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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}

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-9127/UKB-b-9127_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9127/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9127/UKB-b-9127_data.vcf.gz ...
Read summary statistics for 8127821 SNPs.
Dropped 6355 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, 1283353 SNPs remain.
After merging with regression SNP LD, 1283353 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0149 (0.0016)
Lambda GC: 1.187
Mean Chi^2: 1.1993
Intercept: 1.0827 (0.0073)
Ratio: 0.4148 (0.0366)
Analysis finished at Thu Oct 17 14:43:44 2019
Total time elapsed: 1.0m:27.03s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9436,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 1.6334e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 4,
    "n_p_sig": 217,
    "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": 76061,
    "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": 1283353,
    "ldsc_nsnp_merge_regression_ld": 1283353,
    "ldsc_observed_scale_h2_beta": 0.0149,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.0827,
    "ldsc_intercept_se": 0.0073,
    "ldsc_lambda_gc": 1.187,
    "ldsc_mean_chisq": 1.1993,
    "ldsc_ratio": 0.415
}

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 8121495 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 8127821 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 8.659123e+00 5.763242e+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 7.871230e+07 5.639743e+07 828.0000000 3.229088e+07 6.919111e+07 1.145325e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.600000e-06 1.917800e-03 -0.0183591 -8.861000e-04 6.500000e-06 8.889000e-04 2.032510e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.548400e-03 9.581000e-04 0.0006848 7.962000e-04 1.121500e-03 2.048200e-03 8.448200e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.760674e-01 2.949321e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.760678e-01 2.949064e-01 0.0000000 2.145246e-01 4.675698e-01 7.318526e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.420163e-01 2.605692e-01 0.0075660 3.305300e-02 1.316000e-01 3.835160e-01 9.924340e-01 ▇▂▂▁▁
numeric AF_reference 76061 0.9906419 NA NA NA NA NA NA NA 2.413766e-01 2.524218e-01 0.0000000 3.394570e-02 1.473640e-01 3.789940e-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 -0.0005988 0.0012608 0.6300007 0.6348442 0.623704 0.7821490 NA
1 54676 rs2462492 C T 0.0001012 0.0012487 0.9400001 0.9354225 0.400430 NA NA
1 86028 rs114608975 T C -0.0017450 0.0019969 0.3800004 0.3821880 0.103501 0.0277556 NA
1 91536 rs6702460 G T -0.0015107 0.0012306 0.2200002 0.2195873 0.456971 0.4207270 NA
1 234313 rs8179466 C T 0.0038396 0.0024277 0.1100001 0.1137435 0.074444 NA NA
1 534192 rs6680723 C T 0.0001994 0.0014049 0.8900000 0.8871174 0.241023 NA NA
1 546697 rs12025928 A G 0.0011728 0.0017520 0.5000000 0.5032278 0.913453 NA NA
1 693731 rs12238997 A G 0.0006642 0.0011765 0.5700002 0.5723638 0.116292 0.1417730 NA
1 705882 rs72631875 G A -0.0017835 0.0017236 0.2999998 0.3008064 0.067396 0.0315495 NA
1 706368 rs55727773 A G 0.0001986 0.0008719 0.8200001 0.8197862 0.515769 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0005364 0.0010528 0.6100002 0.6103881 0.137831 0.2052720 NA
22 51219387 rs9616832 T C -0.0015378 0.0013663 0.2599998 0.2603670 0.073734 0.0654952 NA
22 51219704 rs147475742 G A -0.0028132 0.0018320 0.1199999 0.1246403 0.041908 0.0473243 NA
22 51221190 rs369304721 G A -0.0014666 0.0018274 0.4199997 0.4222387 0.049691 NA NA
22 51221731 rs115055839 T C -0.0016267 0.0013670 0.2300001 0.2340687 0.073220 0.0625000 NA
22 51222100 rs114553188 G T 0.0011221 0.0016102 0.4899999 0.4858737 0.054362 0.0880591 NA
22 51223637 rs375798137 G A 0.0010991 0.0016179 0.5000000 0.4969346 0.053999 0.0788738 NA
22 51229805 rs9616985 T C -0.0015693 0.0013719 0.2500000 0.2526960 0.073052 0.0730831 NA
22 51232488 rs376461333 A G 0.0045104 0.0032362 0.1600000 0.1634046 0.019977 NA NA
22 51237063 rs3896457 T C -0.0002277 0.0008387 0.7899998 0.7860539 0.297958 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623704 ES:SE:LP:AF:ID  -0.000598755:0.00126075:0.200659:0.623704:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.40043  ES:SE:LP:AF:ID  0.000101172:0.00124866:0.0268721:0.40043:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103501 ES:SE:LP:AF:ID  -0.001745:0.00199686:0.420216:0.103501:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456971 ES:SE:LP:AF:ID  -0.00151068:0.00123057:0.657577:0.456971:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074444 ES:SE:LP:AF:ID  0.00383956:0.00242766:0.958607:0.074444:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241023 ES:SE:LP:AF:ID  0.000199435:0.00140494:0.05061:0.241023:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913453 ES:SE:LP:AF:ID  0.00117285:0.00175204:0.30103:0.913453:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116292 