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-4540/UKB-b-4540_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4540/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:25 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4540/UKB-b-4540_data.vcf.gz ...
Read summary statistics for 5966305 SNPs.
Dropped 2692 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, 1212363 SNPs remain.
After merging with regression SNP LD, 1212363 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0029 (0.0015)
Lambda GC: 1.4102
Mean Chi^2: 1.4043
Intercept: 1.3772 (0.009)
Ratio: 0.9329 (0.0222)
Analysis finished at Thu Oct 17 14:45:38 2019
Total time elapsed: 1.0m:13.22s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9241,
    "inflation_factor": 1.4295,
    "mean_EFFECT": 1.7898e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 128,
    "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": 53869,
    "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": 1212363,
    "ldsc_nsnp_merge_regression_ld": 1212363,
    "ldsc_observed_scale_h2_beta": 0.0029,
    "ldsc_observed_scale_h2_se": 0.0015,
    "ldsc_intercept_beta": 1.3772,
    "ldsc_intercept_se": 0.009,
    "ldsc_lambda_gc": 1.4102,
    "ldsc_mean_chisq": 1.4043,
    "ldsc_ratio": 0.933
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
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 5963631 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 5966305 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 8.671630e+00 5.762507e+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 7.857175e+07 5.651940e+07 828.0000000 3.198214e+07 6.902002e+07 1.144870e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.800000e-06 5.370000e-04 -0.0040873 -3.213000e-04 1.000000e-07 3.221000e-04 5.443500e-03 ▁▃▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.314000e-04 1.430000e-04 0.0002841 3.136000e-04 3.758000e-04 5.152000e-04 1.465500e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.456640e-01 3.008128e-01 0.0000000 1.700000e-01 4.199997e-01 7.099994e-01 1.000000e+00 ▇▆▆▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.456646e-01 3.007892e-01 0.0000000 1.718459e-01 4.228094e-01 7.051353e-01 9.999995e-01 ▇▆▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.130153e-01 2.529928e-01 0.0346300 9.835700e-02 2.314170e-01 4.784050e-01 9.653700e-01 ▇▃▂▂▁
numeric AF_reference 53869 0.9909711 NA NA NA NA NA NA NA 3.094410e-01 2.462170e-01 0.0000000 1.072280e-01 2.390180e-01 4.688500e-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 -0.0007255 0.0005222 0.1600000 0.1647002 0.623761 0.7821490 NA
1 54676 rs2462492 C T -0.0001758 0.0005173 0.7300002 0.7339413 0.400407 NA NA
1 86028 rs114608975 T C 0.0010209 0.0008271 0.2200002 0.2171244 0.103554 0.0277556 NA
1 91536 rs6702460 G T -0.0007603 0.0005094 0.1400000 0.1355608 0.456863 0.4207270 NA
1 234313 rs8179466 C T 0.0001874 0.0010043 0.8499999 0.8520194 0.074508 NA NA
1 534192 rs6680723 C T -0.0003421 0.0005818 0.5600000 0.5565213 0.240956 NA NA
1 546697 rs12025928 A G -0.0004198 0.0007259 0.5600000 0.5629843 0.913475 NA NA
1 693731 rs12238997 A G -0.0009730 0.0004876 0.0460002 0.0459758 0.116333 0.1417730 NA
1 705882 rs72631875 G A 0.0008642 0.0007145 0.2300001 0.2264627 0.067296 0.0315495 NA
1 706368 rs55727773 A G 0.0004110 0.0003612 0.2599998 0.2551178 0.515654 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0004986 0.0005655 0.3800004 0.3779472 0.073622 0.0826677 NA
22 51219006 rs28729663 G A -0.0012460 0.0004365 0.0043000 0.0043109 0.137953 0.2052720 NA
22 51219387 rs9616832 T C -0.0004169 0.0005666 0.4600002 0.4618388 0.073745 0.0654952 NA
22 51219704 rs147475742 G A -0.0000775 0.0007592 0.9199999 0.9186931 0.041961 0.0473243 NA
22 51221190 rs369304721 G A -0.0003193 0.0007580 0.6700003 0.6736202 0.049735 NA NA
22 51221731 rs115055839 T C -0.0004806 0.0005670 0.4000000 0.3966381 0.073236 0.0625000 NA
22 51222100 rs114553188 G T -0.0017176 0.0006675 0.0100000 0.0100720 0.054466 0.0880591 NA
22 51223637 rs375798137 G A -0.0017539 0.0006707 0.0089000 0.0089211 0.054094 0.0788738 NA
22 51229805 rs9616985 T C -0.0004711 0.0005690 0.4100001 0.4077335 0.073071 0.0730831 NA
22 51237063 rs3896457 T C 0.0004428 0.0003480 0.2000000 0.2032774 0.297981 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623761 ES:SE:LP:AF:ID  -0.000725526:0.000522173:0.79588:0.623761:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400407 ES:SE:LP:AF:ID  -0.000175834:0.00051733:0.136677:0.400407:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103554 ES:SE:LP:AF:ID  0.00102087:0.000827144:0.657577:0.103554:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456863 ES:SE:LP:AF:ID  -0.000760258:0.000509376:0.853872:0.456863:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074508 ES:SE:LP:AF:ID  0.000187352:0.00100434:0.0705811:0.074508:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240956 ES:SE:LP:AF:ID  -0.000342125:0.000581829:0.251812:0.240956:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913475 ES:SE:LP:AF:ID  -0.000419848:0.00072586:0.251812:0.913475:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116333 ES:SE:LP:AF:ID  -0.000973032:0.000487585:1.33724:0.116333:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067296 ES:SE:LP:AF:ID  0.000864181:0.000714482:0.638272:0.067296:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515654 ES:SE:LP:AF:ID  0.000411024:0.000361179:0.585027:0.515654:rs12029736
