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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_1578.vcf.gz --id UKB-b:5716 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1578.txt.gz --cohort_controls 326801 --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-5716/UKB-b-5716_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5716/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:37 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5716/UKB-b-5716_data.vcf.gz ...
Read summary statistics for 9792592 SNPs.
Dropped 14232 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, 1289026 SNPs remain.
After merging with regression SNP LD, 1289026 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0296 (0.0021)
Lambda GC: 1.2187
Mean Chi^2: 1.2417
Intercept: 1.0522 (0.0072)
Ratio: 0.2158 (0.0296)
Analysis finished at Thu Oct 17 14:47:09 2019
Total time elapsed: 1.0m:31.87s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9496,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 4,
    "n_p_sig": 55,
    "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": 180800,
    "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": 1289026,
    "ldsc_nsnp_merge_regression_ld": 1289026,
    "ldsc_observed_scale_h2_beta": 0.0296,
    "ldsc_observed_scale_h2_se": 0.0021,
    "ldsc_intercept_beta": 1.0522,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.2187,
    "ldsc_mean_chisq": 1.2417,
    "ldsc_ratio": 0.216
}

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 9778426 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 9792592 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 8.622997e+00 5.748740e+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 7.885740e+07 5.628808e+07 828.0000000 3.258148e+07 6.948109e+07 1.145914e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.447000e-04 9.955600e-03 -0.1431400 -3.259500e-03 -4.260000e-05 3.113300e-03 1.466770e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.076300e-03 6.590600e-03 0.0020188 2.466600e-03 4.109500e-03 9.416400e-03 1.053870e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.765503e-01 2.953313e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▆▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.765510e-01 2.953055e-01 0.0000000 2.145557e-01 4.681005e-01 7.326787e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.044000e-01 2.568225e-01 0.0010710 1.355500e-02 7.928100e-02 3.182550e-01 9.989290e-01 ▇▂▁▁▁
numeric AF_reference 180800 0.9815371 NA NA NA NA NA NA NA 2.074678e-01 2.483853e-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 0.0013357 0.0037102 0.7199992 0.7188349 0.623748 0.7821490 NA
1 54676 rs2462492 C T -0.0070748 0.0036774 0.0539995 0.0543729 0.400564 NA NA
1 86028 rs114608975 T C -0.0054877 0.0058745 0.3500000 0.3502244 0.103621 0.0277556 NA
1 91536 rs6702460 G T -0.0028566 0.0036234 0.4299995 0.4304822 0.456801 0.4207270 NA
1 234313 rs8179466 C T 0.0070198 0.0071405 0.3300000 0.3255580 0.074487 NA NA
1 534192 rs6680723 C T 0.0011688 0.0041403 0.7800007 0.7777150 0.241129 NA NA
1 546697 rs12025928 A G -0.0056631 0.0051677 0.2700001 0.2731384 0.913635 NA NA
1 693731 rs12238997 A G 0.0043786 0.0034700 0.2099999 0.2069978 0.116241 0.1417730 NA
1 705882 rs72631875 G A 0.0043484 0.0050742 0.3900004 0.3914675 0.067388 0.0315495 NA
1 706368 rs55727773 A G -0.0003629 0.0025699 0.8900000 0.8877095 0.516232 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0082858 0.0053976 0.1199999 0.1247599 0.041914 0.0473243 NA
22 51219766 rs182321900 C T -0.0400972 0.0257895 0.1199999 0.1199966 0.001851 NA NA
22 51220146 rs868950473 C T -0.0461964 0.0254965 0.0700003 0.0700059 0.001905 NA NA
22 51221190 rs369304721 G A -0.0050282 0.0053862 0.3500000 0.3505408 0.049601 NA NA
22 51221731 rs115055839 T C -0.0040173 0.0040292 0.3200000 0.3187503 0.073089 0.0625000 NA
22 51222100 rs114553188 G T -0.0090476 0.0047457 0.0569994 0.0565901 0.054306 0.0880591 NA
22 51223637 rs375798137 G A -0.0089633 0.0047680 0.0599998 0.0601260 0.053950 0.0788738 NA
22 51229805 rs9616985 T C -0.0037128 0.0040436 0.3599996 0.3585259 0.072922 0.0730831 NA
22 51232488 rs376461333 A G -0.0039326 0.0095370 0.6800001 0.6800854 0.019944 NA NA
22 51237063 rs3896457 T C 0.0002524 0.0024713 0.9199999 0.9186551 0.298280 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623748 ES:SE:LP:AF:ID  0.00133574:0.00371022:0.142668:0.623748:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400564 ES:SE:LP:AF:ID  -0.00707478:0.0036774:1.26761:0.400564:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103621 ES:SE:LP:AF:ID  -0.00548767:0.00587448:0.455932:0.103621:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456801 ES:SE:LP:AF:ID  -0.0028566:0.00362344:0.366532:0.456801:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074487 ES:SE:LP:AF:ID  0.00701979:0.00714046:0.481486:0.074487:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241129 ES:SE:LP:AF:ID  0.00116879:0.00414027:0.107905:0.241129:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913635 ES:SE:LP:AF:ID  -0.00566315:0.00516775:0.568636:0.913635:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116241 ES:SE:LP:AF:ID  0.00437863:0.00346997:0.677781:0.116241:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067388 ES:SE:LP:AF:ID  0.00434839:0.00507421:0.408935:0.067388:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.516232 ES:SE:LP:AF:ID  -0.000362878:0.0025699:0.05061:0.516232:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032809 ES:SE:LP:AF:ID  -0.00477275:0.00649258:0.337242:0.032809:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036412 ES:SE:LP:AF:ID  -0.00443886:0.00589718:0.346787:0.036412:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036525 ES:SE:LP:AF:ID  -0.00441731:0.00587509:0.346787:0.036525:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036225 ES:SE:LP:AF:ID  -0.00365104:0.0059174:0.267606:0.036225:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016284 ES:SE:LP:AF:ID  -0.0137943:0.0091241:0.886057:0.016284:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036755 ES:SE:LP:AF:ID  -0.00379445:0.0058524:0.283997:0.036755:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036845 ES:SE:LP:AF:ID  -0.00363236:0.00583321:0.275724:0.036845:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101224 ES:SE:LP:AF:ID  0.00431646:0.00424062:0.508638:0.101224:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959477 ES:SE:LP:AF:ID  0.00494464:0.00563553:0.420216:0.959477:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031515 ES:SE:LP:AF:ID  0.00337183:0.0101749:0.130768:0.031515:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053184 ES:SE:LP:AF:ID  -0.0108892:0.00811173:0.744727:0.053184:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036379 ES:SE:LP:AF:ID  -0.00469049:0.00587052:0.376751:0.036379:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036689 ES:SE:LP:AF:ID  -0.00472074:0.0058168:0.376751:0.036689:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843619 ES:SE:LP:AF:ID  -0.00112307:0.00300818:0.148742:0.843619:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056182 ES:SE:LP:AF:ID  0.00729031:0.00485519:0.886057:0.056182:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122274 ES:SE:LP:AF:ID  0.00285335:0.00329067:0.408935:0.122274:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025995 ES:SE:LP:AF:ID  -0.0126955:0.00804126:0.958607:0.025995:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121526 ES:SE:LP:AF:ID  0.00302522:0.00329198:0.443698:0.121526:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132303 ES:SE:LP:AF:ID  0.00383801:0.00324346:0.619789:0.132303:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011074 ES:SE:LP:AF:ID  0.00800286:0.0118411:0.30103:0.011074:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005695 ES:SE:LP:AF:ID  0.0101995:0.0152149:0.30103:0.005695:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002152 ES:SE:LP:AF:ID  -0.0179122:0.0264619:0.30103:0.002152:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036608 ES:SE:LP:AF:ID  -0.00476535:0.00575841:0.387216:0.036608:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839329 ES:SE:LP:AF:ID  -0.000717879:0.00291394:0.091515:0.839329:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838999 ES:SE:LP:AF:ID  -0.00061734:0.00291134:0.0809219:0.838999:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869938 ES:SE:LP:AF:ID  -0.00223998:0.00312306:0.327902:0.869938:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129707 ES:SE:LP:AF:ID  0.00200905:0.00312969:0.283997:0.129707:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037119 ES:SE:LP:AF:ID  -0.0040154:0.00566079:0.318759:0.037119:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037358 ES:SE:LP:AF:ID  -0.00409478:0.00562493:0.327902:0.037358:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869328 ES:SE:LP:AF:ID  -0.00216454:0.00311746:0.309804:0.869328:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869427 ES:SE:LP:AF:ID  -0.00207482:0.0031187:0.29243:0.869427:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037306 ES:SE:LP:AF:ID  -0.00378143:0.00565076:0.30103:0.037306:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86933  ES:SE:LP:AF:ID  -0.0021279:0.00311744:0.309804:0.86933:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005157 ES:SE:LP:AF:ID  -0.00817084:0.0159027:0.21467:0.005157:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00512  ES:SE:LP:AF:ID  -0.00832407:0.0159484:0.221849:0.00512:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838474 ES:SE:LP:AF:ID  -0.000795578:0.00290344:0.107905:0.838474:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03732  ES:SE:LP:AF:ID  -0.00358458:0.00565864:0.275724:0.03732:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.839108 ES:SE:LP:AF:ID  -0.000801236:0.00291169:0.107905:0.839108:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013894 ES:SE:LP:AF:ID  0.0121206:0.0100933:0.638272:0.013894:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005496 ES:SE:LP:AF:ID  0.0126547:0.0157051:0.376751:0.005496:rs184270342