ES:SE:LP:AF:ID  0.000664204:0.00117647:0.244125:0.116292:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067396 ES:SE:LP:AF:ID  -0.00178346:0.00172364:0.522879:0.067396:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515769 ES:SE:LP:AF:ID  0.000198628:0.000871864:0.0861861:0.515769:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033064 ES:SE:LP:AF:ID  0.00079371:0.00219621:0.142668:0.033064:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036692 ES:SE:LP:AF:ID  0.000694949:0.00199472:0.136677:0.036692:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036809 ES:SE:LP:AF:ID  0.000730466:0.00198718:0.148742:0.036809:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036504 ES:SE:LP:AF:ID  0.000567047:0.00200163:0.107905:0.036504:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016429 ES:SE:LP:AF:ID  -0.00197757:0.00308182:0.283997:0.016429:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037047 ES:SE:LP:AF:ID  0.000667955:0.00197939:0.130768:0.037047:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037143 ES:SE:LP:AF:ID  0.000731518:0.00197264:0.148742:0.037143:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101342 ES:SE:LP:AF:ID  0.0012362:0.0014369:0.408935:0.101342:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959108 ES:SE:LP:AF:ID  -0.000773259:0.00190472:0.167491:0.959108:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031515 ES:SE:LP:AF:ID  -0.000617685:0.00344721:0.0655015:0.031515:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053259 ES:SE:LP:AF:ID  -0.00424265:0.0027492:0.920819:0.053259:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036653 ES:SE:LP:AF:ID  0.000788448:0.0019854:0.161151:0.036653:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036977 ES:SE:LP:AF:ID  0.000844539:0.00196721:0.173925:0.036977:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843261 ES:SE:LP:AF:ID  -0.000296043:0.00101964:0.113509:0.843261:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055886 ES:SE:LP:AF:ID  -0.00151422:0.00165137:0.443698:0.055886:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122287 ES:SE:LP:AF:ID  0.000276266:0.00111579:0.09691:0.122287:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025702 ES:SE:LP:AF:ID  0.000139733:0.00274648:0.0177288:0.025702:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121523 ES:SE:LP:AF:ID  0.000265566:0.00111628:0.091515:0.121523:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132384 ES:SE:LP:AF:ID  0.000430112:0.0011002:0.154902:0.132384:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011046 ES:SE:LP:AF:ID  -0.00391942:0.00402304:0.481486:0.011046:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036898 ES:SE:LP:AF:ID  0.00114408:0.0019471:0.251812:0.036898:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838914 ES:SE:LP:AF:ID  4.39896e-05:0.000987344:0.0177288:0.838914:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838565 ES:SE:LP:AF:ID  1.66409e-05:0.000986326:0.00436481:0.838565:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869813 ES:SE:LP:AF:ID  -0.000132232:0.00105852:0.0457575:0.869813:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129814 ES:SE:LP:AF:ID  0.000172804:0.00106072:0.0604807:0.129814:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03742  ES:SE:LP:AF:ID  0.0010409:0.00191382:0.229148:0.03742:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037653 ES:SE:LP:AF:ID  0.000913367:0.00190212:0.200659:0.037653:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869183 ES:SE:LP:AF:ID  -0.000163403:0.00105652:0.0555173:0.869183:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869288 ES:SE:LP:AF:ID  -0.000135603:0.00105695:0.0457575:0.869288:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037617 ES:SE:LP:AF:ID  0.000987983:0.00191011:0.221849:0.037617:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869184 ES:SE:LP:AF:ID  -0.000175671:0.0010565:0.0604807:0.869184:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838019 ES:SE:LP:AF:ID  -6.04196e-05:0.000983599:0.0222764:0.838019:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037628 ES:SE:LP:AF:ID  0.00109597:0.00191289:0.244125:0.037628:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838653 ES:SE:LP:AF:ID  -8.91183e-05:0.000986366:0.0315171:0.838653:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013804 ES:SE:LP:AF:ID  -0.00120166:0.00344301:0.136677:0.013804:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839741 ES:SE:LP:AF:ID  0.000136366:0.000999584:0.05061:0.839741:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869439 ES:SE:LP:AF:ID  -0.000170075:0.00105519:0.0604807:0.869439:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868984 ES:SE:LP:AF:ID  -0.000192214:0.0010525:0.0655015:0.868984:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867962 ES:SE:LP:AF:ID  -7.78667e-05:0.00105055:0.0268721:0.867962:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869122 ES:SE:LP:AF:ID  -0.000184225:0.00105336:0.0655015:0.869122:rs4951929