Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

QQ plot

qq_plot

AF plot

af_plot

P-Z plot

pz_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 /data/cromwell-executions/qc/746747eb-99a7-44b0-94d4-cafd3c362001/call-ldsc/inputs/-261043605/ieu-b-4908.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4908/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Thu Mar  3 20:48:37 2022
Reading summary statistics from /data/cromwell-executions/qc/746747eb-99a7-44b0-94d4-cafd3c362001/call-ldsc/inputs/-261043605/ieu-b-4908.vcf.gz ...
Read summary statistics for 0 SNPs.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
Traceback (most recent call last):
  File "./ldsc/ldsc.py", line 647, in <module>
    sumstats.estimate_h2(args, log)
  File "/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
    args, log, args.h2)
  File "/ldsc/ldscore/sumstats.py", line 252, in _read_ld_sumstats
    sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log)
  File "/ldsc/ldscore/sumstats.py", line 238, in _merge_and_log
    raise ValueError(msg.format(N=len(sumstats), F=noun))
ValueError: After merging with reference panel LD, 0 SNPs remain.

Analysis finished at Thu Mar  3 20:49:24 2022
Total time elapsed: 47.53s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.5875,
    "inflation_factor": 0.999,
    "mean_EFFECT": 0.0023,
    "n": "-Inf",
    "n_snps": 7246993,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 75734,
    "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": 0,
    "ldsc_nsnp_merge_regression_ld": "NA",
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": "NA",
    "ldsc_intercept_se": "NA",
    "ldsc_lambda_gc": "NA",
    "ldsc_mean_chisq": "NA",
    "ldsc_ratio": "NA"
}

