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-18596/UKB-b-18596_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18596/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-18596/UKB-b-18596_data.vcf.gz ...
Read summary statistics for 9066363 SNPs.
Dropped 9029 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, 1287332 SNPs remain.
After merging with regression SNP LD, 1287332 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0321 (0.0016)
Lambda GC: 1.287
Mean Chi^2: 1.3214
Intercept: 1.0337 (0.0073)
Ratio: 0.1047 (0.0229)
Analysis finished at Thu Oct 17 14:42:01 2019
Total time elapsed: 1.0m:42.12s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9479,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 6,
    "n_p_sig": 24,
    "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": 96518,
    "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": 1287332,
    "ldsc_nsnp_merge_regression_ld": 1287332,
    "ldsc_observed_scale_h2_beta": 0.0321,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.0337,
    "ldsc_intercept_se": 0.0073,
    "ldsc_lambda_gc": 1.287,
    "ldsc_mean_chisq": 1.3214,
    "ldsc_ratio": 0.1049
}

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 TRUE
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 9057375 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 9066363 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 8.641759e+00 5.757686e+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 7.879356e+07 5.633233e+07 828.0000000 3.244607e+07 6.936208e+07 1.145319e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.590000e-05 3.058800e-03 -0.0329223 -1.222200e-03 2.210000e-05 1.279400e-03 3.429460e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.360200e-03 1.754100e-03 0.0008446 1.008500e-03 1.554200e-03 3.238500e-03 1.959040e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.696503e-01 2.967642e-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 4.696496e-01 2.967383e-01 0.0000000 2.050463e-01 4.586435e-01 7.265637e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.188667e-01 2.584045e-01 0.0032860 1.983700e-02 9.959900e-02 3.450210e-01 9.967140e-01 ▇▂▁▁▁
numeric AF_reference 96518 0.9893543 NA NA NA NA NA NA NA 2.192628e-01 2.502845e-01 0.0000000 1.697280e-02 1.172120e-01 3.436500e-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 -0.0044819 0.0015540 0.0039000 0.0039246 0.623794 0.7821490 NA
1 54676 rs2462492 C T 0.0008628 0.0015394 0.5800000 0.5751341 0.400412 NA NA
1 86028 rs114608975 T C 0.0036665 0.0024614 0.1400000 0.1363322 0.103543 0.0277556 NA
1 91536 rs6702460 G T -0.0021394 0.0015158 0.1600000 0.1581380 0.456865 0.4207270 NA
1 234313 rs8179466 C T 0.0014430 0.0029884 0.6300007 0.6292019 0.074516 NA NA
1 534192 rs6680723 C T -0.0001592 0.0017315 0.9299999 0.9267539 0.240948 NA NA
1 546697 rs12025928 A G 0.0029248 0.0021597 0.1800002 0.1756473 0.913458 NA NA
1 693731 rs12238997 A G -0.0022756 0.0014510 0.1199999 0.1168103 0.116328 0.1417730 NA
1 705882 rs72631875 G A -0.0014568 0.0021262 0.4899999 0.4932384 0.067284 0.0315495 NA
1 706368 rs55727773 A G 0.0004728 0.0010749 0.6600001 0.6600007 0.515613 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0005586 0.0012970 0.6700003 0.6666801 0.137952 0.2052720 NA
22 51219387 rs9616832 T C 0.0018046 0.0016835 0.2800000 0.2837667 0.073740 0.0654952 NA
22 51219704 rs147475742 G A 0.0011738 0.0022558 0.5999997 0.6028259 0.041958 0.0473243 NA
22 51221190 rs369304721 G A 0.0015173 0.0022522 0.5000000 0.5005183 0.049729 NA NA
22 51221731 rs115055839 T C 0.0018792 0.0016846 0.2599998 0.2646416 0.073230 0.0625000 NA
22 51222100 rs114553188 G T -0.0008938 0.0019831 0.6499995 0.6522140 0.054472 0.0880591 NA
22 51223637 rs375798137 G A -0.0007693 0.0019927 0.6999999 0.6994681 0.054101 0.0788738 NA
22 51229805 rs9616985 T C 0.0020254 0.0016907 0.2300001 0.2309213 0.073065 0.0730831 NA
22 51232488 rs376461333 A G -0.0058348 0.0039830 0.1400000 0.1429489 0.020044 NA NA
22 51237063 rs3896457 T C -0.0011864 0.0010341 0.2500000 0.2512868 0.297957 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623794 ES:SE:LP:AF:ID  -0.0044819:0.00155397:2.40894:0.623794:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400412 ES:SE:LP:AF:ID  0.000862838:0.00153939:0.236572:0.400412:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103543 ES:SE:LP:AF:ID  0.00366652:0.00246143:0.853872:0.103543:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456865 ES:SE:LP:AF:ID  -0.00213937:0.00151582:0.79588:0.456865:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074516 ES:SE:LP:AF:ID  0.00144296:0.00298842:0.200659:0.074516:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240948 ES:SE:LP:AF:ID  -0.000159178:0.00173152:0.0315171:0.240948:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913458 ES:SE:LP:AF:ID  0.00292481:0.00215968:0.744727:0.913458:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116328 