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_103220.vcf.gz --id UKB-b:143 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_103220.txt.gz --cohort_controls 64939 --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-143/UKB-b-143_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-143/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:23 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-143/UKB-b-143_data.vcf.gz ...
Read summary statistics for 8624966 SNPs.
Dropped 7370 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, 1285751 SNPs remain.
After merging with regression SNP LD, 1285751 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0082 (0.0063)
Lambda GC: 1.0165
Mean Chi^2: 1.0217
Intercept: 1.0112 (0.0064)
Ratio: 0.5179 (0.2957)
Analysis finished at Thu Oct 17 14:45:04 2019
Total time elapsed: 1.0m:41.0s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 8,
    "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": 83091,
    "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": 1285751,
    "ldsc_nsnp_merge_regression_ld": 1285751,
    "ldsc_observed_scale_h2_beta": 0.0082,
    "ldsc_observed_scale_h2_se": 0.0063,
    "ldsc_intercept_beta": 1.0112,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0165,
    "ldsc_mean_chisq": 1.0217,
    "ldsc_ratio": 0.5161
}

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 TRUE
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 8617630 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 8624966 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 8.650339e+00 5.760980e+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 7.877007e+07 5.637171e+07 828.0000000 3.238529e+07 6.929181e+07 1.145687e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.320000e-05 2.859800e-03 -0.0271934 -1.209000e-03 -1.690000e-05 1.162500e-03 3.822770e-02 ▁▃▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.348400e-03 1.603000e-03 0.0009327 1.098400e-03 1.619500e-03 3.174500e-03 1.607810e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.977991e-01 2.889940e-01 0.0000000 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.977996e-01 2.889694e-01 0.0000000 2.472845e-01 4.970777e-01 7.476802e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291181e-01 2.594887e-01 0.0053900 2.519000e-02 1.138980e-01 3.626270e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83091 0.9903662 NA NA NA NA NA NA NA 2.288673e-01 2.514593e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 -0.0008808 0.0017188 0.6100002 0.6083392 0.623810 0.7821490 NA
1 54676 rs2462492 C T 0.0005865 0.0017139 0.7300002 0.7321970 0.399147 NA NA
1 86028 rs114608975 T C 0.0006450 0.0027282 0.8100000 0.8131108 0.103534 0.0277556 NA
1 91536 rs6702460 G T -0.0020191 0.0016857 0.2300001 0.2309985 0.455905 0.4207270 NA
1 234313 rs8179466 C T -0.0017088 0.0033336 0.6100002 0.6082192 0.074461 NA NA
1 534192 rs6680723 C T -0.0001776 0.0019200 0.9299999 0.9262838 0.242047 NA NA
1 546697 rs12025928 A G 0.0017721 0.0023823 0.4600002 0.4569637 0.912870 NA NA
1 693731 rs12238997 A G 0.0011574 0.0016009 0.4700002 0.4696910 0.117320 0.1417730 NA
1 705882 rs72631875 G A -0.0027791 0.0023336 0.2300001 0.2336831 0.067690 0.0315495 NA
1 706368 rs55727773 A G -0.0016912 0.0011883 0.1499999 0.1546552 0.513311 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0003422 0.0014454 0.8100000 0.8128199 0.136328 0.2052720 NA
22 51219387 rs9616832 T C 0.0011910 0.0018839 0.5300002 0.5272514 0.071801 0.0654952 NA
22 51219704 rs147475742 G A -0.0015414 0.0025061 0.5400003 0.5385336 0.041189 0.0473243 NA
22 51221190 rs369304721 G A 0.0013200 0.0025239 0.5999997 0.6009673 0.048372 NA NA
22 51221731 rs115055839 T C 0.0013943 0.0018843 0.4600002 0.4593128 0.071351 0.0625000 NA
22 51222100 rs114553188 G T -0.0001803 0.0021834 0.9299999 0.9342040 0.054859 0.0880591 NA
22 51223637 rs375798137 G A -0.0001458 0.0021948 0.9500000 0.9470340 0.054479 0.0788738 NA
22 51229805 rs9616985 T C 0.0013724 0.0018901 0.4700002 0.4677659 0.071256 0.0730831 NA
22 51232488 rs376461333 A G 0.0024941 0.0043530 0.5700002 0.5666790 0.020463 NA NA
22 51237063 rs3896457 T C -0.0009428 0.0011411 0.4100001 0.4086724 0.298373 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62381  ES:SE:LP:AF:ID  -0.000880817:0.00171885:0.21467:0.62381:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399147 ES:SE:LP:AF:ID  0.000586494:0.00171387:0.136677:0.399147:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103534 ES:SE:LP:AF:ID  0.000644987:0.0027282:0.091515:0.103534:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455905 ES:SE:LP:AF:ID  -0.00201908:0.00168567:0.638272:0.455905:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074461 ES:SE:LP:AF:ID  -0.00170885:0.00333358:0.21467:0.074461:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242047 ES:SE:LP:AF:ID  -0.000177641:0.00192:0.0315171:0.242047:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91287  ES:SE:LP:AF:ID  