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_5118.vcf.gz --id UKB-b:11207 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5118.txt.gz --cohort_controls 83410 --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-11207/UKB-b-11207_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11207/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11207/UKB-b-11207_data.vcf.gz ...
Read summary statistics for 8878036 SNPs.
Dropped 8206 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, 1286800 SNPs remain.
After merging with regression SNP LD, 1286800 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0366 (0.0055)
Lambda GC: 1.0652
Mean Chi^2: 1.0784
Intercept: 1.0198 (0.0063)
Ratio: 0.2518 (0.0809)
Analysis finished at Thu Oct 17 14:41:58 2019
Total time elapsed: 1.0m:39.09s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9474,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 5.2431e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 78,
    "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": 88966,
    "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": 1286800,
    "ldsc_nsnp_merge_regression_ld": 1286800,
    "ldsc_observed_scale_h2_beta": 0.0366,
    "ldsc_observed_scale_h2_se": 0.0055,
    "ldsc_intercept_beta": 1.0198,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0652,
    "ldsc_mean_chisq": 1.0784,
    "ldsc_ratio": 0.2526
}

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 8869869 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 8878036 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 8.646674e+00 5.759678e+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 7.877942e+07 5.634531e+07 828.0000000 3.241435e+07 6.933640e+07 1.145509e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 5.200000e-06 1.566740e-02 -0.1722960 -6.322000e-03 3.100000e-06 6.296400e-03 1.753300e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.260710e-02 8.998700e-03 0.0047285 5.612800e-03 8.480700e-03 1.723070e-02 1.079190e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.925490e-01 2.914212e-01 0.0000000 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.925494e-01 2.913963e-01 0.0000000 2.381800e-01 4.904376e-01 7.451120e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.231463e-01 2.588611e-01 0.0041970 2.201400e-02 1.057010e-01 3.522500e-01 9.958030e-01 ▇▂▁▁▁
numeric AF_reference 88966 0.9899791 NA NA NA NA NA NA NA 2.232303e-01 2.508099e-01 0.0000000 1.936900e-02 1.228040e-01 3.502400e-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 -0.0062769 0.0087285 0.4700002 0.4720641 0.623505 0.7821490 NA
1 54676 rs2462492 C T -0.0080486 0.0086659 0.3500000 0.3530089 0.399158 NA NA
1 86028 rs114608975 T C 0.0285681 0.0137504 0.0379997 0.0377444 0.103979 0.0277556 NA
1 91536 rs6702460 G T -0.0178032 0.0085181 0.0369999 0.0366148 0.455954 0.4207270 NA
1 234313 rs8179466 C T 0.0027711 0.0167658 0.8700001 0.8687227 0.074560 NA NA
1 534192 rs6680723 C T -0.0044300 0.0097566 0.6499995 0.6497929 0.240218 NA NA
1 546697 rs12025928 A G -0.0251734 0.0120910 0.0369999 0.0373429 0.912844 NA NA
1 693731 rs12238997 A G -0.0064164 0.0081183 0.4299995 0.4293142 0.117537 0.1417730 NA
1 705882 rs72631875 G A 0.0169949 0.0119022 0.1499999 0.1533267 0.067410 0.0315495 NA
1 706368 rs55727773 A G 0.0049254 0.0060198 0.4100001 0.4132476 0.515046 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0041823 0.0072754 0.5700002 0.5653876 0.137482 0.2052720 NA
22 51219387 rs9616832 T C -0.0020696 0.0094740 0.8300000 0.8270818 0.072750 0.0654952 NA
22 51219704 rs147475742 G A -0.0078296 0.0126166 0.5300002 0.5348791 0.041805 0.0473243 NA
22 51221190 rs369304721 G A -0.0012870 0.0126985 0.9199999 0.9192753 0.049124 NA NA
22 51221731 rs115055839 T C -0.0023654 0.0094754 0.8000000 0.8028673 0.072308 0.0625000 NA
22 51222100 rs114553188 G T -0.0134227 0.0111014 0.2300001 0.2266245 0.054502 0.0880591 NA
22 51223637 rs375798137 G A -0.0140116 0.0111573 0.2099999 0.2091800 0.054135 0.0788738 NA
22 51229805 rs9616985 T C -0.0029366 0.0095108 0.7600007 0.7575023 0.072184 0.0730831 NA
22 51232488 rs376461333 A G -0.0394830 0.0224692 0.0790005 0.0788828 0.020055 NA NA
22 51237063 rs3896457 T C 0.0007686 0.0057987 0.8900000 0.8945514 0.297913 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623505 ES:SE:LP:AF:ID  -0.00627688:0.0087285:0.327902:0.623505:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399158 ES:SE:LP:AF:ID  -0.00804859:0.00866587:0.455932:0.399158:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103979 ES:SE:LP:AF:ID  0.0285681:0.0137504:1.42022:0.103979:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455954 ES:SE:LP:AF:ID  -0.0178032:0.00851814:1.4318:0.455954:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07456  ES:SE:LP:AF:ID  0.00277107:0.0167658:0.0604807:0.07456:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240218 ES:SE:LP:AF:ID  -0.00442997:0.00975657:0.187087:0.240218:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912844 