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_4526.vcf.gz --id UKB-b:4062 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4526.txt.gz --cohort_controls 152348 --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-4062/UKB-b-4062_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4062/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:59 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4062/UKB-b-4062_data.vcf.gz ...
Read summary statistics for 9329577 SNPs.
Dropped 10451 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, 1288016 SNPs remain.
After merging with regression SNP LD, 1288016 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0606 (0.0043)
Lambda GC: 1.19
Mean Chi^2: 1.2024
Intercept: 1.0227 (0.0072)
Ratio: 0.1123 (0.0355)
Analysis finished at Thu Oct 17 14:45:48 2019
Total time elapsed: 1.0m:48.82s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9488,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 5,
    "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": 114030,
    "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": 1288016,
    "ldsc_nsnp_merge_regression_ld": 1288016,
    "ldsc_observed_scale_h2_beta": 0.0606,
    "ldsc_observed_scale_h2_se": 0.0043,
    "ldsc_intercept_beta": 1.0227,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.19,
    "ldsc_mean_chisq": 1.2024,
    "ldsc_ratio": 0.1122
}

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 9319178 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 9329577 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 8.634365e+00 5.753890e+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 7.881304e+07 5.630827e+07 828.0000000 3.250558e+07 6.939349e+07 1.145423e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 6.890000e-05 9.642600e-03 -0.1103910 -3.605600e-03 2.110000e-05 3.655100e-03 1.226060e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.353700e-03 5.841900e-03 0.0024516 2.950400e-03 4.671600e-03 1.007650e-02 8.506720e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.785137e-01 2.946006e-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 4.785141e-01 2.945750e-01 0.0000000 2.176215e-01 4.709526e-01 7.337638e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.133040e-01 2.577573e-01 0.0022980 1.728600e-02 9.184300e-02 3.348420e-01 9.977020e-01 ▇▂▁▁▁
numeric AF_reference 114030 0.9877776 NA NA NA NA NA NA NA 2.143842e-01 2.495383e-01 0.0000000 1.457670e-02 1.104230e-01 3.348640e-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 0.0033252 0.0045168 0.4600002 0.4616258 0.623729 0.7821490 NA
1 54676 rs2462492 C T -0.0019685 0.0044871 0.6600001 0.6608681 0.399322 NA NA
1 86028 rs114608975 T C -0.0050873 0.0071520 0.4799997 0.4768954 0.103787 0.0277556 NA
1 91536 rs6702460 G T -0.0046814 0.0044212 0.2900000 0.2896732 0.456290 0.4207270 NA
1 234313 rs8179466 C T 0.0029856 0.0087292 0.7300002 0.7323302 0.074509 NA NA
1 534192 rs6680723 C T 0.0041564 0.0050543 0.4100001 0.4108770 0.241206 NA NA
1 546697 rs12025928 A G 0.0049006 0.0062700 0.4299995 0.4344554 0.913049 NA NA
1 693731 rs12238997 A G 0.0096551 0.0042112 0.0219999 0.0218630 0.117018 0.1417730 NA
1 705882 rs72631875 G A -0.0059379 0.0061636 0.3400001 0.3353545 0.067592 0.0315495 NA
1 706368 rs55727773 A G -0.0051111 0.0031192 0.1000000 0.1012969 0.514959 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0005375 0.0037789 0.8900000 0.8868962 0.137056 0.2052720 NA
22 51219387 rs9616832 T C 0.0004940 0.0049156 0.9199999 0.9199583 0.072681 0.0654952 NA
22 51219704 rs147475742 G A 0.0028324 0.0065618 0.6700003 0.6659994 0.041637 0.0473243 NA
22 51221190 rs369304721 G A 0.0036600 0.0065758 0.5800000 0.5778143 0.049091 NA NA
22 51221731 rs115055839 T C 0.0006334 0.0049194 0.9000000 0.8975545 0.072152 0.0625000 NA
22 51222100 rs114553188 G T -0.0021258 0.0057566 0.7099994 0.7119164 0.054465 0.0880591 NA
22 51223637 rs375798137 G A -0.0021642 0.0057861 0.7099994 0.7083781 0.054076 0.0788738 NA
22 51229805 rs9616985 T C 0.0005864 0.0049376 0.9100000 0.9054586 0.072007 0.0730831 NA
22 51232488 rs376461333 A G 0.0000719 0.0115572 1.0000000 0.9950383 0.020177 NA NA
22 51237063 rs3896457 T C 0.0006097 0.0030010 0.8400000 0.8390045 0.297594 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623729 ES:SE:LP:AF:ID  0.00332518:0.00451685:0.337242:0.623729:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399322 ES:SE:LP:AF:ID  -0.00196854:0.00448706:0.180456:0.399322:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103787 ES:SE:LP:AF:ID  -0.00508728:0.00715204:0.318759:0.103787:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45629  ES:SE:LP:AF:ID  -0.00468137:0.00442123:0.537602:0.45629:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074509 ES:SE:LP:AF:ID  0.00298562:0.00872918:0.136677:0.074509:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241206 ES:SE:LP:AF:ID  0.00415641:0.0050543:0.387216:0.241206:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913049 ES:SE:LP:AF:ID  