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_6143_3.vcf.gz --id UKB-b:17155 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6143_3.txt.gz --cohort_cases 51847 --cohort_controls 193517 --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-17155/UKB-b-17155_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17155/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:35 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17155/UKB-b-17155_data.vcf.gz ...
Read summary statistics for 8292892 SNPs.
Dropped 6692 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, 1284401 SNPs remain.
After merging with regression SNP LD, 1284401 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0312 (0.0027)
Lambda GC: 1.2112
Mean Chi^2: 1.2239
Intercept: 1.0732 (0.0077)
Ratio: 0.327 (0.0344)
Analysis finished at Thu Oct 17 14:46:54 2019
Total time elapsed: 1.0m:19.65s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9446,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 8.5817e-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": 77949,
    "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": 1284401,
    "ldsc_nsnp_merge_regression_ld": 1284401,
    "ldsc_observed_scale_h2_beta": 0.0312,
    "ldsc_observed_scale_h2_se": 0.0027,
    "ldsc_intercept_beta": 1.0732,
    "ldsc_intercept_se": 0.0077,
    "ldsc_lambda_gc": 1.2112,
    "ldsc_mean_chisq": 1.2239,
    "ldsc_ratio": 0.3269
}

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 8286230 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 8292892 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 8.655368e+00 5.762383e+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 7.873566e+07 5.638619e+07 828.0000000 3.232768e+07 6.922815e+07 1.145478e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 8.600000e-06 3.282000e-03 -0.0349067 -1.489400e-03 -5.400000e-06 1.493100e-03 3.320400e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.621800e-03 1.678800e-03 0.0011202 1.307000e-03 1.868300e-03 3.493000e-03 1.782820e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.734517e-01 2.952565e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.734525e-01 2.952298e-01 0.0000000 2.110503e-01 4.635162e-01 7.290175e-01 9.999998e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.375972e-01 2.602641e-01 0.0067510 3.026000e-02 1.256890e-01 3.762890e-01 9.932490e-01 ▇▂▂▁▁
numeric AF_reference 77949 0.9906005 NA NA NA NA NA NA NA 2.370913e-01 2.521512e-01 0.0000000 3.015180e-02 1.415730e-01 3.724040e-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 0.0010846 0.0020613 0.5999997 0.5987596 0.623695 0.7821490 NA
1 54676 rs2462492 C T -0.0013268 0.0020407 0.5199996 0.5155662 0.400681 NA NA
1 86028 rs114608975 T C -0.0023523 0.0032617 0.4700002 0.4707979 0.103516 0.0277556 NA
1 91536 rs6702460 G T -0.0014988 0.0020115 0.4600002 0.4561899 0.456668 0.4207270 NA
1 234313 rs8179466 C T 0.0008270 0.0039474 0.8300000 0.8340607 0.074802 NA NA
1 534192 rs6680723 C T 0.0006142 0.0022972 0.7899998 0.7891931 0.240941 NA NA
1 546697 rs12025928 A G 0.0009761 0.0028681 0.7300002 0.7336191 0.913746 NA NA
1 693731 rs12238997 A G 0.0002863 0.0019197 0.8800001 0.8814296 0.116422 0.1417730 NA
1 705882 rs72631875 G A 0.0006951 0.0028266 0.8100000 0.8057515 0.066929 0.0315495 NA
1 706368 rs55727773 A G -0.0009218 0.0014241 0.5199996 0.5174219 0.515436 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0018314 0.0017161 0.2900000 0.2858766 0.138606 0.2052720 NA
22 51219387 rs9616832 T C 0.0026620 0.0022265 0.2300001 0.2318687 0.074194 0.0654952 NA
22 51219704 rs147475742 G A 0.0026036 0.0029793 0.3800004 0.3821817 0.042309 0.0473243 NA
22 51221190 rs369304721 G A 0.0036636 0.0029759 0.2200002 0.2182843 0.050099 NA NA
22 51221731 rs115055839 T C 0.0028321 0.0022279 0.2000000 0.2036626 0.073696 0.0625000 NA
22 51222100 rs114553188 G T 0.0004334 0.0026300 0.8700001 0.8691185 0.054577 0.0880591 NA
22 51223637 rs375798137 G A 0.0003319 0.0026427 0.9000000 0.9000649 0.054212 0.0788738 NA
22 51229805 rs9616985 T C 0.0027701 0.0022363 0.2200002 0.2154541 0.073513 0.0730831 NA
22 51232488 rs376461333 A G 0.0027130 0.0052804 0.6100002 0.6074084 0.020032 NA NA
22 51237063 rs3896457 T C 0.0009199 0.0013718 0.5000000 0.5024958 0.297423 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623695 ES:SE:LP:AF:ID  0.00108462:0.00206129:0.221849:0.623695:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400681 ES:SE:LP:AF:ID  -0.00132684:0.00204068:0.283997:0.400681:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103516 ES:SE:LP:AF:ID  -0.00235227:0.00326169:0.327902:0.103516:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456668 ES:SE:LP:AF:ID  -0.00149881:0.00201146:0.337242:0.456668:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074802 ES:SE:LP:AF:ID  0.000826956:0.00394735:0.0809219:0.074802:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240941 ES:SE:LP:AF:ID  0.000614168:0.00229717:0.102373:0.240941:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913746 ES:SE:LP:AF:ID  