0.00177211:0.00238233:0.337242:0.91287:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11732  ES:SE:LP:AF:ID  0.0011574:0.00160087:0.327902:0.11732:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06769  ES:SE:LP:AF:ID  -0.00277913:0.00233359:0.638272:0.06769:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513311 ES:SE:LP:AF:ID  -0.00169124:0.00118827:0.823909:0.513311:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033668 ES:SE:LP:AF:ID  0.000333149:0.00296238:0.0409586:0.033668:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037448 ES:SE:LP:AF:ID  0.000310204:0.00268664:0.0409586:0.037448:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037635 ES:SE:LP:AF:ID  0.000255068:0.00267256:0.0362122:0.037635:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037211 ES:SE:LP:AF:ID  -0.000103123:0.00269701:0.0132283:0.037211:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016284 ES:SE:LP:AF:ID  0.000679995:0.00422964:0.0604807:0.016284:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037852 ES:SE:LP:AF:ID  0.000237067:0.0026634:0.0315171:0.037852:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037946 ES:SE:LP:AF:ID  0.000208999:0.00265498:0.0268721:0.037946:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102707 ES:SE:LP:AF:ID  0.000494695:0.00193948:0.09691:0.102707:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958098 ES:SE:LP:AF:ID  -0.000366804:0.00256428:0.05061:0.958098:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031691 ES:SE:LP:AF:ID  0.000951413:0.00469493:0.0757207:0.031691:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052726 ES:SE:LP:AF:ID  -0.00333772:0.00378158:0.420216:0.052726:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03744  ES:SE:LP:AF:ID  -9.82596e-05:0.00267243:0.0132283:0.03744:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037709 ES:SE:LP:AF:ID  -0.000107337:0.00265028:0.0132283:0.037709:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84144  ES:SE:LP:AF:ID  -0.000780547:0.00138458:0.244125:0.84144:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056329 ES:SE:LP:AF:ID  -0.00230342:0.00224914:0.508638:0.056329:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123081 ES:SE:LP:AF:ID  0.00089226:0.00152067:0.251812:0.123081:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025133 ES:SE:LP:AF:ID  -0.000850557:0.00378679:0.0861861:0.025133:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122334 ES:SE:LP:AF:ID  0.000919626:0.00152116:0.259637:0.122334:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134137 ES:SE:LP:AF:ID  0.000858148:0.0014934:0.244125:0.134137:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  -0.00104954:0.00532178:0.0757207:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.0119104:0.00675799:1.10791:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.03759  ES:SE:LP:AF:ID  -0.000232746:0.00262576:0.0315171:0.03759:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837027 ES:SE:LP:AF:ID  -0.00124198:0.00133941:0.455932:0.837027:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836731 ES:SE:LP:AF:ID  -0.00128296:0.00133842:0.468521:0.836731:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868557 ES:SE:LP:AF:ID  -0.0014639:0.0014381:0.508638:0.868557:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.13101  ES:SE:LP:AF:ID  0.00170159:0.0014418:0.619789:0.13101:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038036 ES:SE:LP:AF:ID  -0.000153658:0.00258456:0.0222764:0.038036:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038282 ES:SE:LP:AF:ID  -0.000137997:0.00256853:0.0177288:0.038282:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86797  ES:SE:LP:AF:ID  -0.00158838:0.00143579:0.568636:0.86797:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868044 ES:SE:LP:AF:ID  -0.00160003:0.00143638:0.568636:0.868044:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.03819  ES:SE:LP:AF:ID  -0.000152833:0.00258038:0.0222764:0.03819:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867981 ES:SE:LP:AF:ID  -0.00159729:0.00143576:0.568636:0.867981:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  0.00100027:0.00719558:0.05061:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836157 ES:SE:LP:AF:ID  -0.00136282:0.00133451:0.508638:0.836157:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038193 ES:SE:LP:AF:ID  -0.000215083:0.00258415:0.0315171:0.038193:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836791 ES:SE:LP:AF:ID  -0.0014099:0.00133816:0.537602:0.836791:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013032 ES:SE:LP:AF:ID  0.00313822:0.00484075:0.283997:0.013032:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005663 ES:SE:LP:AF:ID  0.00181157:0.00718615:0.09691:0.005663:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838107 ES:SE:LP:AF:ID  -0.00109769:0.00135699:0.376751:0.838107:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868223 ES:SE:LP:AF:ID  -0.00157468:0.00143391:0.568636:0.868223:rs3115858