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_2463.vcf.gz --id UKB-b:13346 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2463.txt.gz --cohort_cases 44502 --cohort_controls 415887 --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-13346/UKB-b-13346_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13346/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13346/UKB-b-13346_data.vcf.gz ...
Read summary statistics for 8074242 SNPs.
Dropped 6260 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, 1282991 SNPs remain.
After merging with regression SNP LD, 1282991 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0187 (0.0017)
Lambda GC: 1.1493
Mean Chi^2: 1.1823
Intercept: 1.0156 (0.0077)
Ratio: 0.0858 (0.0422)
Analysis finished at Thu Oct 17 14:43:52 2019
Total time elapsed: 1.0m:32.73s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9434,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -4.928e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 18,
    "n_p_sig": 794,
    "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": 75443,
    "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": 1282991,
    "ldsc_nsnp_merge_regression_ld": 1282991,
    "ldsc_observed_scale_h2_beta": 0.0187,
    "ldsc_observed_scale_h2_se": 0.0017,
    "ldsc_intercept_beta": 1.0156,
    "ldsc_intercept_se": 0.0077,
    "ldsc_lambda_gc": 1.1493,
    "ldsc_mean_chisq": 1.1823,
    "ldsc_ratio": 0.0856
}
 

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 8068010 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 8074242 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 8.659894e+00 5.763470e+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 7.871120e+07 5.640493e+07 828.0000000 3.228190e+07 6.918740e+07 1.145381e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -5.000000e-07 1.607200e-03 -0.0164336 -7.543000e-04 -2.700000e-06 7.532000e-04 1.620840e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.327400e-03 8.118000e-04 0.0005941 6.895000e-04 9.666000e-04 1.751800e-03 7.317400e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.813635e-01 2.938879e-01 0.0000000 2.200002e-01 4.700002e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.813637e-01 2.938618e-01 0.0000000 2.220676e-01 4.749727e-01 7.360042e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.434927e-01 2.606437e-01 0.0078650 3.403000e-02 1.336350e-01 3.858428e-01 9.921350e-01 ▇▂▂▁▁
numeric AF_reference 75443 0.9906563 NA NA NA NA NA NA NA 2.428192e-01 2.524914e-01 0.0000000 3.534350e-02 1.491610e-01 3.811900e-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 0.0009562 0.0010931 0.3800004 0.3817152 0.623786 0.7821490 NA
1 54676 rs2462492 C T -0.0004141 0.0010829 0.6999999 0.7021539 0.400412 NA NA
1 86028 rs114608975 T C -0.0002936 0.0017314 0.8700001 0.8653647 0.103546 0.0277556 NA
1 91536 rs6702460 G T 0.0003161 0.0010662 0.7700005 0.7668929 0.456858 0.4207270 NA
1 234313 rs8179466 C T 0.0007558 0.0021025 0.7199992 0.7192295 0.074506 NA NA
1 534192 rs6680723 C T 0.0008865 0.0012181 0.4700002 0.4667426 0.240957 NA NA
1 546697 rs12025928 A G -0.0003244 0.0015196 0.8300000 0.8309684 0.913488 NA NA
1 693731 rs12238997 A G 0.0001471 0.0010206 0.8900000 0.8853791 0.116345 0.1417730 NA
1 705882 rs72631875 G A -0.0008291 0.0014959 0.5800000 0.5794087 0.067264 0.0315495 NA
1 706368 rs55727773 A G -0.0000018 0.0007561 1.0000000 0.9980656 0.515587 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0008014 0.0009122 0.3800004 0.3796784 0.137944 0.2052720 NA
22 51219387 rs9616832 T C 0.0006408 0.0011841 0.5900000 0.5883664 0.073747 0.0654952 NA
22 51219704 rs147475742 G A -0.0001518 0.0015867 0.9199999 0.9238045 0.041966 0.0473243 NA
22 51221190 rs369304721 G A 0.0005364 0.0015842 0.7300002 0.7349291 0.049733 NA NA
22 51221731 rs115055839 T C 0.0005618 0.0011849 0.6400000 0.6353745 0.073237 0.0625000 NA
22 51222100 rs114553188 G T 0.0004668 0.0013951 0.7400005 0.7379258 0.054451 0.0880591 NA
22 51223637 rs375798137 G A 0.0004854 0.0014018 0.7300002 0.7291370 0.054079 0.0788738 NA
22 51229805 rs9616985 T C 0.0005563 0.0011892 0.6400000 0.6399020 0.073071 0.0730831 NA
22 51232488 rs376461333 A G -0.0022296 0.0028019 0.4299995 0.4261795 0.020038 NA NA
22 51237063 rs3896457 T C 0.0014460 0.0007273 0.0470002 0.0467717 0.298007 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623786 ES:SE:LP:AF:ID  0.00095617:0.00109309:0.420216:0.623786:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400412 ES:SE:LP:AF:ID  -0.000414109:0.00108288:0.154902:0.400412:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103546 ES:SE:LP:AF:ID  -0.000293566:0.00173145:0.0604807:0.103546:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456858 ES:SE:LP:AF:ID  0.000316063:0.00106619:0.113509:0.456858:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074506 ES:SE:LP:AF:ID  0.000755825:0.0021025:0.142668:0.074506:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240957 ES:SE:LP:AF:ID  0.000886491:0.00121806:0.327902:0.240957:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913488 ES:SE:LP:AF:ID  -0.000324382:0.00151964:0.0809219:0.913488:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116345 ES:SE:LP:AF:ID  0.000147129:0.00102064:0.05061:0.116345:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067264 ES:SE:LP:AF:ID  -0.000829095:0.00149589:0.236572:0.067264:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515587 ES:SE:LP:AF:ID  -1.83294e-06:0.000756053:-0:0.515587:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033022 ES:SE:LP:AF:ID  0.000889526:0.0019058:0.19382:0.033022:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03664  ES:SE:LP:AF:ID  0.000123479:0.00173105:0.0268721:0.03664:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036756 ES:SE:LP:AF:ID  4.82045e-05:0.00172455:0.00877392:0.036756:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036456 ES:SE:LP:AF:ID  0.00015786:0.00173693:0.0315171:0.036456:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016413 ES:SE:LP:AF:ID  0.00178352:0.00267446:0.30103:0.016413:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036995 ES:SE:LP:AF:ID  0.000335267:0.00171772:0.0705811:0.036995:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037092 ES:SE:LP:AF:ID  0.000338599:0.00171179:0.0757207:0.037092:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101204 ES:SE:LP:AF:ID  0.000788719:0.00124746:0.275724:0.101204:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959075 ES:SE:LP:AF:ID  0.000153213:0.00165101:0.0315171:0.959075:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031455 ES:SE:LP:AF:ID  -0.000605243:0.00299717:0.0757207:0.031455:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053262 ES:SE:LP:AF:ID  -0.000168916:0.0023841:0.0268721:0.053262:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036611 ES:SE:LP:AF:ID  0.000235638:0.00172287:0.05061:0.036611:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036927 ES:SE:LP:AF:ID  0.000170169:0.00170723:0.0362122:0.036927:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843178 ES:SE:LP:AF:ID  7.30028e-05:0.000884562:0.0315171:0.843178:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05594  ES:SE:LP:AF:ID  -0.00200365:0.00143193:0.79588:0.05594:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122326 ES:SE:LP:AF:ID  -3.38615e-05:0.000968197:0.0132283:0.122326:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025702 ES:SE:LP:AF:ID  -8.05693e-05:0.0023822:0.0132283:0.025702:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121569 ES:SE:LP:AF:ID  -4.82609e-05:0.000968587:0.0177288:0.121569:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13237  ES:SE:LP:AF:ID  -0.000163877:0.000954649:0.0655015:0.13237:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011135 ES:SE:LP:AF:ID  -0.000495242:0.00347153:0.05061:0.011135:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036842 ES:SE:LP:AF:ID  0.000130234:0.00168993:0.0268721:0.036842:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838924 ES:SE:LP:AF:ID  0.000421674:0.000856644:0.207608:0.838924:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838551 ES:SE:LP:AF:ID  0.000413994:0.000855722:0.200659:0.838551:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869771 ES:SE:LP:AF:ID  0.000390634:0.000918219:0.173925:0.869771:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129878 ES:SE:LP:AF:ID  -0.000329345:0.000920094:0.142668:0.129878:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03735  ES:SE:LP:AF:ID  5.10185e-05:0.00166137:0.00877392:0.03735:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037594 ES:SE:LP:AF:ID  9.19199e-05:0.00165088:0.0177288:0.037594:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  0.0004258:0.000916425:0.19382:0.869113:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869211 ES:SE:LP:AF:ID  0.00043015:0.000916789:0.19382:0.869211:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037553 ES:SE:LP:AF:ID  4.56139e-05:0.00165801:0.00877392:0.037553:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869116 ES:SE:LP:AF:ID  0.000406406:0.000916407:0.180456:0.869116:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838005 ES:SE:LP:AF:ID  0.000462919:0.000853356:0.229148:0.838005:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037566 ES:SE:LP:AF:ID  4.00296e-06:0.00166033:-0:0.037566:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838638 ES:SE:LP:AF:ID  0.000490942:0.000855763:0.244125:0.838638:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013773 ES:SE:LP:AF:ID  -0.0045436:0.00298722:0.886057:0.013773:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839752 ES:SE:LP:AF:ID  0.000556242:0.000867317:0.283997:0.839752:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869396 ES:SE:LP:AF:ID  0.00045447:0.000915343:0.207608:0.869396:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868943 ES:SE:LP:AF:ID  0.000418675:0.000913039:0.187087:0.868943:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867894 ES:SE:LP:AF:ID  0.000503456:0.000911291:0.236572:0.867894:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869085 ES:SE:LP:AF:ID  0.000467314:0.000913786:0.21467:0.869085:rs4951929