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_20240.vcf.gz --id UKB-b:18683 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20240.txt.gz --cohort_controls 106162 --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-18683/UKB-b-18683_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18683/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-18683/UKB-b-18683_data.vcf.gz ...
Read summary statistics for 9064681 SNPs.
Dropped 8996 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, 1287331 SNPs remain.
After merging with regression SNP LD, 1287331 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.116 (0.0072)
Lambda GC: 1.2132
Mean Chi^2: 1.2496
Intercept: 1.0056 (0.0074)
Ratio: 0.0226 (0.0297)
Analysis finished at Thu Oct 17 14:41:59 2019
Total time elapsed: 1.0m:40.32s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9479,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 6,
    "n_p_sig": 431,
    "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": 96287,
    "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": 1287331,
    "ldsc_nsnp_merge_regression_ld": 1287331,
    "ldsc_observed_scale_h2_beta": 0.116,
    "ldsc_observed_scale_h2_se": 0.0072,
    "ldsc_intercept_beta": 1.0056,
    "ldsc_intercept_se": 0.0074,
    "ldsc_lambda_gc": 1.2132,
    "ldsc_mean_chisq": 1.2496,
    "ldsc_ratio": 0.0224
}

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 9055727 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 9064681 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 8.641512e+00 5.757753e+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 7.879298e+07 5.633524e+07 828.0000000 3.244336e+07 6.935463e+07 1.145365e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.959000e-04 2.207050e-02 -0.3035880 -9.012000e-03 -7.280000e-05 8.763700e-03 2.310470e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.718770e-02 1.276780e-02 0.0061555 7.347400e-03 1.132160e-02 2.358650e-02 1.394640e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.775371e-01 2.955089e-01 0.0000000 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.775367e-01 2.954844e-01 0.0000000 2.153436e-01 4.694086e-01 7.344158e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.188914e-01 2.583988e-01 0.0032970 1.984800e-02 9.966700e-02 3.450940e-01 9.967030e-01 ▇▂▁▁▁
numeric AF_reference 96287 0.9893778 NA NA NA NA NA NA NA 2.192939e-01 2.502781e-01 0.0000000 1.697280e-02 1.172120e-01 3.436500e-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 0.0153710 0.0112990 0.1700000 0.1737078 0.623900 0.7821490 NA
1 54676 rs2462492 C T 0.0017990 0.0112016 0.8700001 0.8724061 0.399696 NA NA
1 86028 rs114608975 T C -0.0370248 0.0179746 0.0389996 0.0394138 0.103327 0.0277556 NA
1 91536 rs6702460 G T 0.0066796 0.0110304 0.5400003 0.5448039 0.456480 0.4207270 NA
1 234313 rs8179466 C T 0.0291447 0.0219021 0.1800002 0.1832941 0.074157 NA NA
1 534192 rs6680723 C T 0.0063512 0.0125771 0.6100002 0.6135698 0.241080 NA NA
1 546697 rs12025928 A G 0.0095989 0.0156919 0.5400003 0.5407280 0.912963 NA NA
1 693731 rs12238997 A G -0.0173754 0.0105642 0.1000000 0.1000227 0.116794 0.1417730 NA
1 705882 rs72631875 G A 0.0122217 0.0153729 0.4299995 0.4266043 0.067855 0.0315495 NA
1 706368 rs55727773 A G 0.0031860 0.0078250 0.6800001 0.6838909 0.515940 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0021127 0.0094985 0.8200001 0.8239875 0.136548 0.2052720 NA
22 51219387 rs9616832 T C 0.0035713 0.0123316 0.7700005 0.7721174 0.072882 0.0654952 NA
22 51219704 rs147475742 G A -0.0037209 0.0165603 0.8200001 0.8222206 0.041305 0.0473243 NA
22 51221190 rs369304721 G A 0.0144932 0.0164705 0.3800004 0.3788869 0.049287 NA NA
22 51221731 rs115055839 T C 0.0051452 0.0123412 0.6800001 0.6767442 0.072332 0.0625000 NA
22 51222100 rs114553188 G T -0.0010912 0.0145333 0.9400001 0.9401478 0.054056 0.0880591 NA
22 51223637 rs375798137 G A -0.0007524 0.0146082 0.9599999 0.9589240 0.053689 0.0788738 NA
22 51229805 rs9616985 T C 0.0044000 0.0123861 0.7199992 0.7224139 0.072151 0.0730831 NA
22 51232488 rs376461333 A G 0.0230281 0.0293108 0.4299995 0.4320712 0.019741 NA NA
22 51237063 rs3896457 T C 0.0088019 0.0075023 0.2399999 0.2407036 0.298329 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.6239   ES:SE:LP:AF:ID  0.015371:0.011299:0.769551:0.6239:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399696 ES:SE:LP:AF:ID  0.00179901:0.0112016:0.0604807:0.399696:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103327 ES:SE:LP:AF:ID  -0.0370248:0.0179746:1.40894:0.103327:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45648  ES:SE:LP:AF:ID  0.00667962:0.0110304:0.267606:0.45648:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074157 ES:SE:LP:AF:ID  0.0291447:0.0219021:0.744727:0.074157:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24108  ES:SE:LP:AF:ID  0.00635124:0.0125771:0.21467:0.24108:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912963 