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_1588.vcf.gz --id UKB-b:5174 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1588.txt.gz --cohort_controls 327634 --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-5174/UKB-b-5174_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5174/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:00 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5174/UKB-b-5174_data.vcf.gz ...
Read summary statistics for 9793971 SNPs.
Dropped 14243 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, 1289029 SNPs remain.
After merging with regression SNP LD, 1289029 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0514 (0.0028)
Lambda GC: 1.3412
Mean Chi^2: 1.3988
Intercept: 1.0644 (0.0091)
Ratio: 0.1615 (0.0229)
Analysis finished at Thu Oct 17 14:46:34 2019
Total time elapsed: 1.0m:33.33s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9497,
    "inflation_factor": 1.2544,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 22,
    "n_p_sig": 1038,
    "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": 180990,
    "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": 1289029,
    "ldsc_nsnp_merge_regression_ld": 1289029,
    "ldsc_observed_scale_h2_beta": 0.0514,
    "ldsc_observed_scale_h2_se": 0.0028,
    "ldsc_intercept_beta": 1.0644,
    "ldsc_intercept_se": 0.0091,
    "ldsc_lambda_gc": 1.3412,
    "ldsc_mean_chisq": 1.3988,
    "ldsc_ratio": 0.1615
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
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 9779794 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 9793971 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 8.622975e+00 5.748717e+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 7.885759e+07 5.628780e+07 828.0000000 3.258186e+07 6.948112e+07 1.145913e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.140000e-05 8.319900e-03 -0.1428000 -2.718300e-03 1.050000e-05 2.738100e-03 1.749140e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.783400e-03 5.388800e-03 0.0016493 2.015000e-03 3.357600e-03 7.694900e-03 8.612210e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.639353e-01 2.979798e-01 0.0000000 2.000000e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.639362e-01 2.979545e-01 0.0000000 1.970212e-01 4.512828e-01 7.215770e-01 1.000000e+00 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.043779e-01 2.568224e-01 0.0010690 1.354600e-02 7.925300e-02 3.182120e-01 9.989310e-01 ▇▂▁▁▁
numeric AF_reference 180990 0.9815203 NA NA NA NA NA NA NA 2.074526e-01 2.483833e-01 0.0000000 1.198080e-02 1.008390e-01 3.218850e-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 -0.0082376 0.0030305 0.0065999 0.0065637 0.623715 0.7821490 NA
1 54676 rs2462492 C T 0.0028330 0.0030035 0.3500000 0.3455533 0.400569 NA NA
1 86028 rs114608975 T C 0.0096992 0.0047972 0.0430002 0.0431914 0.103629 0.0277556 NA
1 91536 rs6702460 G T 0.0028742 0.0029597 0.3300000 0.3314890 0.456744 0.4207270 NA
1 234313 rs8179466 C T -0.0036478 0.0058342 0.5300002 0.5318113 0.074476 NA NA
1 534192 rs6680723 C T 0.0023380 0.0033815 0.4899999 0.4893010 0.241134 NA NA
1 546697 rs12025928 A G 0.0038829 0.0042200 0.3599996 0.3575083 0.913625 NA NA
1 693731 rs12238997 A G -0.0002950 0.0028335 0.9199999 0.9170879 0.116263 0.1417730 NA
1 705882 rs72631875 G A -0.0064415 0.0041440 0.1199999 0.1200862 0.067402 0.0315495 NA
1 706368 rs55727773 A G -0.0015068 0.0020991 0.4700002 0.4728520 0.516227 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0075027 0.0044110 0.0890000 0.0889566 0.041883 0.0473243 NA
22 51219766 rs182321900 C T 0.0241224 0.0210199 0.2500000 0.2511345 0.001860 NA NA
22 51220146 rs868950473 C T 0.0219264 0.0207830 0.2900000 0.2914180 0.001914 NA NA
22 51221190 rs369304721 G A 0.0097968 0.0044021 0.0259998 0.0260494 0.049579 NA NA
22 51221731 rs115055839 T C 0.0053016 0.0032924 0.1100001 0.1073414 0.073078 0.0625000 NA
22 51222100 rs114553188 G T 0.0085639 0.0038779 0.0269998 0.0272184 0.054302 0.0880591 NA
22 51223637 rs375798137 G A 0.0086340 0.0038963 0.0269998 0.0266945 0.053942 0.0788738 NA
22 51229805 rs9616985 T C 0.0054985 0.0033041 0.0959997 0.0960829 0.072914 0.0730831 NA
22 51232488 rs376461333 A G 0.0072244 0.0077943 0.3500000 0.3539884 0.019937 NA NA
22 51237063 rs3896457 T C -0.0020586 0.0020195 0.3100002 0.3080386 0.298205 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623715 ES:SE:LP:AF:ID  -0.00823757:0.00303052:2.18046:0.623715:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400569 ES:SE:LP:AF:ID  0.00283305:0.0030035:0.455932:0.400569:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103629 ES:SE:LP:AF:ID  0.00969924:0.0047972:1.36653:0.103629:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456744 ES:SE:LP:AF:ID  0.00287425:0.00295973:0.481486:0.456744:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074476 ES:SE:LP:AF:ID  -0.00364782:0.00583424:0.275724:0.074476:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241134 ES:SE:LP:AF:ID  0.00233804:0.0033815:0.309804:0.241134:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913625 