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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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_20157.vcf.gz --id UKB-b:20140 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20157.txt.gz --cohort_controls 99477 --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-20140/UKB-b-20140_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20140/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:06 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20140/UKB-b-20140_data.vcf.gz ...
Read summary statistics for 9015068 SNPs.
Dropped 8782 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, 1287173 SNPs remain.
After merging with regression SNP LD, 1287173 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1399 (0.0072)
Lambda GC: 1.2423
Mean Chi^2: 1.278
Intercept: 1.0031 (0.0076)
Ratio: 0.0112 (0.0274)
Analysis finished at Thu Oct 17 14:43:42 2019
Total time elapsed: 1.0m:35.34s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9477,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 8,
    "n_p_sig": 1245,
    "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": 94023,
    "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": 1287173,
    "ldsc_nsnp_merge_regression_ld": 1287173,
    "ldsc_observed_scale_h2_beta": 0.1399,
    "ldsc_observed_scale_h2_se": 0.0072,
    "ldsc_intercept_beta": 1.0031,
    "ldsc_intercept_se": 0.0076,
    "ldsc_lambda_gc": 1.2423,
    "ldsc_mean_chisq": 1.278,
    "ldsc_ratio": 0.0112
}

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 TRUE
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 9006327 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 9015068 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 8.643109e+00 5.758167e+00 1.000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.878886e+07 5.633844e+07 828.000000 3.243485e+07 6.934962e+07 1.145370e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.123000e-04 1.524420e-02 -0.174833 -6.177700e-03 5.530000e-05 6.324800e-03 1.664180e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.183880e-02 8.697600e-03 0.004295 5.116700e-03 7.844000e-03 1.623730e-02 9.758360e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.732215e-01 2.963799e-01 0.000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.732225e-01 2.963553e-01 0.000000 2.098206e-01 4.635340e-01 7.303559e-01 9.999997e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.199860e-01 2.585246e-01 0.003519 2.038700e-02 1.011850e-01 3.469770e-01 9.964810e-01 ▇▂▁▁▁
numeric AF_reference 94023 0.9895705 NA NA NA NA NA NA NA 2.202921e-01 2.504157e-01 0.000000 1.757190e-02 1.186100e-01 3.454470e-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 -0.0020323 0.0078790 0.8000000 0.7964514 0.624013 0.7821490 NA
1 54676 rs2462492 C T 0.0007742 0.0078206 0.9199999 0.9211462 0.399523 NA NA
1 86028 rs114608975 T C -0.0048465 0.0125136 0.6999999 0.6985375 0.103467 0.0277556 NA
1 91536 rs6702460 G T -0.0042179 0.0076977 0.5800000 0.5837289 0.456310 0.4207270 NA
1 234313 rs8179466 C T -0.0291732 0.0152844 0.0560003 0.0563019 0.074073 NA NA
1 534192 rs6680723 C T -0.0040351 0.0087696 0.6499995 0.6454311 0.241113 NA NA
1 546697 rs12025928 A G -0.0321527 0.0109462 0.0033000 0.0033104 0.913086 NA NA
1 693731 rs12238997 A G 0.0153986 0.0073624 0.0359998 0.0364808 0.116998 0.1417730 NA
1 705882 rs72631875 G A 0.0110528 0.0107204 0.2999998 0.3025379 0.067863 0.0315495 NA
1 706368 rs55727773 A G -0.0051412 0.0054614 0.3500000 0.3465176 0.515618 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0060985 0.0066273 0.3599996 0.3574659 0.136654 0.2052720 NA
22 51219387 rs9616832 T C 0.0015231 0.0086152 0.8600001 0.8596722 0.072656 0.0654952 NA
22 51219704 rs147475742 G A 0.0035796 0.0115750 0.7600007 0.7571313 0.041171 0.0473243 NA
22 51221190 rs369304721 G A 0.0014065 0.0115004 0.9000000 0.9026627 0.049238 NA NA
22 51221731 rs115055839 T C 0.0016705 0.0086242 0.8499999 0.8464111 0.072115 0.0625000 NA
22 51222100 rs114553188 G T 0.0114961 0.0101159 0.2599998 0.2557730 0.054345 0.0880591 NA
22 51223637 rs375798137 G A 0.0101776 0.0101694 0.3200000 0.3169204 0.053970 0.0788738 NA
22 51229805 rs9616985 T C 0.0021813 0.0086544 0.8000000 0.8010079 0.071954 0.0730831 NA
22 51232488 rs376461333 A G 0.0054906 0.0204637 0.7899998 0.7884628 0.019798 NA NA
