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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_2010.vcf.gz --id UKB-b:19957 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2010.txt.gz --cohort_cases 94665 --cohort_controls 351144 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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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-19957/UKB-b-19957_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19957/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19957/UKB-b-19957_data.vcf.gz ...
Read summary statistics for 8974238 SNPs.
Dropped 8620 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, 1287043 SNPs remain.
After merging with regression SNP LD, 1287043 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0423 (0.0026)
Lambda GC: 1.3147
Mean Chi^2: 1.3995
Intercept: 1.0132 (0.0088)
Ratio: 0.0332 (0.0221)
Analysis finished at Thu Oct 17 14:43:46 2019
Total time elapsed: 1.0m:41.04s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9475,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 22,
    "n_p_sig": 2290,
    "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": 92432,
    "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": 1287043,
    "ldsc_nsnp_merge_regression_ld": 1287043,
    "ldsc_observed_scale_h2_beta": 0.0423,
    "ldsc_observed_scale_h2_se": 0.0026,
    "ldsc_intercept_beta": 1.0132,
    "ldsc_intercept_se": 0.0088,
    "ldsc_lambda_gc": 1.3147,
    "ldsc_mean_chisq": 1.3995,
    "ldsc_ratio": 0.033
}

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 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 8965657 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 8974238 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 8.644141e+00 5.758352e+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 7.879166e+07 5.633860e+07 828.0000000 3.243229e+07 6.935819e+07 1.145498e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.730000e-05 2.943600e-03 -0.0336084 -1.225500e-03 1.310000e-05 1.252300e-03 3.089920e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.279900e-03 1.660300e-03 0.0008350 9.941000e-04 1.517700e-03 3.125100e-03 1.935330e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.667397e-01 2.979815e-01 0.0000000 2.000000e-01 4.600002e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.667412e-01 2.979565e-01 0.0000000 2.003974e-01 4.556278e-01 7.245037e-01 9.999998e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.209038e-01 2.586265e-01 0.0036980 2.085200e-02 1.024850e-01 3.485940e-01 9.963020e-01 ▇▂▁▁▁
numeric AF_reference 92432 0.9897003 NA NA NA NA NA NA NA 2.211270e-01 2.505325e-01 0.0000000 1.817090e-02 1.198080e-01 3.468450e-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 -0.0014610 0.0015357 0.3400001 0.3414159 0.623675 0.7821490 NA
1 54676 rs2462492 C T -0.0010477 0.0015213 0.4899999 0.4910328 0.400476 NA NA
1 86028 rs114608975 T C 0.0003295 0.0024332 0.8900000 0.8922968 0.103557 0.0277556 NA
1 91536 rs6702460 G T -0.0024621 0.0014984 0.1000000 0.1003595 0.456799 0.4207270 NA
1 234313 rs8179466 C T -0.0043259 0.0029549 0.1400000 0.1432017 0.074475 NA NA
1 534192 rs6680723 C T 0.0026709 0.0017109 0.1199999 0.1184984 0.241025 NA NA
1 546697 rs12025928 A G 0.0004325 0.0021345 0.8400000 0.8394150 0.913460 NA NA
1 693731 rs12238997 A G -0.0010156 0.0014342 0.4799997 0.4788615 0.116282 0.1417730 NA
1 705882 rs72631875 G A 0.0027430 0.0021014 0.1900002 0.1917817 0.067292 0.0315495 NA
1 706368 rs55727773 A G -0.0016755 0.0010622 0.1100001 0.1146908 0.515727 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0004294 0.0012824 0.7400005 0.7377229 0.137913 0.2052720 NA
22 51219387 rs9616832 T C 0.0004927 0.0016634 0.7700005 0.7671003 0.073808 0.0654952 NA
22 51219704 rs147475742 G A 0.0005614 0.0022294 0.8000000 0.8011942 0.041980 0.0473243 NA
22 51221190 rs369304721 G A 0.0014315 0.0022254 0.5199996 0.5200548 0.049775 NA NA
22 51221731 rs115055839 T C 0.0004578 0.0016644 0.7800007 0.7832641 0.073297 0.0625000 NA
22 51222100 rs114553188 G T -0.0000683 0.0019630 0.9699999 0.9722391 0.054332 0.0880591 NA
22 51223637 rs375798137 G A -0.0000687 0.0019725 0.9699999 0.9722270 0.053960 0.0788738 NA
22 51229805 rs9616985 T C 0.0005487 0.0016705 0.7400005 0.7425323 0.073133 0.0730831 NA
22 51232488 rs376461333 A G 0.0006860 0.0039421 0.8600001 0.8618598 0.019982 NA NA
