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_5157.vcf.gz --id UKB-b:10629 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5157.txt.gz --cohort_controls 51739 --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-10629/UKB-b-10629_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10629/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-10629/UKB-b-10629_data.vcf.gz ...
Read summary statistics for 8294417 SNPs.
Dropped 6707 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, 1284536 SNPs remain.
After merging with regression SNP LD, 1284536 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1222 (0.0116)
Lambda GC: 1.1118
Mean Chi^2: 1.141
Intercept: 1.0176 (0.0069)
Ratio: 0.1251 (0.0487)
Analysis finished at Thu Oct 17 14:41:53 2019
Total time elapsed: 1.0m:34.9s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9448,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 8,
    "n_p_sig": 312,
    "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": 78008,
    "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": 1284536,
    "ldsc_nsnp_merge_regression_ld": 1284536,
    "ldsc_observed_scale_h2_beta": 0.1222,
    "ldsc_observed_scale_h2_se": 0.0116,
    "ldsc_intercept_beta": 1.0176,
    "ldsc_intercept_se": 0.0069,
    "ldsc_lambda_gc": 1.1118,
    "ldsc_mean_chisq": 1.141,
    "ldsc_ratio": 0.1248
}

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 8287740 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 8294417 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 8.655572e+00 5.762567e+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 7.873256e+07 5.638994e+07 828.0000000 3.232064e+07 6.921496e+07 1.145525e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.790000e-05 1.697960e-02 -0.3448540 -7.659800e-03 -6.550000e-05 7.553000e-03 1.663830e-01 ▁▁▁▇▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.396820e-02 8.941500e-03 0.0059627 6.965000e-03 9.955400e-03 1.861770e-02 9.246840e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.864613e-01 2.927216e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.864606e-01 2.926956e-01 0.0000000 2.297557e-01 4.819434e-01 7.403827e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.375904e-01 2.602450e-01 0.0067650 3.029100e-02 1.256860e-01 3.762730e-01 9.932350e-01 ▇▂▂▁▁
numeric AF_reference 78008 0.9905951 NA NA NA NA NA NA NA 2.371012e-01 2.521546e-01 0.0000000 3.015180e-02 1.415730e-01 3.724040e-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 -0.0043023 0.0110004 0.6999999 0.6957197 0.622556 0.7821490 NA
1 54676 rs2462492 C T 0.0029654 0.0109148 0.7899998 0.7858634 0.400233 NA NA
1 86028 rs114608975 T C -0.0026754 0.0174090 0.8800001 0.8778641 0.103837 0.0277556 NA
1 91536 rs6702460 G T -0.0110473 0.0107534 0.2999998 0.3042647 0.456529 0.4207270 NA
1 234313 rs8179466 C T 0.0130340 0.0210883 0.5400003 0.5365306 0.074586 NA NA
1 534192 rs6680723 C T 0.0041438 0.0123226 0.7400005 0.7366612 0.240330 NA NA
1 546697 rs12025928 A G -0.0056440 0.0152159 0.7099994 0.7106933 0.912577 NA NA
1 693731 rs12238997 A G 0.0337437 0.0102209 0.0009600 0.0009619 0.118123 0.1417730 NA
1 705882 rs72631875 G A 0.0010814 0.0150264 0.9400001 0.9426268 0.067243 0.0315495 NA
1 706368 rs55727773 A G -0.0028908 0.0075917 0.6999999 0.7033609 0.514796 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0184946 0.0091713 0.0439997 0.0437403 0.136948 0.2052720 NA
22 51219387 rs9616832 T C -0.0149044 0.0119052 0.2099999 0.2105977 0.072814 0.0654952 NA
22 51219704 rs147475742 G A -0.0075367 0.0158002 0.6300007 0.6333624 0.042238 0.0473243 NA
22 51221190 rs369304721 G A -0.0153668 0.0158690 0.3300000 0.3328679 0.049451 NA NA
22 51221731 rs115055839 T C -0.0148607 0.0119074 0.2099999 0.2120229 0.072402 0.0625000 NA
22 51222100 rs114553188 G T -0.0174197 0.0140376 0.2099999 0.2146311 0.054138 0.0880591 NA
22 51223637 rs375798137 G A -0.0168481 0.0141076 0.2300001 0.2323775 0.053769 0.0788738 NA
22 51229805 rs9616985 T C -0.0138209 0.0119564 0.2500000 0.2477051 0.072230 0.0730831 NA
22 51232488 rs376461333 A G -0.0228599 0.0282903 0.4199997 0.4190634 0.020058 NA NA
22 51237063 rs3896457 T C 0.0139455 0.0073217 0.0569994 0.0568209 0.298978 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622556 ES:SE:LP:AF:ID  -0.00430231:0.0110004:0.154902:0.622556:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400233 ES:SE:LP:AF:ID  0.0029654:0.0109148:0.102373:0.400233:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103837 ES:SE:LP:AF:ID  -0.00267537:0.017409:0.0555173:0.103837:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456529 ES:SE:LP:AF:ID  -0.0110473:0.0107534:0.522879:0.456529:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074586 ES:SE:LP:AF:ID  0.013034:0.0210883:0.267606:0.074586:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24033  ES:SE:LP:AF:ID  0.00414382:0.0123226:0.130768:0.24033:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912577 ES:SE:LP:AF:ID  -0.00564396:0.0152159:0.148742:0.912577:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.118123 ES:SE:LP:AF:ID  0.0337437:0.0102209:3.01773:0.118123:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067243 ES:SE:LP:AF:ID  0.00108143:0.0150264:0.0268721:0.067243:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514796 ES:SE:LP:AF:ID  -0.00289084:0.00759174:0.154902:0.514796:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033291 ES:SE:LP:AF:ID  -0.0184387:0.019044:0.481486:0.033291:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036976 ES:SE:LP:AF:ID  -0.0212979:0.0173009:0.657577:0.036976:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03704  ES:SE:LP:AF:ID  -0.0207637:0.01725:0.638272:0.03704:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036719 ES:SE:LP:AF:ID  -0.0214754:0.0173756:0.657577:0.036719:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016613 ES:SE:LP:AF:ID  0.0398611:0.0267703:0.853872:0.016613:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03731  ES:SE:LP:AF:ID  -0.0203103:0.0171748:0.619789:0.03731:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037371 ES:SE:LP:AF:ID  -0.0178803:0.0171266:0.522879:0.037371:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10035  ES:SE:LP:AF:ID  -0.0137785:0.012615:0.568636:0.10035:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958335 ES:SE:LP:AF:ID  0.0156926:0.0164597:0.468521:0.958335:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031982 ES:SE:LP:AF:ID  0.00452403:0.0300046:0.0555173:0.031982:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052612 ES:SE:LP:AF:ID  -0.00649639:0.0241875:0.102373:0.052612:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036815 ES:SE:LP:AF:ID  -0.0183125:0.0172525:0.537602:0.036815:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037115 ES:SE:LP:AF:ID  -0.0185214:0.0171066:0.552842:0.037115:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841186 ES:SE:LP:AF:ID  -0.0238541:0.00887008:2.14267:0.841186:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055891 ES:SE:LP:AF:ID  0.0356647:0.0144642:1.85387:0.055891:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123744 ES:SE:LP:AF:ID  0.0318192:0.009719:2.95861:0.123744:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025692 ES:SE:LP:AF:ID  -0.0192935:0.0240376:0.376751:0.025692:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122859 ES:SE:LP:AF:ID  0.0321868:0.00972553:3.03152:0.122859:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133453 ES:SE:LP:AF:ID  0.0222225:0.00959272:1.67778:0.133453:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011301 ES:SE:LP:AF:ID  -0.00520186:0.0344861:0.0555173:0.011301:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037112 ES:SE:LP:AF:ID  -0.015388:0.0169065:0.443698:0.037112:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837091 ES:SE:LP:AF:ID  -0.0211632:0.00859329:1.85387:0.837091:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.8367   ES:SE:LP:AF:ID  -0.0205329:0.00858357:1.76955:0.8367:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86806  ES:SE:LP:AF:ID  -0.0278981:0.00920875:2.61979:0.86806:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131592 ES:SE:LP:AF:ID  0.0269378:0.00922591:2.45593:0.131592:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037504 ES:SE:LP:AF:ID  -0.0157208:0.0166427:0.468521:0.037504:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037766 ES:SE:LP:AF:ID  -0.0153666:0.0165365:0.455932:0.037766:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867427 ES:SE:LP:AF:ID  -0.0269634:0.00919186:2.46852:0.867427:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867525 ES:SE:LP:AF:ID  -0.0269487:0.00919576:2.46852:0.867525:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037677 ES:SE:LP:AF:ID  -0.0162094:0.0166084:0.481486:0.037677:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867406 ES:SE:LP:AF:ID  -0.0270177:0.00919108:2.48149:0.867406:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836281 ES:SE:LP:AF:ID  -0.0198322:0.00856535:1.67778:0.836281:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.0377   ES:SE:LP:AF:ID  -0.0169117:0.0166299:0.508638:0.0377:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836843 ES:SE:LP:AF:ID  -0.0198354:0.00858777:1.67778:0.836843:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01307  ES:SE:LP:AF:ID  -0.0199342:0.0308919:0.283997:0.01307:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.838019 ES:SE:LP:AF:ID  -0.0222731:0.00869962:2:0.838019:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86779  ES:SE:LP:AF:ID  -0.0278264:0.00918279:2.61979:0.86779:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867402 ES:SE:LP:AF:ID  -0.0276014:0.00916247:2.58503:0.867402:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866159 ES:SE:LP:AF:ID  -0.0279318:0.00913749:2.65758:0.866159:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867524 ES:SE:LP:AF:ID  -0.0275046:0.00917004:2.56864:0.867524:rs4951929