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_6017.vcf.gz --id UKB-b:14149 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6017.txt.gz --cohort_controls 69537 --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-14149/UKB-b-14149_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14149/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:16 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14149/UKB-b-14149_data.vcf.gz ...
Read summary statistics for 8714070 SNPs.
Dropped 7620 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, 1286136 SNPs remain.
After merging with regression SNP LD, 1286136 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0159 (0.0066)
Lambda GC: 1.029
Mean Chi^2: 1.0282
Intercept: 1.0063 (0.006)
Ratio: 0.2233 (0.2126)
Analysis finished at Thu Oct 17 14:44:53 2019
Total time elapsed: 1.0m:37.25s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9468,
    "inflation_factor": 1,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 84783,
    "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": 1286136,
    "ldsc_nsnp_merge_regression_ld": 1286136,
    "ldsc_observed_scale_h2_beta": 0.0159,
    "ldsc_observed_scale_h2_se": 0.0066,
    "ldsc_intercept_beta": 1.0063,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.029,
    "ldsc_mean_chisq": 1.0282,
    "ldsc_ratio": 0.2234
}

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 8706485 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 8714070 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 8.649483e+00 5.760914e+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 7.877019e+07 5.635897e+07 828.0000000 3.239339e+07 6.929995e+07 1.145647e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.310000e-05 6.356000e-03 -0.0881572 -2.601200e-03 1.070000e-05 2.634200e-03 6.330690e-02 ▁▁▇▂▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.206000e-03 3.608500e-03 0.0020272 2.394200e-03 3.560400e-03 7.065700e-03 3.641110e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.965870e-01 2.896579e-01 0.0000001 2.399999e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.965901e-01 2.896330e-01 0.0000001 2.447181e-01 4.954064e-01 7.472406e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.269976e-01 2.592660e-01 0.0050340 2.408100e-02 1.109260e-01 3.588720e-01 9.949660e-01 ▇▂▁▁▁
numeric AF_reference 84783 0.9902706 NA NA NA NA NA NA NA 2.268546e-01 2.512533e-01 0.0000000 2.176520e-02 1.277960e-01 3.562300e-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 -0.0043424 0.0037433 0.2500000 0.2460285 0.623917 0.7821490 NA
1 54676 rs2462492 C T 0.0014930 0.0037191 0.6899999 0.6880894 0.399212 NA NA
1 86028 rs114608975 T C -0.0017344 0.0058949 0.7700005 0.7685828 0.104023 0.0277556 NA
1 91536 rs6702460 G T -0.0022010 0.0036602 0.5500004 0.5476155 0.456271 0.4207270 NA
1 234313 rs8179466 C T -0.0085453 0.0071509 0.2300001 0.2320910 0.074930 NA NA
1 534192 rs6680723 C T -0.0002920 0.0041855 0.9400001 0.9443889 0.240426 NA NA
1 546697 rs12025928 A G 0.0026753 0.0051918 0.6100002 0.6063528 0.912563 NA NA
1 693731 rs12238997 A G -0.0018714 0.0034827 0.5900000 0.5910285 0.117206 0.1417730 NA
1 705882 rs72631875 G A 0.0063039 0.0050764 0.2099999 0.2143092 0.067977 0.0315495 NA
1 706368 rs55727773 A G 0.0064664 0.0025806 0.0120000 0.0122190 0.513916 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0029739 0.0031206 0.3400001 0.3405971 0.136749 0.2052720 NA
22 51219387 rs9616832 T C 0.0060845 0.0040687 0.1299999 0.1347994 0.072414 0.0654952 NA
22 51219704 rs147475742 G A 0.0081813 0.0054424 0.1299999 0.1327778 0.041167 0.0473243 NA
22 51221190 rs369304721 G A 0.0088248 0.0054654 0.1100001 0.1063832 0.048648 NA NA
22 51221731 rs115055839 T C 0.0058408 0.0040697 0.1499999 0.1512280 0.071924 0.0625000 NA
22 51222100 rs114553188 G T 0.0009676 0.0047608 0.8400000 0.8389371 0.054179 0.0880591 NA
22 51223637 rs375798137 G A 0.0008141 0.0047857 0.8600001 0.8649234 0.053789 0.0788738 NA
22 51229805 rs9616985 T C 0.0061717 0.0040846 0.1299999 0.1307985 0.071798 0.0730831 NA
22 51232488 rs376461333 A G 0.0041124 0.0096657 0.6700003 0.6705027 0.019912 NA NA
22 51237063 rs3896457 T C -0.0019360 0.0024809 0.4400003 0.4351953 0.297729 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623917 ES:SE:LP:AF:ID  -0.00434238:0.00374327:0.60206:0.623917:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399212 ES:SE:LP:AF:ID  0.00149303:0.0037191:0.161151:0.399212:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104023 ES:SE:LP:AF:ID  -0.00173444:0.00589486:0.113509:0.104023:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456271 ES:SE:LP:AF:ID  -0.00220099:0.00366016:0.259637:0.456271:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07493  ES:SE:LP:AF:ID  -0.0085453:0.00715094:0.638272:0.07493:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240426 ES:SE:LP:AF:ID  -0.000291961:0.00418554:0.0268721:0.240426:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912563 ES:SE:LP:AF:ID  