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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_103190.vcf.gz --id UKB-b:5427 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_103190.txt.gz --cohort_controls 64945 --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-5427/UKB-b-5427_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5427/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5427/UKB-b-5427_data.vcf.gz ...
Read summary statistics for 8624990 SNPs.
Dropped 7372 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, 1285749 SNPs remain.
After merging with regression SNP LD, 1285749 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0046 (0.006)
Lambda GC: 0.9939
Mean Chi^2: 1.0008
Intercept: 1.0067 (0.0063)
Ratio: 8.5958 (8.1269)
Analysis finished at Thu Oct 17 14:46:46 2019
Total time elapsed: 1.0m:27.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 83097,
    "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": 1285749,
    "ldsc_nsnp_merge_regression_ld": 1285749,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0067,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 0.9939,
    "ldsc_mean_chisq": 1.0008,
    "ldsc_ratio": 8.375
}

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 TRUE
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 8617652 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 8624990 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 8.650330e+00 5.760984e+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 7.877023e+07 5.637167e+07 828.0000000 3.238547e+07 6.929213e+07 1.145691e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.970000e-05 6.918800e-03 -0.0653546 -2.875300e-03 -2.640000e-05 2.839700e-03 7.786120e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.700500e-03 3.891200e-03 0.0022640 2.666100e-03 3.931000e-03 7.705800e-03 3.902840e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.998448e-01 2.886506e-01 0.0000001 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.998458e-01 2.886248e-01 0.0000001 2.498971e-01 5.004255e-01 7.496816e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291173e-01 2.594883e-01 0.0053900 2.519000e-02 1.138950e-01 3.626260e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83097 0.9903656 NA NA NA NA NA NA NA 2.288665e-01 2.514591e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 0.0064513 0.0041724 0.1199999 0.1220603 0.623810 0.7821490 NA
1 54676 rs2462492 C T 0.0015515 0.0041602 0.7099994 0.7091888 0.399152 NA NA
1 86028 rs114608975 T C -0.0141437 0.0066224 0.0329997 0.0327007 0.103534 0.0277556 NA
1 91536 rs6702460 G T 0.0043875 0.0040919 0.2800000 0.2836120 0.455915 0.4207270 NA
1 234313 rs8179466 C T 0.0004299 0.0080921 0.9599999 0.9576340 0.074459 NA NA
1 534192 rs6680723 C T -0.0023026 0.0046604 0.6200004 0.6212505 0.242061 NA NA
1 546697 rs12025928 A G -0.0013690 0.0057829 0.8100000 0.8128637 0.912867 NA NA
1 693731 rs12238997 A G -0.0035502 0.0038860 0.3599996 0.3609275 0.117320 0.1417730 NA
1 705882 rs72631875 G A -0.0014255 0.0056646 0.8000000 0.8013077 0.067693 0.0315495 NA
1 706368 rs55727773 A G -0.0005596 0.0028845 0.8499999 0.8461854 0.513289 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0006765 0.0035084 0.8499999 0.8471068 0.136315 0.2052720 NA
22 51219387 rs9616832 T C -0.0071065 0.0045729 0.1199999 0.1201716 0.071794 0.0654952 NA
22 51219704 rs147475742 G A -0.0098315 0.0060829 0.1100001 0.1060381 0.041193 0.0473243 NA
22 51221190 rs369304721 G A -0.0044685 0.0061265 0.4700002 0.4657731 0.048368 NA NA
22 51221731 rs115055839 T C -0.0072609 0.0045739 0.1100001 0.1124053 0.071345 0.0625000 NA
22 51222100 rs114553188 G T 0.0067474 0.0052999 0.2000000 0.2029774 0.054854 0.0880591 NA
22 51223637 rs375798137 G A 0.0066406 0.0053276 0.2099999 0.2125985 0.054474 0.0788738 NA
22 51229805 rs9616985 T C -0.0079351 0.0045880 0.0840001 0.0837114 0.071249 0.0730831 NA
22 51232488 rs376461333 A G 0.0064967 0.0105665 0.5400003 0.5386583 0.020461 NA NA
22 51237063 rs3896457 T C -0.0025939 0.0027697 0.3500000 0.3490108 0.298405 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62381  ES:SE:LP:AF:ID  0.00645131:0.00417241:0.920819:0.62381:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399152 ES:SE:LP:AF:ID  0.00155154:0.00416023:0.148742:0.399152:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103534 ES:SE:LP:AF:ID  -0.0141437:0.00662239:1.48149:0.103534:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455915 ES:SE:LP:AF:ID  0.00438749:0.00409189:0.552842:0.455915:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074459 ES:SE:LP:AF:ID  0.000429877:0.00809212:0.0177288:0.074459:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242061 ES:SE:LP:AF:ID  -0.00230262:0.00466043:0.207608:0.242061:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912867 ES:SE:LP:AF:ID  -0.00136901:0.00578292:0.091515:0.912867:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11732  ES:SE:LP:AF:ID  -0.00355021:0.00388596:0.443698:0.11732:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067693 ES:SE:LP:AF:ID  -0.00142552:0.00566458:0.09691:0.067693:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513289 ES:SE:LP:AF:ID  -0.000559554:0.00288448:0.0705811:0.513289:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033673 ES:SE:LP:AF:ID  -0.000241544:0.00719012:0.0132283:0.033673:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037452 ES:SE:LP:AF:ID  -0.0010832:0.00652114:0.0604807:0.037452:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037639 ES:SE:LP:AF:ID  -0.000984551:0.00648702:0.0555173:0.037639:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037215 ES:SE:LP:AF:ID  -0.00115164:0.00654629:0.0655015:0.037215:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  0.00435243:0.0102673:0.173925:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037855 ES:SE:LP:AF:ID  -0.000690019:0.00646484:0.0409586:0.037855:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03795  ES:SE:LP:AF:ID  -0.00115872:0.00644434:0.0655015:0.03795:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102739 ES:SE:LP:AF:ID  0.00431961:0.00470703:0.443698:0.102739:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958095 ES:SE:LP:AF:ID  -0.00164128:0.00622423:0.102373:0.958095:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031691 ES:SE:LP:AF:ID  0.00483644:0.0113966:0.173925:0.031691:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052722 ES:SE:LP:AF:ID  -0.00527994:0.00917969:0.244125:0.052722:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037444 ES:SE:LP:AF:ID  -0.000684614:0.00648665:0.0362122:0.037444:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037712 ES:SE:LP:AF:ID  -0.00130966:0.00643293:0.0757207:0.037712:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841439 ES:SE:LP:AF:ID  0.00216478:0.00336068:0.283997:0.841439:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056338 ES:SE:LP:AF:ID  -0.000709076:0.00545914:0.0457575:0.056338:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123085 ES:SE:LP:AF:ID  -0.00427704:0.00369121:0.60206:0.123085:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025132 ES:SE:LP:AF:ID  -0.00186893:0.0091923:0.0757207:0.025132:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122338 ES:SE:LP:AF:ID  -0.004176:0.0036924:0.585027:0.122338:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13414  ES:SE:LP:AF:ID  -0.0032871:0.00362476:0.443698:0.13414:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  -0.0263526:0.0129185:1.38722:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.00112466:0.0164049:0.0222764:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037594 ES:SE:LP:AF:ID  -0.00224109:0.00637334:0.136677:0.037594:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837027 ES:SE:LP:AF:ID  0.00281304:0.00325105:0.408935:0.837027:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836731 ES:SE:LP:AF:ID  0.0024586:0.00324867:0.346787:0.836731:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868554 ES:SE:LP:AF:ID  0.00363067:0.00349081:0.522879:0.868554:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131012 ES:SE:LP:AF:ID  -0.00299481:0.00349978:0.408935:0.131012:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03804  ES:SE:LP:AF:ID  -0.00135618:0.00627334:0.0809219:0.03804:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038286 ES:SE:LP:AF:ID  -0.00129539:0.00623446:0.0757207:0.038286:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867968 ES:SE:LP:AF:ID  0.00301069:0.0034852:0.408935:0.867968:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868042 ES:SE:LP:AF:ID  0.00309798:0.00348663:0.431798:0.868042:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038194 ES:SE:LP:AF:ID  -0.00163941:0.00626325:0.102373:0.038194:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867979 ES:SE:LP:AF:ID  0.00312871:0.00348512:0.431798:0.867979:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.00828289:0.0174671:0.19382:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836157 ES:SE:LP:AF:ID  0.00243873:0.00323916:0.346787:0.836157:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038197 ES:SE:LP:AF:ID  -0.00152355:0.0062724:0.091515:0.038197:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83679  ES:SE:LP:AF:ID  0.00253076:0.00324803:0.356547:0.83679:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.00118014:0.0117508:0.0362122:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.00403931:0.0174442:0.0861861:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838106 ES:SE:LP:AF:ID  0.00254301:0.00329374:0.356547:0.838106:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86822  ES:SE:LP:AF:ID  0.00316644:0.00348063:0.443698:0.86822:rs3115858