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_5102.vcf.gz --id UKB-b:4331 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5102.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-4331/UKB-b-4331_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4331/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4331/UKB-b-4331_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: 0.0467 (0.0064)
Lambda GC: 1.0738
Mean Chi^2: 1.0788
Intercept: 1.004 (0.0066)
Ratio: 0.0507 (0.0836)
Analysis finished at Thu Oct 17 14:45:44 2019
Total time elapsed: 1.0m:34.36s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9474,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 0.0467,
    "ldsc_observed_scale_h2_se": 0.0064,
    "ldsc_intercept_beta": 1.004,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0738,
    "ldsc_mean_chisq": 1.0788,
    "ldsc_ratio": 0.0508
}

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 3.380000e-05 1.562860e-02 -0.1733530 -6.286400e-03 1.400000e-05 6.351900e-03 1.803340e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.260170e-02 8.994800e-03 0.0047268 5.610300e-03 8.477000e-03 1.722330e-02 1.078750e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.920776e-01 2.910092e-01 0.0000001 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 4.920772e-01 2.909855e-01 0.0000001 2.381970e-01 4.894783e-01 7.440235e-01 1.000000e+00 ▇▇▇▇▇
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 0.0052251 0.0087245 0.5500004 0.5492361 0.623505 0.7821490 NA
1 54676 rs2462492 C T 0.0016202 0.0086619 0.8499999 0.8516244 0.399158 NA NA
1 86028 rs114608975 T C -0.0138019 0.0137441 0.3200000 0.3152796 0.103979 0.0277556 NA
1 91536 rs6702460 G T -0.0099123 0.0085142 0.2399999 0.2443412 0.455954 0.4207270 NA
1 234313 rs8179466 C T 0.0261202 0.0167581 0.1199999 0.1190766 0.074560 NA NA
1 534192 rs6680723 C T 0.0032417 0.0097521 0.7400005 0.7395786 0.240218 NA NA
1 546697 rs12025928 A G -0.0116605 0.0120854 0.3300000 0.3346240 0.912844 NA NA
1 693731 rs12238997 A G 0.0007055 0.0081145 0.9299999 0.9307217 0.117537 0.1417730 NA
1 705882 rs72631875 G A 0.0051805 0.0118967 0.6600001 0.6632333 0.067410 0.0315495 NA
1 706368 rs55727773 A G -0.0063148 0.0060170 0.2900000 0.2939498 0.515046 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0100005 0.0072719 0.1700000 0.1690611 0.137482 0.2052720 NA
22 51219387 rs9616832 T C 0.0179534 0.0094695 0.0580003 0.0579711 0.072750 0.0654952 NA
22 51219704 rs147475742 G A 0.0292986 0.0126106 0.0200000 0.0201614 0.041805 0.0473243 NA
22 51221190 rs369304721 G A 0.0303462 0.0126925 0.0170000 0.0168082 0.049124 NA NA
22 51221731 rs115055839 T C 0.0183788 0.0094709 0.0519996 0.0523123 0.072308 0.0625000 NA
22 51222100 rs114553188 G T 0.0090106 0.0110961 0.4199997 0.4167628 0.054502 0.0880591 NA
22 51223637 rs375798137 G A 0.0093289 0.0111520 0.4000000 0.4028615 0.054135 0.0788738 NA
22 51229805 rs9616985 T C 0.0183072 0.0095063 0.0539995 0.0541295 0.072184 0.0730831 NA
22 51232488 rs376461333 A G 0.0196084 0.0224585 0.3800004 0.3826114 0.020055 NA NA
22 51237063 rs3896457 T C 0.0094020 0.0057960 0.1000000 0.1047672 0.297913 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623505 ES:SE:LP:AF:ID  0.00522513:0.00872447:0.259637:0.623505:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399158 ES:SE:LP:AF:ID  0.00162017:0.00866187:0.0705811:0.399158:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103979 ES:SE:LP:AF:ID  -0.0138019:0.0137441:0.49485:0.103979:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455954 ES:SE:LP:AF:ID  -0.00991228:0.00851421:0.619789:0.455954:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07456  ES:SE:LP:AF:ID  0.0261202:0.0167581:0.920819:0.07456:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240218 ES:SE:LP:AF:ID  0.0032417:0.00975207:0.130768:0.240218:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912844 ES:SE:LP:AF:ID  -0.0116605:0.0120854:0.481486:0.912844:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117537 ES:SE:LP:AF:ID  0.000705452:0.00811453:0.0315171:0.117537:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06741  ES:SE:LP:AF:ID  0.00518046:0.0118967:0.180456:0.06741:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515046 ES:SE:LP:AF:ID  -0.00631485:0.00601703:0.537602:0.515046:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033464 ES:SE:LP:AF:ID  -0.0221331:0.0150434:0.853872:0.033464:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037138 