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_101430.vcf.gz --id UKB-b:5064 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_101430.txt.gz --cohort_controls 64939 --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-5064/UKB-b-5064_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5064/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:59 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5064/UKB-b-5064_data.vcf.gz ...
Read summary statistics for 8624999 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: 0.0034 (0.0066)
Lambda GC: 1.0144
Mean Chi^2: 1.0173
Intercept: 1.013 (0.0061)
Ratio: 0.7512 (0.3532)
Analysis finished at Thu Oct 17 14:46:18 2019
Total time elapsed: 1.0m:19.52s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0001,
    "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": 83093,
    "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": 0.0034,
    "ldsc_observed_scale_h2_se": 0.0066,
    "ldsc_intercept_beta": 1.013,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0144,
    "ldsc_mean_chisq": 1.0173,
    "ldsc_ratio": 0.7514
}

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 1.000000 3 58 0 8617661 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 8624999 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 8.650345e+00 5.760987e+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 7.877022e+07 5.637170e+07 828.0000000 3.238547e+07 6.929212e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -1.090000e-04 1.127850e-02 -0.1115220 -4.797800e-03 -8.680000e-05 4.559300e-03 1.441160e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 9.258300e-03 6.319800e-03 0.0036771 4.330100e-03 6.384500e-03 1.251540e-02 6.339280e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.975004e-01 2.893917e-01 0.0000001 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.974984e-01 2.893654e-01 0.0000001 2.461799e-01 4.969311e-01 7.482091e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.291173e-01 2.594885e-01 0.0053900 2.518900e-02 1.138970e-01 3.626190e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83093 0.990366 NA NA NA NA NA NA NA 2.288665e-01 2.514592e-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 -0.0102633 0.0067767 0.1299999 0.1298978 0.623784 0.7821490 NA
1 54676 rs2462492 C T 0.0067734 0.0067568 0.3200000 0.3161287 0.399151 NA NA
1 86028 rs114608975 T C -0.0031505 0.0107554 0.7700005 0.7695820 0.103544 0.0277556 NA
1 91536 rs6702460 G T -0.0071443 0.0066459 0.2800000 0.2823727 0.455911 0.4207270 NA
1 234313 rs8179466 C T -0.0228185 0.0131441 0.0830004 0.0825592 0.074455 NA NA
1 534192 rs6680723 C T -0.0031676 0.0075695 0.6800001 0.6756006 0.242058 NA NA
1 546697 rs12025928 A G -0.0085706 0.0093915 0.3599996 0.3614575 0.912859 NA NA
1 693731 rs12238997 A G -0.0033338 0.0063116 0.5999997 0.5973524 0.117309 0.1417730 NA
1 705882 rs72631875 G A 0.0072441 0.0092001 0.4299995 0.4310469 0.067694 0.0315495 NA
1 706368 rs55727773 A G 0.0019686 0.0046853 0.6700003 0.6743657 0.513303 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0009390 0.0056980 0.8700001 0.8691061 0.136319 0.2052720 NA
22 51219387 rs9616832 T C -0.0032381 0.0074264 0.6600001 0.6628158 0.071808 0.0654952 NA
22 51219704 rs147475742 G A 0.0007200 0.0098790 0.9400001 0.9418969 0.041197 0.0473243 NA
22 51221190 rs369304721 G A 0.0007713 0.0099491 0.9400001 0.9382033 0.048380 NA NA
22 51221731 rs115055839 T C -0.0024104 0.0074280 0.7499995 0.7455618 0.071359 0.0625000 NA
22 51222100 rs114553188 G T 0.0045112 0.0086080 0.5999997 0.6002245 0.054851 0.0880591 NA
22 51223637 rs375798137 G A 0.0045025 0.0086529 0.5999997 0.6028218 0.054471 0.0788738 NA
22 51229805 rs9616985 T C -0.0023877 0.0074510 0.7499995 0.7486198 0.071264 0.0730831 NA
22 51232488 rs376461333 A G -0.0079951 0.0171608 0.6400000 0.6412936 0.020463 NA NA
22 51237063 rs3896457 T C 0.0011500 0.0044986 0.8000000 0.7982227 0.298390 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623784 ES:SE:LP:AF:ID  -0.0102633:0.00677667:0.886057:0.623784:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399151 ES:SE:LP:AF:ID  0.00677335:0.00675683:0.49485:0.399151:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103544 ES:SE:LP:AF:ID  -0.00315049:0.0107554:0.113509:0.103544:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455911 ES:SE:LP:AF:ID  -0.00714433:0.00664586:0.552842:0.455911:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074455 ES:SE:LP:AF:ID  -0.0228185:0.0131441:1.08092:0.074455:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242058 ES:SE:LP:AF:ID  -0.00316763:0.00756948:0.167491:0.242058:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912859 ES:SE:LP:AF:ID  -0.00857063:0.00939153:0.443698:0.912859:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117309 ES:SE:LP:AF:ID  -0.00333384:0.00631156:0.221849:0.117309:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067694 ES:SE:LP:AF:ID  0.00724414:0.00920006:0.366532:0.067694:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513303 ES:SE:LP:AF:ID  0.00196859:0.00468529:0.173925:0.513303:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033676 ES:SE:LP:AF:ID  0.00381552:0.0116774:0.130768:0.033676:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037456 ES:SE:LP:AF:ID  0.00141809:0.0105909:0.05061:0.037456:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037642 ES:SE:LP:AF:ID  0.0014858:0.0105355:0.05061:0.037642:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037219 ES:SE:LP:AF:ID  0.00201612:0.0106317:0.0705811:0.037219:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016269 ES:SE:LP:AF:ID  0.01373:0.0166842:0.387216:0.016269:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037859 ES:SE:LP:AF:ID  0.00174196:0.0104995:0.0604807:0.037859:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037953 ES:SE:LP:AF:ID  0.0016251:0.0104662:0.0555173:0.037953:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102742 ES:SE:LP:AF:ID  0.0021244:0.0076448:0.107905:0.102742:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958091 ES:SE:LP:AF:ID  9.4024e-05:0.0101087:0.00436481:0.958091:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031693 ES:SE:LP:AF:ID  0.00469218:0.018509:0.09691:0.031693:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052725 ES:SE:LP:AF:ID  -0.00220938:0.0149087:0.0555173:0.052725:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037447 ES:SE:LP:AF:ID  0.00117464:0.0105349:0.0409586:0.037447:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037716 ES:SE:LP:AF:ID  0.00220403:0.0104476:0.0809219:0.037716:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841456 ES:SE:LP:AF:ID  0.00290375:0.00545876:0.229148:0.841456:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056327 ES:SE:LP:AF:ID  -0.0117279:0.00886726:0.721246:0.056327:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123064 ES:SE:LP:AF:ID  -0.00362573:0.00599589:0.259637:0.123064:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025128 ES:SE:LP:AF:ID  0.00790388:0.0149311:0.221849:0.025128:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122317 ES:SE:LP:AF:ID  -0.00384692:0.00599782:0.283997:0.122317:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134137 ES:SE:LP:AF:ID  -0.00585949:0.00588718:0.49485:0.134137:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  -0.000614517:0.0209806:0.00877392:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  -0.0557986:0.0266428:1.4437:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037598 ES:SE:LP:AF:ID  0.00172306:0.0103508:0.0604807:0.037598:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837043 ES:SE:LP:AF:ID  0.00424359:0.00528059:0.376751:0.837043:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836747 ES:SE:LP:AF:ID  0.00471434:0.00527672:0.431798:0.836747:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868573 ES:SE:LP:AF:ID  0.00592277:0.00567019:0.522879:0.868573:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130994 ES:SE:LP:AF:ID  -0.00599109:0.00568476:0.537602:0.130994:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038043 ES:SE:LP:AF:ID  0.00391962:0.0101884:0.154902:0.038043:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03829  ES:SE:LP:AF:ID  0.00421175:0.0101253:0.167491:0.03829:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867986 ES:SE:LP:AF:ID  0.00619821:0.00566107:0.568636:0.867986:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86806  ES:SE:LP:AF:ID  0.00619649:0.00566339:0.568636:0.86806:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038198 ES:SE:LP:AF:ID  0.00278086:0.010172:0.107905:0.038198:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867997 ES:SE:LP:AF:ID  0.00637584:0.00566093:0.585027:0.867997:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.0158476:0.0283679:0.236572:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836172 ES:SE:LP:AF:ID  0.00484047:0.00526127:0.443698:0.836172:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038201 ES:SE:LP:AF:ID  0.00246524:0.0101869:0.091515:0.038201:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836806 ES:SE:LP:AF:ID  0.00484431:0.00527569:0.443698:0.836806:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013032 ES:SE:LP:AF:ID  -0.0253274:0.0190842:0.744727:0.013032:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005663 ES:SE:LP:AF:ID  -0.0140161:0.0283308:0.207608:0.005663:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838122 ES:SE:LP:AF:ID  0.00416882:0.00534996:0.356547:0.838122:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868238 ES:SE:LP:AF:ID  0.00574955:0.00565364:0.508638:0.868238:rs3115858