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-19115/UKB-b-19115_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19115/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:59 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19115/UKB-b-19115_data.vcf.gz ...
Read summary statistics for 6503542 SNPs.
Dropped 3525 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, 1243865 SNPs remain.
After merging with regression SNP LD, 1243865 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0014 (0.0011)
Lambda GC: 1.0478
Mean Chi^2: 1.0516
Intercept: 1.0387 (0.0066)
Ratio: 0.75 (0.1271)
Analysis finished at Thu Oct 17 14:42:15 2019
Total time elapsed: 1.0m:15.53s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9309,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 3.77e-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": 59647,
    "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": 1243865,
    "ldsc_nsnp_merge_regression_ld": 1243865,
    "ldsc_observed_scale_h2_beta": 0.0014,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0387,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0478,
    "ldsc_mean_chisq": 1.0516,
    "ldsc_ratio": 0.75
}

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 6500038 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 6503542 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 8.667016e+00 5.763739e+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 7.857199e+07 5.647441e+07 828.0000000 3.204582e+07 6.900535e+07 1.144675e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.800000e-06 6.327000e-04 -0.0049158 -3.561000e-04 -3.000000e-07 3.568000e-04 5.937500e-03 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.763000e-04 2.303000e-04 0.0003486 3.892000e-04 4.830000e-04 7.070000e-04 3.020300e-03 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.941737e-01 2.905693e-01 0.0000004 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.941743e-01 2.905426e-01 0.0000004 2.411902e-01 4.924797e-01 7.458578e-01 9.999996e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.929978e-01 2.574185e-01 0.0237410 7.647300e-02 2.035940e-01 4.547030e-01 9.762580e-01 ▇▃▂▂▁
numeric AF_reference 59647 0.9908285 NA NA NA NA NA NA NA 2.905314e-01 2.498054e-01 0.0000000 8.586260e-02 2.132590e-01 4.464860e-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 0.0001096 0.0006414 0.8600001 0.8642899 0.623779 0.7821490 NA
1 54676 rs2462492 C T 0.0005278 0.0006356 0.4100001 0.4063137 0.400400 NA NA
1 86028 rs114608975 T C -0.0009365 0.0010160 0.3599996 0.3566503 0.103557 0.0277556 NA
1 91536 rs6702460 G T 0.0009055 0.0006257 0.1499999 0.1478535 0.456857 0.4207270 NA
1 234313 rs8179466 C T 0.0004319 0.0012337 0.7300002 0.7262426 0.074511 NA NA
1 534192 rs6680723 C T -0.0004028 0.0007147 0.5700002 0.5730067 0.240948 NA NA
1 546697 rs12025928 A G -0.0011002 0.0008916 0.2200002 0.2172185 0.913471 NA NA
1 693731 rs12238997 A G 0.0008727 0.0005990 0.1499999 0.1451070 0.116327 0.1417730 NA
1 705882 rs72631875 G A 0.0001130 0.0008776 0.9000000 0.8975845 0.067312 0.0315495 NA
1 706368 rs55727773 A G -0.0008317 0.0004437 0.0610000 0.0608656 0.515675 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0003214 0.0006933 0.6400000 0.6429787 0.073608 0.0826677 NA
22 51219006 rs28729663 G A 0.0005174 0.0005352 0.3300000 0.3337350 0.137947 0.2052720 NA
22 51219387 rs9616832 T C 0.0002417 0.0006947 0.7300002 0.7278784 0.073733 0.0654952 NA
22 51219704 rs147475742 G A 0.0012019 0.0009308 0.2000000 0.1966496 0.041959 0.0473243 NA
22 51221190 rs369304721 G A 0.0004169 0.0009294 0.6499995 0.6537707 0.049727 NA NA
22 51221731 rs115055839 T C 0.0001938 0.0006952 0.7800007 0.7803984 0.073223 0.0625000 NA
22 51222100 rs114553188 G T 0.0008165 0.0008185 0.3200000 0.3184465 0.054461 0.0880591 NA
22 51223637 rs375798137 G A 0.0008562 0.0008224 0.2999998 0.2978644 0.054090 0.0788738 NA
22 51229805 rs9616985 T C 0.0001731 0.0006977 0.8000000 0.8040229 0.073060 0.0730831 NA
22 51237063 rs3896457 T C 0.0001455 0.0004267 0.7300002 0.7330888 0.297984 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623779 ES:SE:LP:AF:ID  0.00010962:0.000641368:0.0655015:0.623779:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.4004   ES:SE:LP:AF:ID  0.000527767:0.000635559:0.387216:0.4004:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103557 ES:SE:LP:AF:ID  -0.000936515:0.001016:0.443698:0.103557:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456857 ES:SE:LP:AF:ID  0.000905456:0.00062568:0.823909:0.456857:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074511 ES:SE:LP:AF:ID  0.000431939:0.00123366:0.136677:0.074511:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240948 ES:SE:LP:AF:ID  -0.000402834:0.000714715:0.244125:0.240948:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913471 ES:SE:LP:AF:ID  -0.00110017:0.000891578:0.657577:0.913471:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116327 ES:SE:LP:AF:ID  