ES:SE:LP:AF:ID  -0.0251734:0.012091:1.4318:0.912844:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117537 ES:SE:LP:AF:ID  -0.00641641:0.00811828:0.366532:0.117537:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06741  ES:SE:LP:AF:ID  0.0169949:0.0119022:0.823909:0.06741:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515046 ES:SE:LP:AF:ID  0.00492536:0.00601981:0.387216:0.515046:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033464 ES:SE:LP:AF:ID  -0.0309505:0.0150503:1.39794:0.033464:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037138 ES:SE:LP:AF:ID  -0.0261218:0.0136803:1.25181:0.037138:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037222 ES:SE:LP:AF:ID  -0.0256832:0.0136355:1.22185:0.037222:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036892 ES:SE:LP:AF:ID  -0.0261734:0.0137356:1.24413:0.036892:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016769 ES:SE:LP:AF:ID  -0.0165011:0.0211033:0.366532:0.016769:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037477 ES:SE:LP:AF:ID  -0.0248133:0.0135795:1.16749:0.037477:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037563 ES:SE:LP:AF:ID  -0.0251475:0.0135365:1.20066:0.037563:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100858 ES:SE:LP:AF:ID  -0.0054109:0.00998099:0.229148:0.100858:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958184 ES:SE:LP:AF:ID  0.0222272:0.0130151:1.05552:0.958184:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03191  ES:SE:LP:AF:ID  0.00990669:0.0236716:0.167491:0.03191:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052678 ES:SE:LP:AF:ID  -0.000740752:0.0191441:0.0132283:0.052678:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037038 ES:SE:LP:AF:ID  -0.0266063:0.0136287:1.29243:0.037038:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037361 ES:SE:LP:AF:ID  -0.0278815:0.013512:1.40894:0.037361:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841197 ES:SE:LP:AF:ID  0.0110582:0.00702734:0.920819:0.841197:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055916 ES:SE:LP:AF:ID  0.0074634:0.0114659:0.283997:0.055916:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12353  ES:SE:LP:AF:ID  -0.0079496:0.00770479:0.522879:0.12353:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025888 ES:SE:LP:AF:ID  0.0262023:0.0189348:0.769551:0.025888:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122691 ES:SE:LP:AF:ID  -0.00791916:0.00770948:0.522879:0.122691:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133313 ES:SE:LP:AF:ID  -0.0122515:0.00760058:0.958607:0.133313:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01125  ES:SE:LP:AF:ID  0.00581719:0.0274876:0.0809219:0.01125:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006038 ES:SE:LP:AF:ID  -0.0159065:0.0345522:0.187087:0.006038:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037341 ES:SE:LP:AF:ID  -0.0269917:0.0133587:1.36653:0.037341:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837078 ES:SE:LP:AF:ID  0.0116975:0.00680249:1.0655:0.837078:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836668 ES:SE:LP:AF:ID  0.0113888:0.0067954:1.02687:0.836668:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868267 ES:SE:LP:AF:ID  0.00777301:0.007295:0.537602:0.868267:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131381 ES:SE:LP:AF:ID  -0.00756639:0.00731001:0.522879:0.131381:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037784 ES:SE:LP:AF:ID  -0.0259325:0.0131496:1.3098:0.037784:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038035 ES:SE:LP:AF:ID  -0.0257667:0.0130682:1.3098:0.038035:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867594 ES:SE:LP:AF:ID  0.00747102:0.00728137:0.522879:0.867594:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867673 ES:SE:LP:AF:ID  0.00740523:0.00728402:0.508638:0.867673:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037976 ES:SE:LP:AF:ID  -0.0268078:0.0131227:1.38722:0.037976:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867592 ES:SE:LP:AF:ID  0.00751892:0.0072809:0.522879:0.867592:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00506  ES:SE:LP:AF:ID  -0.000408488:0.0378023:0.00436481:0.00506:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005027 ES:SE:LP:AF:ID  -0.0028707:0.0379118:0.0268721:0.005027:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836191 ES:SE:LP:AF:ID  0.0116618:0.00677998:1.07058:0.836191:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037994 ES:SE:LP:AF:ID  -0.0268427:0.0131398:1.38722:0.037994:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836792 ES:SE:LP:AF:ID  0.011421:0.00679852:1.03152:0.836792:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013255 ES:SE:LP:AF:ID  -0.0477063:0.0243082:1.30103:0.013255:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005432 ES:SE:LP:AF:ID  0.0104485:0.0371392:0.107905:0.005432:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838    ES:SE:LP:AF:ID  0.0127932:0.00688941:1.20066:0.838:rs3131965