0.00490061:0.00627005:0.366532:0.913049:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117018 ES:SE:LP:AF:ID  0.00965509:0.00421116:1.65758:0.117018:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067592 ES:SE:LP:AF:ID  -0.00593787:0.00616355:0.468521:0.067592:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514959 ES:SE:LP:AF:ID  -0.0051111:0.00311919:1:0.514959:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033522 ES:SE:LP:AF:ID  0.00203179:0.00779952:0.102373:0.033522:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037226 ES:SE:LP:AF:ID  0.000389047:0.00708246:0.0177288:0.037226:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037355 ES:SE:LP:AF:ID  0.000982914:0.00705467:0.05061:0.037355:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037008 ES:SE:LP:AF:ID  0.001555:0.00710849:0.0809219:0.037008:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016361 ES:SE:LP:AF:ID  0.0146582:0.0110917:0.721246:0.016361:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037595 ES:SE:LP:AF:ID  0.000884873:0.00702646:0.0457575:0.037595:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03769  ES:SE:LP:AF:ID  0.00064655:0.00700381:0.0315171:0.03769:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10149  ES:SE:LP:AF:ID  -0.0049928:0.00513843:0.481486:0.10149:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958359 ES:SE:LP:AF:ID  -0.000263946:0.0067577:0.0132283:0.958359:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031697 ES:SE:LP:AF:ID  0.0153985:0.0123443:0.677781:0.031697:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052644 ES:SE:LP:AF:ID  -0.00776068:0.00996568:0.356547:0.052644:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037165 ES:SE:LP:AF:ID  0.00189441:0.00705219:0.102373:0.037165:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037495 ES:SE:LP:AF:ID  0.0013746:0.0069893:0.0757207:0.037495:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841892 ES:SE:LP:AF:ID  -0.0085105:0.00364494:1.69897:0.841892:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056115 ES:SE:LP:AF:ID  0.0111668:0.00592134:1.22915:0.056115:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122968 ES:SE:LP:AF:ID  0.00908569:0.00399633:1.63827:0.122968:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025781 ES:SE:LP:AF:ID  -0.00243401:0.0098247:0.09691:0.025781:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122188 ES:SE:LP:AF:ID  0.00921044:0.0039983:1.67778:0.122188:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133439 ES:SE:LP:AF:ID  0.00648827:0.00393401:1.00436:0.133439:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011247 ES:SE:LP:AF:ID  0.00346048:0.0142667:0.091515:0.011247:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005842 ES:SE:LP:AF:ID  0.0230584:0.0182359:0.677781:0.005842:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037439 ES:SE:LP:AF:ID  0.000314969:0.00691726:0.0177288:0.037439:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837606 ES:SE:LP:AF:ID  -0.00817783:0.00352929:1.69897:0.837606:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83722  ES:SE:LP:AF:ID  -0.00748927:0.00352534:1.46852:0.83722:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868895 ES:SE:LP:AF:ID  -0.00915642:0.00378391:1.79588:0.868895:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.1308   ES:SE:LP:AF:ID  0.00813379:0.00379143:1.49485:0.1308:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037934 ES:SE:LP:AF:ID  0.000603414:0.00680389:0.0315171:0.037934:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038186 ES:SE:LP:AF:ID  0.000927005:0.00676081:0.05061:0.038186:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868218 ES:SE:LP:AF:ID  -0.00857147:0.0037763:1.63827:0.868218:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868325 ES:SE:LP:AF:ID  -0.00866576:0.003778:1.65758:0.868325:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038118 ES:SE:LP:AF:ID  0.000387372:0.00679011:0.0222764:0.038118:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868214 ES:SE:LP:AF:ID  -0.00865987:0.00377605:1.65758:0.868214:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005133 ES:SE:LP:AF:ID  0.00296482:0.019453:0.0555173:0.005133:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005103 ES:SE:LP:AF:ID  0.00327148:0.0194957:0.0604807:0.005103:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836699 ES:SE:LP:AF:ID  -0.00800988:0.00351614:1.63827:0.836699:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038129 ES:SE:LP:AF:ID  0.000234196:0.00679935:0.0132283:0.038129:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837331 ES:SE:LP:AF:ID  -0.00802334:0.00352589:1.63827:0.837331:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013274 ES:SE:LP:AF:ID  -0.00625761:0.0126092:0.207608:0.013274:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005463 ES:SE:LP:AF:ID  0.00446364:0.0191541:0.0861861:0.005463:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838556 ES:SE:LP:AF:ID  -0.00937411:0.0035741:2.06048:0.838556:rs3131965