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_767.vcf.gz --id UKB-b:8013 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_767.txt.gz --cohort_controls 260069 --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-8013/UKB-b-8013_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8013/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8013/UKB-b-8013_data.vcf.gz ...
Read summary statistics for 9672486 SNPs.
Dropped 13047 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, 1288727 SNPs remain.
After merging with regression SNP LD, 1288727 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0202 (0.0019)
Lambda GC: 1.1155
Mean Chi^2: 1.1278
Intercept: 1.025 (0.0058)
Ratio: 0.1953 (0.0457)
Analysis finished at Thu Oct 17 14:42:48 2019
Total time elapsed: 1.0m:38.59s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9496,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 5,
    "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": 156649,
    "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": 1288727,
    "ldsc_nsnp_merge_regression_ld": 1288727,
    "ldsc_observed_scale_h2_beta": 0.0202,
    "ldsc_observed_scale_h2_se": 0.0019,
    "ldsc_intercept_beta": 1.025,
    "ldsc_intercept_se": 0.0058,
    "ldsc_lambda_gc": 1.1155,
    "ldsc_mean_chisq": 1.1278,
    "ldsc_ratio": 0.1956
}

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 9659503 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 9672486 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 8.626432e+00 5.750294e+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 7.883951e+07 5.629392e+07 828.0000000 3.255993e+07 6.944422e+07 1.145707e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.300000e-05 1.134460e-02 -0.1541160 -3.729400e-03 1.440000e-05 3.734700e-03 1.922970e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.333000e-03 7.386800e-03 0.0024908 3.029700e-03 4.979400e-03 1.123160e-02 1.312530e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.864185e-01 2.928598e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.864169e-01 2.928337e-01 0.0000000 2.290293e-01 4.814097e-01 7.406505e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.066183e-01 2.570345e-01 0.0013460 1.445500e-02 8.240500e-02 3.225420e-01 9.986540e-01 ▇▂▁▁▁
numeric AF_reference 156649 0.9838047 NA NA NA NA NA NA NA 2.090566e-01 2.486326e-01 0.0000000 1.257990e-02 1.030350e-01 3.248800e-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 -0.0102966 0.0045886 0.0250000 0.0248361 0.623918 0.7821490 NA
1 54676 rs2462492 C T 0.0008788 0.0045403 0.8499999 0.8465250 0.400621 NA NA
1 86028 rs114608975 T C 0.0141045 0.0072522 0.0519996 0.0517917 0.103567 0.0277556 NA
1 91536 rs6702460 G T -0.0014329 0.0044725 0.7499995 0.7486745 0.456755 0.4207270 NA
1 234313 rs8179466 C T 0.0023136 0.0087874 0.7899998 0.7923289 0.074761 NA NA
1 534192 rs6680723 C T 0.0008395 0.0051082 0.8700001 0.8694600 0.241098 NA NA
1 546697 rs12025928 A G 0.0139745 0.0063765 0.0280001 0.0284103 0.913634 NA NA
1 693731 rs12238997 A G -0.0041532 0.0042731 0.3300000 0.3310789 0.116274 0.1417730 NA
1 705882 rs72631875 G A -0.0064950 0.0062859 0.2999998 0.3014792 0.066956 0.0315495 NA
1 706368 rs55727773 A G -0.0019020 0.0031678 0.5500004 0.5482286 0.515480 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0110455 0.0066333 0.0959997 0.0958790 0.042173 0.0473243 NA
22 51219766 rs182321900 C T -0.0269313 0.0305918 0.3800004 0.3786731 0.001995 NA NA
22 51220146 rs868950473 C T -0.0209538 0.0302984 0.4899999 0.4892005 0.002045 NA NA
22 51221190 rs369304721 G A 0.0099723 0.0066241 0.1299999 0.1322032 0.049948 NA NA
22 51221731 rs115055839 T C 0.0084985 0.0049568 0.0860003 0.0864323 0.073521 0.0625000 NA
22 51222100 rs114553188 G T -0.0052203 0.0058535 0.3700002 0.3724841 0.054464 0.0880591 NA
22 51223637 rs375798137 G A -0.0046597 0.0058817 0.4299995 0.4282170 0.054101 0.0788738 NA
22 51229805 rs9616985 T C 0.0084913 0.0049750 0.0879995 0.0878630 0.073350 0.0730831 NA
22 51232488 rs376461333 A G -0.0194720 0.0117392 0.0969996 0.0971730 0.020029 NA NA
22 51237063 rs3896457 T C 0.0018638 0.0030509 0.5400003 0.5412650 0.297261 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623918 ES:SE:LP:AF:ID  -0.0102966:0.00458862:1.60206:0.623918:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400621 ES:SE:LP:AF:ID  0.000878786:0.00454026:0.0705811:0.400621:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103567 ES:SE:LP:AF:ID  0.0141045:0.00725219:1.284:0.103567:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456755 ES:SE:LP:AF:ID  -0.00143294:0.00447252:0.124939:0.456755:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074761 ES:SE:LP:AF:ID  0.0023136:0.00878735:0.102373:0.074761:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241098 ES:SE:LP:AF:ID  0.000839496:0.00510815:0.0604807:0.241098:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913634 ES:SE:LP:AF:ID  0.0139745:0.00637647:1.55284:0.913634:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116274 