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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_4440.vcf.gz --id UKB-b:10830 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4440.txt.gz --cohort_controls 34111 --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-10830/UKB-b-10830_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10830/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-10830/UKB-b-10830_data.vcf.gz ...
Read summary statistics for 7701619 SNPs.
Dropped 5544 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, 1279506 SNPs remain.
After merging with regression SNP LD, 1279506 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0585 (0.0152)
Lambda GC: 1.0269
Mean Chi^2: 1.0294
Intercept: 0.9901 (0.0061)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:50 2019
Total time elapsed: 1.0m:31.09s

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": 71687,
    "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": 1279506,
    "ldsc_nsnp_merge_regression_ld": 1279506,
    "ldsc_observed_scale_h2_beta": 0.0585,
    "ldsc_observed_scale_h2_se": 0.0152,
    "ldsc_intercept_beta": 0.9901,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0269,
    "ldsc_mean_chisq": 1.0294,
    "ldsc_ratio": -0.3367
}

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 1.000000 3 58 0 7696099 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 7701619 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.661831e+00 5.763950e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.868724e+07 5.643701e+07 828.0000000 3.220325e+07 6.914614e+07 1.145692e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 3.440000e-05 1.321610e-02 -0.1153990 -6.411600e-03 -3.200000e-05 6.440900e-03 1.300560e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.147670e-02 6.446500e-03 0.0055463 6.381600e-03 8.666800e-03 1.490490e-02 6.629350e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.961024e-01 2.897107e-01 0.0000001 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.961022e-01 2.896845e-01 0.0000001 2.444625e-01 4.946052e-01 7.469530e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.540677e-01 2.607906e-01 0.0102610 4.154200e-02 1.487330e-01 4.015830e-01 9.897390e-01 ▇▂▂▁▁
numeric AF_reference 71687 0.990692 NA NA NA NA NA NA NA 2.531334e-01 2.526671e-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 -0.0027460 0.0102439 0.7899998 0.7886486 0.623601 0.7821490 NA
1 54676 rs2462492 C T -0.0062523 0.0101508 0.5400003 0.5379318 0.398324 NA NA
1 86028 rs114608975 T C 0.0167871 0.0160848 0.2999998 0.2966416 0.104140 0.0277556 NA
1 91536 rs6702460 G T -0.0069205 0.0099833 0.4899999 0.4881767 0.455374 0.4207270 NA
1 234313 rs8179466 C T 0.0011670 0.0197313 0.9500000 0.9528373 0.074701 NA NA
1 534192 rs6680723 C T 0.0140118 0.0114557 0.2200002 0.2212809 0.240532 NA NA
1 546697 rs12025928 A G 0.0220314 0.0139884 0.1199999 0.1152619 0.911434 NA NA
1 693731 rs12238997 A G 0.0151737 0.0094323 0.1100001 0.1076829 0.118120 0.1417730 NA
1 705882 rs72631875 G A -0.0148316 0.0138993 0.2900000 0.2859378 0.068297 0.0315495 NA
1 706368 rs55727773 A G -0.0025693 0.0070289 0.7099994 0.7147084 0.514467 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0055273 0.0085815 0.5199996 0.5195146 0.137010 0.2052720 NA
22 51219387 rs9616832 T C -0.0071824 0.0111178 0.5199996 0.5182618 0.073396 0.0654952 NA
22 51219704 rs147475742 G A -0.0027304 0.0148151 0.8499999 0.8537820 0.042178 0.0473243 NA
22 51221190 rs369304721 G A -0.0114240 0.0148847 0.4400003 0.4427846 0.049644 NA NA
22 51221731 rs115055839 T C -0.0069268 0.0111194 0.5300002 0.5333198 0.073003 0.0625000 NA
22 51222100 rs114553188 G T -0.0051941 0.0132024 0.6899999 0.6940075 0.053333 0.0880591 NA
22 51223637 rs375798137 G A -0.0054854 0.0132773 0.6800001 0.6795048 0.052932 0.0788738 NA
22 51229805 rs9616985 T C -0.0070526 0.0111643 0.5300002 0.5275758 0.072835 0.0730831 NA
22 51232488 rs376461333 A G -0.0293618 0.0271433 0.2800000 0.2793712 0.019235 NA NA
22 51237063 rs3896457 T C 0.0082232 0.0068147 0.2300001 0.2275549 0.298534 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623601 ES:SE:LP:AF:ID  -0.00274604:0.0102439:0.102373:0.623601:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398324 ES:SE:LP:AF:ID  -0.00625232:0.0101508:0.267606:0.398324:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10414  ES:SE:LP:AF:ID  0.0167871:0.0160848:0.522879:0.10414:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455374 ES:SE:LP:AF:ID  -0.00692052:0.00998327:0.309804:0.455374:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074701 ES:SE:LP:AF:ID  0.00116699:0.0197313:0.0222764:0.074701:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240532 ES:SE:LP:AF:ID  0.0140118:0.0114557:0.657577:0.240532:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.911434 ES:SE:LP:AF:ID  0.0220314:0.0139884:0.920819:0.911434:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11812  