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-1867/UKB-b-1867_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1867/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-1867/UKB-b-1867_data.vcf.gz ...
Read summary statistics for 6655382 SNPs.
Dropped 3779 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, 1250797 SNPs remain.
After merging with regression SNP LD, 1250797 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0212 (0.0015)
Lambda GC: 1.1924
Mean Chi^2: 1.2171
Intercept: 1.0199 (0.0075)
Ratio: 0.0915 (0.0344)
Analysis finished at Thu Oct 17 14:41:33 2019
Total time elapsed: 1.0m:14.3s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9324,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 7.0074e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 5,
    "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": 61108,
    "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": 1250797,
    "ldsc_nsnp_merge_regression_ld": 1250797,
    "ldsc_observed_scale_h2_beta": 0.0212,
    "ldsc_observed_scale_h2_se": 0.0015,
    "ldsc_intercept_beta": 1.0199,
    "ldsc_intercept_se": 0.0075,
    "ldsc_lambda_gc": 1.1924,
    "ldsc_mean_chisq": 1.2171,
    "ldsc_ratio": 0.0917
}

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 6651625 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 6655382 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 8.664178e+00 5.763797e+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 7.859938e+07 5.648067e+07 828.0000000 3.206581e+07 6.904817e+07 1.144966e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.000000e-06 7.332000e-04 -0.0058262 -4.128000e-04 4.400000e-06 4.225000e-04 6.074200e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.345000e-04 2.662000e-04 0.0003740 4.190000e-04 5.254000e-04 7.845000e-04 3.242400e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.731175e-01 2.960677e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.731197e-01 2.960445e-01 0.0000000 2.091777e-01 4.638858e-01 7.291583e-01 9.999997e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.875912e-01 2.582755e-01 0.0212780 7.098700e-02 1.960780e-01 4.479370e-01 9.787220e-01 ▇▃▂▂▁
numeric AF_reference 61108 0.9908183 NA NA NA NA NA NA NA 2.853780e-01 2.505190e-01 0.0000000 8.007190e-02 2.064700e-01 4.400960e-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 0.0006623 0.0006880 0.3400001 0.3356773 0.623753 0.7821490 NA
1 54676 rs2462492 C T 0.0008701 0.0006815 0.2000000 0.2017370 0.400435 NA NA
1 86028 rs114608975 T C -0.0004053 0.0010898 0.7099994 0.7099740 0.103548 0.0277556 NA
1 91536 rs6702460 G T 0.0013274 0.0006712 0.0479999 0.0479640 0.456835 0.4207270 NA
1 234313 rs8179466 C T 0.0024812 0.0013238 0.0610000 0.0608919 0.074495 NA NA
1 534192 rs6680723 C T 0.0008166 0.0007666 0.2900000 0.2868241 0.240974 NA NA
1 546697 rs12025928 A G 0.0002690 0.0009565 0.7800007 0.7785491 0.913470 NA NA
1 693731 rs12238997 A G 0.0010056 0.0006424 0.1199999 0.1175175 0.116360 0.1417730 NA
1 705882 rs72631875 G A 0.0004570 0.0009414 0.6300007 0.6273360 0.067283 0.0315495 NA
1 706368 rs55727773 A G 0.0002579 0.0004760 0.5900000 0.5879449 0.515638 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0004027 0.0007440 0.5900000 0.5883335 0.073617 0.0826677 NA
22 51219006 rs28729663 G A -0.0003516 0.0005742 0.5400003 0.5403966 0.137953 0.2052720 NA
22 51219387 rs9616832 T C -0.0004137 0.0007454 0.5800000 0.5789019 0.073740 0.0654952 NA
22 51219704 rs147475742 G A -0.0002936 0.0009990 0.7700005 0.7688656 0.041940 0.0473243 NA
22 51221190 rs369304721 G A -0.0010249 0.0009972 0.2999998 0.3040621 0.049736 NA NA
22 51221731 rs115055839 T C -0.0004046 0.0007459 0.5900000 0.5875498 0.073230 0.0625000 NA
22 51222100 rs114553188 G T -0.0002750 0.0008780 0.7499995 0.7541464 0.054467 0.0880591 NA
22 51223637 rs375798137 G A -0.0003180 0.0008822 0.7199992 0.7185126 0.054097 0.0788738 NA
22 51229805 rs9616985 T C -0.0003242 0.0007486 0.6600001 0.6649382 0.073065 0.0730831 NA
22 51237063 rs3896457 T C 0.0004328 0.0004578 0.3400001 0.3444821 0.297970 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623753 ES:SE:LP:AF:ID  0.000662343:0.000687976:0.468521:0.623753:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400435 ES:SE:LP:AF:ID  0.000870078:0.000681549:0.69897:0.400435:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103548 ES:SE:LP:AF:ID  -0.000405298:0.00108983:0.148742:0.103548:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456835 ES:SE:LP:AF:ID  0.00132739:0.000671183:1.31876:0.456835:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074495 ES:SE:LP:AF:ID  0.00248119:0.00132381:1.21467:0.074495:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240974 ES:SE:LP:AF:ID  0.000816553:0.000766634:0.537602:0.240974:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91347  ES:SE:LP:AF:ID  0.000268965:0.000956455:0.107905:0.91347:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11636  ES:SE:LP:AF:ID  