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_4093.vcf.gz --id UKB-b:8221 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4093.txt.gz --cohort_controls 147338 --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-8221/UKB-b-8221_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8221/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8221/UKB-b-8221_data.vcf.gz ...
Read summary statistics for 9307185 SNPs.
Dropped 10321 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, 1287965 SNPs remain.
After merging with regression SNP LD, 1287965 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0014 (0.0031)
Lambda GC: 1.0075
Mean Chi^2: 1.0029
Intercept: 1.0071 (0.0058)
Ratio: 2.4342 (1.987)
Analysis finished at Thu Oct 17 14:43:09 2019
Total time elapsed: 1.0m:43.41s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9487,
    "inflation_factor": 1,
    "mean_EFFECT": -9.4812e-09,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 12,
    "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": 112118,
    "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": 1287965,
    "ldsc_nsnp_merge_regression_ld": 1287965,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0071,
    "ldsc_intercept_se": 0.0058,
    "ldsc_lambda_gc": 1.0075,
    "ldsc_mean_chisq": 1.0029,
    "ldsc_ratio": 2.4483
}

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 9296916 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 9307185 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 8.635205e+00 5.754136e+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 7.881133e+07 5.630810e+07 828.0000000 3.250190e+07 6.939478e+07 1.145414e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 0.000000e+00 1.305000e-03 -0.0164774 -4.912000e-04 -1.710000e-05 4.456000e-04 2.417480e-02 ▁▅▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.020500e-03 8.056000e-04 0.0003423 4.118000e-04 6.506000e-04 1.398900e-03 1.187570e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.991969e-01 2.881282e-01 0.0000000 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 4.991980e-01 2.881015e-01 0.0000000 2.499136e-01 4.978023e-01 7.480896e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.137728e-01 2.578167e-01 0.0023760 1.749500e-02 9.251100e-02 3.356530e-01 9.976240e-01 ▇▂▁▁▁
numeric AF_reference 112118 0.9879536 NA NA NA NA NA NA NA 2.147861e-01 2.496065e-01 0.0000000 1.477640e-02 1.110220e-01 3.354630e-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 0.0005435 0.0006312 0.3900004 0.3891947 0.623753 0.7821490 NA
1 54676 rs2462492 C T 0.0005032 0.0006268 0.4199997 0.4220659 0.399286 NA NA
1 86028 rs114608975 T C -0.0009904 0.0009991 0.3200000 0.3215001 0.103801 0.0277556 NA
1 91536 rs6702460 G T -0.0001517 0.0006176 0.8100000 0.8059911 0.456124 0.4207270 NA
1 234313 rs8179466 C T -0.0007110 0.0012200 0.5600000 0.5600479 0.074452 NA NA
1 534192 rs6680723 C T -0.0004663 0.0007055 0.5099998 0.5086076 0.241206 NA NA
1 546697 rs12025928 A G 0.0010352 0.0008757 0.2399999 0.2371764 0.913048 NA NA
1 693731 rs12238997 A G 0.0003528 0.0005887 0.5500004 0.5490106 0.116893 0.1417730 NA
1 705882 rs72631875 G A -0.0010878 0.0008602 0.2099999 0.2059949 0.067674 0.0315495 NA
1 706368 rs55727773 A G -0.0001566 0.0004358 0.7199992 0.7192734 0.514999 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0001700 0.0005277 0.7499995 0.7474095 0.137101 0.2052720 NA
22 51219387 rs9616832 T C -0.0000023 0.0006862 1.0000000 0.9973322 0.072767 0.0654952 NA
22 51219704 rs147475742 G A 0.0005869 0.0009162 0.5199996 0.5218097 0.041650 0.0473243 NA
22 51221190 rs369304721 G A -0.0000082 0.0009183 0.9900000 0.9928905 0.049118 NA NA
22 51221731 rs115055839 T C 0.0000175 0.0006867 0.9800000 0.9796697 0.072246 0.0625000 NA
22 51222100 rs114553188 G T -0.0005034 0.0008043 0.5300002 0.5314050 0.054414 0.0880591 NA
22 51223637 rs375798137 G A -0.0004828 0.0008085 0.5500004 0.5504102 0.054022 0.0788738 NA
22 51229805 rs9616985 T C -0.0001040 0.0006892 0.8800001 0.8800060 0.072111 0.0730831 NA
22 51232488 rs376461333 A G -0.0013188 0.0016138 0.4100001 0.4138045 0.020176 NA NA
22 51237063 rs3896457 T C -0.0004957 0.0004191 0.2399999 0.2368476 0.297698 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623753 ES:SE:LP:AF:ID  0.000543543:0.000631235:0.408935:0.623753:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399286 ES:SE:LP:AF:ID  0.000503232:0.000626813:0.376751:0.399286:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103801 ES:SE:LP:AF:ID  -0.000990441:0.000999053:0.49485:0.103801:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456124 ES:SE:LP:AF:ID  -0.000151692:0.000617636:0.091515:0.456124:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074452 ES:SE:LP:AF:ID  -0.000711009:0.00122005:0.251812:0.074452:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241206 ES:SE:LP:AF:ID  -0.000466344:0.000705505:0.29243:0.241206:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913048 ES:SE:LP:AF:ID  0.0010352:0.000875749:0.619789:0.913048:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116893 