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_103090.vcf.gz --id UKB-b:6373 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_103090.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-6373/UKB-b-6373_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6373/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6373/UKB-b-6373_data.vcf.gz ...
Read summary statistics for 8624973 SNPs.
Dropped 7372 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: -0.0056 (0.0071)
Lambda GC: 1.0171
Mean Chi^2: 1.0084
Intercept: 1.0155 (0.0061)
Ratio: 1.8514 (0.7231)
Analysis finished at Thu Oct 17 14:41:54 2019
Total time elapsed: 1.0m:35.73s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 6,
    "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": 83097,
    "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": 1.0155,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0171,
    "ldsc_mean_chisq": 1.0084,
    "ldsc_ratio": 1.8452
}

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 8617635 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 8624973 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 8.650348e+00 5.761001e+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 7.877012e+07 5.637175e+07 828.0000000 3.238533e+07 6.929174e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.990000e-05 3.980300e-03 -0.0452709 -1.655200e-03 -1.620000e-05 1.626300e-03 5.345900e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.262000e-03 2.226600e-03 0.0012956 1.525600e-03 2.249500e-03 4.409500e-03 2.233310e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.984648e-01 2.889782e-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 4.984656e-01 2.889520e-01 0.0000000 2.479700e-01 4.979708e-01 7.488673e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291177e-01 2.594885e-01 0.0053900 2.519000e-02 1.138970e-01 3.626230e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83097 0.9903655 NA NA NA NA NA NA NA 2.288668e-01 2.514592e-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 -0.0050152 0.0023876 0.0359998 0.0356836 0.623817 0.7821490 NA
1 54676 rs2462492 C T 0.0046656 0.0023808 0.0500000 0.0500301 0.399153 NA NA
1 86028 rs114608975 T C 0.0058399 0.0037898 0.1199999 0.1233264 0.103532 0.0277556 NA
1 91536 rs6702460 G T -0.0001624 0.0023418 0.9400001 0.9447074 0.455932 0.4207270 NA
1 234313 rs8179466 C T -0.0034828 0.0046307 0.4500005 0.4519948 0.074456 NA NA
1 534192 rs6680723 C T -0.0025082 0.0026669 0.3500000 0.3469683 0.242064 NA NA
1 546697 rs12025928 A G -0.0046552 0.0033093 0.1600000 0.1595160 0.912866 NA NA
1 693731 rs12238997 A G -0.0057793 0.0022238 0.0094000 0.0093522 0.117322 0.1417730 NA
1 705882 rs72631875 G A 0.0027319 0.0032413 0.4000000 0.3993267 0.067701 0.0315495 NA
1 706368 rs55727773 A G 0.0013601 0.0016507 0.4100001 0.4099581 0.513295 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0023391 0.0020077 0.2399999 0.2439882 0.136301 0.2052720 NA
22 51219387 rs9616832 T C 0.0040197 0.0026170 0.1199999 0.1245323 0.071780 0.0654952 NA
22 51219704 rs147475742 G A 0.0071356 0.0034811 0.0400000 0.0403836 0.041185 0.0473243 NA
22 51221190 rs369304721 G A 0.0062964 0.0035057 0.0719996 0.0724848 0.048365 NA NA
22 51221731 rs115055839 T C 0.0041088 0.0026175 0.1199999 0.1164766 0.071330 0.0625000 NA
22 51222100 rs114553188 G T -0.0009628 0.0030328 0.7499995 0.7508983 0.054854 0.0880591 NA
22 51223637 rs375798137 G A -0.0009269 0.0030486 0.7600007 0.7611062 0.054473 0.0788738 NA
22 51229805 rs9616985 T C 0.0038714 0.0026256 0.1400000 0.1403523 0.071235 0.0730831 NA
22 51232488 rs376461333 A G -0.0028829 0.0060465 0.6300007 0.6335106 0.020462 NA NA
22 51237063 rs3896457 T C -0.0001331 0.0015849 0.9299999 0.9330883 0.298386 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623817 ES:SE:LP:AF:ID  -0.00501521:0.00238761:1.4437:0.623817:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399153 ES:SE:LP:AF:ID  0.00466559:0.00238076:1.30103:0.399153:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103532 ES:SE:LP:AF:ID  0.00583987:0.00378976:0.920819:0.103532:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455932 ES:SE:LP:AF:ID  -0.000162411:0.00234175:0.0268721:0.455932:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074456 ES:SE:LP:AF:ID  -0.00348275:0.00463074:0.346787:0.074456:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242064 ES:SE:LP:AF:ID  -0.00250822:0.00266694:0.455932:0.242064:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912866 ES:SE:LP:AF:ID  -0.00465521:0.00330931:0.79588:0.912866:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117322 ES:SE:LP:AF:ID  -0.00577931:0.00222375:2.02687:0.117322:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067701 