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-17729/UKB-b-17729_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17729/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17729/UKB-b-17729_data.vcf.gz ...
Read summary statistics for 8831953 SNPs.
Dropped 8033 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, 1286512 SNPs remain.
After merging with regression SNP LD, 1286512 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0872 (0.0028)
Lambda GC: 1.6658
Mean Chi^2: 1.8635
Intercept: 1.0863 (0.0101)
Ratio: 0.0999 (0.0117)
Analysis finished at Thu Oct 17 14:41:59 2019
Total time elapsed: 1.0m:41.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.947,
    "inflation_factor": 1.4921,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 93,
    "n_p_sig": 6364,
    "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": 87759,
    "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": 1286512,
    "ldsc_nsnp_merge_regression_ld": 1286512,
    "ldsc_observed_scale_h2_beta": 0.0872,
    "ldsc_observed_scale_h2_se": 0.0028,
    "ldsc_intercept_beta": 1.0863,
    "ldsc_intercept_se": 0.0101,
    "ldsc_lambda_gc": 1.6658,
    "ldsc_mean_chisq": 1.8635,
    "ldsc_ratio": 0.0999
}

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 TRUE
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 8823957 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 8831953 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 8.648476e+00 5.760052e+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 7.877308e+07 5.634275e+07 828.0000000 3.240823e+07 6.932944e+07 1.145508e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.830000e-05 2.738800e-03 -0.0255398 -1.216300e-03 1.380000e-05 1.258500e-03 2.995960e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.992800e-03 1.410900e-03 0.0007551 8.950000e-04 1.346800e-03 2.718600e-03 1.342280e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.347359e-01 3.050092e-01 0.0000000 1.499999e-01 4.100001e-01 6.999999e-01 1.000000e+00 ▇▅▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.347372e-01 3.049847e-01 0.0000000 1.540712e-01 4.092806e-01 6.984923e-01 1.000000e+00 ▇▅▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.241537e-01 2.589772e-01 0.0044010 2.253700e-02 1.070000e-01 3.541580e-01 9.955990e-01 ▇▂▁▁▁
numeric AF_reference 87759 0.9900635 NA NA NA NA NA NA NA 2.241495e-01 2.509223e-01 0.0000000 1.996810e-02 1.240020e-01 3.518370e-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 -0.0033367 0.0013882 0.0160000 0.0162333 0.623772 0.7821490 NA
1 54676 rs2462492 C T 0.0032993 0.0013751 0.0160000 0.0164306 0.400421 NA NA
1 86028 rs114608975 T C -0.0003307 0.0021984 0.8800001 0.8804235 0.103585 0.0277556 NA
1 91536 rs6702460 G T -0.0004156 0.0013540 0.7600007 0.7588691 0.456816 0.4207270 NA
1 234313 rs8179466 C T -0.0022665 0.0026686 0.4000000 0.3956907 0.074526 NA NA
1 534192 rs6680723 C T -0.0008156 0.0015467 0.5999997 0.5979884 0.240962 NA NA
1 546697 rs12025928 A G 0.0002159 0.0019296 0.9100000 0.9109100 0.913481 NA NA
1 693731 rs12238997 A G 0.0003182 0.0012958 0.8100000 0.8060478 0.116349 0.1417730 NA
1 705882 rs72631875 G A -0.0024603 0.0018995 0.2000000 0.1952384 0.067273 0.0315495 NA
1 706368 rs55727773 A G -0.0001605 0.0009601 0.8700001 0.8672497 0.515645 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0005427 0.0011603 0.6400000 0.6399979 0.137923 0.2052720 NA
22 51219387 rs9616832 T C 0.0006238 0.0015062 0.6800001 0.6787603 0.073721 0.0654952 NA
22 51219704 rs147475742 G A 0.0034148 0.0020186 0.0909997 0.0907071 0.041940 0.0473243 NA
22 51221190 rs369304721 G A 0.0007775 0.0020154 0.6999999 0.6996680 0.049710 NA NA
22 51221731 rs115055839 T C 0.0007264 0.0015072 0.6300007 0.6298297 0.073212 0.0625000 NA
22 51222100 rs114553188 G T -0.0016441 0.0017739 0.3500000 0.3540128 0.054457 0.0880591 NA
22 51223637 rs375798137 G A -0.0018301 0.0017825 0.2999998 0.3045705 0.054086 0.0788738 NA
22 51229805 rs9616985 T C 0.0006748 0.0015126 0.6600001 0.6555092 0.073046 0.0730831 NA
22 51232488 rs376461333 A G -0.0023986 0.0035600 0.5000000 0.5004589 0.020058 NA NA
22 51237063 rs3896457 T C 0.0013460 0.0009248 0.1499999 0.1455629 0.297994 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623772 ES:SE:LP:AF:ID  -0.00333674:0.00138821:1.79588:0.623772:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400421 ES:SE:LP:AF:ID  0.00329927:0.00137515:1.79588:0.400421:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103585 ES:SE:LP:AF:ID  -0.000330706:0.00219837:0.0555173:0.103585:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456816 ES:SE:LP:AF:ID  -0.000415623:0.00135397:0.119186:0.456816:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074526 ES:SE:LP:AF:ID  -0.00226653:0.00266857:0.39794:0.074526:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240962 ES:SE:LP:AF:ID  -0.000815568:0.0015467:0.221849:0.240962:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913481 ES:SE:LP:AF:ID  