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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_20121.vcf.gz --id UKB-b:10768 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20121.txt.gz --cohort_controls 13634 --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-10768/UKB-b-10768_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10768/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-10768/UKB-b-10768_data.vcf.gz ...
Read summary statistics for 6389927 SNPs.
Dropped 3344 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, 1237763 SNPs remain.
After merging with regression SNP LD, 1237763 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0101 (0.0336)
Lambda GC: 0.997
Mean Chi^2: 1.0041
Intercept: 1.0069 (0.0064)
Ratio: 1.6752 (1.5595)
Analysis finished at Thu Oct 17 14:41:27 2019
Total time elapsed: 1.0m:8.54s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9295,
    "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": 58547,
    "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": 1237763,
    "ldsc_nsnp_merge_regression_ld": 1237763,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0069,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 0.997,
    "ldsc_mean_chisq": 1.0041,
    "ldsc_ratio": 1.6829
}

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 6386604 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 6389927 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 8.666376e+00 5.763888e+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 7.858224e+07 5.650567e+07 828.0000000 3.200881e+07 6.901432e+07 1.145115e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.223000e-04 2.042210e-02 -0.1574560 -1.170270e-02 -6.630000e-05 1.153820e-02 1.626740e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.902980e-02 7.325400e-03 0.0116595 1.306380e-02 1.608430e-02 2.321090e-02 1.059590e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 5.003968e-01 2.892990e-01 0.0000004 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 5.003983e-01 2.892751e-01 0.0000004 2.493209e-01 5.006986e-01 7.516470e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.970892e-01 2.566702e-01 0.0256720 8.080100e-02 2.093500e-01 4.596990e-01 9.743280e-01 ▇▃▂▂▁
numeric AF_reference 58547 0.9908376 NA NA NA NA NA NA NA 2.943935e-01 2.491993e-01 0.0000000 9.005590e-02 2.184500e-01 4.512780e-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 0.0069415 0.0214865 0.7499995 0.7466473 0.621466 0.7821490 NA
1 54676 rs2462492 C T 0.0335658 0.0213481 0.1199999 0.1158790 0.401363 NA NA
1 86028 rs114608975 T C 0.0047000 0.0335314 0.8900000 0.8885284 0.104723 0.0277556 NA
1 91536 rs6702460 G T -0.0095217 0.0209249 0.6499995 0.6490803 0.455410 0.4207270 NA
1 234313 rs8179466 C T -0.1088050 0.0423466 0.0100000 0.0101877 0.073480 NA NA
1 534192 rs6680723 C T 0.0145389 0.0240685 0.5500004 0.5458015 0.241161 NA NA
1 546697 rs12025928 A G -0.0031567 0.0309918 0.9199999 0.9188710 0.916466 NA NA
1 693731 rs12238997 A G -0.0292350 0.0203506 0.1499999 0.1508411 0.114878 0.1417730 NA
1 705882 rs72631875 G A 0.0120554 0.0301331 0.6899999 0.6891037 0.065901 0.0315495 NA
1 706368 rs55727773 A G 0.0142277 0.0148977 0.3400001 0.3395642 0.517689 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0380274 0.0232032 0.1000000 0.1012370 0.075375 0.0826677 NA
22 51219006 rs28729663 G A 0.0195743 0.0178975 0.2700001 0.2740914 0.140144 0.2052720 NA
22 51219387 rs9616832 T C 0.0370769 0.0232490 0.1100001 0.1107628 0.075533 0.0654952 NA
22 51219704 rs147475742 G A 0.0138735 0.0313916 0.6600001 0.6585258 0.042293 0.0473243 NA
22 51221190 rs369304721 G A 0.0249079 0.0310477 0.4199997 0.4224105 0.051132 NA NA
22 51221731 rs115055839 T C 0.0393741 0.0232543 0.0899995 0.0904181 0.075063 0.0625000 NA
22 51222100 rs114553188 G T -0.0082341 0.0273968 0.7600007 0.7637579 0.054781 0.0880591 NA
22 51223637 rs375798137 G A -0.0086545 0.0275205 0.7499995 0.7531608 0.054396 0.0788738 NA
22 51229805 rs9616985 T C 0.0347318 0.0233505 0.1400000 0.1369062 0.074794 0.0730831 NA
22 51237063 rs3896457 T C -0.0039873 0.0143848 0.7800007 0.7816376 0.298962 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.621466 ES:SE:LP:AF:ID  0.0069415:0.0214865:0.124939:0.621466:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401363 ES:SE:LP:AF:ID  0.0335658:0.0213481:0.920819:0.401363:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104723 ES:SE:LP:AF:ID  0.00469998:0.0335314:0.05061:0.104723:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45541  ES:SE:LP:AF:ID  -0.00952167:0.0209249:0.187087:0.45541:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07348  ES:SE:LP:AF:ID  -0.108805:0.0423466:2:0.07348:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241161 ES:SE:LP:AF:ID  0.0145389:0.0240685:0.259637:0.241161:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.916466 ES:SE:LP:AF:ID  -0.0031567:0.0309918:0.0362122:0.916466:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.114878 