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_103120.vcf.gz --id UKB-b:15508 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_103120.txt.gz --cohort_cases 15643 --cohort_controls 1259 --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-15508/UKB-b-15508_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15508/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15508/UKB-b-15508_data.vcf.gz ...
Read summary statistics for 6584135 SNPs.
Dropped 3675 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, 1247504 SNPs remain.
After merging with regression SNP LD, 1247504 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.022 (0.0256)
Lambda GC: 1.0131
Mean Chi^2: 1.0109
Intercept: 1.0036 (0.0063)
Ratio: 0.3248 (0.5784)
Analysis finished at Thu Oct 17 14:45:26 2019
Total time elapsed: 1.0m:17.19s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9317,
    "inflation_factor": 1,
    "mean_EFFECT": 7.7691e-06,
    "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": 60460,
    "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": 1247504,
    "ldsc_nsnp_merge_regression_ld": 1247504,
    "ldsc_observed_scale_h2_beta": 0.022,
    "ldsc_observed_scale_h2_se": 0.0256,
    "ldsc_intercept_beta": 1.0036,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0131,
    "ldsc_mean_chisq": 1.0109,
    "ldsc_ratio": 0.3303
}

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 6580481 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 6584135 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 8.665031e+00 5.763305e+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 7.858478e+07 5.647416e+07 828.0000000 3.205841e+07 6.903217e+07 1.144804e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.800000e-06 4.998300e-03 -0.0433992 -2.774900e-03 3.220000e-05 2.821500e-03 4.001660e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.601100e-03 1.888200e-03 0.0027423 3.070700e-03 3.830500e-03 5.669700e-03 2.310660e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.980970e-01 2.891025e-01 0.0000002 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 4.980963e-01 2.890752e-01 0.0000002 2.478008e-01 4.970487e-01 7.485010e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.901019e-01 2.578933e-01 0.0223750 7.351200e-02 1.995750e-01 4.510030e-01 9.776250e-01 ▇▃▂▂▁
numeric AF_reference 60460 0.9908173 NA NA NA NA NA NA NA 2.877602e-01 2.502007e-01 0.0000000 8.266770e-02 2.096650e-01 4.430910e-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 -0.0057423 0.0050610 0.2599998 0.2565418 0.625690 0.7821490 NA
1 54676 rs2462492 C T 0.0018881 0.0050859 0.7099994 0.7104521 0.397947 NA NA
1 86028 rs114608975 T C 0.0011969 0.0080887 0.8800001 0.8823616 0.103020 0.0277556 NA
1 91536 rs6702460 G T -0.0047928 0.0049778 0.3400001 0.3356333 0.457175 0.4207270 NA
1 234313 rs8179466 C T 0.0062477 0.0098227 0.5199996 0.5247479 0.073983 NA NA
1 534192 rs6680723 C T -0.0144364 0.0056202 0.0100000 0.0102086 0.244252 NA NA
1 546697 rs12025928 A G -0.0046282 0.0070523 0.5099998 0.5116586 0.912571 NA NA
1 693731 rs12238997 A G -0.0001913 0.0046778 0.9699999 0.9673760 0.118464 0.1417730 NA
1 705882 rs72631875 G A -0.0048027 0.0068616 0.4799997 0.4839676 0.068153 0.0315495 NA
1 706368 rs55727773 A G -0.0034472 0.0035026 0.3300000 0.3250226 0.514327 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0102160 0.0055401 0.0649995 0.0651826 0.072073 0.0826677 NA
22 51219006 rs28729663 G A -0.0044075 0.0042914 0.2999998 0.3043904 0.134668 0.2052720 NA
22 51219387 rs9616832 T C -0.0102201 0.0055582 0.0659994 0.0659550 0.071901 0.0654952 NA
22 51219704 rs147475742 G A -0.0169684 0.0073833 0.0219999 0.0215489 0.041139 0.0473243 NA
22 51221190 rs369304721 G A -0.0129074 0.0074644 0.0840001 0.0837726 0.048120 NA NA
22 51221731 rs115055839 T C -0.0104411 0.0055617 0.0599998 0.0604726 0.071394 0.0625000 NA
22 51222100 rs114553188 G T -0.0005857 0.0065241 0.9299999 0.9284634 0.053700 0.0880591 NA
22 51223637 rs375798137 G A -0.0005112 0.0065595 0.9400001 0.9378779 0.053307 0.0788738 NA
22 51229805 rs9616985 T C -0.0103693 0.0055716 0.0629999 0.0627278 0.071418 0.0730831 NA
22 51237063 rs3896457 T C 0.0024002 0.0033532 0.4700002 0.4741305 0.298352 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62569  ES:SE:LP:AF:ID  -0.00574227:0.00506104:0.585027:0.62569:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.397947 ES:SE:LP:AF:ID  0.00188812:0.00508586:0.148742:0.397947:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10302  ES:SE:LP:AF:ID  0.00119693:0.00808867:0.0555173:0.10302:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457175 ES:SE:LP:AF:ID  -0.00479278:0.00497781:0.468521:0.457175:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073983 ES:SE:LP:AF:ID  0.00624766:0.00982269:0.283997:0.073983:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.244252 ES:SE:LP:AF:ID  -0.0144364:0.00562016:2:0.244252:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912571 ES:SE:LP:AF:ID  -0.00462815:0.00705234:0.29243:0.912571:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.118464 