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_102150.vcf.gz --id UKB-b:9857 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102150.txt.gz --cohort_controls 64944 --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-9857/UKB-b-9857_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9857/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:58 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9857/UKB-b-9857_data.vcf.gz ...
Read summary statistics for 8624957 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, 1285749 SNPs remain.
After merging with regression SNP LD, 1285749 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0064 (0.0076)
Lambda GC: 1.0024
Mean Chi^2: 1.0041
Intercept: 1.0123 (0.0063)
Ratio: 2.9963 (1.5432)
Analysis finished at Thu Oct 17 14:44:17 2019
Total time elapsed: 1.0m:18.96s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 2.4191e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0123,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0024,
    "ldsc_mean_chisq": 1.0041,
    "ldsc_ratio": 3
}

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 8617620 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 8624957 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 8.650357e+00 5.760991e+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 7.877021e+07 5.637169e+07 828.0000000 3.238547e+07 6.929208e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.400000e-06 4.559500e-03 -0.0445904 -1.917300e-03 -2.910000e-05 1.848400e-03 5.818060e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.760400e-03 2.566800e-03 0.0014935 1.758800e-03 2.593100e-03 5.083200e-03 2.574550e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.996627e-01 2.885625e-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 4.996628e-01 2.885363e-01 0.0000000 2.502194e-01 4.990473e-01 7.493031e-01 9.999992e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291179e-01 2.594882e-01 0.0053900 2.519100e-02 1.138990e-01 3.626200e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83096 0.9903656 NA NA NA NA NA NA NA 2.288670e-01 2.514590e-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 0.0029448 0.0027524 0.2800000 0.2846630 0.623821 0.7821490 NA
1 54676 rs2462492 C T -0.0071761 0.0027445 0.0089000 0.0089297 0.399144 NA NA
1 86028 rs114608975 T C 0.0001205 0.0043688 0.9800000 0.9779922 0.103534 0.0277556 NA
1 91536 rs6702460 G T -0.0078440 0.0026993 0.0037000 0.0036620 0.455917 0.4207270 NA
1 234313 rs8179466 C T -0.0032090 0.0053381 0.5500004 0.5477477 0.074456 NA NA
1 534192 rs6680723 C T 0.0015419 0.0030743 0.6200004 0.6159818 0.242066 NA NA
1 546697 rs12025928 A G 0.0048683 0.0038145 0.2000000 0.2018652 0.912861 NA NA
1 693731 rs12238997 A G 0.0019639 0.0025634 0.4400003 0.4435986 0.117316 0.1417730 NA
1 705882 rs72631875 G A -0.0008041 0.0037365 0.8300000 0.8296111 0.067698 0.0315495 NA
1 706368 rs55727773 A G -0.0000147 0.0019028 0.9900000 0.9938185 0.513297 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0016779 0.0023144 0.4700002 0.4684520 0.136309 0.2052720 NA
22 51219387 rs9616832 T C -0.0012092 0.0030167 0.6899999 0.6885262 0.071787 0.0654952 NA
22 51219704 rs147475742 G A -0.0016657 0.0040126 0.6800001 0.6780601 0.041193 0.0473243 NA
22 51221190 rs369304721 G A -0.0003831 0.0040415 0.9199999 0.9244879 0.048366 NA NA
22 51221731 rs115055839 T C -0.0008171 0.0030173 0.7899998 0.7865494 0.071338 0.0625000 NA
22 51222100 rs114553188 G T -0.0040638 0.0034961 0.2500000 0.2450896 0.054854 0.0880591 NA
22 51223637 rs375798137 G A -0.0039667 0.0035144 0.2599998 0.2590222 0.054474 0.0788738 NA
22 51229805 rs9616985 T C -0.0008940 0.0030266 0.7700005 0.7677086 0.071243 0.0730831 NA
22 51232488 rs376461333 A G -0.0027477 0.0069703 0.6899999 0.6934381 0.020462 NA NA
22 51237063 rs3896457 T C -0.0006740 0.0018271 0.7099994 0.7121884 0.298389 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623821 ES:SE:LP:AF:ID  0.00294479:0.00275239:0.552842:0.623821:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399144 ES:SE:LP:AF:ID  -0.00717608:0.00274448:2.05061:0.399144:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103534 ES:SE:LP:AF:ID  0.00012052:0.00436885:0.00877392:0.103534:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455917 ES:SE:LP:AF:ID  -0.00784401:0.00269934:2.4318:0.455917:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074456 ES:SE:LP:AF:ID  -0.00320895:0.00533812:0.259637:0.074456:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242066 ES:SE:LP:AF:ID  0.00154192:0.00307429:0.207608:0.242066:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912861 ES:SE:LP:AF:ID  0.00486829:0.00381451:0.69897:0.912861:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117316 ES:SE:LP:AF:ID  