ES:SE:LP:AF:ID  -0.029235:0.0203506:0.823909:0.114878:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.065901 ES:SE:LP:AF:ID  0.0120554:0.0301331:0.161151:0.065901:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.517689 ES:SE:LP:AF:ID  0.0142277:0.0148977:0.468521:0.517689:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.031801 ES:SE:LP:AF:ID  0.0157486:0.0383619:0.167491:0.031801:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035498 ES:SE:LP:AF:ID  0.000590258:0.0347876:0.00436481:0.035498:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035692 ES:SE:LP:AF:ID  -0.000179693:0.0345995:-0:0.035692:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035329 ES:SE:LP:AF:ID  0.000448995:0.0348933:0.00436481:0.035329:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.035865 ES:SE:LP:AF:ID  3.5428e-05:0.0344926:-0:0.035865:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03606  ES:SE:LP:AF:ID  -0.00417644:0.034326:0.0457575:0.03606:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100321 ES:SE:LP:AF:ID  0.00113613:0.0247558:0.0177288:0.100321:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959773 ES:SE:LP:AF:ID  0.0216345:0.032843:0.29243:0.959773:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031412 ES:SE:LP:AF:ID  0.0918164:0.0590187:0.920819:0.031412:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053064 ES:SE:LP:AF:ID  -0.0197378:0.0469675:0.173925:0.053064:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.035689 ES:SE:LP:AF:ID  -0.00483814:0.0345274:0.05061:0.035689:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.035824 ES:SE:LP:AF:ID  -0.00398796:0.0342785:0.0409586:0.035824:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.845469 ES:SE:LP:AF:ID  0.0335556:0.0176736:1.23657:0.845469:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.054879 ES:SE:LP:AF:ID  -0.072924:0.0285982:1.95861:0.054879:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.1207   ES:SE:LP:AF:ID  -0.034523:0.019321:1.13077:0.1207:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.026762 ES:SE:LP:AF:ID  0.00017093:0.0458247:-0:0.026762:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.119967 ES:SE:LP:AF:ID  -0.0338078:0.0193185:1.09691:0.119967:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.130271 ES:SE:LP:AF:ID  -0.0162757:0.0190656:0.408935:0.130271:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.035719 ES:SE:LP:AF:ID  -0.00736195:0.0338797:0.0809219:0.035719:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.841474 ES:SE:LP:AF:ID  0.0285435:0.0171537:1.01773:0.841474:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.841132 ES:SE:LP:AF:ID  0.0280487:0.0171335:1:0.841132:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.871175 ES:SE:LP:AF:ID  0.0289451:0.0183662:0.920819:0.871175:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.128104 ES:SE:LP:AF:ID  -0.0250354:0.0184115:0.769551:0.128104:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036297 ES:SE:LP:AF:ID  -0.00339481:0.0332571:0.0362122:0.036297:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036563 ES:SE:LP:AF:ID  -0.000653269:0.0330096:0.00877392:0.036563:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870561 ES:SE:LP:AF:ID  0.027654:0.0183363:0.886057:0.870561:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870671 ES:SE:LP:AF:ID  0.0275911:0.0183424:0.886057:0.870671:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036686 ES:SE:LP:AF:ID  0.00250094:0.0331057:0.0268721:0.036686:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.870552 ES:SE:LP:AF:ID  0.0275738:0.0183356:0.886057:0.870552:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.840236 ES:SE:LP:AF:ID  0.026486:0.0170658:0.920819:0.840236:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036719 ES:SE:LP:AF:ID  0.00391057:0.0331488:0.0409586:0.036719:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.840793 ES:SE:LP:AF:ID  0.0265667:0.0171162:0.920819:0.840793:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.841537 ES:SE:LP:AF:ID  0.0272915:0.0173375:0.920819:0.841537:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.870794 ES:SE:LP:AF:ID  0.0272072:0.0183002:0.853872:0.870794:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.870251 ES:SE:LP:AF:ID  0.0256382:0.0182527:0.79588:0.870251:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.86944  ES:SE:LP:AF:ID  0.0255383:0.0182287:0.79588:0.86944:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870489 ES:SE:LP:AF:ID  0.025915:0.0182699:0.79588:0.870489:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870493 ES:SE:LP:AF:ID  0.0259439:0.0182715:0.79588:0.870493:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870494 ES:SE:LP:AF:ID  0.0258951:0.0182712:0.79588:0.870494:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870916 ES:SE:LP:AF:ID  0.0270702:0.0183187:0.853872:0.870916:rs3131954