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_1060.vcf.gz --id UKB-b:6811 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1060.txt.gz --cohort_controls 364465 --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-6811/UKB-b-6811_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6811/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6811/UKB-b-6811_data.vcf.gz ...
Read summary statistics for 9851191 SNPs.
Dropped 14735 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0394 (0.002)
Lambda GC: 1.2537
Mean Chi^2: 1.2883
Intercept: 1.0153 (0.007)
Ratio: 0.0529 (0.0244)
Analysis finished at Thu Oct 17 14:41:56 2019
Total time elapsed: 1.0m:39.06s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 4,
    "n_p_sig": 703,
    "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": 184834,
    "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": 1289166,
    "ldsc_nsnp_merge_regression_ld": 1289166,
    "ldsc_observed_scale_h2_beta": 0.0394,
    "ldsc_observed_scale_h2_se": 0.002,
    "ldsc_intercept_beta": 1.0153,
    "ldsc_intercept_se": 0.007,
    "ldsc_lambda_gc": 1.2537,
    "ldsc_mean_chisq": 1.2883,
    "ldsc_ratio": 0.0531
}

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 9836524 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 9851191 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 8.622801e+00 5.748318e+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 7.886062e+07 5.628355e+07 828.0000000 3.259045e+07 6.948861e+07 1.145919e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.910000e-05 6.437100e-03 -0.0892365 -2.045500e-03 1.060000e-05 2.058300e-03 1.082520e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.566400e-03 4.327200e-03 0.0012767 1.563500e-03 2.621700e-03 6.049200e-03 6.637470e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.744951e-01 2.956467e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.744953e-01 2.956222e-01 0.0000000 2.117655e-01 4.653329e-01 7.307965e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035173e-01 2.568680e-01 0.0009610 1.317100e-02 7.792800e-02 3.164780e-01 9.990380e-01 ▇▂▁▁▁
numeric AF_reference 184834 0.9812374 NA NA NA NA NA NA NA 2.068487e-01 2.482935e-01 0.0000000 1.198080e-02 9.984030e-02 3.202880e-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 -0.0001872 0.0023502 0.9400001 0.9365093 0.623712 0.7821490 NA
1 54676 rs2462492 C T 0.0001883 0.0023300 0.9400001 0.9355987 0.400541 NA NA
1 86028 rs114608975 T C -0.0051691 0.0037224 0.1600000 0.1649422 0.103636 0.0277556 NA
1 91536 rs6702460 G T 0.0008619 0.0022927 0.7099994 0.7069782 0.456730 0.4207270 NA
1 234313 rs8179466 C T -0.0022862 0.0045235 0.6100002 0.6132790 0.074422 NA NA
1 534192 rs6680723 C T 0.0033838 0.0026234 0.2000000 0.1971040 0.240961 NA NA
1 546697 rs12025928 A G 0.0007096 0.0032666 0.8300000 0.8280303 0.913453 NA NA
1 693731 rs12238997 A G 0.0003304 0.0021952 0.8800001 0.8803680 0.116146 0.1417730 NA
1 705882 rs72631875 G A 0.0004498 0.0032202 0.8900000 0.8889057 0.067176 0.0315495 NA
1 706368 rs55727773 A G -0.0004118 0.0016256 0.8000000 0.8000024 0.516010 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0038132 0.0034205 0.2599998 0.2649314 0.041756 0.0473243 NA
22 51219766 rs182321900 C T -0.0052342 0.0159922 0.7400005 0.7434437 0.001915 NA NA
22 51220146 rs868950473 C T -0.0079668 0.0158646 0.6200004 0.6155447 0.001959 NA NA
22 51221190 rs369304721 G A -0.0044227 0.0034130 0.2000000 0.1950283 0.049522 NA NA
22 51221731 rs115055839 T C -0.0034605 0.0025532 0.1800002 0.1753053 0.072898 0.0625000 NA
22 51222100 rs114553188 G T 0.0000716 0.0029992 0.9800000 0.9809536 0.054438 0.0880591 NA
22 51223637 rs375798137 G A -0.0002065 0.0030137 0.9500000 0.9453713 0.054063 0.0788738 NA
22 51229805 rs9616985 T C -0.0033570 0.0025625 0.1900002 0.1901793 0.072742 0.0730831 NA
22 51232488 rs376461333 A G 0.0009195 0.0060347 0.8800001 0.8788949 0.019999 NA NA
22 51237063 rs3896457 T C 0.0012450 0.0015640 0.4299995 0.4260416 0.298175 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623712 ES:SE:LP:AF:ID  -0.000187213:0.00235021:0.0268721:0.623712:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400541 ES:SE:LP:AF:ID  0.00018827:0.00232999:0.0268721:0.400541:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103636 ES:SE:LP:AF:ID  -0.00516913:0.00372244:0.79588:0.103636:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45673  ES:SE:LP:AF:ID  0.000861855:0.00229267:0.148742:0.45673:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074422 ES:SE:LP:AF:ID  -0.00228619:0.00452354:0.21467:0.074422:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240961 ES:SE:LP:AF:ID  0.00338377:0.00262339:0.69897:0.240961:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913453 ES:SE:LP:AF:ID  0.000709592:0.00326657:0.0809219:0.913453:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116146 ES:SE:LP:AF:ID  