22 51237063 rs3896457 T C 0.0068762 0.0052417 0.1900002 0.1895754 0.298164 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624013 ES:SE:LP:AF:ID  -0.00203233:0.00787901:0.09691:0.624013:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399523 ES:SE:LP:AF:ID  0.000774159:0.00782058:0.0362122:0.399523:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103467 ES:SE:LP:AF:ID  -0.00484646:0.0125136:0.154902:0.103467:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45631  ES:SE:LP:AF:ID  -0.00421793:0.00769771:0.236572:0.45631:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074073 ES:SE:LP:AF:ID  -0.0291732:0.0152844:1.25181:0.074073:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241113 ES:SE:LP:AF:ID  -0.00403507:0.00876963:0.187087:0.241113:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913086 ES:SE:LP:AF:ID  -0.0321527:0.0109462:2.48149:0.913086:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116998 ES:SE:LP:AF:ID  0.0153986:0.00736237:1.4437:0.116998:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067863 ES:SE:LP:AF:ID  0.0110528:0.0107204:0.522879:0.067863:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515618 ES:SE:LP:AF:ID  -0.00514122:0.00546145:0.455932:0.515618:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032881 ES:SE:LP:AF:ID  0.0197283:0.0137995:0.823909:0.032881:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036522 ES:SE:LP:AF:ID  0.0153452:0.0125202:0.657577:0.036522:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036626 ES:SE:LP:AF:ID  0.0146581:0.012475:0.619789:0.036626:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03632  ES:SE:LP:AF:ID  0.0158469:0.0125688:0.677781:0.03632:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016662 ES:SE:LP:AF:ID  -0.00345083:0.0191624:0.0655015:0.016662:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036861 ES:SE:LP:AF:ID  0.0128505:0.0124275:0.522879:0.036861:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036982 ES:SE:LP:AF:ID  0.0137984:0.0123837:0.568636:0.036982:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102103 ES:SE:LP:AF:ID  0.000215891:0.00895541:0.00877392:0.102103:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95939  ES:SE:LP:AF:ID  -0.018327:0.0119696:0.886057:0.95939:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031175 ES:SE:LP:AF:ID  0.032683:0.0219397:0.853872:0.031175:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053284 ES:SE:LP:AF:ID  -0.0250121:0.0171916:0.823909:0.053284:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036475 ES:SE:LP:AF:ID  0.0153701:0.0124604:0.657577:0.036475:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036756 ES:SE:LP:AF:ID  0.0132135:0.0123535:0.552842:0.036756:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842901 ES:SE:LP:AF:ID  -0.0140061:0.0063941:1.55284:0.842901:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056112 ES:SE:LP:AF:ID  0.00927155:0.0103383:0.431798:0.056112:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122992 ES:SE:LP:AF:ID  0.0130927:0.0069905:1.21467:0.122992:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02475  ES:SE:LP:AF:ID  -0.0213996:0.0175189:0.657577:0.02475:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122226 ES:SE:LP:AF:ID  0.013667:0.00699443:1.29243:0.122226:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132682 ES:SE:LP:AF:ID  0.0159833:0.00689374:1.69897:0.132682:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011094 ES:SE:LP:AF:ID  0.0603559:0.02524:1.76955:0.011094:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005556 ES:SE:LP:AF:ID  0.02168:0.0329488:0.29243:0.005556:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036679 ES:SE:LP:AF:ID  0.0144333:0.0122265:0.619789:0.036679:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838468 ES:SE:LP:AF:ID  -0.0146941:0.00619254:1.74473:0.838468:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838103 ES:SE:LP:AF:ID  -0.014865:0.00618571:1.79588:0.838103:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869003 ES:SE:LP:AF:ID  -0.0141051:0.00662987:1.48149:0.869003:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130663 ES:SE:LP:AF:ID  0.0137449:0.0066439:1.40894:0.130663:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037184 ES:SE:LP:AF:ID  0.0163372:0.0120217:0.769551:0.037184:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037432 ES:SE:LP:AF:ID  0.015861:0.0119436:0.744727:0.037432:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868337 ES:SE:LP:AF:ID  -0.014139:0.00661727:1.48149:0.868337:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868432 ES:SE:LP:AF:ID  -0.0144466:0.00662033:1.5376:0.868432:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037385 ES:SE:LP:AF:ID  0.0160617:0.011998:0.744727:0.037385:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86834  ES:SE:LP:AF:ID  -0.0141384:0.00661705:1.48149:0.86834:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005306 ES:SE:LP:AF:ID  0.0040202:0.0333163:0.0457575:0.005306:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005277 ES:SE:LP:AF:ID  0.0019964:0.0333747:0.0222764:0.005277:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837507 ES:SE:LP:AF:ID  -0.0148145:0.00616819:1.79588:0.837507:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037419 ES:SE:LP:AF:ID  0.0159733:0.0120118:0.744727:0.037419:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838089 ES:SE:LP:AF:ID  -0.0148774:0.00618455:1.79588:0.838089:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013728 ES:SE:LP:AF:ID  0.00846825:0.021574:0.161151:0.013728:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005406 ES:SE:LP:AF:ID  0.0259595:0.0336461:0.356547:0.005406:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839275 ES:SE:LP:AF:ID  -0.0156161:0.00626828:1.88606:0.839275:rs3131965