22 51237063 rs3896457 T C -0.0001933 0.0010220 0.8499999 0.8499596 0.298073 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623675 ES:SE:LP:AF:ID  -0.00146104:0.00153572:0.468521:0.623675:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400476 ES:SE:LP:AF:ID  -0.00104769:0.00152133:0.309804:0.400476:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103557 ES:SE:LP:AF:ID  0.000329457:0.00243324:0.05061:0.103557:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456799 ES:SE:LP:AF:ID  -0.00246209:0.00149843:1:0.456799:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074475 ES:SE:LP:AF:ID  -0.00432589:0.0029549:0.853872:0.074475:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241025 ES:SE:LP:AF:ID  0.00267093:0.00171092:0.920819:0.241025:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91346  ES:SE:LP:AF:ID  0.000432535:0.00213448:0.0757207:0.91346:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116282 ES:SE:LP:AF:ID  -0.0010156:0.00143419:0.318759:0.116282:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067292 ES:SE:LP:AF:ID  0.002743:0.00210139:0.721246:0.067292:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515727 ES:SE:LP:AF:ID  -0.00167551:0.00106216:0.958607:0.515727:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033015 ES:SE:LP:AF:ID  -0.00022894:0.00267802:0.0315171:0.033015:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036635 ES:SE:LP:AF:ID  -0.000201013:0.00243253:0.0315171:0.036635:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036748 ES:SE:LP:AF:ID  -0.000282283:0.00242349:0.0409586:0.036748:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036443 ES:SE:LP:AF:ID  0.000216688:0.00244106:0.0315171:0.036443:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016401 ES:SE:LP:AF:ID  0.000927295:0.00375981:0.091515:0.016401:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036986 ES:SE:LP:AF:ID  0.000139628:0.00241389:0.0222764:0.036986:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037085 ES:SE:LP:AF:ID  9.2605e-05:0.00240556:0.0132283:0.037085:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101237 ES:SE:LP:AF:ID  0.000297914:0.00175241:0.0604807:0.101237:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959106 ES:SE:LP:AF:ID  0.000637534:0.00232055:0.107905:0.959106:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031427 ES:SE:LP:AF:ID  0.00210645:0.00421649:0.207608:0.031427:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.0533   ES:SE:LP:AF:ID  0.00197735:0.00334803:0.259637:0.0533:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036601 ES:SE:LP:AF:ID  -0.000289892:0.00242122:0.0457575:0.036601:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036914 ES:SE:LP:AF:ID  -9.24746e-05:0.00239929:0.0132283:0.036914:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84324  ES:SE:LP:AF:ID  0.000906109:0.00124291:0.327902:0.84324:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055871 ES:SE:LP:AF:ID  -0.000391605:0.0020133:0.0705811:0.055871:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122274 ES:SE:LP:AF:ID  -0.000416681:0.00136041:0.119186:0.122274:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025697 ES:SE:LP:AF:ID  -0.00729422:0.00334644:1.5376:0.025697:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121519 ES:SE:LP:AF:ID  -0.00043642:0.00136097:0.124939:0.121519:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132336 ES:SE:LP:AF:ID  -0.00112023:0.00134141:0.39794:0.132336:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011133 ES:SE:LP:AF:ID  0.00247644:0.00487905:0.21467:0.011133:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005729 ES:SE:LP:AF:ID  -0.00023883:0.0062803:0.0132283:0.005729:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036827 ES:SE:LP:AF:ID  -0.000343294:0.00237513:0.05061:0.036827:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838972 ES:SE:LP:AF:ID  0.000307542:0.00120368:0.09691:0.838972:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838609 ES:SE:LP:AF:ID  0.000408841:0.00120239:0.136677:0.838609:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869818 ES:SE:LP:AF:ID  -8.02853e-05:0.0012902:0.0222764:0.869818:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129818 ES:SE:LP:AF:ID  3.66437e-05:0.00129286:0.00877392:0.129818:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037342 ES:SE:LP:AF:ID  0.000240073:0.00233465:0.0362122:0.037342:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037585 ES:SE:LP:AF:ID  0.000188737:0.00231994:0.0268721:0.037585:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869174 ES:SE:LP:AF:ID  -4.38514e-05:0.00128771:0.0132283:0.869174:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869275 ES:SE:LP:AF:ID  -5.47084e-05:0.00128823:0.0132283:0.869275:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037545 ES:SE:LP:AF:ID  0.000349056:0.00232992:0.0555173:0.037545:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869176 ES:SE:LP:AF:ID  -5.16787e-05:0.00128768:0.0132283:0.869176:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00511  ES:SE:LP:AF:ID  0.00875575:0.00662027:0.721246:0.00511:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005077 ES:SE:LP:AF:ID  0.0087033:0.00663745:0.721246:0.005077:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838071 ES:SE:LP:AF:ID  0.000326038:0.00119911:0.102373:0.838071:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037558 ES:SE:LP:AF:ID  0.000232157:0.00233322:0.0362122:0.037558:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838698 ES:SE:LP:AF:ID  0.000336075:0.00120247:0.107905:0.838698:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013801 ES:SE:LP:AF:ID  -7.35752e-05:0.00419174:0.00436481:0.013801:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005546 ES:SE:LP:AF:ID  0.00201891:0.00647649:0.119186:0.005546:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839801 ES:SE:LP:AF:ID  7.40913e-06:0.00121873:-0:0.839801:rs3131965