0.00196393:0.00256344:0.356547:0.117316:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067698 ES:SE:LP:AF:ID  -0.000804094:0.00373649:0.0809219:0.067698:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513297 ES:SE:LP:AF:ID  -1.4742e-05:0.00190283:0.00436481:0.513297:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033672 ES:SE:LP:AF:ID  0.00510822:0.00474316:0.552842:0.033672:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037453 ES:SE:LP:AF:ID  0.00438038:0.00430169:0.508638:0.037453:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037639 ES:SE:LP:AF:ID  0.00440049:0.00427924:0.522879:0.037639:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037216 ES:SE:LP:AF:ID  0.0038436:0.00431835:0.431798:0.037216:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  0.000711268:0.00677293:0.0362122:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037856 ES:SE:LP:AF:ID  0.00410954:0.00426461:0.468521:0.037856:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03795  ES:SE:LP:AF:ID  0.00392634:0.00425108:0.443698:0.03795:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102723 ES:SE:LP:AF:ID  0.000823168:0.00310555:0.102373:0.102723:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958094 ES:SE:LP:AF:ID  -0.00284543:0.00410587:0.309804:0.958094:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031688 ES:SE:LP:AF:ID  -0.000761636:0.00751821:0.0362122:0.031688:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052725 ES:SE:LP:AF:ID  -0.00397586:0.00605544:0.29243:0.052725:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037444 ES:SE:LP:AF:ID  0.00421461:0.004279:0.49485:0.037444:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037713 ES:SE:LP:AF:ID  0.0041305:0.00424355:0.481486:0.037713:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841443 ES:SE:LP:AF:ID  -0.00221226:0.00221692:0.49485:0.841443:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056334 ES:SE:LP:AF:ID  -0.0013308:0.00360125:0.148742:0.056334:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123081 ES:SE:LP:AF:ID  0.0022597:0.00243497:0.455932:0.123081:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025132 ES:SE:LP:AF:ID  0.00254978:0.00606378:0.173925:0.025132:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122333 ES:SE:LP:AF:ID  0.00220341:0.00243575:0.431798:0.122333:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134136 ES:SE:LP:AF:ID  0.00292609:0.00239113:0.657577:0.134136:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  0.00951451:0.00852178:0.585027:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.0292547:0.0108216:2.16115:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037595 ES:SE:LP:AF:ID  0.00398419:0.00420423:0.468521:0.037595:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837032 ES:SE:LP:AF:ID  -0.00210429:0.00214461:0.481486:0.837032:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836736 ES:SE:LP:AF:ID  -0.0020987:0.00214304:0.481486:0.836736:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86856  ES:SE:LP:AF:ID  -0.00161657:0.00230279:0.318759:0.86856:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131007 ES:SE:LP:AF:ID  0.00154816:0.0023087:0.30103:0.131007:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03804  ES:SE:LP:AF:ID  0.00504614:0.00413827:0.657577:0.03804:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038287 ES:SE:LP:AF:ID  0.00481594:0.00411261:0.619789:0.038287:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867973 ES:SE:LP:AF:ID  -0.00150618:0.00229909:0.29243:0.867973:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868047 ES:SE:LP:AF:ID  -0.00139791:0.00230003:0.267606:0.868047:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038195 ES:SE:LP:AF:ID  0.00497484:0.0041316:0.638272:0.038195:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867984 ES:SE:LP:AF:ID  -0.00150832:0.00229903:0.29243:0.867984:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.00488127:0.0115223:0.173925:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836162 ES:SE:LP:AF:ID  -0.00213649:0.00213677:0.49485:0.836162:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038198 ES:SE:LP:AF:ID  0.00483862:0.00413764:0.619789:0.038198:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836796 ES:SE:LP:AF:ID  -0.00212048:0.00214262:0.49485:0.836796:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.0064751:0.0077515:0.39794:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  -0.0136385:0.0115072:0.619789:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838112 ES:SE:LP:AF:ID  -0.0021322:0.00217278:0.481486:0.838112:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868225 ES:SE:LP:AF:ID  -0.00154167:0.00229607:0.30103:0.868225:rs3115858