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_100540.vcf.gz --id UKB-b:1795 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_100540.txt.gz --cohort_controls 64942 --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-1795/UKB-b-1795_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1795/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1795/UKB-b-1795_data.vcf.gz ...
Read summary statistics for 8624981 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, 1285749 SNPs remain.
After merging with regression SNP LD, 1285749 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0136 (0.0077)
Lambda GC: 1.0192
Mean Chi^2: 1.0197
Intercept: 1.0022 (0.007)
Ratio: 0.1099 (0.3562)
Analysis finished at Thu Oct 17 14:41:55 2019
Total time elapsed: 1.0m:36.6s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 3,
    "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": 1285749,
    "ldsc_nsnp_merge_regression_ld": 1285749,
    "ldsc_observed_scale_h2_beta": 0.0136,
    "ldsc_observed_scale_h2_se": 0.0077,
    "ldsc_intercept_beta": 1.0022,
    "ldsc_intercept_se": 0.007,
    "ldsc_lambda_gc": 1.0192,
    "ldsc_mean_chisq": 1.0197,
    "ldsc_ratio": 0.1117
}

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 8617643 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 8624981 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 8.650332e+00 5.760994e+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 7.877028e+07 5.637176e+07 828.0000000 3.238547e+07 6.929217e+07 1.145691e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.130000e-05 3.872900e-03 -0.0424729 -1.644100e-03 -2.720000e-05 1.568700e-03 5.140210e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.175900e-03 2.167900e-03 0.0012614 1.485400e-03 2.190100e-03 4.293200e-03 2.174310e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.974366e-01 2.893279e-01 0.0000000 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 4.974360e-01 2.893008e-01 0.0000000 2.463711e-01 4.960168e-01 7.483809e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291176e-01 2.594884e-01 0.0053900 2.519000e-02 1.138950e-01 3.626220e-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 -0.0007216 0.0023246 0.7600007 0.7562489 0.623803 0.7821490 NA
1 54676 rs2462492 C T 0.0018536 0.0023178 0.4199997 0.4238868 0.399139 NA NA
1 86028 rs114608975 T C 0.0038452 0.0036896 0.2999998 0.2973301 0.103537 0.0277556 NA
1 91536 rs6702460 G T -0.0014630 0.0022798 0.5199996 0.5210590 0.455920 0.4207270 NA
1 234313 rs8179466 C T -0.0079273 0.0045085 0.0790005 0.0786968 0.074451 NA NA
1 534192 rs6680723 C T -0.0071531 0.0025967 0.0059000 0.0058756 0.242051 NA NA
1 546697 rs12025928 A G -0.0007876 0.0032216 0.8100000 0.8068725 0.912858 NA NA
1 693731 rs12238997 A G -0.0014313 0.0021650 0.5099998 0.5085377 0.117318 0.1417730 NA
1 705882 rs72631875 G A 0.0012277 0.0031556 0.6999999 0.6972405 0.067703 0.0315495 NA
1 706368 rs55727773 A G 0.0022496 0.0016070 0.1600000 0.1615554 0.513280 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0015215 0.0019547 0.4400003 0.4363472 0.136306 0.2052720 NA
22 51219387 rs9616832 T C 0.0027065 0.0025477 0.2900000 0.2880818 0.071797 0.0654952 NA
22 51219704 rs147475742 G A 0.0046812 0.0033892 0.1700000 0.1672133 0.041187 0.0473243 NA
22 51221190 rs369304721 G A 0.0016135 0.0034130 0.6400000 0.6364019 0.048374 NA NA
22 51221731 rs115055839 T C 0.0027494 0.0025482 0.2800000 0.2806230 0.071348 0.0625000 NA
22 51222100 rs114553188 G T 0.0005635 0.0029531 0.8499999 0.8486657 0.054841 0.0880591 NA
22 51223637 rs375798137 G A 0.0005452 0.0029685 0.8499999 0.8542912 0.054461 0.0788738 NA
22 51229805 rs9616985 T C 0.0031801 0.0025561 0.2099999 0.2134565 0.071253 0.0730831 NA
22 51232488 rs376461333 A G 0.0038772 0.0058871 0.5099998 0.5101527 0.020459 NA NA
22 51237063 rs3896457 T C -0.0007274 0.0015431 0.6400000 0.6373561 0.298404 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623803 ES:SE:LP:AF:ID  -0.00072158:0.0023246:0.119186:0.623803:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399139 ES:SE:LP:AF:ID  0.00185356:0.00231783:0.376751:0.399139:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103537 ES:SE:LP:AF:ID  0.00384516:0.00368955:0.522879:0.103537:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45592  ES:SE:LP:AF:ID  -0.00146296:0.00227977:0.283997:0.45592:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074451 ES:SE:LP:AF:ID  -0.00792727:0.00450849:1.10237:0.074451:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242051 ES:SE:LP:AF:ID  -0.00715306:0.00259673:2.22915:0.242051:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912858 ES:SE:LP:AF:ID  -0.000787551:0.00322156:0.091515:0.912858:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117318 ES:SE:LP:AF:ID  -0.00143133:0.00216502:0.29243:0.117318:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067703 ES:SE:LP:AF:ID  0.0012277:0.00315565:0.154902:0.067703:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51328  ES:SE:LP:AF:ID  0.00224963:0.00160704:0.79588:0.51328:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033667 ES:SE:LP:AF:ID  -0.00129474:0.00400622:0.124939:0.033667:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037446 ES:SE:LP:AF:ID  -0.0021886:0.00363345:0.259637:0.037446:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037633 ES:SE:LP:AF:ID  -0.0018572:0.00361444:0.21467:0.037633:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037209 ES:SE:LP:AF:ID  -0.00178258:0.00364747:0.200659:0.037209:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016284 ES:SE:LP:AF:ID  0.00411112:0.00572017:0.327902:0.016284:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03785  ES:SE:LP:AF:ID  -0.00131318:0.00360206:0.142668:0.03785:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037944 ES:SE:LP:AF:ID  -0.0013737:0.00359063:0.154902:0.037944:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102742 ES:SE:LP:AF:ID  -0.00402581:0.00262243:0.920819:0.102742:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.9581   ES:SE:LP:AF:ID  -2.11869e-05:0.00346796:-0:0.9581:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031693 ES:SE:LP:AF:ID  0.00217345:0.00634934:0.136677:0.031693:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052725 ES:SE:LP:AF:ID  0.00499921:0.00511424:0.481486:0.052725:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037438 ES:SE:LP:AF:ID  -0.00122083:0.00361421:0.130768:0.037438:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037707 ES:SE:LP:AF:ID  -0.00118263:0.00358422:0.130768:0.037707:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841446 ES:SE:LP:AF:ID  0.000552817:0.00187237:0.113509:0.841446:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056339 ES:SE:LP:AF:ID  -0.0055037:0.00304142:1.1549:0.056339:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123083 ES:SE:LP:AF:ID  -0.0014286:0.00205651:0.309804:0.123083:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025126 ES:SE:LP:AF:ID  0.00283619:0.00512203:0.236572:0.025126:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122336 ES:SE:LP:AF:ID  -0.00135326:0.00205718:0.29243:0.122336:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134132 ES:SE:LP:AF:ID  -0.00189605:0.00201951:0.455932:0.134132:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  -0.0125942:0.00719718:1.09691:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  -0.00554975:0.00913953:0.267606:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037588 ES:SE:LP:AF:ID  -0.00169715:0.00355107:0.200659:0.037588:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837035 ES:SE:LP:AF:ID  0.000995572:0.00181129:0.236572:0.837035:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836739 ES:SE:LP:AF:ID  0.000741497:0.00180996:0.167491:0.836739:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868556 ES:SE:LP:AF:ID  0.00114222:0.00194486:0.251812:0.868556:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131011 ES:SE:LP:AF:ID  -0.00146242:0.00194985:0.346787:0.131011:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038034 ES:SE:LP:AF:ID  -0.00168733:0.00349536:0.200659:0.038034:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03828  ES:SE:LP:AF:ID  -0.00180572:0.00347369:0.221849:0.03828:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867969 ES:SE:LP:AF:ID  0.00093771:0.00194173:0.200659:0.867969:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868043 ES:SE:LP:AF:ID  0.000924548:0.00194253:0.200659:0.868043:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038188 ES:SE:LP:AF:ID  -0.00177212:0.00348973:0.21467:0.038188:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86798  ES:SE:LP:AF:ID  0.000939754:0.00194168:0.200659:0.86798:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.000412075:0.00973132:0.0132283:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836164 ES:SE:LP:AF:ID  0.000709908:0.00180466:0.161151:0.836164:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038191 ES:SE:LP:AF:ID  -0.00179254:0.00349483:0.21467:0.038191:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836798 ES:SE:LP:AF:ID  0.000694959:0.00180961:0.154902:0.836798:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  -0.010422:0.00654664:0.958607:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.00105041:0.00971857:0.0409586:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838113 ES:SE:LP:AF:ID  0.000669267:0.00183507:0.142668:0.838113:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868221 ES:SE:LP:AF:ID  0.00124797:0.00193918:0.283997:0.868221:rs3115858