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_4836.vcf.gz --id UKB-b:5436 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4836.txt.gz --cohort_controls 150898 --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-5436/UKB-b-5436_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5436/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5436/UKB-b-5436_data.vcf.gz ...
Read summary statistics for 9323304 SNPs.
Dropped 10413 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, 1287994 SNPs remain.
After merging with regression SNP LD, 1287994 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0321 (0.0036)
Lambda GC: 1.1082
Mean Chi^2: 1.1097
Intercept: 1.0158 (0.0071)
Ratio: 0.1437 (0.0652)
Analysis finished at Thu Oct 17 14:46:46 2019
Total time elapsed: 1.0m:27.71s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9488,
    "inflation_factor": 1.0966,
    "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": 113448,
    "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": 1287994,
    "ldsc_nsnp_merge_regression_ld": 1287994,
    "ldsc_observed_scale_h2_beta": 0.0321,
    "ldsc_observed_scale_h2_se": 0.0036,
    "ldsc_intercept_beta": 1.0158,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.1082,
    "ldsc_mean_chisq": 1.1097,
    "ldsc_ratio": 0.144
}

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 9312943 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 9323304 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 8.634527e+00 5.753900e+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 7.881303e+07 5.630853e+07 828.0000000 3.250492e+07 6.939352e+07 1.145430e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.580000e-05 1.001510e-02 -0.1475020 -3.661600e-03 1.540000e-05 3.710700e-03 1.275080e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.737100e-03 6.135300e-03 0.0025835 3.109400e-03 4.920100e-03 1.060350e-02 8.958360e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.875414e-01 2.917336e-01 0.0000002 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.875402e-01 2.917086e-01 0.0000002 2.321077e-01 4.829120e-01 7.401677e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.134355e-01 2.577742e-01 0.0023200 1.734200e-02 9.202100e-02 3.350612e-01 9.976800e-01 ▇▂▁▁▁
numeric AF_reference 113448 0.9878318 NA NA NA NA NA NA NA 2.144950e-01 2.495579e-01 0.0000000 1.477640e-02 1.106230e-01 3.350640e-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 -0.0050394 0.0047623 0.2900000 0.2899647 0.623809 0.7821490 NA
1 54676 rs2462492 C T 0.0050009 0.0047305 0.2900000 0.2904380 0.399332 NA NA
1 86028 rs114608975 T C 0.0122336 0.0075377 0.1000000 0.1045910 0.103773 0.0277556 NA
1 91536 rs6702460 G T 0.0035068 0.0046607 0.4500005 0.4518029 0.456262 0.4207270 NA
1 234313 rs8179466 C T 0.0121314 0.0091966 0.1900002 0.1871306 0.074530 NA NA
1 534192 rs6680723 C T -0.0075646 0.0053266 0.1600000 0.1555599 0.241219 NA NA
1 546697 rs12025928 A G 0.0048392 0.0066035 0.4600002 0.4636661 0.912995 NA NA
1 693731 rs12238997 A G -0.0047501 0.0044385 0.2800000 0.2845212 0.117050 0.1417730 NA
1 705882 rs72631875 G A -0.0063388 0.0064910 0.3300000 0.3287902 0.067669 0.0315495 NA
1 706368 rs55727773 A G 0.0028205 0.0032877 0.3900004 0.3909437 0.515027 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0008186 0.0039814 0.8400000 0.8370956 0.137140 0.2052720 NA
22 51219387 rs9616832 T C -0.0036683 0.0051782 0.4799997 0.4786984 0.072762 0.0654952 NA
22 51219704 rs147475742 G A -0.0007775 0.0069189 0.9100000 0.9105243 0.041622 0.0473243 NA
22 51221190 rs369304721 G A 0.0003611 0.0069309 0.9599999 0.9584509 0.049121 NA NA
22 51221731 rs115055839 T C -0.0042205 0.0051822 0.4199997 0.4153970 0.072236 0.0625000 NA
22 51222100 rs114553188 G T 0.0032437 0.0060686 0.5900000 0.5929903 0.054455 0.0880591 NA
22 51223637 rs375798137 G A 0.0020980 0.0060999 0.7300002 0.7308938 0.054065 0.0788738 NA
22 51229805 rs9616985 T C -0.0039992 0.0052011 0.4400003 0.4419420 0.072095 0.0730831 NA
22 51232488 rs376461333 A G 0.0016078 0.0121851 0.9000000 0.8950260 0.020166 NA NA
22 51237063 rs3896457 T C -0.0037190 0.0031614 0.2399999 0.2394375 0.297695 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623809 ES:SE:LP:AF:ID  -0.00503944:0.00476228:0.537602:0.623809:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399332 ES:SE:LP:AF:ID  0.00500087:0.00473047:0.537602:0.399332:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103773 ES:SE:LP:AF:ID  0.0122336:0.00753768:1:0.103773:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456262 ES:SE:LP:AF:ID  0.00350677:0.0046607:0.346787:0.456262:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07453  ES:SE:LP:AF:ID  0.0121314:0.00919662:0.721246:0.07453:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241219 ES:SE:LP:AF:ID  -0.00756459:0.00532656:0.79588:0.241219:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912995 ES:SE:LP:AF:ID  0.00483917:0.00660346:0.337242:0.912995:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11705  