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_20230.vcf.gz --id UKB-b:19585 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20230.txt.gz --cohort_controls 113410 --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-19585/UKB-b-19585_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19585/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19585/UKB-b-19585_data.vcf.gz ...
Read summary statistics for 9114753 SNPs.
Dropped 9239 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, 1287462 SNPs remain.
After merging with regression SNP LD, 1287462 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1152 (0.0071)
Lambda GC: 1.2398
Mean Chi^2: 1.2817
Intercept: 1.024 (0.0082)
Ratio: 0.085 (0.0289)
Analysis finished at Thu Oct 17 14:43:05 2019
Total time elapsed: 1.0m:38.99s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.948,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 6,
    "n_p_sig": 1579,
    "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": 98967,
    "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": 1287462,
    "ldsc_nsnp_merge_regression_ld": 1287462,
    "ldsc_observed_scale_h2_beta": 0.1152,
    "ldsc_observed_scale_h2_se": 0.0071,
    "ldsc_intercept_beta": 1.024,
    "ldsc_intercept_se": 0.0082,
    "ldsc_lambda_gc": 1.2398,
    "ldsc_mean_chisq": 1.2817,
    "ldsc_ratio": 0.0852
}

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 TRUE
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 9105557 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 9114753 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 8.640322e+00 5.756799e+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 7.879257e+07 5.632590e+07 828.0000000 3.245579e+07 6.936258e+07 1.145255e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.510000e-05 1.475250e-02 -0.1718930 -5.854000e-03 5.780000e-05 5.996300e-03 1.484570e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.135040e-02 8.531000e-03 0.0040124 4.797100e-03 7.429000e-03 1.558080e-02 9.067770e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.723910e-01 2.960504e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.723918e-01 2.960255e-01 0.0000000 2.088498e-01 4.626999e-01 7.286211e-01 9.999997e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.177895e-01 2.582660e-01 0.0030870 1.933300e-02 9.813300e-02 3.430920e-01 9.969130e-01 ▇▂▁▁▁
numeric AF_reference 98967 0.9891421 NA NA NA NA NA NA NA 2.182963e-01 2.501321e-01 0.0000000 1.657350e-02 1.158150e-01 3.418530e-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 -0.0074140 0.0073615 0.3100002 0.3138708 0.623800 0.7821490 NA
1 54676 rs2462492 C T 0.0064881 0.0073023 0.3700002 0.3742729 0.399424 NA NA
1 86028 rs114608975 T C 0.0043005 0.0117076 0.7099994 0.7133775 0.103390 0.0277556 NA
1 91536 rs6702460 G T 0.0008376 0.0071910 0.9100000 0.9072733 0.456405 0.4207270 NA
1 234313 rs8179466 C T -0.0284574 0.0142801 0.0460002 0.0462832 0.074106 NA NA
1 534192 rs6680723 C T -0.0097237 0.0082009 0.2399999 0.2357456 0.241035 NA NA
1 546697 rs12025928 A G -0.0182096 0.0102152 0.0749998 0.0746517 0.913001 NA NA
1 693731 rs12238997 A G 0.0047550 0.0068737 0.4899999 0.4890891 0.117135 0.1417730 NA
1 705882 rs72631875 G A 0.0060469 0.0100252 0.5500004 0.5463943 0.067800 0.0315495 NA
1 706368 rs55727773 A G -0.0012755 0.0051002 0.8000000 0.8025206 0.515918 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0022455 0.0061915 0.7199992 0.7168428 0.136561 0.2052720 NA
22 51219387 rs9616832 T C -0.0064070 0.0080420 0.4299995 0.4256330 0.072768 0.0654952 NA
22 51219704 rs147475742 G A -0.0008057 0.0108209 0.9400001 0.9406451 0.041180 0.0473243 NA
22 51221190 rs369304721 G A -0.0102658 0.0107586 0.3400001 0.3399851 0.049184 NA NA
22 51221731 rs115055839 T C -0.0066059 0.0080496 0.4100001 0.4118469 0.072209 0.0625000 NA
22 51222100 rs114553188 G T 0.0029620 0.0094559 0.7499995 0.7540966 0.054210 0.0880591 NA
22 51223637 rs375798137 G A 0.0018778 0.0095040 0.8400000 0.8433772 0.053844 0.0788738 NA
22 51229805 rs9616985 T C -0.0062977 0.0080787 0.4400003 0.4356612 0.072036 0.0730831 NA
22 51232488 rs376461333 A G -0.0017533 0.0191130 0.9299999 0.9269115 0.019775 NA NA
22 51237063 rs3896457 T C 0.0091336 0.0048949 0.0619998 0.0620467 0.298492 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.6238   ES:SE:LP:AF:ID  -0.007414:0.00736149:0.508638:0.6238:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399424 ES:SE:LP:AF:ID  0.00648807:0.00730229:0.431798:0.399424:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10339  ES:SE:LP:AF:ID  0.00430048:0.0117076:0.148742:0.10339:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456405 ES:SE:LP:AF:ID  0.000837602:0.00719104:0.0409586:0.456405:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074106 ES:SE:LP:AF:ID  -0.0284574:0.0142801:1.33724:0.074106:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241035 ES:SE:LP:AF:ID  -0.00972366:0.00820086:0.619789:0.241035:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913001 