ES:SE:LP:AF:ID  -0.0041532:0.00427309:0.481486:0.116274:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066956 ES:SE:LP:AF:ID  -0.00649502:0.00628591:0.522879:0.066956:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51548  ES:SE:LP:AF:ID  -0.001902:0.0031678:0.259637:0.51548:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033272 ES:SE:LP:AF:ID  0.00111594:0.00795826:0.05061:0.033272:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036895 ES:SE:LP:AF:ID  0.0022221:0.00723232:0.119186:0.036895:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036999 ES:SE:LP:AF:ID  0.00239837:0.0072074:0.130768:0.036999:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036713 ES:SE:LP:AF:ID  0.00119723:0.00725651:0.0604807:0.036713:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016474 ES:SE:LP:AF:ID  -0.0220786:0.0111826:1.31876:0.016474:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037258 ES:SE:LP:AF:ID  0.00292117:0.00717637:0.167491:0.037258:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037357 ES:SE:LP:AF:ID  0.00199632:0.00715215:0.107905:0.037357:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101134 ES:SE:LP:AF:ID  0.0124542:0.00523311:1.76955:0.101134:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958719 ES:SE:LP:AF:ID  -0.00019642:0.00688924:0.00877392:0.958719:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031518 ES:SE:LP:AF:ID  0.00200918:0.0125225:0.0604807:0.031518:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053243 ES:SE:LP:AF:ID  0.00491611:0.00997796:0.207608:0.053243:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036856 ES:SE:LP:AF:ID  0.00146355:0.00720018:0.0757207:0.036856:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037172 ES:SE:LP:AF:ID  0.00099159:0.00713397:0.05061:0.037172:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842876 ES:SE:LP:AF:ID  0.00481734:0.00369888:0.721246:0.842876:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055838 ES:SE:LP:AF:ID  0.00269365:0.00600542:0.187087:0.055838:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122306 ES:SE:LP:AF:ID  -0.00488904:0.00405208:0.638272:0.122306:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02572  ES:SE:LP:AF:ID  0.0093402:0.00997919:0.455932:0.02572:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121517 ES:SE:LP:AF:ID  -0.00488993:0.00405392:0.638272:0.121517:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132501 ES:SE:LP:AF:ID  -0.00273291:0.00399475:0.309804:0.132501:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011228 ES:SE:LP:AF:ID  -0.0315348:0.0145142:1.52288:0.011228:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005737 ES:SE:LP:AF:ID  -0.0223538:0.0187235:0.638272:0.005737:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002329 ES:SE:LP:AF:ID  -0.0073977:0.0310972:0.091515:0.002329:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.037107 ES:SE:LP:AF:ID  0.000926174:0.00705971:0.0457575:0.037107:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838687 ES:SE:LP:AF:ID  0.00341814:0.00358372:0.468521:0.838687:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838271 ES:SE:LP:AF:ID  0.00351137:0.00357938:0.481486:0.838271:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869728 ES:SE:LP:AF:ID  0.00325898:0.00384225:0.39794:0.869728:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.12995  ES:SE:LP:AF:ID  -0.00317849:0.00384963:0.387216:0.12995:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037614 ES:SE:LP:AF:ID  5.57329e-06:0.00694002:-0:0.037614:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037866 ES:SE:LP:AF:ID  -0.000155503:0.00689569:0.00877392:0.037866:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869031 ES:SE:LP:AF:ID  0.0031607:0.00383415:0.387216:0.869031:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869133 ES:SE:LP:AF:ID  0.00326609:0.00383577:0.408935:0.869133:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037819 ES:SE:LP:AF:ID  0.000732362:0.00692607:0.0362122:0.037819:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869036 ES:SE:LP:AF:ID  0.00321085:0.00383418:0.39794:0.869036:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005127 ES:SE:LP:AF:ID  0.012181:0.0197006:0.267606:0.005127:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005093 ES:SE:LP:AF:ID  0.0119573:0.0197527:0.267606:0.005093:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.83771  ES:SE:LP:AF:ID  0.00345169:0.00356943:0.481486:0.83771:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037834 ES:SE:LP:AF:ID  0.000420661:0.00693563:0.0222764:0.037834:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838363 ES:SE:LP:AF:ID  0.00340752:0.00357956:0.468521:0.838363:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.0137   ES:SE:LP:AF:ID  -0.00577651:0.0125625:0.187087:0.0137:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005544 ES:SE:LP:AF:ID  -0.0151396:0.019337:0.366532:0.005544:rs184270342