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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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_20022.vcf.gz --id UKB-b:13378 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20022.txt.gz --cohort_controls 261932 --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-13378/UKB-b-13378_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13378/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:24 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13378/UKB-b-13378_data.vcf.gz ...
Read summary statistics for 9662430 SNPs.
Dropped 13034 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, 1288710 SNPs remain.
After merging with regression SNP LD, 1288710 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1028 (0.005)
Lambda GC: 1.3913
Mean Chi^2: 1.6064
Intercept: 1.0838 (0.0126)
Ratio: 0.1383 (0.0207)
Analysis finished at Thu Oct 17 14:44:06 2019
Total time elapsed: 1.0m:42.01s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9494,
    "inflation_factor": 1.2544,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 143,
    "n_p_sig": 8399,
    "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": 157568,
    "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": 1288710,
    "ldsc_nsnp_merge_regression_ld": 1288710,
    "ldsc_observed_scale_h2_beta": 0.1028,
    "ldsc_observed_scale_h2_se": 0.005,
    "ldsc_intercept_beta": 1.0838,
    "ldsc_intercept_se": 0.0126,
    "ldsc_lambda_gc": 1.3913,
    "ldsc_mean_chisq": 1.6064,
    "ldsc_ratio": 0.1382
}

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 9649459 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 9662430 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 8.626131e+00 5.750241e+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 7.883900e+07 5.629144e+07 828.0000000 3.256175e+07 6.944372e+07 1.145680e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.190000e-05 1.223550e-02 -0.2345600 -4.334400e-03 -2.850000e-05 4.273500e-03 2.915860e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.708800e-03 7.720200e-03 0.0026048 3.170900e-03 5.205800e-03 1.172840e-02 1.358010e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.607414e-01 3.000976e-01 0.0000000 1.900002e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.607422e-01 3.000731e-01 0.0000000 1.909410e-01 4.475879e-01 7.208602e-01 9.999998e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.067267e-01 2.570004e-01 0.0013370 1.451300e-02 8.261600e-02 3.227438e-01 9.986630e-01 ▇▂▁▁▁
numeric AF_reference 157568 0.9836927 NA NA NA NA NA NA NA 2.091256e-01 2.486395e-01 0.0000000 1.257990e-02 1.032350e-01 3.250800e-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 0.0044810 0.0047942 0.3500000 0.3499631 0.623293 0.7821490 NA
1 54676 rs2462492 C T 0.0028001 0.0047478 0.5600000 0.5553454 0.401012 NA NA
1 86028 rs114608975 T C 0.0097602 0.0076035 0.2000000 0.1992671 0.103354 0.0277556 NA
1 91536 rs6702460 G T 0.0083653 0.0046783 0.0739997 0.0737575 0.456999 0.4207270 NA
1 234313 rs8179466 C T -0.0153290 0.0092131 0.0959997 0.0961485 0.074504 NA NA
1 534192 rs6680723 C T -0.0024127 0.0053400 0.6499995 0.6514054 0.241071 NA NA
1 546697 rs12025928 A G -0.0056056 0.0066569 0.4000000 0.3997509 0.913419 NA NA
1 693731 rs12238997 A G -0.0005640 0.0044743 0.9000000 0.8996920 0.116296 0.1417730 NA
1 705882 rs72631875 G A 0.0018025 0.0065641 0.7800007 0.7836278 0.067281 0.0315495 NA
1 706368 rs55727773 A G -0.0023897 0.0033133 0.4700002 0.4707654 0.516129 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0171389 0.0069466 0.0140001 0.0136163 0.042217 0.0473243 NA
22 51219766 rs182321900 C T -0.0230201 0.0325122 0.4799997 0.4789173 0.001939 NA NA
22 51220146 rs868950473 C T -0.0222047 0.0322931 0.4899999 0.4917054 0.001981 NA NA
22 51221190 rs369304721 G A -0.0113213 0.0069401 0.1000000 0.1028291 0.049985 NA NA
22 51221731 rs115055839 T C -0.0066457 0.0051858 0.2000000 0.2000112 0.073730 0.0625000 NA
22 51222100 rs114553188 G T -0.0031886 0.0061044 0.5999997 0.6014271 0.054745 0.0880591 NA
22 51223637 rs375798137 G A -0.0031786 0.0061330 0.5999997 0.6042597 0.054377 0.0788738 NA
22 51229805 rs9616985 T C -0.0065074 0.0052040 0.2099999 0.2111352 0.073577 0.0730831 NA
22 51232488 rs376461333 A G -0.0110081 0.0123202 0.3700002 0.3715894 0.020022 NA NA
22 51237063 rs3896457 T C 0.0009915 0.0031966 0.7600007 0.7564243 0.297632 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623293 ES:SE:LP:AF:ID  0.00448097:0.00479422:0.455932:0.623293:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401012 ES:SE:LP:AF:ID  0.0028001:0.00474779:0.251812:0.401012:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103354 ES:SE:LP:AF:ID  0.00976015:0.00760348:0.69897:0.103354:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456999 ES:SE:LP:AF:ID  0.00836532:0.00467829:1.13077:0.456999:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074504 ES:SE:LP:AF:ID  -0.015329:0.00921314:1.01773:0.074504:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241071 ES:SE:LP:AF:ID  -0.00241266:0.00533998:0.187087:0.241071:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913419 