ES:SE:LP:AF:ID  0.000352777:0.000588704:0.259637:0.116893:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067674 ES:SE:LP:AF:ID  -0.00108783:0.000860179:0.677781:0.067674:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514999 ES:SE:LP:AF:ID  -0.000156631:0.000435776:0.142668:0.514999:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033479 ES:SE:LP:AF:ID  0.000749765:0.00109037:0.309804:0.033479:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037191 ES:SE:LP:AF:ID  0.00052563:0.000989796:0.221849:0.037191:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037317 ES:SE:LP:AF:ID  0.000528584:0.000985927:0.229148:0.037317:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036971 ES:SE:LP:AF:ID  0.000440609:0.000993444:0.180456:0.036971:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016329 ES:SE:LP:AF:ID  0.000495763:0.00155037:0.124939:0.016329:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03755  ES:SE:LP:AF:ID  0.000372459:0.000982087:0.154902:0.03755:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037642 ES:SE:LP:AF:ID  0.000371774:0.000979013:0.154902:0.037642:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101548 ES:SE:LP:AF:ID  -0.000924177:0.000717802:0.69897:0.101548:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958414 ES:SE:LP:AF:ID  -0.000331556:0.000944611:0.136677:0.958414:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031796 ES:SE:LP:AF:ID  0.0014959:0.00171939:0.420216:0.031796:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052667 ES:SE:LP:AF:ID  0.000107171:0.00139314:0.0268721:0.052667:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037117 ES:SE:LP:AF:ID  0.000411471:0.000985828:0.167491:0.037117:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037437 ES:SE:LP:AF:ID  0.000299604:0.000977106:0.119186:0.037437:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842108 ES:SE:LP:AF:ID  -0.000278578:0.000509599:0.236572:0.842108:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056073 ES:SE:LP:AF:ID  -2.9162e-05:0.000827356:0.0132283:0.056073:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122811 ES:SE:LP:AF:ID  0.000456893:0.000558716:0.387216:0.122811:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025711 ES:SE:LP:AF:ID  0.000159868:0.00137522:0.0409586:0.025711:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122034 ES:SE:LP:AF:ID  0.000472899:0.00055899:0.39794:0.122034:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133269 ES:SE:LP:AF:ID  0.000157009:0.000549952:0.107905:0.133269:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011225 ES:SE:LP:AF:ID  0.00205759:0.0019946:0.522879:0.011225:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005838 ES:SE:LP:AF:ID  -0.0040215:0.00254812:0.958607:0.005838:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037382 ES:SE:LP:AF:ID  0.000256245:0.000967124:0.102373:0.037382:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837763 ES:SE:LP:AF:ID  -0.000221561:0.000493272:0.187087:0.837763:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83738  ES:SE:LP:AF:ID  -0.000190128:0.000492698:0.154902:0.83738:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869018 ES:SE:LP:AF:ID  -0.000411376:0.000528849:0.356547:0.869018:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130669 ES:SE:LP:AF:ID  0.000374423:0.000529905:0.318759:0.130669:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037878 ES:SE:LP:AF:ID  0.000264233:0.00095122:0.107905:0.037878:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038137 ES:SE:LP:AF:ID  0.000332936:0.000945089:0.142668:0.038137:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868354 ES:SE:LP:AF:ID  -0.000397349:0.000527798:0.346787:0.868354:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868458 ES:SE:LP:AF:ID  -0.000402848:0.000528004:0.346787:0.868458:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.03806  ES:SE:LP:AF:ID  0.0003439:0.00094931:0.142668:0.03806:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868346 ES:SE:LP:AF:ID  -0.000395876:0.000527763:0.346787:0.868346:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005164 ES:SE:LP:AF:ID  0.000480681:0.00270872:0.0655015:0.005164:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005134 ES:SE:LP:AF:ID  0.000491459:0.00271456:0.0655015:0.005134:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836876 ES:SE:LP:AF:ID  -0.00021261:0.000491449:0.173925:0.836876:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038071 ES:SE:LP:AF:ID  0.000370132:0.000950606:0.154902:0.038071:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837507 ES:SE:LP:AF:ID  -0.000201377:0.000492816:0.167491:0.837507:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013264 ES:SE:LP:AF:ID  -0.000757911:0.00176156:0.173925:0.013264:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005486 ES:SE:LP:AF:ID  0.00373762:0.00267181:0.79588:0.005486:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838724 ES:SE:LP:AF:ID  -0.000223329:0.000499596:0.187087:0.838724:rs3131965