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_4620.vcf.gz --id UKB-b:1464 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4620.txt.gz --cohort_controls 58290 --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-1464/UKB-b-1464_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1464/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:44 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1464/UKB-b-1464_data.vcf.gz ...
Read summary statistics for 8466872 SNPs.
Dropped 7047 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, 1285232 SNPs remain.
After merging with regression SNP LD, 1285232 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0274 (0.0088)
Lambda GC: 1.0416
Mean Chi^2: 1.0412
Intercept: 1.0093 (0.0065)
Ratio: 0.2258 (0.1583)
Analysis finished at Thu Oct 17 14:45:19 2019
Total time elapsed: 1.0m:34.31s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9455,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0.0001,
    "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": 80423,
    "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": 1285232,
    "ldsc_nsnp_merge_regression_ld": 1285232,
    "ldsc_observed_scale_h2_beta": 0.0274,
    "ldsc_observed_scale_h2_se": 0.0088,
    "ldsc_intercept_beta": 1.0093,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0416,
    "ldsc_mean_chisq": 1.0412,
    "ldsc_ratio": 0.2257
}

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 8459858 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 8466872 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 8.652989e+00 5.761706e+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 7.874846e+07 5.637705e+07 828.0000000 3.235511e+07 6.925711e+07 1.145500e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 8.590000e-05 1.458440e-02 -0.1521360 -6.168900e-03 3.170000e-05 6.307900e-03 1.654300e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.200840e-02 7.956800e-03 0.0049210 5.791200e-03 8.411700e-03 1.613900e-02 7.512180e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.947277e-01 2.904189e-01 0.0000001 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 4.947248e-01 2.903920e-01 0.0000001 2.418877e-01 4.925601e-01 7.468361e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.330882e-01 2.598703e-01 0.0060050 2.751300e-02 1.193460e-01 3.690840e-01 9.939950e-01 ▇▂▂▁▁
numeric AF_reference 80423 0.9905015 NA NA NA NA NA NA NA 2.327190e-01 2.518103e-01 0.0000000 2.635780e-02 1.355830e-01 3.658150e-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 -0.0112663 0.0090656 0.2099999 0.2139599 0.622315 0.7821490 NA
1 54676 rs2462492 C T 0.0103329 0.0090073 0.2500000 0.2513137 0.399660 NA NA
1 86028 rs114608975 T C 0.0078459 0.0142056 0.5800000 0.5807353 0.104808 0.0277556 NA
1 91536 rs6702460 G T -0.0030495 0.0088988 0.7300002 0.7318344 0.456151 0.4207270 NA
1 234313 rs8179466 C T -0.0386612 0.0175871 0.0280001 0.0279298 0.074205 NA NA
1 534192 rs6680723 C T 0.0024139 0.0101517 0.8100000 0.8120472 0.241321 NA NA
1 546697 rs12025928 A G 0.0045019 0.0125928 0.7199992 0.7207179 0.913118 NA NA
1 693731 rs12238997 A G 0.0131445 0.0085500 0.1199999 0.1242044 0.115867 0.1417730 NA
1 705882 rs72631875 G A -0.0163389 0.0123976 0.1900002 0.1875343 0.067570 0.0315495 NA
1 706368 rs55727773 A G -0.0019534 0.0062776 0.7600007 0.7556700 0.514657 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0031253 0.0076094 0.6800001 0.6812799 0.136904 0.2052720 NA
22 51219387 rs9616832 T C -0.0105719 0.0099207 0.2900000 0.2865852 0.071998 0.0654952 NA
22 51219704 rs147475742 G A -0.0153773 0.0132999 0.2500000 0.2476007 0.040864 0.0473243 NA
22 51221190 rs369304721 G A -0.0138139 0.0132641 0.2999998 0.2976666 0.048644 NA NA
22 51221731 rs115055839 T C -0.0098841 0.0099264 0.3200000 0.3193747 0.071493 0.0625000 NA
22 51222100 rs114553188 G T 0.0110816 0.0114973 0.3400001 0.3351243 0.055039 0.0880591 NA
22 51223637 rs375798137 G A 0.0106552 0.0115650 0.3599996 0.3568773 0.054592 0.0788738 NA
22 51229805 rs9616985 T C -0.0085299 0.0099622 0.3900004 0.3918725 0.071351 0.0730831 NA
22 51232488 rs376461333 A G 0.0338714 0.0229563 0.1400000 0.1400854 0.020454 NA NA
22 51237063 rs3896457 T C -0.0050942 0.0060604 0.4000000 0.4005931 0.296842 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622315 ES:SE:LP:AF:ID  -0.0112663:0.00906562:0.677781:0.622315:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39966  ES:SE:LP:AF:ID  0.0103329:0.00900734:0.60206:0.39966:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104808 ES:SE:LP:AF:ID  0.00784591:0.0142056:0.236572:0.104808:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456151 ES:SE:LP:AF:ID  -0.00304948:0.00889875:0.136677:0.456151:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074205 ES:SE:LP:AF:ID  -0.0386612:0.0175871:1.55284:0.074205:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241321 ES:SE:LP:AF:ID  0.00241393:0.0101517:0.091515:0.241321:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913118 ES:SE:LP:AF:ID  