ES:SE:LP:AF:ID  -0.00560555:0.0066569:0.39794:0.913419:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116296 ES:SE:LP:AF:ID  -0.000563991:0.00447433:0.0457575:0.116296:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067281 ES:SE:LP:AF:ID  0.00180246:0.00656408:0.107905:0.067281:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.516129 ES:SE:LP:AF:ID  -0.00238968:0.00331332:0.327902:0.516129:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033026 ES:SE:LP:AF:ID  -0.00295475:0.00834871:0.142668:0.033026:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036647 ES:SE:LP:AF:ID  -0.0014331:0.0075847:0.0705811:0.036647:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036759 ES:SE:LP:AF:ID  -0.00198399:0.00755725:0.102373:0.036759:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036448 ES:SE:LP:AF:ID  -0.00140363:0.0076132:0.0705811:0.036448:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016241 ES:SE:LP:AF:ID  0.00188806:0.0117973:0.0604807:0.016241:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036985 ES:SE:LP:AF:ID  -0.0021735:0.00752907:0.113509:0.036985:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037088 ES:SE:LP:AF:ID  -0.00168469:0.00750278:0.0861861:0.037088:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100993 ES:SE:LP:AF:ID  -0.000333338:0.00547962:0.0222764:0.100993:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959186 ES:SE:LP:AF:ID  0.00221727:0.00725002:0.119186:0.959186:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031375 ES:SE:LP:AF:ID  0.00617499:0.0131967:0.19382:0.031375:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053351 ES:SE:LP:AF:ID  -0.000752792:0.0104329:0.0268721:0.053351:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036609 ES:SE:LP:AF:ID  -0.00257953:0.00755115:0.136677:0.036609:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036905 ES:SE:LP:AF:ID  -0.00277079:0.00748461:0.148742:0.036905:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843431 ES:SE:LP:AF:ID  0.00064155:0.00387963:0.0604807:0.843431:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055987 ES:SE:LP:AF:ID  -0.00197106:0.00626514:0.124939:0.055987:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122168 ES:SE:LP:AF:ID  0.000795909:0.00424489:0.0705811:0.122168:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025654 ES:SE:LP:AF:ID  -0.0190528:0.0104615:1.16115:0.025654:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121395 ES:SE:LP:AF:ID  0.000651785:0.00424695:0.0555173:0.121395:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13241  ES:SE:LP:AF:ID  -0.00146873:0.00418038:0.136677:0.13241:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011208 ES:SE:LP:AF:ID  0.00538838:0.0151429:0.142668:0.011208:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005705 ES:SE:LP:AF:ID  0.0163088:0.0196519:0.387216:0.005705:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002184 ES:SE:LP:AF:ID  -0.0522409:0.0338405:0.920819:0.002184:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036854 ES:SE:LP:AF:ID  -0.00103334:0.00740376:0.05061:0.036854:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839077 ES:SE:LP:AF:ID  -0.000300064:0.00375524:0.0268721:0.839077:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838727 ES:SE:LP:AF:ID  3.91706e-05:0.00375134:0.00436481:0.838727:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.870013 ES:SE:LP:AF:ID  -7.57977e-05:0.00402649:0.00877392:0.870013:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129636 ES:SE:LP:AF:ID  -3.69407e-05:0.00403491:0.00436481:0.129636:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037354 ES:SE:LP:AF:ID  -0.00259161:0.00728178:0.142668:0.037354:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037591 ES:SE:LP:AF:ID  -0.00296169:0.00723658:0.167491:0.037591:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869403 ES:SE:LP:AF:ID  0.000381514:0.00401949:0.0362122:0.869403:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869489 ES:SE:LP:AF:ID  0.000574284:0.00402113:0.05061:0.869489:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037551 ES:SE:LP:AF:ID  -0.00277357:0.0072679:0.154902:0.037551:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869397 ES:SE:LP:AF:ID  0.000369126:0.00401924:0.0315171:0.869397:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005092 ES:SE:LP:AF:ID  -0.00976599:0.0206659:0.19382:0.005092:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005059 ES:SE:LP:AF:ID  -0.00879919:0.0207148:0.173925:0.005059:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.8382   ES:SE:LP:AF:ID  0.000325662:0.00374159:0.0315171:0.8382:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037567 ES:SE:LP:AF:ID  -0.00293066:0.00727807:0.161151:0.037567:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838836 ES:SE:LP:AF:ID  0.000535277:0.00375232:0.05061:0.838836:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013698 ES:SE:LP:AF:ID  -0.00682638:0.0131281:0.221849:0.013698:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005445 ES:SE:LP:AF:ID  0.00144678:0.0204144:0.0268721:0.005445:rs184270342