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_2704.vcf.gz --id UKB-b:2482 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2704.txt.gz --cohort_controls 192526 --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-2482/UKB-b-2482_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2482/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2482/UKB-b-2482_data.vcf.gz ...
Read summary statistics for 9475819 SNPs.
Dropped 11446 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, 1288324 SNPs remain.
After merging with regression SNP LD, 1288324 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0086 (0.0022)
Lambda GC: 1.0634
Mean Chi^2: 1.064
Intercept: 1.0317 (0.0056)
Ratio: 0.4954 (0.0876)
Analysis finished at Thu Oct 17 14:44:32 2019
Total time elapsed: 1.0m:43.71s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.949,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 3,
    "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": 130398,
    "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": 1288324,
    "ldsc_nsnp_merge_regression_ld": 1288324,
    "ldsc_observed_scale_h2_beta": 0.0086,
    "ldsc_observed_scale_h2_se": 0.0022,
    "ldsc_intercept_beta": 1.0317,
    "ldsc_intercept_se": 0.0056,
    "ldsc_lambda_gc": 1.0634,
    "ldsc_mean_chisq": 1.064,
    "ldsc_ratio": 0.4953
}

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 9464433 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 9475819 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 8.629684e+00 5.752226e+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 7.883495e+07 5.631148e+07 828.0000000 3.253848e+07 6.941481e+07 1.145674e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.173000e-04 1.004760e-02 -0.1401210 -3.534300e-03 -5.670000e-05 3.340100e-03 1.326210e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.542700e-03 6.271500e-03 0.0024002 2.906000e-03 4.674900e-03 1.027270e-02 8.590930e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.926769e-01 2.904856e-01 0.0000000 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.926758e-01 2.904619e-01 0.0000000 2.395694e-01 4.899365e-01 7.441342e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.102947e-01 2.573797e-01 0.0018180 1.597100e-02 8.753400e-02 3.292270e-01 9.981820e-01 ▇▂▁▁▁
numeric AF_reference 130398 0.9862389 NA NA NA NA NA NA NA 2.118843e-01 2.491089e-01 0.0000000 1.357830e-02 1.070290e-01 3.304710e-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 -0.0023299 0.0044287 0.5999997 0.5988153 0.623427 0.7821490 NA
1 54676 rs2462492 C T 0.0024360 0.0043794 0.5800000 0.5780389 0.400840 NA NA
1 86028 rs114608975 T C 0.0017967 0.0070185 0.8000000 0.7979570 0.103536 0.0277556 NA
1 91536 rs6702460 G T 0.0005112 0.0043127 0.9100000 0.9056486 0.457185 0.4207270 NA
1 234313 rs8179466 C T -0.0085549 0.0085324 0.3200000 0.3160338 0.074303 NA NA
1 534192 rs6680723 C T -0.0013265 0.0049262 0.7899998 0.7877197 0.241168 NA NA
1 546697 rs12025928 A G -0.0015608 0.0061332 0.8000000 0.7991237 0.913439 NA NA
1 693731 rs12238997 A G 0.0033749 0.0041290 0.4100001 0.4137190 0.116336 0.1417730 NA
1 705882 rs72631875 G A -0.0010150 0.0060427 0.8700001 0.8666048 0.067163 0.0315495 NA
1 706368 rs55727773 A G -0.0018972 0.0030553 0.5300002 0.5346204 0.514891 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0168369 0.0064042 0.0086000 0.0085628 0.042128 0.0473243 NA
22 51219766 rs182321900 C T -0.0495449 0.0306994 0.1100001 0.1065552 0.001851 NA NA
22 51220146 rs868950473 C T -0.0500895 0.0304417 0.1000000 0.0998824 0.001890 NA NA
22 51221190 rs369304721 G A -0.0097679 0.0063946 0.1299999 0.1266284 0.049966 NA NA
22 51221731 rs115055839 T C -0.0081834 0.0047812 0.0870001 0.0869761 0.073643 0.0625000 NA
22 51222100 rs114553188 G T -0.0045649 0.0056293 0.4199997 0.4174142 0.054726 0.0880591 NA
22 51223637 rs375798137 G A -0.0045386 0.0056566 0.4199997 0.4223512 0.054350 0.0788738 NA
22 51229805 rs9616985 T C -0.0084498 0.0047984 0.0779992 0.0782443 0.073510 0.0730831 NA
22 51232488 rs376461333 A G -0.0139042 0.0113143 0.2200002 0.2191074 0.020061 NA NA
22 51237063 rs3896457 T C 0.0029309 0.0029430 0.3200000 0.3193076 0.297520 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623427 ES:SE:LP:AF:ID  -0.00232994:0.00442866:0.221849:0.623427:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.40084  ES:SE:LP:AF:ID  0.00243602:0.00437935:0.236572:0.40084:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  0.00179668:0.0070185:0.09691:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457185 ES:SE:LP:AF:ID  0.000511183:0.00431273:0.0409586:0.457185:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074303 ES:SE:LP:AF:ID  -0.0085549:0.00853236:0.49485:0.074303:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241168 ES:SE:LP:AF:ID  -0.0013265:0.00492623:0.102373:0.241168:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913439 ES:SE:LP:AF:ID  -0.00156079:0.00613322:0.09691:0.913439:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116336 