ES:SE:LP:AF:ID  0.00959893:0.0156919:0.267606:0.912963:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116794 ES:SE:LP:AF:ID  -0.0173754:0.0105642:1:0.116794:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067855 ES:SE:LP:AF:ID  0.0122217:0.0153729:0.366532:0.067855:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51594  ES:SE:LP:AF:ID  0.00318601:0.00782497:0.167491:0.51594:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033039 ES:SE:LP:AF:ID  -0.0500385:0.0197473:1.95861:0.033039:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036653 ES:SE:LP:AF:ID  -0.0445026:0.0179299:1.88606:0.036653:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036781 ES:SE:LP:AF:ID  -0.046107:0.0178567:2.00877:0.036781:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036476 ES:SE:LP:AF:ID  -0.0467125:0.0179893:2.02687:0.036476:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016611 ES:SE:LP:AF:ID  0.0171671:0.0275321:0.275724:0.016611:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037025 ES:SE:LP:AF:ID  -0.0438018:0.0177857:1.85387:0.037025:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037139 ES:SE:LP:AF:ID  -0.043078:0.0177227:1.82391:0.037139:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101647 ES:SE:LP:AF:ID  -0.00872645:0.0129015:0.30103:0.101647:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959224 ES:SE:LP:AF:ID  0.044724:0.0171287:2.04576:0.959224:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031172 ES:SE:LP:AF:ID  0.0233412:0.0314658:0.337242:0.031172:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053428 ES:SE:LP:AF:ID  0.0419457:0.0245978:1.05552:0.053428:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036647 ES:SE:LP:AF:ID  -0.0448906:0.0178307:1.92082:0.036647:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036957 ES:SE:LP:AF:ID  -0.0480556:0.0176723:2.18709:0.036957:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843042 ES:SE:LP:AF:ID  0.0201247:0.00915966:1.55284:0.843042:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055867 ES:SE:LP:AF:ID  -0.0365656:0.0148458:1.85387:0.055867:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.1227   ES:SE:LP:AF:ID  -0.0156809:0.0100239:0.920819:0.1227:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024749 ES:SE:LP:AF:ID  -0.000718896:0.0251173:0.00877392:0.024749:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121914 ES:SE:LP:AF:ID  -0.0153654:0.0100308:0.886057:0.121914:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132526 ES:SE:LP:AF:ID  -0.0233511:0.00987871:1.74473:0.132526:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011055 ES:SE:LP:AF:ID  0.00357155:0.0361939:0.0362122:0.011055:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005653 ES:SE:LP:AF:ID  -0.0936749:0.0466981:1.34679:0.005653:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036864 ES:SE:LP:AF:ID  -0.0420612:0.0174958:1.79588:0.036864:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838633 ES:SE:LP:AF:ID  0.0202635:0.00886913:1.65758:0.838633:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838285 ES:SE:LP:AF:ID  0.0202092:0.00886022:1.63827:0.838285:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869339 ES:SE:LP:AF:ID  0.0169245:0.00950461:1.12494:0.869339:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130314 ES:SE:LP:AF:ID  -0.0162026:0.00952561:1.05061:0.130314:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037353 ES:SE:LP:AF:ID  -0.0437958:0.0172034:1.95861:0.037353:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037604 ES:SE:LP:AF:ID  -0.0439676:0.017091:2:0.037604:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868686 ES:SE:LP:AF:ID  0.0170973:0.00948713:1.14267:0.868686:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868794 ES:SE:LP:AF:ID  0.0173039:0.00949204:1.16749:0.868794:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037561 ES:SE:LP:AF:ID  -0.0440408:0.0171689:2:0.037561:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868695 ES:SE:LP:AF:ID  0.0169535:0.00948698:1.13077:0.868695:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005215 ES:SE:LP:AF:ID  -0.0794961:0.0482446:1.00436:0.005215:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005187 ES:SE:LP:AF:ID  -0.078872:0.0483287:1:0.005187:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837716 ES:SE:LP:AF:ID  0.0196351:0.00883576:1.58503:0.837716:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037603 ES:SE:LP:AF:ID  -0.0454933:0.0171879:2.09152:0.037603:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838311 ES:SE:LP:AF:ID  0.0197474:0.00885972:1.58503:0.838311:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01346  ES:SE:LP:AF:ID  0.00313591:0.031278:0.0362122:0.01346:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005454 ES:SE:LP:AF:ID  -0.0683513:0.0480096:0.823909:0.005454:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839479 ES:SE:LP:AF:ID  0.021034:0.00897853:1.72125:0.839479:rs3131965