ES:SE:LP:AF:ID  -0.000191318:0.00467776:0.0132283:0.118464:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068153 ES:SE:LP:AF:ID  -0.00480267:0.00686159:0.318759:0.068153:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514327 ES:SE:LP:AF:ID  -0.00344724:0.00350262:0.481486:0.514327:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033147 ES:SE:LP:AF:ID  -0.00544184:0.00878338:0.267606:0.033147:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036884 ES:SE:LP:AF:ID  -0.0031596:0.00797393:0.161151:0.036884:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037009 ES:SE:LP:AF:ID  -0.00313806:0.00793441:0.161151:0.037009:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036677 ES:SE:LP:AF:ID  -0.00267193:0.00799664:0.130768:0.036677:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.037252 ES:SE:LP:AF:ID  -0.00305337:0.00790734:0.154902:0.037252:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03741  ES:SE:LP:AF:ID  -0.00298354:0.00787029:0.154902:0.03741:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102694 ES:SE:LP:AF:ID  -0.00141076:0.00569787:0.09691:0.102694:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95914  ES:SE:LP:AF:ID  0.00197219:0.00763569:0.09691:0.95914:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031343 ES:SE:LP:AF:ID  0.0192293:0.0139801:0.769551:0.031343:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052791 ES:SE:LP:AF:ID  -0.00562187:0.0111028:0.21467:0.052791:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036886 ES:SE:LP:AF:ID  -0.00260202:0.00792791:0.130768:0.036886:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037076 ES:SE:LP:AF:ID  -0.00293518:0.00785982:0.148742:0.037076:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84129  ES:SE:LP:AF:ID  0.000291125:0.00406391:0.0268721:0.84129:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056196 ES:SE:LP:AF:ID  -0.000604071:0.00661944:0.0315171:0.056196:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.124333 ES:SE:LP:AF:ID  0.000460593:0.00444255:0.0362122:0.124333:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025148 ES:SE:LP:AF:ID  0.00680173:0.0111266:0.267606:0.025148:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.123474 ES:SE:LP:AF:ID  0.000842964:0.00444749:0.0705811:0.123474:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134215 ES:SE:LP:AF:ID  -0.0018251:0.0043882:0.167491:0.134215:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036972 ES:SE:LP:AF:ID  -0.0029519:0.00780443:0.148742:0.036972:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837137 ES:SE:LP:AF:ID  0.000858899:0.00392364:0.0809219:0.837137:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836765 ES:SE:LP:AF:ID  0.000731575:0.00391921:0.0705811:0.836765:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868033 ES:SE:LP:AF:ID  -0.000898991:0.00420198:0.0809219:0.868033:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131826 ES:SE:LP:AF:ID  0.00144993:0.00420717:0.136677:0.131826:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037433 ES:SE:LP:AF:ID  -0.00312455:0.00766811:0.167491:0.037433:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037658 ES:SE:LP:AF:ID  -0.0033908:0.00762073:0.180456:0.037658:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86735  ES:SE:LP:AF:ID  -0.00114762:0.0041925:0.107905:0.86735:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867409 ES:SE:LP:AF:ID  -0.00109586:0.00419419:0.102373:0.867409:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037609 ES:SE:LP:AF:ID  -0.00271332:0.00765416:0.142668:0.037609:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867379 ES:SE:LP:AF:ID  -0.00113398:0.00419256:0.102373:0.867379:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836275 ES:SE:LP:AF:ID  0.000496018:0.00391122:0.0457575:0.836275:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037616 ES:SE:LP:AF:ID  -0.00262446:0.00766641:0.136677:0.037616:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836866 ES:SE:LP:AF:ID  0.000466538:0.00392112:0.0409586:0.836866:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.838169 ES:SE:LP:AF:ID  0.00060202:0.00397857:0.0555173:0.838169:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.867797 ES:SE:LP:AF:ID  -0.000717276:0.00419042:0.0655015:0.867797:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867412 ES:SE:LP:AF:ID  -0.00077729:0.0041816:0.0705811:0.867412:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866173 ES:SE:LP:AF:ID  -0.000829699:0.00416977:0.0757207:0.866173:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867555 ES:SE:LP:AF:ID  -0.000717053:0.00418473:0.0655015:0.867555:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.867566 ES:SE:LP:AF:ID  -0.000718111:0.00418505:0.0655015:0.867566:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.867577 ES:SE:LP:AF:ID  -0.000712157:0.00418526:0.0655015:0.867577:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.868031 ES:SE:LP:AF:ID  -0.000614939:0.00419532:0.0555173:0.868031:rs3131954