ES:SE:LP:AF:ID  -0.00227564:0.00145102:0.920819:0.116328:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067284 ES:SE:LP:AF:ID  -0.00145683:0.00212624:0.309804:0.067284:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515613 ES:SE:LP:AF:ID  0.000472844:0.00107486:0.180456:0.515613:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033001 ES:SE:LP:AF:ID  -0.00418138:0.0027098:0.920819:0.033001:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036618 ES:SE:LP:AF:ID  -0.00404545:0.0024614:1:0.036618:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036735 ES:SE:LP:AF:ID  -0.00422844:0.00245208:1.07058:0.036735:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036435 ES:SE:LP:AF:ID  -0.0042847:0.00246972:1.08092:0.036435:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016406 ES:SE:LP:AF:ID  -0.00539832:0.00380296:0.79588:0.016406:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036974 ES:SE:LP:AF:ID  -0.00411285:0.00244239:1.03621:0.036974:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03707  ES:SE:LP:AF:ID  -0.00407989:0.00243401:1.02687:0.03707:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101218 ES:SE:LP:AF:ID  -0.000771507:0.00177326:0.180456:0.101218:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.9591   ES:SE:LP:AF:ID  0.00324747:0.00234762:0.769551:0.9591:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031445 ES:SE:LP:AF:ID  0.0027425:0.00426246:0.283997:0.031445:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.05326  ES:SE:LP:AF:ID  0.00492372:0.00338968:0.823909:0.05326:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03659  ES:SE:LP:AF:ID  -0.00439141:0.00244973:1.13668:0.03659:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036907 ES:SE:LP:AF:ID  -0.0043416:0.00242741:1.13077:0.036907:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843217 ES:SE:LP:AF:ID  0.00284463:0.00125752:1.61979:0.843217:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055923 ES:SE:LP:AF:ID  -0.00181645:0.00203587:0.431798:0.055923:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122311 ES:SE:LP:AF:ID  -0.00238328:0.00137642:1.08092:0.122311:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025715 ES:SE:LP:AF:ID  0.00521935:0.00338542:0.920819:0.025715:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121553 ES:SE:LP:AF:ID  -0.00230823:0.00137702:1.02687:0.121553:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  -0.00274927:0.00135716:1.36653:0.132335:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011123 ES:SE:LP:AF:ID  -0.0059366:0.00493729:0.638272:0.011123:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005697 ES:SE:LP:AF:ID  0.00672398:0.00637187:0.537602:0.005697:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.03682  ES:SE:LP:AF:ID  -0.00408199:0.00240292:1.05061:0.03682:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838951 ES:SE:LP:AF:ID  0.00254131:0.0012178:1.4318:0.838951:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838581 ES:SE:LP:AF:ID  0.0025512:0.0012165:1.4437:0.838581:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869775 ES:SE:LP:AF:ID  0.00172129:0.00130536:0.721246:0.869775:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129877 ES:SE:LP:AF:ID  -0.00180183:0.00130801:0.769551:0.129877:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037331 ES:SE:LP:AF:ID  -0.00366654:0.00236218:0.920819:0.037331:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037575 ES:SE:LP:AF:ID  -0.0036269:0.00234724:0.920819:0.037575:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869118 ES:SE:LP:AF:ID  0.00178612:0.00130281:0.769551:0.869118:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869216 ES:SE:LP:AF:ID  0.00179518:0.00130333:0.769551:0.869216:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037532 ES:SE:LP:AF:ID  -0.00358086:0.00235743:0.886057:0.037532:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869121 ES:SE:LP:AF:ID  0.00177251:0.00130278:0.769551:0.869121:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005126 ES:SE:LP:AF:ID  -0.0031255:0.00668672:0.19382:0.005126:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005091 ES:SE:LP:AF:ID  -0.00321017:0.00670426:0.200659:0.005091:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838034 ES:SE:LP:AF:ID  0.00257819:0.00121311:1.46852:0.838034:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037544 ES:SE:LP:AF:ID  -0.00377199:0.00236077:0.958607:0.037544:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838665 ES:SE:LP:AF:ID  0.00257401:0.00121653:1.46852:0.838665:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013776 ES:SE:LP:AF:ID  0.00543657:0.00424583:0.69897:0.013776:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.00555  ES:SE:LP:AF:ID  -0.00287567:0.00655023:0.180456:0.00555:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839775 ES:SE:LP:AF:ID  0.00239747:0.00123296:1.284:0.839775:rs3131965