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-10559/UKB-b-10559_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10559/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-10559/UKB-b-10559_data.vcf.gz ...
Read summary statistics for 8875900 SNPs.
Dropped 8196 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, 1286697 SNPs remain.
After merging with regression SNP LD, 1286697 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0217 (0.0016)
Lambda GC: 1.2435
Mean Chi^2: 1.2705
Intercept: 1.0772 (0.0083)
Ratio: 0.2854 (0.0306)
Analysis finished at Thu Oct 17 14:41:54 2019
Total time elapsed: 1.0m:35.28s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9472,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 110,
    "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": 89015,
    "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": 1286697,
    "ldsc_nsnp_merge_regression_ld": 1286697,
    "ldsc_observed_scale_h2_beta": 0.0217,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.0772,
    "ldsc_intercept_se": 0.0083,
    "ldsc_lambda_gc": 1.2435,
    "ldsc_mean_chisq": 1.2705,
    "ldsc_ratio": 0.2854
}

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 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 8867743 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 8875900 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 8.646748e+00 5.759635e+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 7.878376e+07 5.634384e+07 828.0000000 3.241837e+07 6.934214e+07 1.145594e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.540000e-05 2.670300e-03 -0.0296737 -1.087300e-03 1.100000e-05 1.121600e-03 2.774940e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.051700e-03 1.464600e-03 0.0007693 9.132000e-04 1.380400e-03 2.803500e-03 1.784110e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.708322e-01 2.962245e-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 4.708349e-01 2.961983e-01 0.0000000 2.073085e-01 4.604817e-01 7.272732e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.231364e-01 2.588655e-01 0.0041810 2.199900e-02 1.056550e-01 3.523800e-01 9.958190e-01 ▇▂▁▁▁
numeric AF_reference 89015 0.9899712 NA NA NA NA NA NA NA 2.231963e-01 2.508014e-01 0.0000000 1.936900e-02 1.228040e-01 3.502400e-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 0.0001098 0.0014153 0.9400001 0.9381795 0.623759 0.7821490 NA
1 54676 rs2462492 C T 0.0014638 0.0014022 0.2999998 0.2965172 0.400398 NA NA
1 86028 rs114608975 T C -0.0026490 0.0022418 0.2399999 0.2373377 0.103557 0.0277556 NA
1 91536 rs6702460 G T 0.0016347 0.0013806 0.2399999 0.2363840 0.456847 0.4207270 NA
1 234313 rs8179466 C T 0.0007359 0.0027220 0.7899998 0.7868836 0.074509 NA NA
1 534192 rs6680723 C T 0.0032806 0.0015770 0.0379997 0.0375023 0.240954 NA NA
1 546697 rs12025928 A G 0.0032629 0.0019675 0.0969996 0.0972380 0.913481 NA NA
1 693731 rs12238997 A G -0.0011102 0.0013215 0.4000000 0.4008476 0.116339 0.1417730 NA
1 705882 rs72631875 G A -0.0027223 0.0019367 0.1600000 0.1598404 0.067284 0.0315495 NA
1 706368 rs55727773 A G -0.0012686 0.0009790 0.2000000 0.1950364 0.515644 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0000181 0.0011814 0.9900000 0.9877516 0.137955 0.2052720 NA
22 51219387 rs9616832 T C -0.0001989 0.0015335 0.9000000 0.8967932 0.073747 0.0654952 NA
22 51219704 rs147475742 G A -0.0020810 0.0020549 0.3100002 0.3111957 0.041960 0.0473243 NA
22 51221190 rs369304721 G A -0.0005112 0.0020516 0.8000000 0.8032069 0.049735 NA NA
22 51221731 rs115055839 T C -0.0001901 0.0015345 0.9000000 0.9014240 0.073237 0.0625000 NA
22 51222100 rs114553188 G T -0.0002860 0.0018065 0.8700001 0.8742301 0.054461 0.0880591 NA
22 51223637 rs375798137 G A -0.0003307 0.0018153 0.8600001 0.8554348 0.054090 0.0788738 NA
22 51229805 rs9616985 T C -0.0001681 0.0015400 0.9100000 0.9130632 0.073072 0.0730831 NA
22 51232488 rs376461333 A G -0.0042291 0.0036280 0.2399999 0.2437379 0.020043 NA NA
22 51237063 rs3896457 T C 0.0012942 0.0009419 0.1700000 0.1694439 0.297989 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623759 ES:SE:LP:AF:ID  0.000109772:0.00141535:0.0268721:0.623759:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400398 ES:SE:LP:AF:ID  0.00146379:0.00140219:0.522879:0.400398:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103557 ES:SE:LP:AF:ID  -0.00264905:0.00224179:0.619789:0.103557:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456847 ES:SE:LP:AF:ID  0.00163472:0.00138059:0.619789:0.456847:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074509 ES:SE:LP:AF:ID  0.000735927:0.00272203:0.102373:0.074509:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240954 ES:SE:LP:AF:ID  0.00328056:0.001577:1.42022:0.240954:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913481 ES:SE:LP:AF:ID  0.00326289:0.0019675:1.01323:0.913481:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116339 ES:SE:LP:AF:ID  -0.00111022:0.00132152:0.39794:0.116339:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067284 ES:SE:LP:AF:ID  -0.00272227:0.00193672:0.79588:0.067284:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515644 ES:SE:LP:AF:ID  -0.00126855:0.000978953:0.69897:0.515644:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033006 ES:SE:LP:AF:ID  0.000237004:0.00246803:0.0362122:0.033006:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036622 ES:SE:LP:AF:ID  -0.000254095:0.00224185:0.0409586:0.036622:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036738 ES:SE:LP:AF:ID  -0.000400469:0.00223337:0.0655015:0.036738:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036438 ES:SE:LP:AF:ID  -0.000503481:0.00224945:0.0861861:0.036438:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016412 ES:SE:LP:AF:ID  -0.00360709:0.00346298:0.522879:0.016412:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036978 ES:SE:LP:AF:ID  -0.000323945:0.00222451:0.0555173:0.036978:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037074 ES:SE:LP:AF:ID  -0.000440139:0.00221688:0.0757207:0.037074:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101203 ES:SE:LP:AF:ID  0.00242721:0.00161524:0.886057:0.101203:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959094 ES:SE:LP:AF:ID  -0.000543947:0.00213811:0.09691:0.959094:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031451 ES:SE:LP:AF:ID  -0.00120969:0.00388159:0.119186:0.031451:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053255 ES:SE:LP:AF:ID  -0.00422092:0.00308781:0.769551:0.053255:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036591 ES:SE:LP:AF:ID  -0.000477877:0.00223126:0.0809219:0.036591:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036908 ES:SE:LP:AF:ID  -0.000413047:0.00221095:0.0705811:0.036908:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843198 ES:SE:LP:AF:ID  0.000424984:0.00114528:0.148742:0.843198:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05592  ES:SE:LP:AF:ID  -0.000990161:0.00185432:0.229148:0.05592:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122322 ES:SE:LP:AF:ID  -0.000881:0.0012536:0.318759:0.122322:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025715 ES:SE:LP:AF:ID  0.00623517:0.00308353:1.36653:0.025715:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121564 ES:SE:LP:AF:ID  -0.00077282:0.00125412:0.267606:0.121564:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132339 ES:SE:LP:AF:ID  -1.97463e-05:0.00123608:0.00436481:0.132339:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011136 ES:SE:LP:AF:ID  0.00138609:0.00449407:0.119186:0.011136:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005698 ES:SE:LP:AF:ID  -0.00633174:0.00580303:0.552842:0.005698:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036822 ES:SE:LP:AF:ID  -0.000321617:0.0021886:0.0555173:0.036822:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83894  ES:SE:LP:AF:ID  0.000773426:0.00110913:0.309804:0.83894:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838568 ES:SE:LP:AF:ID  0.000641072:0.00110794:0.251812:0.838568:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869768 ES:SE:LP:AF:ID  0.00100624:0.00118885:0.39794:0.869768:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129884 ES:SE:LP:AF:ID  -0.000927723:0.00119128:0.356547:0.129884:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037334 ES:SE:LP:AF:ID  -0.000411345:0.00215147:0.0705811:0.037334:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037578 ES:SE:LP:AF:ID  -0.000376373:0.00213787:0.0655015:0.037578:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869109 ES:SE:LP:AF:ID  0.000880621:0.00118652:0.337242:0.869109:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869207 ES:SE:LP:AF:ID  0.000929385:0.00118699:0.366532:0.869207:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037535 ES:SE:LP:AF:ID  -0.000333511:0.00214712:0.0555173:0.037535:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  0.000859819:0.0011865:0.327902:0.869113:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00512  ES:SE:LP:AF:ID  -0.00734178:0.00609416:0.638272:0.00512:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005086 ES:SE:LP:AF:ID  -0.00703049:0.00611014:0.60206:0.005086:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838021 ES:SE:LP:AF:ID  0.000757595:0.00110486:0.309804:0.838021:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037548 ES:SE:LP:AF:ID  -0.000399354:0.00215015:0.0705811:0.037548:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838652 ES:SE:LP:AF:ID  0.000700195:0.00110797:0.275724:0.838652:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013773 ES:SE:LP:AF:ID  0.00472754:0.00386771:0.657577:0.013773:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005545 ES:SE:LP:AF:ID  -0.00419716:0.00596916:0.318759:0.005545:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839765 ES:SE:LP:AF:ID  0.000902275:0.00112295:0.376751:0.839765:rs3131965