ES:SE:LP:AF:ID  -0.0182096:0.0102152:1.12494:0.913001:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117135 ES:SE:LP:AF:ID  0.00475496:0.00687373:0.309804:0.117135:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.0678   ES:SE:LP:AF:ID  0.00604692:0.0100252:0.259637:0.0678:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515918 ES:SE:LP:AF:ID  -0.00127549:0.0051002:0.09691:0.515918:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032802 ES:SE:LP:AF:ID  0.0155094:0.0129109:0.638272:0.032802:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036399 ES:SE:LP:AF:ID  0.00918287:0.0117242:0.366532:0.036399:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036515 ES:SE:LP:AF:ID  0.00891184:0.0116782:0.346787:0.036515:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036212 ES:SE:LP:AF:ID  0.0104975:0.0117642:0.431798:0.036212:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016683 ES:SE:LP:AF:ID  -0.0191041:0.0178888:0.537602:0.016683:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036745 ES:SE:LP:AF:ID  0.00861221:0.0116332:0.337242:0.036745:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036861 ES:SE:LP:AF:ID  0.00870393:0.0115923:0.346787:0.036861:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10176  ES:SE:LP:AF:ID  -0.00540527:0.008396:0.283997:0.10176:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959484 ES:SE:LP:AF:ID  -0.0119994:0.0111985:0.552842:0.959484:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031198 ES:SE:LP:AF:ID  -0.000302521:0.0204943:0.00436481:0.031198:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053333 ES:SE:LP:AF:ID  -0.0182064:0.016066:0.585027:0.053333:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036347 ES:SE:LP:AF:ID  0.00971374:0.0116674:0.387216:0.036347:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036656 ES:SE:LP:AF:ID  0.0101099:0.0115627:0.420216:0.036656:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842954 ES:SE:LP:AF:ID  -0.00496533:0.00597234:0.387216:0.842954:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055973 ES:SE:LP:AF:ID  0.000915571:0.00966987:0.0362122:0.055973:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123073 ES:SE:LP:AF:ID  0.00401817:0.00652718:0.267606:0.123073:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02478  ES:SE:LP:AF:ID  -0.00785873:0.0163661:0.200659:0.02478:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.1223   ES:SE:LP:AF:ID  0.0043614:0.00653089:0.30103:0.1223:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132539 ES:SE:LP:AF:ID  0.00757546:0.00644129:0.619789:0.132539:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011076 ES:SE:LP:AF:ID  0.0427854:0.0235493:1.16115:0.011076:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005648 ES:SE:LP:AF:ID  0.000375164:0.0304705:0.00436481:0.005648:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036574 ES:SE:LP:AF:ID  0.0117175:0.0114445:0.508638:0.036574:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838637 ES:SE:LP:AF:ID  -0.00790149:0.00578391:0.769551:0.838637:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838282 ES:SE:LP:AF:ID  -0.00808518:0.00577789:0.79588:0.838282:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869066 ES:SE:LP:AF:ID  -0.00748572:0.00619118:0.638272:0.869066:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130599 ES:SE:LP:AF:ID  0.00741808:0.00620422:0.638272:0.130599:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03708  ES:SE:LP:AF:ID  0.0135356:0.0112493:0.638272:0.03708:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037326 ES:SE:LP:AF:ID  0.0127433:0.0111758:0.60206:0.037326:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868402 ES:SE:LP:AF:ID  -0.00739832:0.00617935:0.638272:0.868402:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868499 ES:SE:LP:AF:ID  -0.00751324:0.00618228:0.657577:0.868499:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037285 ES:SE:LP:AF:ID  0.0124638:0.0112266:0.568636:0.037285:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86841  ES:SE:LP:AF:ID  -0.00746207:0.00617923:0.638272:0.86841:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005173 ES:SE:LP:AF:ID  0.0336106:0.0315938:0.537602:0.005173:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005144 ES:SE:LP:AF:ID  0.032421:0.0316481:0.508638:0.005144:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837711 ES:SE:LP:AF:ID  -0.00788615:0.00576176:0.769551:0.837711:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037322 ES:SE:LP:AF:ID  0.0123733:0.0112398:0.568636:0.037322:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838306 ES:SE:LP:AF:ID  -0.00795125:0.00577717:0.769551:0.838306:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013578 ES:SE:LP:AF:ID  0.00994788:0.0203038:0.207608:0.013578:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005494 ES:SE:LP:AF:ID  -0.0148673:0.0311538:0.200659:0.005494:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839436 ES:SE:LP:AF:ID  -0.00762971:0.00585401:0.721246:0.839436:rs3131965