0.835859:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868094 ES:SE:LP:AF:ID  -0.0115774:0.00949668:0.657577:0.868094:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131531 ES:SE:LP:AF:ID  0.00983424:0.00951754:0.522879:0.131531:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038348 ES:SE:LP:AF:ID  0.00545498:0.0171197:0.124939:0.038348:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038621 ES:SE:LP:AF:ID  0.00358521:0.0170091:0.0809219:0.038621:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867417 ES:SE:LP:AF:ID  -0.0109667:0.00947953:0.60206:0.867417:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867557 ES:SE:LP:AF:ID  -0.0110225:0.00948531:0.60206:0.867557:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038579 ES:SE:LP:AF:ID  0.00362591:0.0170661:0.0809219:0.038579:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867431 ES:SE:LP:AF:ID  -0.0110286:0.0094792:0.619789:0.867431:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.835409 ES:SE:LP:AF:ID  -0.0134307:0.00884486:0.886057:0.835409:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038521 ES:SE:LP:AF:ID  0.00359004:0.0171065:0.0809219:0.038521:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836091 ES:SE:LP:AF:ID  -0.0126188:0.00887137:0.823909:0.836091:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013057 ES:SE:LP:AF:ID  -0.0113797:0.0322133:0.142668:0.013057:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.837471 ES:SE:LP:AF:ID  -0.0135372:0.00898837:0.886057:0.837471:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86766  ES:SE:LP:AF:ID  -0.0110836:0.00946821:0.619789:0.86766:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867239 ES:SE:LP:AF:ID  -0.0104382:0.00944541:0.568636:0.867239:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866007 ES:SE:LP:AF:ID  -0.0103044:0.00942529:0.568636:0.866007:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867317 ES:SE:LP:AF:ID  -0.0105017:0.00945152:0.568636:0.867317:rs4951929