ES:SE:LP:AF:ID  -0.00475013:0.00443847:0.552842:0.11705:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067669 ES:SE:LP:AF:ID  -0.00633878:0.00649097:0.481486:0.067669:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515027 ES:SE:LP:AF:ID  0.00282055:0.00328771:0.408935:0.515027:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033511 ES:SE:LP:AF:ID  0.00732071:0.00822294:0.431798:0.033511:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037214 ES:SE:LP:AF:ID  0.00581591:0.00746693:0.356547:0.037214:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037343 ES:SE:LP:AF:ID  0.00573955:0.00743749:0.356547:0.037343:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036993 ES:SE:LP:AF:ID  0.00600287:0.00749472:0.376751:0.036993:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016346 ES:SE:LP:AF:ID  0.00139408:0.0116978:0.0409586:0.016346:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037581 ES:SE:LP:AF:ID  0.00613994:0.00740814:0.387216:0.037581:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037675 ES:SE:LP:AF:ID  0.00635675:0.00738433:0.408935:0.037675:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101454 ES:SE:LP:AF:ID  -0.00613119:0.005417:0.585027:0.101454:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958391 ES:SE:LP:AF:ID  -0.0079919:0.00712622:0.585027:0.958391:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031712 ES:SE:LP:AF:ID  0.00641174:0.0130019:0.207608:0.031712:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052634 ES:SE:LP:AF:ID  0.0176015:0.0105091:1.02687:0.052634:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03715  ES:SE:LP:AF:ID  0.00551628:0.00743534:0.337242:0.03715:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037488 ES:SE:LP:AF:ID  0.0047366:0.00736827:0.283997:0.037488:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841915 ES:SE:LP:AF:ID  0.00244263:0.00384247:0.283997:0.841915:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056189 ES:SE:LP:AF:ID  -0.00809524:0.00623727:0.721246:0.056189:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122978 ES:SE:LP:AF:ID  -0.00511292:0.00421259:0.657577:0.122978:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025827 ES:SE:LP:AF:ID  0.0010102:0.0103453:0.0362122:0.025827:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122195 ES:SE:LP:AF:ID  -0.00543348:0.00421467:0.69897:0.122195:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133444 ES:SE:LP:AF:ID  -0.00375696:0.00414712:0.443698:0.133444:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011236 ES:SE:LP:AF:ID  -0.0250235:0.0150455:1.01773:0.011236:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005855 ES:SE:LP:AF:ID  -0.0211304:0.0191999:0.568636:0.005855:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037425 ES:SE:LP:AF:ID  0.00452164:0.00729329:0.267606:0.037425:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837646 ES:SE:LP:AF:ID  0.00100611:0.00372064:0.102373:0.837646:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83726  ES:SE:LP:AF:ID  0.00143022:0.00371644:0.154902:0.83726:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868923 ES:SE:LP:AF:ID  0.00221807:0.00398896:0.236572:0.868923:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130795 ES:SE:LP:AF:ID  -0.00267094:0.0039967:0.30103:0.130795:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037915 ES:SE:LP:AF:ID  0.00460519:0.00717387:0.283997:0.037915:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038168 ES:SE:LP:AF:ID  0.00501851:0.00712821:0.318759:0.038168:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868241 ES:SE:LP:AF:ID  0.00246314:0.00398086:0.267606:0.868241:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86835  ES:SE:LP:AF:ID  0.00247399:0.00398267:0.275724:0.86835:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038101 ES:SE:LP:AF:ID  0.00492534:0.00715924:0.309804:0.038101:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868237 ES:SE:LP:AF:ID  0.00236861:0.0039806:0.259637:0.868237:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005129 ES:SE:LP:AF:ID  0.0320619:0.0205144:0.920819:0.005129:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005099 ES:SE:LP:AF:ID  0.0325541:0.0205595:0.958607:0.005099:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836742 ES:SE:LP:AF:ID  0.00114395:0.00370673:0.119186:0.836742:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038112 ES:SE:LP:AF:ID  0.00464351:0.00716897:0.283997:0.038112:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837371 ES:SE:LP:AF:ID  0.00105764:0.00371695:0.107905:0.837371:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013293 ES:SE:LP:AF:ID  0.000459478:0.013282:0.0132283:0.013293:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005455 ES:SE:LP:AF:ID  -0.0271842:0.0202208:0.744727:0.005455:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838596 ES:SE:LP:AF:ID  0.000757947:0.00376784:0.0757207:0.838596:rs3131965