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_102170.vcf.gz --id UKB-b:998 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102170.txt.gz --cohort_controls 64947 --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-998/UKB-b-998_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-998/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:04 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-998/UKB-b-998_data.vcf.gz ...
Read summary statistics for 8624959 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, 1285748 SNPs remain.
After merging with regression SNP LD, 1285748 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0004 (0.0064)
Lambda GC: 1.0047
Mean Chi^2: 1.0028
Intercept: 1.0033 (0.0059)
Ratio: 1.1804 (2.1302)
Analysis finished at Thu Oct 17 14:44:23 2019
Total time elapsed: 1.0m:18.75s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 1.7022e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 4,
    "n_p_sig": 14,
    "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": 83094,
    "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": 1285748,
    "ldsc_nsnp_merge_regression_ld": 1285748,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0033,
    "ldsc_intercept_se": 0.0059,
    "ldsc_lambda_gc": 1.0047,
    "ldsc_mean_chisq": 1.0028,
    "ldsc_ratio": 1.1786
}

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 8617622 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 8624959 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 5.760985e+00 1.000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.877001e+07 5.637163e+07 8.280e+02 3.238532e+07 6.929181e+07 1.145687e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.700000e-06 2.478700e-03 -2.606e-02 -1.068500e-03 -2.800000e-05 9.982000e-04 4.040210e-02 ▁▇▅▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.050200e-03 1.399400e-03 8.143e-04 9.589000e-04 1.413800e-03 2.771400e-03 1.403670e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.987369e-01 2.880191e-01 0.000e+00 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 4.987354e-01 2.879945e-01 0.000e+00 2.489704e-01 4.983944e-01 7.472987e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291179e-01 2.594882e-01 5.390e-03 2.519000e-02 1.138970e-01 3.626220e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83094 0.9903659 NA NA NA NA NA NA NA 2.288670e-01 2.514590e-01 0.000e+00 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 0.0001976 0.0015006 0.9000000 0.8952362 0.623814 0.7821490 NA
1 54676 rs2462492 C T 0.0008708 0.0014963 0.5600000 0.5605578 0.399146 NA NA
1 86028 rs114608975 T C -0.0022194 0.0023818 0.3500000 0.3514374 0.103533 0.0277556 NA
1 91536 rs6702460 G T 0.0008274 0.0014717 0.5700002 0.5739594 0.455920 0.4207270 NA
1 234313 rs8179466 C T -0.0027762 0.0029104 0.3400001 0.3401412 0.074456 NA NA
1 534192 rs6680723 C T 0.0011592 0.0016762 0.4899999 0.4892171 0.242052 NA NA
1 546697 rs12025928 A G 0.0004708 0.0020798 0.8200001 0.8208980 0.912860 NA NA
1 693731 rs12238997 A G -0.0015290 0.0013977 0.2700001 0.2739707 0.117310 0.1417730 NA
1 705882 rs72631875 G A 0.0013471 0.0020372 0.5099998 0.5084578 0.067700 0.0315495 NA
1 706368 rs55727773 A G 0.0002088 0.0010374 0.8400000 0.8404570 0.513310 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0009533 0.0012618 0.4500005 0.4499485 0.136319 0.2052720 NA
22 51219387 rs9616832 T C 0.0016337 0.0016446 0.3200000 0.3205375 0.071799 0.0654952 NA
22 51219704 rs147475742 G A 0.0002548 0.0021878 0.9100000 0.9072928 0.041192 0.0473243 NA
22 51221190 rs369304721 G A 0.0029585 0.0022033 0.1800002 0.1793472 0.048374 NA NA
22 51221731 rs115055839 T C 0.0019331 0.0016450 0.2399999 0.2399334 0.071350 0.0625000 NA
22 51222100 rs114553188 G T -0.0042068 0.0019062 0.0269998 0.0273175 0.054852 0.0880591 NA
22 51223637 rs375798137 G A -0.0041714 0.0019161 0.0290001 0.0294767 0.054472 0.0788738 NA
22 51229805 rs9616985 T C 0.0019675 0.0016500 0.2300001 0.2331011 0.071255 0.0730831 NA
22 51232488 rs376461333 A G -0.0078722 0.0038003 0.0379997 0.0383174 0.020461 NA NA
22 51237063 rs3896457 T C 0.0013312 0.0009961 0.1800002 0.1814280 0.298394 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623814 ES:SE:LP:AF:ID  0.000197604:0.00150062:0.0457575:0.623814:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399146 ES:SE:LP:AF:ID  0.000870843:0.00149626:0.251812:0.399146:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103533 ES:SE:LP:AF:ID  -0.00221941:0.00238184:0.455932:0.103533:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45592  ES:SE:LP:AF:ID  0.000827428:0.00147169:0.244125:0.45592:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074456 ES:SE:LP:AF:ID  -0.00277621:0.00291042:0.468521:0.074456:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242052 ES:SE:LP:AF:ID  0.00115917:0.00167618:0.309804:0.242052:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91286  ES:SE:LP:AF:ID  