ES:SE:LP:AF:ID  0.00273188:0.00324134:0.39794:0.067701:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513295 ES:SE:LP:AF:ID  0.00136009:0.00165066:0.387216:0.513295:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033666 ES:SE:LP:AF:ID  -0.00117872:0.00411497:0.113509:0.033666:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037445 ES:SE:LP:AF:ID  -0.000604492:0.0037321:0.0604807:0.037445:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037632 ES:SE:LP:AF:ID  -0.000632728:0.00371257:0.0655015:0.037632:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037208 ES:SE:LP:AF:ID  -0.000523333:0.00374652:0.05061:0.037208:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  -0.00952081:0.0058755:0.958607:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037848 ES:SE:LP:AF:ID  -0.000800027:0.00369989:0.0809219:0.037848:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037943 ES:SE:LP:AF:ID  -0.000682791:0.00368811:0.0705811:0.037943:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102731 ES:SE:LP:AF:ID  0.0036525:0.00269375:0.744727:0.102731:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958101 ES:SE:LP:AF:ID  0.000668545:0.00356211:0.0705811:0.958101:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031692 ES:SE:LP:AF:ID  -0.00173538:0.00652173:0.102373:0.031692:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052726 ES:SE:LP:AF:ID  0.0149992:0.00525309:2.36653:0.052726:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037436 ES:SE:LP:AF:ID  -0.000244998:0.00371236:0.0222764:0.037436:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037705 ES:SE:LP:AF:ID  -0.00118694:0.0036816:0.124939:0.037705:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841444 ES:SE:LP:AF:ID  0.00475433:0.00192319:1.88606:0.841444:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056338 ES:SE:LP:AF:ID  -0.00377425:0.003124:0.638272:0.056338:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123087 ES:SE:LP:AF:ID  -0.0059451:0.00211231:2.3098:0.123087:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025132 ES:SE:LP:AF:ID  -0.00667571:0.00526031:0.69897:0.025132:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12234  ES:SE:LP:AF:ID  -0.00586723:0.00211299:2.25964:0.12234:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134135 ES:SE:LP:AF:ID  -0.00477852:0.0020743:1.67778:0.134135:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  0.00774868:0.00739261:0.537602:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  -0.0121542:0.00938771:0.69897:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037587 ES:SE:LP:AF:ID  -0.00116922:0.0036475:0.124939:0.037587:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837032 ES:SE:LP:AF:ID  0.00471227:0.00186044:1.95861:0.837032:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836736 ES:SE:LP:AF:ID  0.00479327:0.00185908:2.00436:0.836736:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868552 ES:SE:LP:AF:ID  0.00506379:0.00199763:1.95861:0.868552:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131014 ES:SE:LP:AF:ID  -0.00541473:0.00200276:2.16115:0.131014:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038033 ES:SE:LP:AF:ID  -0.00103356:0.00359027:0.113509:0.038033:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038279 ES:SE:LP:AF:ID  -0.00107602:0.00356801:0.119186:0.038279:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867966 ES:SE:LP:AF:ID  0.00516215:0.00199442:2.01773:0.867966:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86804  ES:SE:LP:AF:ID  0.00521329:0.00199523:2.04576:0.86804:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038187 ES:SE:LP:AF:ID  -0.00107908:0.00358449:0.119186:0.038187:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867977 ES:SE:LP:AF:ID  0.00515146:0.00199437:2.00877:0.867977:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.0056749:0.00999557:0.244125:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836162 ES:SE:LP:AF:ID  0.00475031:0.00185364:2:0.836162:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03819  ES:SE:LP:AF:ID  -0.00111422:0.00358972:0.119186:0.03819:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836796 ES:SE:LP:AF:ID  0.00474899:0.00185871:1.95861:0.836796:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  0.00325694:0.00672441:0.200659:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.00121944:0.00998247:0.0457575:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838112 ES:SE:LP:AF:ID  0.00528358:0.00188488:2.29243:0.838112:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868218 ES:SE:LP:AF:ID  0.00497036:0.0019918:1.88606:0.868218:rs3115858