ES:SE:LP:AF:ID  0.00388293:0.00422001:0.443698:0.913625:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116263 ES:SE:LP:AF:ID  -0.00029498:0.00283355:0.0362122:0.116263:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067402 ES:SE:LP:AF:ID  -0.00644153:0.00414403:0.920819:0.067402:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.516227 ES:SE:LP:AF:ID  -0.00150681:0.00209907:0.327902:0.516227:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032793 ES:SE:LP:AF:ID  -0.000928167:0.00530359:0.0655015:0.032793:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036396 ES:SE:LP:AF:ID  -0.0018821:0.00481725:0.154902:0.036396:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036507 ES:SE:LP:AF:ID  -0.00124074:0.00479931:0.09691:0.036507:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036212 ES:SE:LP:AF:ID  -0.00246708:0.00483347:0.21467:0.036212:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  -0.000172523:0.00745204:0.00877392:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036739 ES:SE:LP:AF:ID  -0.00229325:0.00478072:0.200659:0.036739:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036828 ES:SE:LP:AF:ID  -0.00201779:0.00476511:0.173925:0.036828:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101206 ES:SE:LP:AF:ID  0.000795624:0.003463:0.0861861:0.101206:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959495 ES:SE:LP:AF:ID  0.00224142:0.00460356:0.200659:0.959495:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031516 ES:SE:LP:AF:ID  0.000431962:0.00831066:0.0177288:0.031516:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053195 ES:SE:LP:AF:ID  -0.000397819:0.00662399:0.0222764:0.053195:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036364 ES:SE:LP:AF:ID  -0.00203012:0.0047953:0.173925:0.036364:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036677 ES:SE:LP:AF:ID  -0.00123451:0.00475137:0.09691:0.036677:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84362  ES:SE:LP:AF:ID  -7.31385e-05:0.00245693:0.00877392:0.84362:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056153 ES:SE:LP:AF:ID  -0.000462409:0.00396594:0.0409586:0.056153:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122277 ES:SE:LP:AF:ID  0.000547507:0.0026874:0.0757207:0.122277:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025983 ES:SE:LP:AF:ID  0.0058114:0.00656965:0.420216:0.025983:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121528 ES:SE:LP:AF:ID  0.000593968:0.00268855:0.0809219:0.121528:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132312 ES:SE:LP:AF:ID  0.000325274:0.00264893:0.0457575:0.132312:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011072 ES:SE:LP:AF:ID  -0.0116613:0.00967633:0.638272:0.011072:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005705 ES:SE:LP:AF:ID  0.00153109:0.0124094:0.0457575:0.005705:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002148 ES:SE:LP:AF:ID  0.00985896:0.0216245:0.187087:0.002148:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036593 ES:SE:LP:AF:ID  -0.00102954:0.00470376:0.0809219:0.036593:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839319 ES:SE:LP:AF:ID  -5.46577e-05:0.0023798:0.00877392:0.839319:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838987 ES:SE:LP:AF:ID  9.61341e-05:0.00237765:0.0132283:0.838987:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869934 ES:SE:LP:AF:ID  -0.000333071:0.00255056:0.0457575:0.869934:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129711 ES:SE:LP:AF:ID  0.000207451:0.00255596:0.0268721:0.129711:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037104 ES:SE:LP:AF:ID  -0.000911654:0.00462405:0.0757207:0.037104:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037343 ES:SE:LP:AF:ID  -0.00107346:0.00459483:0.0861861:0.037343:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869322 ES:SE:LP:AF:ID  -0.000262361:0.00254596:0.0362122:0.869322:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869421 ES:SE:LP:AF:ID  -0.000223585:0.00254697:0.0315171:0.869421:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037291 ES:SE:LP:AF:ID  -0.000994619:0.00461589:0.0809219:0.037291:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869324 ES:SE:LP:AF:ID  -0.00023169:0.00254594:0.0315171:0.869324:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005161 ES:SE:LP:AF:ID  -0.000376924:0.0129839:0.00877392:0.005161:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005124 ES:SE:LP:AF:ID  0.000174656:0.0130211:0.00436481:0.005124:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838461 ES:SE:LP:AF:ID  -8.31082e-05:0.00237117:0.0132283:0.838461:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037304 ES:SE:LP:AF:ID  -0.0010621:0.00462238:0.0861861:0.037304:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.839094 ES:SE:LP:AF:ID  -9.98524e-05:0.00237789:0.0132283:0.839094:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.0139   ES:SE:LP:AF:ID  0.00196987:0.00824237:0.091515:0.0139:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005497 ES:SE:LP:AF:ID  0.0228035:0.0128257:1.12494:0.005497:rs184270342