0.00267528:0.00519183:0.21467:0.912563:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117206 ES:SE:LP:AF:ID  -0.00187141:0.00348269:0.229148:0.117206:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067977 ES:SE:LP:AF:ID  0.00630389:0.0050764:0.677781:0.067977:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513916 ES:SE:LP:AF:ID  0.00646635:0.0025806:1.92082:0.513916:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033782 ES:SE:LP:AF:ID  -0.00290231:0.00641691:0.187087:0.033782:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037483 ES:SE:LP:AF:ID  -0.00177286:0.00583473:0.119186:0.037483:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037575 ES:SE:LP:AF:ID  -0.00165458:0.0058146:0.107905:0.037575:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037256 ES:SE:LP:AF:ID  -0.00211033:0.00585671:0.142668:0.037256:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016282 ES:SE:LP:AF:ID  -0.0013103:0.00919132:0.05061:0.016282:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037852 ES:SE:LP:AF:ID  -0.00136675:0.00578919:0.091515:0.037852:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037934 ES:SE:LP:AF:ID  -0.0018935:0.00577125:0.130768:0.037934:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101537 ES:SE:LP:AF:ID  -0.00134041:0.00426393:0.124939:0.101537:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957886 ES:SE:LP:AF:ID  0.000121918:0.00555667:0.00877392:0.957886:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031528 ES:SE:LP:AF:ID  -0.0036943:0.0103441:0.142668:0.031528:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.0526   ES:SE:LP:AF:ID  0.0132782:0.00820751:0.958607:0.0526:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037391 ES:SE:LP:AF:ID  -0.00116894:0.0058115:0.0757207:0.037391:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037723 ES:SE:LP:AF:ID  -0.00157413:0.00576133:0.107905:0.037723:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841078 ES:SE:LP:AF:ID  0.00197818:0.00301082:0.29243:0.841078:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05615  ES:SE:LP:AF:ID  -0.00393553:0.00489458:0.376751:0.05615:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123195 ES:SE:LP:AF:ID  -0.00228071:0.00330376:0.309804:0.123195:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025605 ES:SE:LP:AF:ID  -0.0106269:0.00817002:0.721246:0.025605:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122384 ES:SE:LP:AF:ID  -0.00252741:0.00330604:0.356547:0.122384:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133723 ES:SE:LP:AF:ID  -0.00229845:0.00325173:0.318759:0.133723:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011119 ES:SE:LP:AF:ID  0.00680293:0.0118816:0.244125:0.011119:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006296 ES:SE:LP:AF:ID  1.07865e-05:0.0145004:-0:0.006296:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.03766  ES:SE:LP:AF:ID  -0.00133151:0.00570178:0.0861861:0.03766:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836969 ES:SE:LP:AF:ID  0.00122086:0.00291373:0.167491:0.836969:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836504 ES:SE:LP:AF:ID  0.00149755:0.00291011:0.21467:0.836504:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868542 ES:SE:LP:AF:ID  0.00103391:0.00312619:0.130768:0.868542:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131164 ES:SE:LP:AF:ID  -0.00163813:0.00313247:0.221849:0.131164:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038096 ES:SE:LP:AF:ID  -0.00177501:0.00561504:0.124939:0.038096:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038339 ES:SE:LP:AF:ID  -0.00147854:0.00558093:0.102373:0.038339:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867831 ES:SE:LP:AF:ID  0.00122444:0.00312:0.161151:0.867831:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867935 ES:SE:LP:AF:ID  0.00129815:0.00312158:0.167491:0.867935:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038316 ES:SE:LP:AF:ID  -0.00167037:0.00560147:0.113509:0.038316:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867852 ES:SE:LP:AF:ID  0.00122918:0.00311992:0.161151:0.867852:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005281 ES:SE:LP:AF:ID  -0.00647999:0.0157926:0.167491:0.005281:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005254 ES:SE:LP:AF:ID  -0.00640436:0.015831:0.161151:0.005254:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836056 ES:SE:LP:AF:ID  0.00140888:0.0029045:0.200659:0.836056:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038332 ES:SE:LP:AF:ID  -0.00163624:0.0056093:0.113509:0.038332:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836684 ES:SE:LP:AF:ID  0.00100178:0.00291247:0.136677:0.836684:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.012828 ES:SE:LP:AF:ID  0.0147497:0.0105871:0.79588:0.012828:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005505 ES:SE:LP:AF:ID  0.00448369:0.0158129:0.107905:0.005505:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838092 ES:SE:LP:AF:ID  0.000718996:0.0029539:0.091515:0.838092:rs3131965