0.00450192:0.0125928:0.142668:0.913118:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.115867 ES:SE:LP:AF:ID  0.0131445:0.00855003:0.920819:0.115867:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06757  ES:SE:LP:AF:ID  -0.0163389:0.0123976:0.721246:0.06757:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514657 ES:SE:LP:AF:ID  -0.0019534:0.00627756:0.119186:0.514657:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03415  ES:SE:LP:AF:ID  0.00386051:0.0155624:0.09691:0.03415:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037896 ES:SE:LP:AF:ID  0.000271966:0.0141498:0.00877392:0.037896:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.038085 ES:SE:LP:AF:ID  0.000378653:0.0140843:0.00877392:0.038085:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037675 ES:SE:LP:AF:ID  -0.00156508:0.0142085:0.0409586:0.037675:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016036 ES:SE:LP:AF:ID  0.0556273:0.0225119:1.88606:0.016036:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.038364 ES:SE:LP:AF:ID  0.0010192:0.0140208:0.0268721:0.038364:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.038438 ES:SE:LP:AF:ID  0.000337661:0.0139824:0.00877392:0.038438:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102107 ES:SE:LP:AF:ID  -0.0208943:0.0102715:1.37675:0.102107:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957366 ES:SE:LP:AF:ID  -0.00395142:0.0134385:0.113509:0.957366:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031631 ES:SE:LP:AF:ID  0.0153415:0.0249272:0.267606:0.031631:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052437 ES:SE:LP:AF:ID  -0.0150688:0.020087:0.346787:0.052437:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037926 ES:SE:LP:AF:ID  -0.00226236:0.0140706:0.0604807:0.037926:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.038218 ES:SE:LP:AF:ID  -0.00193881:0.013955:0.05061:0.038218:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842072 ES:SE:LP:AF:ID  -0.0129:0.00736749:1.09691:0.842072:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05601  ES:SE:LP:AF:ID  0.00921284:0.0119562:0.356547:0.05601:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122032 ES:SE:LP:AF:ID  0.0131552:0.008101:1:0.122032:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025868 ES:SE:LP:AF:ID  -0.018755:0.0196563:0.468521:0.025868:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121232 ES:SE:LP:AF:ID  0.0128051:0.00810668:0.958607:0.121232:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133074 ES:SE:LP:AF:ID  0.00478068:0.00795331:0.259637:0.133074:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011015 ES:SE:LP:AF:ID  0.000754273:0.0292004:0.00877392:0.011015:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.038136 ES:SE:LP:AF:ID  -0.00246748:0.0138199:0.0655015:0.038136:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837674 ES:SE:LP:AF:ID  -0.0135258:0.00714143:1.23657:0.837674:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837268 ES:SE:LP:AF:ID  -0.0131542:0.00713303:1.18709:0.837268:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869449 ES:SE:LP:AF:ID  -0.0159785:0.00766185:1.4318:0.869449:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130182 ES:SE:LP:AF:ID  0.0150697:0.00768101:1.30103:0.130182:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038628 ES:SE:LP:AF:ID  -0.00352025:0.0135935:0.09691:0.038628:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038918 ES:SE:LP:AF:ID  -0.00334139:0.0135011:0.09691:0.038918:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868722 ES:SE:LP:AF:ID  -0.0159727:0.0076467:1.4318:0.868722:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86881  ES:SE:LP:AF:ID  -0.0157076:0.00765031:1.39794:0.86881:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038863 ES:SE:LP:AF:ID  -0.00351874:0.0135573:0.09691:0.038863:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868723 ES:SE:LP:AF:ID  -0.0159594:0.00764626:1.4318:0.868723:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836744 ES:SE:LP:AF:ID  -0.0139599:0.00711592:1.30103:0.836744:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038895 ES:SE:LP:AF:ID  -0.00340795:0.0135718:0.09691:0.038895:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83741  ES:SE:LP:AF:ID  -0.0142942:0.0071374:1.34679:0.83741:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013    ES:SE:LP:AF:ID  -0.00787617:0.0256212:0.119186:0.013:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.838413 ES:SE:LP:AF:ID  -0.0143284:0.00722809:1.3279:0.838413:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869082 ES:SE:LP:AF:ID  -0.015523:0.0076382:1.37675:0.869082:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.86862  ES:SE:LP:AF:ID  -0.0148847:0.00761961:1.29243:0.86862:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867394 ES:SE:LP:AF:ID  -0.0151222:0.00760061:1.3279:0.867394:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868715 ES:SE:LP:AF:ID  -0.015053:0.00762395:1.31876:0.868715:rs4951929