Flags

name value
af_correlation TRUE
inflation_factor FALSE
n TRUE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq NA
n_p_sig FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio NA
ldsc_intercept_beta NA
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 132 0.9999818 3 58 0 7242925 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 7246993 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 8.656778e+00 5.769714e+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 7.891601e+07 5.635140e+07 828.0000000 3.250790e+07 6.937929e+07 1.147037e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.321100e-03 1.564145e+00 -917.3020000 -9.883920e-02 3.780000e-05 9.934140e-02 1.058620e+03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.960959e-01 2.110647e+00 0.0775370 9.779510e-02 1.357650e-01 2.391900e-01 1.056850e+03 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.996707e-01 2.892194e-01 0.0000004 2.488301e-01 5.002165e-01 7.501947e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.990752e-01 2.895766e-01 0.0000002 2.477964e-01 4.996316e-01 7.499458e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 1.683334e-01 1.474396e-01 0.0096754 3.631400e-02 1.193600e-01 2.811500e-01 5.000000e-01 ▇▃▂▂▂
numeric AF_reference 75734 0.9895496 NA NA NA NA NA NA NA 2.455154e-01 2.513944e-01 0.0000000 4.053510e-02 1.529550e-01 3.829870e-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 0.114378 0.456916 0.8025271 0.8023352 0.359860 0.7821490 NA
1 54676 rs2462492 C T -0.534404 0.420910 0.2053180 0.2042130 0.388830 NA NA
1 86028 rs114608975 T C 1.318850 0.808291 0.1039319 0.1027527 0.098238 0.0277556 NA
1 91536 rs6702460 G T 0.209953 0.421189 0.6185574 0.6181479 0.453670 0.4207270 NA
1 234313 rs8179466 C T -0.242411 0.687578 0.7246979 0.7244200 0.079244 NA NA
1 534192 rs6680723 C T -0.783669 0.477199 0.1017199 0.1005430 0.240840 NA NA
1 546697 rs12025928 A G -0.461036 0.484088 0.3417661 0.3409040 0.086871 NA NA
1 568800 rs375217967 G A 1.073420 0.947988 0.2585187 0.2575025 0.016076 0.0778754 NA
1 569004 rs9285835 T C -5.293950 2.553610 0.0391201 0.0381607 0.012532 NA NA
1 569624 rs6594035 T C 0.327027 2.586430 0.8994789 0.8993840 0.011422 NA NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219387 rs9616832 T C 0.0341765 0.195083 0.8610639 0.8609307 0.073955 0.0654952 NA
22 51219704 rs147475742 G A -0.0733929 0.275757 0.7903273 0.7901232 0.041917 0.0473243 NA
22 51220517 rs9616980 C G 0.3381740 0.566017 0.5507050 0.5501986 0.011741 0.0027955 NA
22 51221190 rs369304721 G A -0.0286750 0.262365 0.9130509 0.9129691 0.049718 NA NA
22 51221731 rs115055839 T C 0.0338984 0.195402 0.8624051 0.8622739 0.073062 0.0625000 NA
22 51222100 rs114553188 G T -0.3571000 0.216765 0.1006501 0.0994746 0.053551 0.0880591 NA
22 51223637 rs375798137 G A -0.3581860 0.217063 0.1000889 0.0989127 0.053086 0.0788738 NA
22 51229805 rs9616985 T C 0.0338253 0.195734 0.8629301 0.8627987 0.073123 0.0730831 NA
22 51232488 rs376461333 A G -0.5943610 0.423405 0.1615511 0.1603889 0.020104 NA NA
22 51237063 rs3896457 T C 0.1417570 0.106878 0.1858570 0.1847258 0.294910 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.35986  ES:SE:LP:AF:ID  0.114378:0.456916:0.0955403:0.35986:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.38883  ES:SE:LP:AF:ID  -0.534404:0.42091:0.687573:0.38883:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.098238 ES:SE:LP:AF:ID  1.31885:0.808291:0.983251:0.098238:rs114608975
1   91536   rs1251109649    G   T   .   PASS    AF=0.45367  ES:SE:LP:AF:ID  0.209953:0.421189:0.20862:0.45367:rs1251109649
1   234313  rs8179466   C   T   .   PASS    AF=0.079244 ES:SE:LP:AF:ID  -0.242411:0.687578:0.139843:0.079244:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24084  ES:SE:LP:AF:ID  -0.783669:0.477199:0.992594:0.24084:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.086871 ES:SE:LP:AF:ID  -0.461036:0.484088:0.466271:0.086871:rs12025928
1   568800  rs375217967 G   A   .   PASS    AF=0.016076 ES:SE:LP:AF:ID  1.07342:0.947988:0.587508:0.016076:rs375217967
1   569004  rs9285835   T   C   .   PASS    AF=0.012532 ES:SE:LP:AF:ID  -5.29395:2.55361:1.4076:0.012532:rs9285835
1   569624  rs6594035   T   C   .   PASS    AF=0.011422 ES:SE:LP:AF:ID  0.327027:2.58643:0.046009:0.011422:rs6594035
1   693731  rs12238997  A   G   .   PASS    AF=0.12394  ES:SE:LP:AF:ID  0.766406:0.407467:1.21415:0.12394:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.071517 ES:SE:LP:AF:ID  -0.404071:0.54409:0.338809:0.071517:rs72631875