0.000215909:0.00192964:0.0409586:0.913481:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116349 ES:SE:LP:AF:ID  0.00031815:0.00129578:0.091515:0.116349:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067273 ES:SE:LP:AF:ID  -0.00246029:0.00189949:0.69897:0.067273:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -0.000160485:0.000960109:0.0604807:0.515645:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032976 ES:SE:LP:AF:ID  -0.00270716:0.00242153:0.585027:0.032976:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03659  ES:SE:LP:AF:ID  -0.00200454:0.00219948:0.443698:0.03659:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036705 ES:SE:LP:AF:ID  -0.00192142:0.0021912:0.420216:0.036705:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036407 ES:SE:LP:AF:ID  -0.00228006:0.00220691:0.522879:0.036407:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016397 ES:SE:LP:AF:ID  -0.00422003:0.00339829:0.677781:0.016397:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036944 ES:SE:LP:AF:ID  -0.00204916:0.00218252:0.455932:0.036944:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037043 ES:SE:LP:AF:ID  -0.0020829:0.00217499:0.468521:0.037043:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101233 ES:SE:LP:AF:ID  0.00182666:0.00158369:0.60206:0.101233:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959133 ES:SE:LP:AF:ID  0.00277259:0.00209782:0.721246:0.959133:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031452 ES:SE:LP:AF:ID  0.0040476:0.00380586:0.537602:0.031452:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053268 ES:SE:LP:AF:ID  0.000837318:0.00302699:0.107905:0.053268:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036561 ES:SE:LP:AF:ID  -0.00230558:0.00218903:0.537602:0.036561:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036873 ES:SE:LP:AF:ID  -0.00200423:0.00216924:0.443698:0.036873:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84323  ES:SE:LP:AF:ID  0.000164558:0.00112319:0.0555173:0.84323:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055944 ES:SE:LP:AF:ID  0.00264368:0.00181812:0.823909:0.055944:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122334 ES:SE:LP:AF:ID  0.000259016:0.0012292:0.0809219:0.122334:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025728 ES:SE:LP:AF:ID  0.00263284:0.00302315:0.420216:0.025728:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121576 ES:SE:LP:AF:ID  0.000326121:0.00122972:0.102373:0.121576:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132332 ES:SE:LP:AF:ID  0.000635177:0.00121211:0.221849:0.132332:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011138 ES:SE:LP:AF:ID  -0.00328814:0.00440643:0.337242:0.011138:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005701 ES:SE:LP:AF:ID  -0.00363444:0.00568853:0.283997:0.005701:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036789 ES:SE:LP:AF:ID  -0.00161669:0.00214728:0.346787:0.036789:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838985 ES:SE:LP:AF:ID  0.000210067:0.00108778:0.0705811:0.838985:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838616 ES:SE:LP:AF:ID  0.000236193:0.00108663:0.0809219:0.838616:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869779 ES:SE:LP:AF:ID  0.000177253:0.00116587:0.0555173:0.869779:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129873 ES:SE:LP:AF:ID  -0.000162102:0.00116825:0.05061:0.129873:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037298 ES:SE:LP:AF:ID  -0.00119251:0.00211094:0.244125:0.037298:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037541 ES:SE:LP:AF:ID  -0.00113646:0.0020976:0.229148:0.037541:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869122 ES:SE:LP:AF:ID  0.00024315:0.00116362:0.0809219:0.869122:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869218 ES:SE:LP:AF:ID  0.000244911:0.00116408:0.0809219:0.869218:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.0375   ES:SE:LP:AF:ID  -0.00124979:0.00210665:0.259637:0.0375:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869125 ES:SE:LP:AF:ID  0.000231695:0.0011636:0.0757207:0.869125:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005119 ES:SE:LP:AF:ID  -0.00240122:0.00597685:0.161151:0.005119:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005085 ES:SE:LP:AF:ID  -0.00248076:0.00599258:0.167491:0.005085:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838068 ES:SE:LP:AF:ID  0.000226469:0.00108362:0.0809219:0.838068:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037514 ES:SE:LP:AF:ID  -0.00139344:0.0021096:0.29243:0.037514:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838697 ES:SE:LP:AF:ID  0.000210119:0.00108666:0.0705811:0.838697:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013781 ES:SE:LP:AF:ID  0.00493131:0.00379135:0.721246:0.013781:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005531 ES:SE:LP:AF:ID  0.00632497:0.00586186:0.552842:0.005531:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839797 ES:SE:LP:AF:ID  0.00017153:0.00110131:0.0555173:0.839797:rs3131965