1   715265  rs12184267  C   T   .   PASS    AF=0.036615 ES:SE:LP:AF:ID  -0.000366328:0.000827174:0.180456:0.036615:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036731 ES:SE:LP:AF:ID  -0.000383935:0.000824045:0.19382:0.036731:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036431 ES:SE:LP:AF:ID  -0.000434401:0.000829973:0.221849:0.036431:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036971 ES:SE:LP:AF:ID  -0.000358759:0.000820769:0.180456:0.036971:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037067 ES:SE:LP:AF:ID  -0.000329743:0.000817958:0.161151:0.037067:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101209 ES:SE:LP:AF:ID  0.000527778:0.000595911:0.420216:0.101209:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959102 ES:SE:LP:AF:ID  0.000478467:0.0007889:0.267606:0.959102:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.053256 ES:SE:LP:AF:ID  0.00292481:0.0011393:2:0.053256:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036585 ES:SE:LP:AF:ID  -0.000466422:0.000823265:0.244125:0.036585:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036902 ES:SE:LP:AF:ID  -0.000446657:0.000815775:0.236572:0.036902:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843209 ES:SE:LP:AF:ID  0.000648696:0.000422559:0.920819:0.843209:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055926 ES:SE:LP:AF:ID  -0.000458781:0.000684087:0.30103:0.055926:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122317 ES:SE:LP:AF:ID  -0.000770417:0.000462521:1.01773:0.122317:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.12156  ES:SE:LP:AF:ID  -0.000738956:0.000462714:0.958607:0.12156:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132334 ES:SE:LP:AF:ID  -0.000506308:0.000456045:0.568636:0.132334:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036816 ES:SE:LP:AF:ID  -0.000429933:0.000807518:0.229148:0.036816:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83895  ES:SE:LP:AF:ID  0.000570559:0.000409218:0.79588:0.83895:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838578 ES:SE:LP:AF:ID  0.000572375:0.000408777:0.79588:0.838578:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869771 ES:SE:LP:AF:ID  0.00066007:0.000438629:0.886057:0.869771:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129881 ES:SE:LP:AF:ID  -0.000672008:0.000439526:0.886057:0.129881:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037327 ES:SE:LP:AF:ID  -0.000396756:0.000793827:0.207608:0.037327:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037571 ES:SE:LP:AF:ID  -0.000361147:0.000788807:0.187087:0.037571:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  0.000661868:0.000437769:0.886057:0.869113:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869211 ES:SE:LP:AF:ID  0.000660045:0.000437943:0.886057:0.869211:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037528 ES:SE:LP:AF:ID  -0.000425885:0.000792223:0.229148:0.037528:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869116 ES:SE:LP:AF:ID  0.00067008:0.000437761:0.886057:0.869116:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838031 ES:SE:LP:AF:ID  0.000557997:0.000407643:0.769551:0.838031:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037541 ES:SE:LP:AF:ID  -0.000468647:0.000793341:0.259637:0.037541:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838662 ES:SE:LP:AF:ID  0.000562227:0.000408789:0.769551:0.838662:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839773 ES:SE:LP:AF:ID  0.000532628:0.000414316:0.69897:0.839773:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869396 ES:SE:LP:AF:ID  0.000628757:0.000437255:0.823909:0.869396:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868943 ES:SE:LP:AF:ID  0.000619852:0.000436154:0.79588:0.868943:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867895 ES:SE:LP:AF:ID  0.000603224:0.000435318:0.769551:0.867895:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869086 ES:SE:LP:AF:ID  0.000639748:0.000436512:0.853872:0.869086:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869094 ES:SE:LP:AF:ID  0.000640049:0.000436545:0.853872:0.869094:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869102 ES:SE:LP:AF:ID  0.000639632:0.000436555:0.853872:0.869102:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.86958  ES:SE:LP:AF:ID  0.000621893:0.000437755:0.79588:0.86958:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.037591 ES:SE:LP:AF:ID  -0.000567548:0.000788682:0.327902:0.037591:rs114525117
1   760912  rs1048488   C   T   .   PASS    AF=0.83831  ES:SE:LP:AF:ID  0.000585789:0.000406871:0.823909:0.83831:rs1048488
1   760998  rs148828841 C   A   .   PASS    AF=0.037028 ES:SE:LP:AF:ID  -0.000642273:0.000803453:0.376751:0.037028:rs148828841