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_104360.vcf.gz --id UKB-b:5008 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_104360.txt.gz --cohort_controls 64945 --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-5008/UKB-b-5008_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5008/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:58 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5008/UKB-b-5008_data.vcf.gz ...
Read summary statistics for 8624977 SNPs.
Dropped 7371 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, 1285748 SNPs remain.
After merging with regression SNP LD, 1285748 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 3.6755e-05 (0.0064)
Lambda GC: 1.0107
Mean Chi^2: 1.0096
Intercept: 1.0096 (0.0059)
Ratio: 0.9954 (0.6129)
Analysis finished at Thu Oct 17 14:46:21 2019
Total time elapsed: 1.0m:23.02s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -6.6042e-06,
    "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": 83096,
    "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": 1285748,
    "ldsc_nsnp_merge_regression_ld": 1285748,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0096,
    "ldsc_intercept_se": 0.0059,
    "ldsc_lambda_gc": 1.0107,
    "ldsc_mean_chisq": 1.0096,
    "ldsc_ratio": 1
}

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 1.0000000 3 58 0 8617640 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 8624977 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 8.650354e+00 5.760989e+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 7.877011e+07 5.637168e+07 828.0000000 3.238541e+07 6.929195e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -6.600000e-06 6.059200e-03 -0.0617304 -2.573100e-03 -4.740000e-05 2.462600e-03 7.279010e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.987700e-03 3.404600e-03 0.0019810 2.332800e-03 3.439500e-03 6.742300e-03 3.414820e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.979988e-01 2.886693e-01 0.0000001 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.979974e-01 2.886428e-01 0.0000001 2.478593e-01 4.973418e-01 7.471872e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291175e-01 2.594883e-01 0.0053900 2.519000e-02 1.138970e-01 3.626270e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83096 0.9903657 NA NA NA NA NA NA NA 2.288667e-01 2.514591e-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 0.0000657 0.0036507 0.9900000 0.9856387 0.623813 0.7821490 NA
1 54676 rs2462492 C T -0.0013703 0.0036400 0.7099994 0.7065861 0.399141 NA NA
1 86028 rs114608975 T C -0.0040981 0.0057942 0.4799997 0.4793982 0.103536 0.0277556 NA
1 91536 rs6702460 G T 0.0078256 0.0035802 0.0290001 0.0288294 0.455913 0.4207270 NA
1 234313 rs8179466 C T 0.0031018 0.0070802 0.6600001 0.6613238 0.074455 NA NA
1 534192 rs6680723 C T 0.0009459 0.0040776 0.8200001 0.8165546 0.242058 NA NA
1 546697 rs12025928 A G 0.0009914 0.0050595 0.8400000 0.8446539 0.912865 NA NA
1 693731 rs12238997 A G -0.0022130 0.0034001 0.5199996 0.5151204 0.117321 0.1417730 NA
1 705882 rs72631875 G A -0.0049808 0.0049564 0.3100002 0.3149359 0.067687 0.0315495 NA
1 706368 rs55727773 A G -0.0000725 0.0025238 0.9800000 0.9770753 0.513290 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0019408 0.0030698 0.5300002 0.5272555 0.136315 0.2052720 NA
22 51219387 rs9616832 T C 0.0036108 0.0040011 0.3700002 0.3668174 0.071802 0.0654952 NA
22 51219704 rs147475742 G A 0.0006345 0.0053225 0.9100000 0.9051115 0.041193 0.0473243 NA
22 51221190 rs369304721 G A 0.0046805 0.0053602 0.3800004 0.3825605 0.048375 NA NA
22 51221731 rs115055839 T C 0.0035198 0.0040019 0.3800004 0.3791202 0.071352 0.0625000 NA
22 51222100 rs114553188 G T -0.0007153 0.0046377 0.8800001 0.8774198 0.054846 0.0880591 NA
22 51223637 rs375798137 G A -0.0007262 0.0046619 0.8800001 0.8762079 0.054466 0.0788738 NA
22 51229805 rs9616985 T C 0.0039794 0.0040143 0.3200000 0.3215349 0.071257 0.0730831 NA
22 51232488 rs376461333 A G 0.0017787 0.0092457 0.8499999 0.8474432 0.020460 NA NA
22 51237063 rs3896457 T C -0.0013373 0.0024236 0.5800000 0.5810842 0.298381 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623813 ES:SE:LP:AF:ID  6.57128e-05:0.00365067:0.00436481:0.623813:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399141 ES:SE:LP:AF:ID  -0.00137026:0.00364:0.148742:0.399141:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  -0.00409809:0.00579423:0.318759:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455913 ES:SE:LP:AF:ID  0.0078256:0.00358018:1.5376:0.455913:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074455 ES:SE:LP:AF:ID  0.00310176:0.00708025:0.180456:0.074455:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242058 ES:SE:LP:AF:ID  0.000945921:0.00407762:0.0861861:0.242058:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912865 ES:SE:LP:AF:ID  