ES:SE:LP:AF:ID  -0.0226532:0.0136739:1.00877:0.037138:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037222 ES:SE:LP:AF:ID  -0.0226625:0.0136292:1.01773:0.037222:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036892 ES:SE:LP:AF:ID  -0.022488:0.0137292:1:0.036892:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016769 ES:SE:LP:AF:ID  0.00421884:0.0210936:0.0757207:0.016769:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037477 ES:SE:LP:AF:ID  -0.0229256:0.0135732:1.04096:0.037477:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037563 ES:SE:LP:AF:ID  -0.0232048:0.0135303:1.0655:0.037563:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100858 ES:SE:LP:AF:ID  0.0113249:0.00997638:0.585027:0.100858:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958184 ES:SE:LP:AF:ID  0.0212186:0.013009:1:0.958184:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03191  ES:SE:LP:AF:ID  -0.00311175:0.0236607:0.0457575:0.03191:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052678 ES:SE:LP:AF:ID  0.00928631:0.0191352:0.200659:0.052678:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037038 ES:SE:LP:AF:ID  -0.0231907:0.0136224:1.05061:0.037038:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037361 ES:SE:LP:AF:ID  -0.0209068:0.0135058:0.920819:0.037361:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841197 ES:SE:LP:AF:ID  0.00251635:0.0070241:0.142668:0.841197:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055916 ES:SE:LP:AF:ID  -0.00669909:0.0114606:0.251812:0.055916:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12353  ES:SE:LP:AF:ID  0.00196023:0.00770123:0.09691:0.12353:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025888 ES:SE:LP:AF:ID  0.0140558:0.0189261:0.337242:0.025888:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122691 ES:SE:LP:AF:ID  0.00189165:0.00770592:0.091515:0.122691:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133313 ES:SE:LP:AF:ID  -0.00221775:0.00759708:0.113509:0.133313:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01125  ES:SE:LP:AF:ID  -0.0232482:0.0274749:0.39794:0.01125:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006038 ES:SE:LP:AF:ID  -0.042407:0.0345362:0.657577:0.006038:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037341 ES:SE:LP:AF:ID  -0.0218819:0.0133526:1:0.037341:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837078 ES:SE:LP:AF:ID  0.0014614:0.00679936:0.0809219:0.837078:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836668 ES:SE:LP:AF:ID  0.00198226:0.00679226:0.113509:0.836668:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868267 ES:SE:LP:AF:ID  -0.0012675:0.00729164:0.0655015:0.868267:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131381 ES:SE:LP:AF:ID  -0.000605842:0.00730664:0.0315171:0.131381:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037784 ES:SE:LP:AF:ID  -0.0205688:0.0131435:0.920819:0.037784:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038035 ES:SE:LP:AF:ID  -0.0204667:0.0130622:0.920819:0.038035:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867594 ES:SE:LP:AF:ID  -0.000251533:0.00727801:0.0132283:0.867594:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867673 ES:SE:LP:AF:ID  -0.000372798:0.00728065:0.0177288:0.867673:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037976 ES:SE:LP:AF:ID  -0.0215764:0.0131166:1:0.037976:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867592 ES:SE:LP:AF:ID  -0.000145615:0.00727754:0.00877392:0.867592:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00506  ES:SE:LP:AF:ID  -0.0385498:0.0377848:0.508638:0.00506:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005027 ES:SE:LP:AF:ID  -0.0384603:0.0378943:0.508638:0.005027:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836191 ES:SE:LP:AF:ID  0.00249682:0.00677685:0.148742:0.836191:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037994 ES:SE:LP:AF:ID  -0.0226102:0.0131337:1.07058:0.037994:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836792 ES:SE:LP:AF:ID  0.00269791:0.00679538:0.161151:0.836792:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013255 ES:SE:LP:AF:ID  -0.0190798:0.024297:0.366532:0.013255:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005432 ES:SE:LP:AF:ID  -0.00788469:0.0371221:0.0809219:0.005432:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838    ES:SE:LP:AF:ID  0.00206235:0.00688623:0.119186:0.838:rs3131965