1   706368  rs963699400 A   G   .   PASS    AF=0.48937  ES:SE:LP:AF:ID  -0.14414:0.228776:0.276379:0.48937:rs963699400
1   714596  rs149887893 T   C   .   PASS    AF=0.036483 ES:SE:LP:AF:ID  1.20858:1.35812:0.426751:0.036483:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.040536 ES:SE:LP:AF:ID  0.682336:1.37603:0.207334:0.040536:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.040763 ES:SE:LP:AF:ID  0.722097:1.37614:0.221698:0.040763:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.040352 ES:SE:LP:AF:ID  0.762118:1.38924:0.233775:0.040352:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016514 ES:SE:LP:AF:ID  -0.0298361:1.14435:0.00912017:0.016514:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.040909 ES:SE:LP:AF:ID  0.638612:1.36781:0.193167:0.040909:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.041095 ES:SE:LP:AF:ID  0.480538:1.36697:0.139384:0.041095:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099265 ES:SE:LP:AF:ID  0.247439:0.444446:0.237941:0.099265:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.047367 ES:SE:LP:AF:ID  0.284077:0.860915:0.129783:0.047367:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.030038 ES:SE:LP:AF:ID  4.68756:1.50377:2.69354:0.030038:rs12126395
1   725060  rs1445661281    A   T   .   PASS    AF=0.052781 ES:SE:LP:AF:ID  -1.23993:0.680194:1.15841:0.052781:rs1445661281
1   726794  rs28454925  C   G   .   PASS    AF=0.040513 ES:SE:LP:AF:ID  0.577922:1.36935:0.171769:0.040513:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.040678 ES:SE:LP:AF:ID  0.753265:1.37795:0.232791:0.040678:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.16855  ES:SE:LP:AF:ID  -0.551686:0.367142:0.872536:0.16855:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.053483 ES:SE:LP:AF:ID  0.728625:0.647805:0.582195:0.053483:rs148120343
1   731453  rs186002080 G   A   .   PASS    AF=0.015304 ES:SE:LP:AF:ID  -0.832867:0.932818:0.428593:0.015304:rs186002080
1   731718  rs58276399  T   C   .   PASS    AF=0.13048  ES:SE:LP:AF:ID  0.66937:0.385967:1.07559:0.13048:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.021775 ES:SE:LP:AF:ID  -0.502739:0.635447:0.366982:0.021775:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12935  ES:SE:LP:AF:ID  0.680532:0.385286:1.1052:0.12935:rs141242758
1   736289  rs1254887344    T   A   .   PASS    AF=0.13925  ES:SE:LP:AF:ID  0.881096:0.407405:1.50235:0.13925:rs1254887344
1   752478  rs146277091 G   A   .   PASS    AF=0.040427 ES:SE:LP:AF:ID  1.08435:1.3978:0.357951:0.040427:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.17073  ES:SE:LP:AF:ID  -0.89555:0.36026:1.86849:0.17073:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.17249  ES:SE:LP:AF:ID  -0.815472:0.353211:1.6631:0.17249:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.13929  ES:SE:LP:AF:ID  -0.823527:0.355497:1.67192:0.13929:rs3115860
1   753541  rs1388595942    G   A   .   PASS    AF=0.13958  ES:SE:LP:AF:ID  0.761604:0.352902:1.49743:0.13958:rs1388595942
1   754063  rs12184312  G   T   .   PASS    AF=0.040063 ES:SE:LP:AF:ID  0.730975:1.41384:0.217833:0.040063:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.040029 ES:SE:LP:AF:ID  0.661912:1.41704:0.193276:0.040029:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.14121  ES:SE:LP:AF:ID  -0.754688:0.349152:1.50105:0.14121:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.14109  ES:SE:LP:AF:ID  -0.755349:0.349146:1.50312:0.14109:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.040203 ES:SE:LP:AF:ID  0.713266:1.41807:0.21085:0.040203:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.14107  ES:SE:LP:AF:ID  -0.758035:0.349232:1.51083:0.14107:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.17227  ES:SE:LP:AF:ID  -0.821862:0.353567:1.6809:0.17227:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.040591 ES:SE:LP:AF:ID  0.65969:1.38102:0.198413:0.040591:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.1717   ES:SE:LP:AF:ID  -0.81688:0.353231:1.66745:0.1717:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.011824 ES:SE:LP:AF:ID  -0.116548:1.41119:0.0295411:0.011824:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.1692   ES:SE:LP:AF:ID  -0.750209:0.359804:1.42005:0.1692:rs3131965
1   755890  rs1280367067    A   T   .   PASS    AF=0.13948  ES:SE:LP:AF:ID  -0.816024:0.354936:1.65222:0.13948:rs1280367067