0.000976072:0.00286814:0.136677:0.913746:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116422 ES:SE:LP:AF:ID  0.000286334:0.00191968:0.0555173:0.116422:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066929 ES:SE:LP:AF:ID  0.000695103:0.00282665:0.091515:0.066929:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515436 ES:SE:LP:AF:ID  -0.000921829:0.00142406:0.283997:0.515436:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033237 ES:SE:LP:AF:ID  0.00503445:0.00357936:0.79588:0.033237:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036836 ES:SE:LP:AF:ID  0.00364605:0.00325368:0.585027:0.036836:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036942 ES:SE:LP:AF:ID  0.00358858:0.00324245:0.568636:0.036942:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036658 ES:SE:LP:AF:ID  0.00354402:0.00326457:0.552842:0.036658:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016434 ES:SE:LP:AF:ID  -0.000766438:0.00503658:0.0555173:0.016434:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037204 ES:SE:LP:AF:ID  0.00353361:0.00322833:0.568636:0.037204:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037301 ES:SE:LP:AF:ID  0.00378738:0.00321756:0.619789:0.037301:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101051 ES:SE:LP:AF:ID  0.0004829:0.002354:0.0757207:0.101051:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958793 ES:SE:LP:AF:ID  -0.00447879:0.00309932:0.823909:0.958793:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031537 ES:SE:LP:AF:ID  -0.00987609:0.00562708:1.10237:0.031537:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053306 ES:SE:LP:AF:ID  0.00664309:0.00448082:0.853872:0.053306:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036808 ES:SE:LP:AF:ID  0.0034263:0.00323901:0.537602:0.036808:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037114 ES:SE:LP:AF:ID  0.00388361:0.00320976:0.638272:0.037114:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842785 ES:SE:LP:AF:ID  -0.00166253:0.00166232:0.49485:0.842785:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055996 ES:SE:LP:AF:ID  0.000353073:0.00269682:0.0457575:0.055996:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122437 ES:SE:LP:AF:ID  0.000692188:0.0018203:0.154902:0.122437:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025762 ES:SE:LP:AF:ID  -0.0094229:0.00448367:1.4437:0.025762:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121649 ES:SE:LP:AF:ID  0.000554363:0.00182116:0.119186:0.121649:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132632 ES:SE:LP:AF:ID  0.00177336:0.00179521:0.49485:0.132632:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011235 ES:SE:LP:AF:ID  0.0089629:0.00652279:0.769551:0.011235:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037053 ES:SE:LP:AF:ID  0.00376781:0.00317648:0.619789:0.037053:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838548 ES:SE:LP:AF:ID  -0.00139684:0.00161027:0.408935:0.838548:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838134 ES:SE:LP:AF:ID  -0.00133798:0.00160827:0.387216:0.838134:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869551 ES:SE:LP:AF:ID  -0.000708654:0.00172598:0.167491:0.869551:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130136 ES:SE:LP:AF:ID  0.000580184:0.00172908:0.130768:0.130136:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037551 ES:SE:LP:AF:ID  0.00368916:0.00312256:0.619789:0.037551:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037804 ES:SE:LP:AF:ID  0.00362462:0.00310256:0.619789:0.037804:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868852 ES:SE:LP:AF:ID  -0.000670342:0.0017223:0.154902:0.868852:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86895  ES:SE:LP:AF:ID  -0.000736518:0.001723:0.173925:0.86895:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037757 ES:SE:LP:AF:ID  0.004205:0.00311635:0.744727:0.037757:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868857 ES:SE:LP:AF:ID  -0.000707552:0.00172232:0.167491:0.868857:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837569 ES:SE:LP:AF:ID  -0.0015101:0.00160382:0.455932:0.837569:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037772 ES:SE:LP:AF:ID  0.00417281:0.00312065:0.744727:0.037772:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838224 ES:SE:LP:AF:ID  -0.00139634:0.00160837:0.408935:0.838224:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013657 ES:SE:LP:AF:ID  -0.0019391:0.00565884:0.136677:0.013657:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839351 ES:SE:LP:AF:ID  -0.00129119:0.00163022:0.366532:0.839351:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869154 ES:SE:LP:AF:ID  -0.000725104:0.00172046:0.173925:0.869154:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868702 ES:SE:LP:AF:ID  -0.000738378:0.00171611:0.173925:0.868702:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867609 ES:SE:LP:AF:ID  -0.00063389:0.00171249:0.148742:0.867609:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868849 ES:SE:LP:AF:ID  -0.000689683:0.00171759:0.161151:0.868849:rs4951929