0.000330376:0.00219515:0.0555173:0.116146:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067176 ES:SE:LP:AF:ID  0.000449821:0.00322016:0.05061:0.067176:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51601  ES:SE:LP:AF:ID  -0.000411831:0.00162558:0.09691:0.51601:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033024 ES:SE:LP:AF:ID  0.00023183:0.00409734:0.0222764:0.033024:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036651 ES:SE:LP:AF:ID  0.000589149:0.00372085:0.0604807:0.036651:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036775 ES:SE:LP:AF:ID  0.000465093:0.00370617:0.0457575:0.036775:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036462 ES:SE:LP:AF:ID  0.000640559:0.00373345:0.0655015:0.036462:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016351 ES:SE:LP:AF:ID  -0.00496162:0.00576738:0.408935:0.016351:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037005 ES:SE:LP:AF:ID  0.000471527:0.00369171:0.0457575:0.037005:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037097 ES:SE:LP:AF:ID  0.00102638:0.00367964:0.107905:0.037097:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101246 ES:SE:LP:AF:ID  0.00279169:0.00268179:0.522879:0.101246:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959085 ES:SE:LP:AF:ID  -0.00230305:0.00354965:0.283997:0.959085:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031393 ES:SE:LP:AF:ID  0.00793335:0.00646362:0.657577:0.031393:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053247 ES:SE:LP:AF:ID  -0.0101993:0.0051341:1.3279:0.053247:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036611 ES:SE:LP:AF:ID  0.00170738:0.00370373:0.19382:0.036611:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036933 ES:SE:LP:AF:ID  0.00145915:0.0036694:0.161151:0.036933:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843397 ES:SE:LP:AF:ID  -0.00052815:0.00190213:0.107905:0.843397:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05583  ES:SE:LP:AF:ID  0.00119578:0.00307802:0.154902:0.05583:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122131 ES:SE:LP:AF:ID  -0.000198886:0.00208256:0.0362122:0.122131:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025775 ES:SE:LP:AF:ID  -0.00524321:0.00511532:0.508638:0.025775:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121367 ES:SE:LP:AF:ID  -0.000208666:0.00208359:0.0362122:0.121367:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13224  ES:SE:LP:AF:ID  0.000813325:0.00205201:0.161151:0.13224:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01117  ES:SE:LP:AF:ID  0.00669053:0.00744732:0.431798:0.01117:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005656 ES:SE:LP:AF:ID  -0.00180501:0.00966572:0.0705811:0.005656:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002258 ES:SE:LP:AF:ID  0.0286246:0.0161913:1.11351:0.002258:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001016 ES:SE:LP:AF:ID  0.00764088:0.0268274:0.107905:0.001016:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036851 ES:SE:LP:AF:ID  0.00160285:0.00363216:0.180456:0.036851:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839158 ES:SE:LP:AF:ID  -3.13552e-05:0.00184245:0.00436481:0.839158:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838787 ES:SE:LP:AF:ID  -0.000158626:0.00184048:0.0315171:0.838787:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.870018 ES:SE:LP:AF:ID  0.000148577:0.0019754:0.0268721:0.870018:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129636 ES:SE:LP:AF:ID  -0.000389535:0.00197948:0.0757207:0.129636:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037346 ES:SE:LP:AF:ID  0.00162921:0.00357148:0.187087:0.037346:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037597 ES:SE:LP:AF:ID  0.00174042:0.00354836:0.207608:0.037597:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869356 ES:SE:LP:AF:ID  2.05068e-05:0.00197154:0.00436481:0.869356:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86945  ES:SE:LP:AF:ID  0.000148773:0.00197232:0.0268721:0.86945:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037556 ES:SE:LP:AF:ID  0.00135364:0.00356397:0.154902:0.037556:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869362 ES:SE:LP:AF:ID  3.36692e-05:0.00197153:0.00436481:0.869362:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005115 ES:SE:LP:AF:ID  -0.0077727:0.010128:0.356547:0.005115:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005082 ES:SE:LP:AF:ID  -0.00794958:0.010154:0.366532:0.005082:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838241 ES:SE:LP:AF:ID  -0.000127563:0.00183536:0.0268721:0.838241:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037569 ES:SE:LP:AF:ID  0.00113957:0.00356896:0.124939:0.037569:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838868 ES:SE:LP:AF:ID  -6.81659e-05:0.00184048:0.0132283:0.838868:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013845 ES:SE:LP:AF:ID  -0.00422436:0.00640288:0.29243:0.013845:rs181660517