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_41246_1950.vcf.gz --id UKB-b:11856 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1950.txt.gz --cohort_cases 23749 --cohort_controls 421629 --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-11856/UKB-b-11856_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11856/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11856/UKB-b-11856_data.vcf.gz ...
Read summary statistics for 7178623 SNPs.
Dropped 4578 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, 1268824 SNPs remain.
After merging with regression SNP LD, 1268824 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0063 (0.0011)
Lambda GC: 1.0845
Mean Chi^2: 1.0845
Intercept: 1.0293 (0.0068)
Ratio: 0.3463 (0.0799)
Analysis finished at Thu Oct 17 14:42:33 2019
Total time elapsed: 1.0m:23.56s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9373,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -1.2392e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 99,
    "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": 66200,
    "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": 1268824,
    "ldsc_nsnp_merge_regression_ld": 1268824,
    "ldsc_observed_scale_h2_beta": 0.0063,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0293,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.0845,
    "ldsc_mean_chisq": 1.0845,
    "ldsc_ratio": 0.3467
}

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 7174067 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 7178623 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 8.662987e+00 5.763380e+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 7.863770e+07 5.645962e+07 828.0000000 3.215301e+07 6.906094e+07 1.145238e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.200000e-06 9.789000e-04 -0.0087617 -5.153000e-04 -1.600000e-06 5.118000e-04 8.893600e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.571000e-04 4.203000e-04 0.0004595 5.211000e-04 6.793000e-04 1.085700e-03 4.987900e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.885175e-01 2.922219e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.885192e-01 2.921984e-01 0.0000000 2.320848e-01 4.841617e-01 7.424593e-01 9.999993e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.700836e-01 2.602768e-01 0.0147380 5.465700e-02 1.713200e-01 4.247245e-01 9.852610e-01 ▇▃▂▁▁
numeric AF_reference 66200 0.9907782 NA NA NA NA NA NA NA 2.685821e-01 2.522013e-01 0.0000000 6.150160e-02 1.839060e-01 4.177320e-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 -0.0005200 0.0008453 0.5400003 0.5384261 0.623754 0.7821490 NA
1 54676 rs2462492 C T 0.0008343 0.0008376 0.3200000 0.3192400 0.400424 NA NA
1 86028 rs114608975 T C -0.0005028 0.0013391 0.7099994 0.7073199 0.103539 0.0277556 NA
1 91536 rs6702460 G T 0.0001678 0.0008245 0.8400000 0.8387542 0.456851 0.4207270 NA
1 234313 rs8179466 C T -0.0017919 0.0016272 0.2700001 0.2708071 0.074434 NA NA
1 534192 rs6680723 C T -0.0000961 0.0009422 0.9199999 0.9187689 0.240924 NA NA
1 546697 rs12025928 A G -0.0000845 0.0011752 0.9400001 0.9426899 0.913479 NA NA
1 693731 rs12238997 A G -0.0010136 0.0007895 0.2000000 0.1992312 0.116385 0.1417730 NA
1 705882 rs72631875 G A 0.0010603 0.0011569 0.3599996 0.3593913 0.067336 0.0315495 NA
1 706368 rs55727773 A G 0.0010363 0.0005849 0.0759994 0.0764426 0.515677 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0000449 0.0007054 0.9500000 0.9492997 0.137998 0.2052720 NA
22 51219387 rs9616832 T C -0.0009545 0.0009154 0.2999998 0.2970563 0.073782 0.0654952 NA
22 51219704 rs147475742 G A -0.0002013 0.0012265 0.8700001 0.8696406 0.041989 0.0473243 NA
22 51221190 rs369304721 G A -0.0007159 0.0012243 0.5600000 0.5587249 0.049775 NA NA
22 51221731 rs115055839 T C -0.0009558 0.0009159 0.2999998 0.2967086 0.073278 0.0625000 NA
22 51222100 rs114553188 G T 0.0008857 0.0010787 0.4100001 0.4115837 0.054499 0.0880591 NA
22 51223637 rs375798137 G A 0.0008904 0.0010839 0.4100001 0.4114165 0.054126 0.0788738 NA
22 51229805 rs9616985 T C -0.0009420 0.0009193 0.3100002 0.3054946 0.073115 0.0730831 NA
22 51232488 rs376461333 A G 0.0012915 0.0021660 0.5500004 0.5510013 0.020056 NA NA
22 51237063 rs3896457 T C 0.0003434 0.0005624 0.5400003 0.5415118 0.298147 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623754 ES:SE:LP:AF:ID  -0.000520034:0.000845316:0.267606:0.623754:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400424 ES:SE:LP:AF:ID  0.000834299:0.000837632:0.49485:0.400424:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103539 ES:SE:LP:AF:ID  -0.000502787:0.00133913:0.148742:0.103539:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  0.000167785:0.000824548:0.0757207:0.456851:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074434 ES:SE:LP:AF:ID  -0.00179186:0.00162718:0.568636:0.074434:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240924 ES:SE:LP:AF:ID  -9.60915e-05:0.000942216:0.0362122:0.240924:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913479 ES:SE:LP:AF:ID  -8.44881e-05:0.00117525:0.0268721:0.913479:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116385 ES:SE:LP:AF:ID  -0.00101355:0.000789526:0.69897:0.116385:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067336 ES:SE:LP:AF:ID  0.00106031:0.00115688:0.443698:0.067336:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515677 ES:SE:LP:AF:ID  0.00103627:0.000584898:1.11919:0.515677:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033048 ES:SE:LP:AF:ID  -0.00275519:0.00147365:1.20761:0.033048:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036667 ES:SE:LP:AF:ID  -0.00208047:0.00133863:0.920819:0.036667:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036783 ES:SE:LP:AF:ID  -0.00215778:0.00133358:0.958607:0.036783:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036478 ES:SE:LP:AF:ID  -0.00205809:0.00134328:0.886057:0.036478:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016443 ES:SE:LP:AF:ID  0.00380189:0.00206756:1.18046:0.016443:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037017 ES:SE:LP:AF:ID  -0.00215404:0.00132843:1:0.037017:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037118 ES:SE:LP:AF:ID  -0.00210188:0.00132373:0.958607:0.037118:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101243 ES:SE:LP:AF:ID  -0.00114488:0.000964878:0.619789:0.101243:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959064 ES:SE:LP:AF:ID  0.00184366:0.001277:0.823909:0.959064:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031454 ES:SE:LP:AF:ID  0.000647786:0.00231783:0.107905:0.031454:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053304 ES:SE:LP:AF:ID  -0.00166713:0.00184226:0.431798:0.053304:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036626 ES:SE:LP:AF:ID  -0.00214068:0.00133251:0.958607:0.036626:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036945 ES:SE:LP:AF:ID  -0.00211782:0.0013203:0.958607:0.036945:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843106 ES:SE:LP:AF:ID  0.00137457:0.000684177:1.34679:0.843106:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055932 ES:SE:LP:AF:ID  -0.00133699:0.00110771:0.638272:0.055932:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122376 ES:SE:LP:AF:ID  -0.00102453:0.000748865:0.769551:0.122376:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025683 ES:SE:LP:AF:ID  0.00436157:0.00184387:1.74473:0.025683:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121618 ES:SE:LP:AF:ID  -0.00105868:0.000749171:0.79588:0.121618:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132426 ES:SE:LP:AF:ID  -0.00204803:0.000738286:2.25964:0.132426:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036856 ES:SE:LP:AF:ID  -0.00214821:0.00130705:1:0.036856:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.8388   ES:SE:LP:AF:ID  0.00153941:0.000662448:1.69897:0.8388:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838423 ES:SE:LP:AF:ID  0.00151883:0.000661711:1.65758:0.838423:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869671 ES:SE:LP:AF:ID  0.00113813:0.000710083:0.958607:0.869671:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.12999  ES:SE:LP:AF:ID  -0.00108622:0.000711508:0.886057:0.12999:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037373 ES:SE:LP:AF:ID  -0.00211516:0.00128475:1:0.037373:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037618 ES:SE:LP:AF:ID  -0.00203646:0.00127662:0.958607:0.037618:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86901  ES:SE:LP:AF:ID  0.0011277:0.000708674:0.958607:0.86901:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869111 ES:SE:LP:AF:ID  0.00112529:0.000708962:0.958607:0.869111:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037578 ES:SE:LP:AF:ID  -0.00194171:0.00128212:0.886057:0.037578:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869014 ES:SE:LP:AF:ID  0.00111683:0.000708662:0.920819:0.869014:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837872 ES:SE:LP:AF:ID  0.00157157:0.000659874:1.76955:0.837872:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037593 ES:SE:LP:AF:ID  -0.00194376:0.00128389:0.886057:0.037593:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838503 ES:SE:LP:AF:ID  0.00154149:0.000661726:1.69897:0.838503:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839621 ES:SE:LP:AF:ID  0.00155362:0.000670711:1.67778:0.839621:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869299 ES:SE:LP:AF:ID  0.00117631:0.000707875:1.01323:0.869299:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868849 ES:SE:LP:AF:ID  0.00111567:0.000706116:0.958607:0.868849:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867796 ES:SE:LP:AF:ID  0.00117545:0.000704729:1.02228:0.867796:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86899  ES:SE:LP:AF:ID  0.0011574:0.000706684:1:0.86899:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868998 ES:SE:LP:AF:ID  0.00115761:0.000706738:1:0.868998:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869006 ES:SE:LP:AF:ID  0.0011562:0.000706755:1:0.869006:rs3131956