ES:SE:LP:AF:ID  0.00337489:0.00412898:0.387216:0.116336:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067163 ES:SE:LP:AF:ID  -0.00101501:0.00604271:0.0604807:0.067163:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514891 ES:SE:LP:AF:ID  -0.00189722:0.00305526:0.275724:0.514891:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03297  ES:SE:LP:AF:ID  -0.00961062:0.0077093:0.677781:0.03297:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03653  ES:SE:LP:AF:ID  -0.00811283:0.00701081:0.60206:0.03653:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036646 ES:SE:LP:AF:ID  -0.00823216:0.00698477:0.619789:0.036646:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036338 ES:SE:LP:AF:ID  -0.00852329:0.00703601:0.638272:0.036338:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016263 ES:SE:LP:AF:ID  0.0141191:0.0108625:0.721246:0.016263:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036886 ES:SE:LP:AF:ID  -0.00843826:0.00695664:0.638272:0.036886:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036967 ES:SE:LP:AF:ID  -0.00878382:0.00693449:0.677781:0.036967:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101077 ES:SE:LP:AF:ID  0.00325498:0.00504994:0.283997:0.101077:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959208 ES:SE:LP:AF:ID  0.0110677:0.00668652:1.00877:0.959208:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031472 ES:SE:LP:AF:ID  -0.0119669:0.0120881:0.49485:0.031472:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053349 ES:SE:LP:AF:ID  -0.00037932:0.00963545:0.0132283:0.053349:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036489 ES:SE:LP:AF:ID  -0.00751163:0.00697758:0.552842:0.036489:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036834 ES:SE:LP:AF:ID  -0.00863188:0.00691316:0.677781:0.036834:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843223 ES:SE:LP:AF:ID  0.00183239:0.00357889:0.21467:0.843223:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055983 ES:SE:LP:AF:ID  0.00491632:0.00578912:0.39794:0.055983:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122338 ES:SE:LP:AF:ID  0.00322098:0.00391672:0.387216:0.122338:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025656 ES:SE:LP:AF:ID  -0.00530507:0.0096545:0.236572:0.025656:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121547 ES:SE:LP:AF:ID  0.00344597:0.00391848:0.420216:0.121547:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132539 ES:SE:LP:AF:ID  -0.00341146:0.00385822:0.420216:0.132539:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011116 ES:SE:LP:AF:ID  -0.00949867:0.0140673:0.30103:0.011116:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005726 ES:SE:LP:AF:ID  -0.00999859:0.0180449:0.236572:0.005726:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00225  ES:SE:LP:AF:ID  -0.0698539:0.0305214:1.65758:0.00225:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036747 ES:SE:LP:AF:ID  -0.00899664:0.0068439:0.721246:0.036747:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.8389   ES:SE:LP:AF:ID  0.00102239:0.00346374:0.113509:0.8389:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838512 ES:SE:LP:AF:ID  0.00105093:0.00346008:0.119186:0.838512:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869752 ES:SE:LP:AF:ID  -0.00275281:0.00371275:0.337242:0.869752:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129907 ES:SE:LP:AF:ID  0.00327459:0.00372071:0.420216:0.129907:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037233 ES:SE:LP:AF:ID  -0.0083744:0.00673049:0.677781:0.037233:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037507 ES:SE:LP:AF:ID  -0.00815419:0.00668415:0.657577:0.037507:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869076 ES:SE:LP:AF:ID  -0.00277922:0.0037056:0.346787:0.869076:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869152 ES:SE:LP:AF:ID  -0.00296887:0.00370698:0.376751:0.869152:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037456 ES:SE:LP:AF:ID  -0.00856849:0.00671433:0.69897:0.037456:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869073 ES:SE:LP:AF:ID  -0.00281464:0.0037054:0.346787:0.869073:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005152 ES:SE:LP:AF:ID  0.00661205:0.0189966:0.136677:0.005152:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00512  ES:SE:LP:AF:ID  0.0068174:0.0190465:0.142668:0.00512:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837958 ES:SE:LP:AF:ID  0.000959366:0.00345024:0.107905:0.837958:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037472 ES:SE:LP:AF:ID  -0.00881398:0.00672377:0.721246:0.037472:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838595 ES:SE:LP:AF:ID  0.000714983:0.00346003:0.0757207:0.838595:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013666 ES:SE:LP:AF:ID  -0.00243657:0.0121405:0.0757207:0.013666:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005616 ES:SE:LP:AF:ID  0.0245526:0.0184997:0.744727:0.005616:rs184270342