0.000470835:0.00207975:0.0861861:0.91286:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11731  ES:SE:LP:AF:ID  -0.00152899:0.00139766:0.568636:0.11731:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.0677   ES:SE:LP:AF:ID  0.00134708:0.0020372:0.29243:0.0677:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51331  ES:SE:LP:AF:ID  0.000208846:0.00103744:0.0757207:0.51331:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033678 ES:SE:LP:AF:ID  0.000941248:0.00258581:0.142668:0.033678:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037458 ES:SE:LP:AF:ID  0.000533084:0.0023452:0.0861861:0.037458:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037645 ES:SE:LP:AF:ID  0.000481481:0.00233293:0.0757207:0.037645:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037221 ES:SE:LP:AF:ID  0.000580982:0.00235425:0.091515:0.037221:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016282 ES:SE:LP:AF:ID  -0.0011729:0.00369279:0.124939:0.016282:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037862 ES:SE:LP:AF:ID  0.000270348:0.00232496:0.0409586:0.037862:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037956 ES:SE:LP:AF:ID  0.000436538:0.00231758:0.0705811:0.037956:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102733 ES:SE:LP:AF:ID  -0.00320712:0.00169297:1.23657:0.102733:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958088 ES:SE:LP:AF:ID  -0.000773807:0.00223844:0.136677:0.958088:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031691 ES:SE:LP:AF:ID  0.00360834:0.00409894:0.420216:0.031691:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052724 ES:SE:LP:AF:ID  -0.00293169:0.00330158:0.431798:0.052724:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03745  ES:SE:LP:AF:ID  0.000534769:0.0023328:0.0861861:0.03745:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037719 ES:SE:LP:AF:ID  0.000262353:0.00231348:0.0409586:0.037719:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841444 ES:SE:LP:AF:ID  0.000563119:0.00120871:0.19382:0.841444:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056329 ES:SE:LP:AF:ID  -0.000908714:0.00196354:0.19382:0.056329:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123074 ES:SE:LP:AF:ID  -0.000551876:0.00132762:0.167491:0.123074:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025131 ES:SE:LP:AF:ID  0.00676082:0.00330614:1.38722:0.025131:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122327 ES:SE:LP:AF:ID  -0.000719029:0.00132804:0.229148:0.122327:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134136 ES:SE:LP:AF:ID  -0.000747446:0.00130369:0.244125:0.134136:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  0.00308353:0.00464631:0.29243:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.0061   ES:SE:LP:AF:ID  -0.00134639:0.00590024:0.0861861:0.0061:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037601 ES:SE:LP:AF:ID  0.000466673:0.00229204:0.0757207:0.037601:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837032 ES:SE:LP:AF:ID  0.000793796:0.00116928:0.30103:0.837032:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836736 ES:SE:LP:AF:ID  0.000807417:0.00116842:0.309804:0.836736:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868566 ES:SE:LP:AF:ID  0.000878672:0.00125554:0.318759:0.868566:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131001 ES:SE:LP:AF:ID  -0.00095243:0.00125876:0.346787:0.131001:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038046 ES:SE:LP:AF:ID  0.000234703:0.00225608:0.0362122:0.038046:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038293 ES:SE:LP:AF:ID  8.85103e-05:0.0022421:0.0132283:0.038293:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867979 ES:SE:LP:AF:ID  0.000932335:0.00125352:0.337242:0.867979:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868053 ES:SE:LP:AF:ID  0.00105287:0.00125403:0.39794:0.868053:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038201 ES:SE:LP:AF:ID  9.62192e-05:0.00225246:0.0132283:0.038201:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86799  ES:SE:LP:AF:ID  0.000932805:0.00125349:0.337242:0.86799:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.00309867:0.00628229:0.207608:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836162 ES:SE:LP:AF:ID  0.000852982:0.001165:0.337242:0.836162:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038204 ES:SE:LP:AF:ID  0.000104514:0.00225574:0.0177288:0.038204:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836796 ES:SE:LP:AF:ID  0.000845565:0.0011682:0.327902:0.836796:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.00714632:0.00422634:1.04096:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005656 ES:SE:LP:AF:ID  0.010707:0.00627752:1.05552:0.005656:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838112 ES:SE:LP:AF:ID  0.000866853:0.00118464:0.337242:0.838112:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868231 ES:SE:LP:AF:ID  0.000912388:0.00125187:0.327902:0.868231:rs3115858