0.000872711:0.000598964:0.823909:0.116327:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067312 ES:SE:LP:AF:ID  0.000112957:0.000877586:0.0457575:0.067312:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515675 ES:SE:LP:AF:ID  -0.00083171:0.000443704:1.21467:0.515675:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033012 ES:SE:LP:AF:ID  0.000945819:0.00111839:0.39794:0.033012:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036627 ES:SE:LP:AF:ID  0.000866513:0.00101591:0.408935:0.036627:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036744 ES:SE:LP:AF:ID  0.000893237:0.00101206:0.420216:0.036744:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036442 ES:SE:LP:AF:ID  0.00077499:0.00101937:0.346787:0.036442:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036981 ES:SE:LP:AF:ID  0.000810002:0.00100809:0.376751:0.036981:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037078 ES:SE:LP:AF:ID  0.000757029:0.00100462:0.346787:0.037078:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101206 ES:SE:LP:AF:ID  -0.000307366:0.000732026:0.173925:0.101206:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959087 ES:SE:LP:AF:ID  -0.00138042:0.00096891:0.823909:0.959087:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031434 ES:SE:LP:AF:ID  0.000461375:0.00176029:0.102373:0.031434:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053267 ES:SE:LP:AF:ID  -0.00213893:0.00139899:0.886057:0.053267:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036596 ES:SE:LP:AF:ID  0.000859345:0.00101114:0.39794:0.036596:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036912 ES:SE:LP:AF:ID  0.000798999:0.00100194:0.366532:0.036912:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843197 ES:SE:LP:AF:ID  -0.00100161:0.000519071:1.26761:0.843197:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055915 ES:SE:LP:AF:ID  0.000977133:0.000840473:0.619789:0.055915:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122314 ES:SE:LP:AF:ID  0.000717373:0.000568174:0.677781:0.122314:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025723 ES:SE:LP:AF:ID  0.000933997:0.00139727:0.30103:0.025723:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121557 ES:SE:LP:AF:ID  0.000784704:0.000568412:0.769551:0.121557:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  0.000813908:0.000560245:0.823909:0.132335:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036826 ES:SE:LP:AF:ID  0.000858601:0.00099181:0.408935:0.036826:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838939 ES:SE:LP:AF:ID  -0.000975348:0.000502683:1.284:0.838939:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838566 ES:SE:LP:AF:ID  -0.00102191:0.00050214:1.37675:0.838566:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869773 ES:SE:LP:AF:ID  -0.000844653:0.000538821:0.920819:0.869773:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.12988  ES:SE:LP:AF:ID  0.00090226:0.000539913:1.02228:0.12988:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037338 ES:SE:LP:AF:ID  0.000762244:0.000974969:0.366532:0.037338:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037582 ES:SE:LP:AF:ID  0.000760422:0.000968806:0.366532:0.037582:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869114 ES:SE:LP:AF:ID  -0.000892691:0.000537762:1.01323:0.869114:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869212 ES:SE:LP:AF:ID  -0.000881923:0.000537976:1:0.869212:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037541 ES:SE:LP:AF:ID  0.000742689:0.000972994:0.346787:0.037541:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  -0.000886367:0.000537752:1.00436:0.869117:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838019 ES:SE:LP:AF:ID  -0.000938829:0.000500746:1.21467:0.838019:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037553 ES:SE:LP:AF:ID  0.000690242:0.000974373:0.318759:0.037553:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83865  ES:SE:LP:AF:ID  -0.000938851:0.000502153:1.20761:0.83865:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839765 ES:SE:LP:AF:ID  -0.00083097:0.000508948:1:0.839765:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869398 ES:SE:LP:AF:ID  -0.000806658:0.000537134:0.886057:0.869398:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868948 ES:SE:LP:AF:ID  -0.000785166:0.000535787:0.853872:0.868948:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867898 ES:SE:LP:AF:ID  -0.000861728:0.000534753:0.958607:0.867898:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86909  ES:SE:LP:AF:ID  -0.000794731:0.000536223:0.853872:0.86909:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869099 ES:SE:LP:AF:ID  -0.000793152:0.000536264:0.853872:0.869099:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869106 ES:SE:LP:AF:ID  -0.000794859:0.000536277:0.853872:0.869106:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869582 ES:SE:LP:AF:ID  -0.000809744:0.000537745:0.886057:0.869582:rs3131954