0.00100559:0.000642435:0.920819:0.11636:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067283 ES:SE:LP:AF:ID  0.000457038:0.000941415:0.200659:0.067283:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515638 ES:SE:LP:AF:ID  0.000257883:0.00047596:0.229148:0.515638:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033022 ES:SE:LP:AF:ID  0.000840176:0.00119954:0.318759:0.033022:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036635 ES:SE:LP:AF:ID  0.000494858:0.00108967:0.187087:0.036635:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03675  ES:SE:LP:AF:ID  0.000474712:0.00108557:0.180456:0.03675:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03645  ES:SE:LP:AF:ID  0.000474772:0.00109339:0.180456:0.03645:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036993 ES:SE:LP:AF:ID  0.000657984:0.00108123:0.267606:0.036993:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037089 ES:SE:LP:AF:ID  0.000604759:0.00107754:0.244125:0.037089:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101215 ES:SE:LP:AF:ID  -0.000525828:0.000785104:0.30103:0.101215:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959096 ES:SE:LP:AF:ID  -0.000542106:0.00103944:0.221849:0.959096:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031455 ES:SE:LP:AF:ID  -0.000884779:0.00188668:0.19382:0.031455:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053246 ES:SE:LP:AF:ID  -0.00144101:0.00150123:0.468521:0.053246:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036606 ES:SE:LP:AF:ID  0.000379803:0.00108453:0.136677:0.036606:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036923 ES:SE:LP:AF:ID  0.000467499:0.00107463:0.180456:0.036923:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843184 ES:SE:LP:AF:ID  -0.00102133:0.000556782:1.17393:0.843184:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055962 ES:SE:LP:AF:ID  0.00182479:0.000901178:1.36653:0.055962:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122347 ES:SE:LP:AF:ID  0.000903444:0.000609399:0.853872:0.122347:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025694 ES:SE:LP:AF:ID  0.00475906:0.00149965:2.82391:0.025694:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12159  ES:SE:LP:AF:ID  0.000880198:0.000609654:0.823909:0.12159:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132378 ES:SE:LP:AF:ID  0.0013835:0.000600875:1.67778:0.132378:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036835 ES:SE:LP:AF:ID  0.000326184:0.00106384:0.119186:0.036835:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838931 ES:SE:LP:AF:ID  -0.000591426:0.000539254:0.568636:0.838931:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838562 ES:SE:LP:AF:ID  -0.000581222:0.000538669:0.552842:0.838562:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869765 ES:SE:LP:AF:ID  -0.000301435:0.000578034:0.221849:0.869765:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129888 ES:SE:LP:AF:ID  0.000319354:0.000579204:0.236572:0.129888:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037344 ES:SE:LP:AF:ID  0.000359379:0.00104582:0.136677:0.037344:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037587 ES:SE:LP:AF:ID  0.000308465:0.00103921:0.113509:0.037587:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86911  ES:SE:LP:AF:ID  -0.00028798:0.000576901:0.207608:0.86911:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869209 ES:SE:LP:AF:ID  -0.000259736:0.000577132:0.187087:0.869209:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037545 ES:SE:LP:AF:ID  0.000380025:0.00104371:0.142668:0.037545:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  -0.000280453:0.00057689:0.200659:0.869113:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838015 ES:SE:LP:AF:ID  -0.000546986:0.000537178:0.508638:0.838015:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037558 ES:SE:LP:AF:ID  0.000348706:0.00104518:0.130768:0.037558:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838644 ES:SE:LP:AF:ID  -0.000553551:0.000538688:0.522879:0.838644:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839751 ES:SE:LP:AF:ID  -0.000598937:0.00054597:0.568636:0.839751:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86939  ES:SE:LP:AF:ID  -0.000287856:0.000576221:0.207608:0.86939:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868936 ES:SE:LP:AF:ID  -0.000299708:0.000574768:0.221849:0.868936:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867895 ES:SE:LP:AF:ID  -0.000313214:0.000573668:0.229148:0.867895:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86908  ES:SE:LP:AF:ID  -0.000291392:0.000575241:0.21467:0.86908:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869088 ES:SE:LP:AF:ID  -0.000291671:0.000575285:0.21467:0.869088:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869096 ES:SE:LP:AF:ID  -0.000292361:0.000575298:0.21467:0.869096:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869575 ES:SE:LP:AF:ID  -0.000300686:0.000576882:0.221849:0.869575:rs3131954