ES:SE:LP:AF:ID  0.0151737:0.0094323:0.958607:0.11812:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068297 ES:SE:LP:AF:ID  -0.0148316:0.0138993:0.537602:0.068297:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514467 ES:SE:LP:AF:ID  -0.00256934:0.00702889:0.148742:0.514467:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033928 ES:SE:LP:AF:ID  -0.000899076:0.0175781:0.0177288:0.033928:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037744 ES:SE:LP:AF:ID  0.00576952:0.0159437:0.142668:0.037744:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03794  ES:SE:LP:AF:ID  0.00623586:0.015863:0.161151:0.03794:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037588 ES:SE:LP:AF:ID  0.00449834:0.0159937:0.107905:0.037588:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016471 ES:SE:LP:AF:ID  0.0570941:0.0250093:1.65758:0.016471:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.038196 ES:SE:LP:AF:ID  0.00491904:0.0158026:0.119186:0.038196:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.038247 ES:SE:LP:AF:ID  0.00393794:0.0157647:0.09691:0.038247:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101136 ES:SE:LP:AF:ID  -0.00339276:0.0116323:0.113509:0.101136:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957776 ES:SE:LP:AF:ID  -0.000347473:0.0152203:0.00877392:0.957776:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031395 ES:SE:LP:AF:ID  -0.0138463:0.0281649:0.207608:0.031395:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052516 ES:SE:LP:AF:ID  -0.0146423:0.0225241:0.283997:0.052516:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037632 ES:SE:LP:AF:ID  0.00499318:0.0158901:0.124939:0.037632:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037951 ES:SE:LP:AF:ID  0.00495913:0.0157446:0.124939:0.037951:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840475 ES:SE:LP:AF:ID  -0.0153087:0.00820615:1.20761:0.840475:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056085 ES:SE:LP:AF:ID  -0.00201636:0.0133821:0.0555173:0.056085:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12375  ES:SE:LP:AF:ID  0.0158733:0.0089728:1.11351:0.12375:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024884 ES:SE:LP:AF:ID  0.0307726:0.0228185:0.744727:0.024884:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122973 ES:SE:LP:AF:ID  0.0154072:0.0089799:1.0655:0.122973:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134207 ES:SE:LP:AF:ID  0.00948606:0.00886815:0.552842:0.134207:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01146  ES:SE:LP:AF:ID  0.0283966:0.031742:0.431798:0.01146:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.038028 ES:SE:LP:AF:ID  -0.000410112:0.0155649:0.00877392:0.038028:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836435 ES:SE:LP:AF:ID  -0.00785029:0.00795881:0.49485:0.836435:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83603  ES:SE:LP:AF:ID  -0.00771818:0.00794922:0.481486:0.83603:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868335 ES:SE:LP:AF:ID  -0.00753358:0.00851751:0.420216:0.868335:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131264 ES:SE:LP:AF:ID  0.00642585:0.00853684:0.346787:0.131264:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038431 ES:SE:LP:AF:ID  -0.00112497:0.0153314:0.0268721:0.038431:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038713 ES:SE:LP:AF:ID  -0.00131178:0.0152303:0.0315171:0.038713:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86768  ES:SE:LP:AF:ID  -0.00731655:0.0085025:0.408935:0.86768:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86782  ES:SE:LP:AF:ID  -0.00753772:0.00850771:0.420216:0.86782:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038671 ES:SE:LP:AF:ID  -0.00244754:0.0152813:0.0604807:0.038671:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867693 ES:SE:LP:AF:ID  -0.0073807:0.00850222:0.408935:0.867693:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.835572 ES:SE:LP:AF:ID  -0.0076668:0.0079304:0.481486:0.835572:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038612 ES:SE:LP:AF:ID  -0.00258804:0.0153175:0.0604807:0.038612:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836258 ES:SE:LP:AF:ID  -0.00721806:0.00795442:0.443698:0.836258:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013082 ES:SE:LP:AF:ID  0.0250328:0.0288417:0.408935:0.013082:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.837644 ES:SE:LP:AF:ID  -0.00787743:0.00806037:0.481486:0.837644:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86791  ES:SE:LP:AF:ID  -0.00763298:0.00849222:0.431798:0.86791:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867488 ES:SE:LP:AF:ID  -0.00752157:0.00847194:0.431798:0.867488:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866287 ES:SE:LP:AF:ID  -0.00567472:0.00845409:0.30103:0.866287:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86757  ES:SE:LP:AF:ID  -0.00701427:0.00847741:0.387216:0.86757:rs4951929