0.000991381:0.00505951:0.0757207:0.912865:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117321 ES:SE:LP:AF:ID  -0.00221305:0.00340006:0.283997:0.117321:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067687 ES:SE:LP:AF:ID  -0.00498078:0.0049564:0.508638:0.067687:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51329  ES:SE:LP:AF:ID  -7.2523e-05:0.00252379:0.00877392:0.51329:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033679 ES:SE:LP:AF:ID  0.0101552:0.00629054:0.958607:0.033679:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03746  ES:SE:LP:AF:ID  0.00750908:0.00570521:0.721246:0.03746:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037646 ES:SE:LP:AF:ID  0.00705425:0.00567536:0.677781:0.037646:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037223 ES:SE:LP:AF:ID  0.00737694:0.00572722:0.69897:0.037223:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  -0.00975483:0.00898349:0.552842:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037863 ES:SE:LP:AF:ID  0.00710261:0.00565595:0.677781:0.037863:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037957 ES:SE:LP:AF:ID  0.00676986:0.00563801:0.638272:0.037957:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10274  ES:SE:LP:AF:ID  0.00776143:0.00411845:1.22915:0.10274:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958087 ES:SE:LP:AF:ID  -0.00524392:0.00544547:0.468521:0.958087:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031688 ES:SE:LP:AF:ID  -0.0131381:0.00997211:0.721246:0.031688:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  -0.00546935:0.00803178:0.30103:0.052723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037451 ES:SE:LP:AF:ID  0.00570348:0.00567503:0.508638:0.037451:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03772  ES:SE:LP:AF:ID  0.00587263:0.00562802:0.522879:0.03772:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841431 ES:SE:LP:AF:ID  0.00115999:0.00294043:0.161151:0.841431:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056338 ES:SE:LP:AF:ID  -0.00907584:0.00477652:1.24413:0.056338:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123085 ES:SE:LP:AF:ID  -0.00307188:0.00322966:0.468521:0.123085:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025126 ES:SE:LP:AF:ID  0.0157826:0.00804363:1.30103:0.025126:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122338 ES:SE:LP:AF:ID  -0.00298779:0.0032307:0.443698:0.122338:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134148 ES:SE:LP:AF:ID  0.00016642:0.00317146:0.0177288:0.134148:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  -0.0116716:0.0113031:0.522879:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  -0.0242576:0.0143536:1.04096:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037602 ES:SE:LP:AF:ID  0.00616858:0.00557587:0.568636:0.037602:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837027 ES:SE:LP:AF:ID  0.000739197:0.00284454:0.102373:0.837027:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836731 ES:SE:LP:AF:ID  0.000793287:0.00284245:0.107905:0.836731:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868562 ES:SE:LP:AF:ID  0.00263915:0.00305437:0.408935:0.868562:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131005 ES:SE:LP:AF:ID  -0.00252217:0.00306221:0.387216:0.131005:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038048 ES:SE:LP:AF:ID  0.00549331:0.0054884:0.49485:0.038048:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038294 ES:SE:LP:AF:ID  0.00520404:0.00545439:0.468521:0.038294:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867975 ES:SE:LP:AF:ID  0.00278662:0.00304946:0.443698:0.867975:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868049 ES:SE:LP:AF:ID  0.00275016:0.0030507:0.431798:0.868049:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038202 ES:SE:LP:AF:ID  0.00548563:0.00547958:0.49485:0.038202:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867986 ES:SE:LP:AF:ID  0.00278212:0.00304938:0.443698:0.867986:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.000781325:0.015283:0.0177288:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836157 ES:SE:LP:AF:ID  0.00102751:0.00283413:0.142668:0.836157:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038205 ES:SE:LP:AF:ID  0.00557713:0.00548758:0.508638:0.038205:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836791 ES:SE:LP:AF:ID  0.00101031:0.0028419:0.142668:0.836791:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  0.020542:0.0102815:1.33724:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.0150144:0.015263:0.481486:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838107 ES:SE:LP:AF:ID  0.00113739:0.0028819:0.161151:0.838107:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868227 ES:SE:LP:AF:ID  0.00293753:0.00304546:0.481486:0.868227:rs3115858