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_41226_2.vcf.gz --id UKB-b:338 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41226_2.txt.gz --cohort_cases 10605 --cohort_controls 2922 --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-338/UKB-b-338_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-338/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:24 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-338/UKB-b-338_data.vcf.gz ...
Read summary statistics for 6040450 SNPs.
Dropped 2794 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, 1217899 SNPs remain.
After merging with regression SNP LD, 1217899 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0399 (0.0319)
Lambda GC: 1.011
Mean Chi^2: 1.0155
Intercept: 1.0045 (0.0068)
Ratio: 0.2883 (0.4415)
Analysis finished at Thu Oct 17 14:44:36 2019
Total time elapsed: 1.0m:12.12s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9255,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0001,
    "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": 54742,
    "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": 1217899,
    "ldsc_nsnp_merge_regression_ld": 1217899,
    "ldsc_observed_scale_h2_beta": 0.0399,
    "ldsc_observed_scale_h2_se": 0.0319,
    "ldsc_intercept_beta": 1.0045,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.011,
    "ldsc_mean_chisq": 1.0155,
    "ldsc_ratio": 0.2903
}

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 6037675 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 6040450 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 8.670618e+00 5.762209e+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 7.858359e+07 5.652001e+07 828.0000000 3.199238e+07 6.902572e+07 1.145102e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -6.590000e-05 7.858700e-03 -0.0781989 -4.671900e-03 -1.190000e-05 4.628400e-03 6.679210e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.412200e-03 2.522100e-03 0.0048193 5.339200e-03 6.428800e-03 8.884100e-03 2.605200e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.982673e-01 2.895098e-01 0.0000007 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 4.982665e-01 2.894839e-01 0.0000007 2.470318e-01 4.981893e-01 7.489450e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.101766e-01 2.536433e-01 0.0330040 9.522300e-02 2.275040e-01 4.752660e-01 9.669960e-01 ▇▃▂▂▁
numeric AF_reference 54742 0.9909374 NA NA NA NA NA NA NA 3.068365e-01 2.467596e-01 0.0000000 1.042330e-01 2.352240e-01 4.658550e-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 0.0029967 0.0089642 0.7400005 0.7381562 0.626247 0.7821490 NA
1 54676 rs2462492 C T 0.0023016 0.0088258 0.7899998 0.7942602 0.398790 NA NA
1 86028 rs114608975 T C 0.0088151 0.0142074 0.5300002 0.5349575 0.102860 0.0277556 NA
1 91536 rs6702460 G T -0.0089323 0.0087101 0.3100002 0.3051245 0.458478 0.4207270 NA
1 234313 rs8179466 C T 0.0085832 0.0168105 0.6100002 0.6096391 0.075145 NA NA
1 534192 rs6680723 C T 0.0215896 0.0099369 0.0299999 0.0298057 0.240345 NA NA
1 546697 rs12025928 A G -0.0067390 0.0122871 0.5800000 0.5833762 0.913074 NA NA
1 693731 rs12238997 A G -0.0001651 0.0082983 0.9800000 0.9841234 0.116630 0.1417730 NA
1 705882 rs72631875 G A -0.0159015 0.0120170 0.1900002 0.1857521 0.067611 0.0315495 NA
1 706368 rs55727773 A G -0.0000340 0.0061331 1.0000000 0.9955833 0.516434 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0007789 0.0094736 0.9299999 0.9344733 0.074874 0.0826677 NA
22 51219006 rs28729663 G A -0.0057266 0.0073046 0.4299995 0.4330574 0.141031 0.2052720 NA
22 51219387 rs9616832 T C -0.0013000 0.0095114 0.8900000 0.8912900 0.074622 0.0654952 NA
22 51219704 rs147475742 G A 0.0088631 0.0126835 0.4799997 0.4846824 0.043336 0.0473243 NA
22 51221190 rs369304721 G A -0.0057285 0.0127688 0.6499995 0.6536984 0.049972 NA NA
22 51221731 rs115055839 T C -0.0018190 0.0095194 0.8499999 0.8484585 0.074159 0.0625000 NA
22 51222100 rs114553188 G T -0.0105438 0.0111126 0.3400001 0.3427148 0.056209 0.0880591 NA
22 51223637 rs375798137 G A -0.0100193 0.0111488 0.3700002 0.3688185 0.055974 0.0788738 NA
22 51229805 rs9616985 T C -0.0021724 0.0095615 0.8200001 0.8202667 0.073982 0.0730831 NA
22 51237063 rs3896457 T C 0.0026323 0.0059049 0.6600001 0.6557515 0.295583 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.626247 ES:SE:LP:AF:ID  0.00299668:0.00896415:0.130768:0.626247:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39879  ES:SE:LP:AF:ID  0.00230162:0.00882582:0.102373:0.39879:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10286  ES:SE:LP:AF:ID  0.00881507:0.0142074:0.275724:0.10286:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.458478 ES:SE:LP:AF:ID  -0.00893228:0.00871012:0.508638:0.458478:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.075145 ES:SE:LP:AF:ID  0.00858325:0.0168105:0.21467:0.075145:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240345 ES:SE:LP:AF:ID  0.0215896:0.00993693:1.52288:0.240345:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913074 ES:SE:LP:AF:ID  -0.00673898:0.0122871:0.236572:0.913074:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11663  ES:SE:LP:AF:ID  -0.000165133:0.00829829:0.00877392:0.11663:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067611 ES:SE:LP:AF:ID  -0.0159015:0.012017:0.721246:0.067611:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.516434 ES:SE:LP:AF:ID  -3.39504e-05:0.00613313:-0:0.516434:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03376  ES:SE:LP:AF:ID  -0.00196442:0.0153008:0.0457575:0.03376:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037247 ES:SE:LP:AF:ID  -0.00487623:0.0139651:0.136677:0.037247:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037421 ES:SE:LP:AF:ID  -0.0050309:0.013893:0.142668:0.037421:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03697  ES:SE:LP:AF:ID  -0.0064362:0.0140249:0.187087:0.03697:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.037466 ES:SE:LP:AF:ID  -0.00501176:0.0138846:0.142668:0.037466:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037646 ES:SE:LP:AF:ID  -0.00469798:0.0138226:0.136677:0.037646:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099146 ES:SE:LP:AF:ID  -0.0110551:0.010187:0.552842:0.099146:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957546 ES:SE:LP:AF:ID  0.00324786:0.0131734:0.091515:0.957546:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.052974 ES:SE:LP:AF:ID  -0.0103224:0.0196065:0.221849:0.052974:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036994 ES:SE:LP:AF:ID  -0.0056688:0.0139405:0.167491:0.036994:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037337 ES:SE:LP:AF:ID  -0.00519684:0.0138066:0.148742:0.037337:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842163 ES:SE:LP:AF:ID  0.000340913:0.00718754:0.0177288:0.842163:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055015 ES:SE:LP:AF:ID  0.00048135:0.0117551:0.0132283:0.055015:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122276 ES:SE:LP:AF:ID  0.000198198:0.00789414:0.00877392:0.122276:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121511 ES:SE:LP:AF:ID  0.000532439:0.00789399:0.0222764:0.121511:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132098 ES:SE:LP:AF:ID  0.00013681:0.00777275:0.00436481:0.132098:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.037465 ES:SE:LP:AF:ID  -0.00506912:0.0136354:0.148742:0.037465:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837925 ES:SE:LP:AF:ID  -0.000560441:0.00694902:0.0268721:0.837925:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83751  ES:SE:LP:AF:ID  -0.000193831:0.0069458:0.00877392:0.83751:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869116 ES:SE:LP:AF:ID  -0.00208508:0.00745183:0.107905:0.869116:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130441 ES:SE:LP:AF:ID  0.00125452:0.00747951:0.0604807:0.130441:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037993 ES:SE:LP:AF:ID  -0.00499551:0.0133894:0.148742:0.037993:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03822  ES:SE:LP:AF:ID  -0.00492968:0.0133112:0.148742:0.03822:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868332 ES:SE:LP:AF:ID  -0.00168199:0.00744075:0.0861861:0.868332:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868441 ES:SE:LP:AF:ID  -0.00146526:0.00744251:0.0757207:0.868441:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038123 ES:SE:LP:AF:ID  -0.00366534:0.0133791:0.107905:0.038123:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86836  ES:SE:LP:AF:ID  -0.00161929:0.00744069:0.0809219:0.86836:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837095 ES:SE:LP:AF:ID  -0.000239883:0.00692635:0.0132283:0.837095:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038054 ES:SE:LP:AF:ID  -0.00365671:0.0134171:0.102373:0.038054:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837784 ES:SE:LP:AF:ID  -0.000190175:0.00694765:0.00877392:0.837784:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.838988 ES:SE:LP:AF:ID  8.31809e-05:0.0070349:0.00436481:0.838988:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868717 ES:SE:LP:AF:ID  -0.00231643:0.00742562:0.119186:0.868717:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.8683   ES:SE:LP:AF:ID  -0.00250794:0.00740673:0.136677:0.8683:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866928 ES:SE:LP:AF:ID  -0.00171474:0.00739877:0.0861861:0.866928:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868373 ES:SE:LP:AF:ID  -0.00212126:0.00741325:0.113509:0.868373:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868396 ES:SE:LP:AF:ID  -0.00208693:0.00741347:0.107905:0.868396:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.868401 ES:SE:LP:AF:ID  -0.00208175:0.00741374:0.107905:0.868401:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.868897 ES:SE:LP:AF:ID  -0.00213298:0.00743375:0.113509:0.868897:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.038072 ES:SE:LP:AF:ID  -0.000583124:0.0133351:0.0132283:0.038072:rs114525117
1   760912  rs1048488   C   T   .   PASS    AF=0.837683 ES:SE:LP:AF:ID  -0.00174145:0.00690827:0.09691:0.837683:rs1048488