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_101230.vcf.gz --id UKB-b:13281 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_101230.txt.gz --cohort_controls 64944 --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-13281/UKB-b-13281_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13281/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13281/UKB-b-13281_data.vcf.gz ...
Read summary statistics for 8624989 SNPs.
Dropped 7371 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, 1285748 SNPs remain.
After merging with regression SNP LD, 1285748 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.01 (0.0065)
Lambda GC: 0.9984
Mean Chi^2: 0.9948
Intercept: 1.0076 (0.0065)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:43:49 2019
Total time elapsed: 1.0m:31.03s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -1.9565e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 7,
    "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": 83094,
    "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": 1285748,
    "ldsc_nsnp_merge_regression_ld": 1285748,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0076,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 0.9984,
    "ldsc_mean_chisq": 0.9948,
    "ldsc_ratio": -1.4615
}

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 8617652 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 8624989 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 8.650336e+00 5.760994e+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 7.877017e+07 5.637167e+07 828.0000000 3.238545e+07 6.929183e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.000000e-06 3.897700e-03 -0.0490993 -1.641700e-03 -2.840000e-05 1.581700e-03 5.820680e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.219000e-03 2.197300e-03 0.0012784 1.505600e-03 2.219800e-03 4.351400e-03 2.203840e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 5.003261e-01 2.879318e-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 5.003240e-01 2.879054e-01 0.0000000 2.517796e-01 4.998522e-01 7.491409e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291172e-01 2.594883e-01 0.0053900 2.519000e-02 1.138980e-01 3.626240e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83094 0.9903659 NA NA NA NA NA NA NA 2.288663e-01 2.514590e-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 0.0023596 0.0023562 0.3200000 0.3166086 0.623808 0.7821490 NA
1 54676 rs2462492 C T 0.0027014 0.0023493 0.2500000 0.2501881 0.399142 NA NA
1 86028 rs114608975 T C -0.0029306 0.0037397 0.4299995 0.4332531 0.103540 0.0277556 NA
1 91536 rs6702460 G T 0.0028504 0.0023107 0.2200002 0.2173616 0.455922 0.4207270 NA
1 234313 rs8179466 C T 0.0015652 0.0045697 0.7300002 0.7319536 0.074458 NA NA
1 534192 rs6680723 C T 0.0050229 0.0026319 0.0560003 0.0563319 0.242049 NA NA
1 546697 rs12025928 A G 0.0030037 0.0032655 0.3599996 0.3576644 0.912861 NA NA
1 693731 rs12238997 A G 0.0026909 0.0021947 0.2200002 0.2201592 0.117301 0.1417730 NA
1 705882 rs72631875 G A 0.0008933 0.0031987 0.7800007 0.7800495 0.067697 0.0315495 NA
1 706368 rs55727773 A G -0.0013849 0.0016289 0.4000000 0.3952063 0.513305 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0006009 0.0019812 0.7600007 0.7616662 0.136325 0.2052720 NA
22 51219387 rs9616832 T C -0.0021561 0.0025822 0.4000000 0.4037290 0.071803 0.0654952 NA
22 51219704 rs147475742 G A -0.0021548 0.0034350 0.5300002 0.5304601 0.041193 0.0473243 NA
22 51221190 rs369304721 G A -0.0005147 0.0034593 0.8800001 0.8817193 0.048376 NA NA
22 51221731 rs115055839 T C -0.0020776 0.0025827 0.4199997 0.4211629 0.071354 0.0625000 NA
22 51222100 rs114553188 G T 0.0035229 0.0029928 0.2399999 0.2391530 0.054855 0.0880591 NA
22 51223637 rs375798137 G A 0.0034452 0.0030085 0.2500000 0.2521300 0.054474 0.0788738 NA
22 51229805 rs9616985 T C -0.0021023 0.0025907 0.4199997 0.4170833 0.071258 0.0730831 NA
22 51232488 rs376461333 A G 0.0051531 0.0059669 0.3900004 0.3877956 0.020462 NA NA
22 51237063 rs3896457 T C 0.0010029 0.0015641 0.5199996 0.5214029 0.298394 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623808 ES:SE:LP:AF:ID  0.00235958:0.00235616:0.49485:0.623808:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399142 ES:SE:LP:AF:ID  0.00270142:0.00234928:0.60206:0.399142:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.10354  ES:SE:LP:AF:ID  -0.00293056:0.00373969:0.366532:0.10354:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455922 ES:SE:LP:AF:ID  0.00285042:0.0023107:0.657577:0.455922:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074458 ES:SE:LP:AF:ID  0.00156524:0.00456967:0.136677:0.074458:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242049 ES:SE:LP:AF:ID  0.00502293:0.00263193:1.25181:0.242049:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912861 ES:SE:LP:AF:ID  0.00300366:0.00326547:0.443698:0.912861:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117301 ES:SE:LP:AF:ID  0.00269092:0.00219469:0.657577:0.117301:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067697 ES:SE:LP:AF:ID  0.000893257:0.00319872:0.107905:0.067697:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513305 ES:SE:LP:AF:ID  -0.00138493:0.00162892:0.39794:0.513305:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03368  ES:SE:LP:AF:ID  0.000415629:0.00405998:0.0362122:0.03368:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03746  ES:SE:LP:AF:ID  0.000306809:0.0036822:0.0315171:0.03746:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037647 ES:SE:LP:AF:ID  0.00034787:0.00366294:0.0362122:0.037647:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037223 ES:SE:LP:AF:ID  0.000382671:0.00369641:0.0362122:0.037223:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  -0.00234045:0.00579804:0.161151:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037863 ES:SE:LP:AF:ID  0.000273252:0.00365041:0.0268721:0.037863:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037958 ES:SE:LP:AF:ID  0.000287145:0.00363883:0.0268721:0.037958:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102744 ES:SE:LP:AF:ID  -0.00106203:0.0026581:0.161151:0.102744:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958086 ES:SE:LP:AF:ID  -0.000760759:0.00351457:0.0809219:0.958086:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031688 ES:SE:LP:AF:ID  -0.00497455:0.00643621:0.356547:0.031688:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052718 ES:SE:LP:AF:ID  0.00187144:0.00518479:0.142668:0.052718:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037451 ES:SE:LP:AF:ID  0.000337893:0.00366273:0.0315171:0.037451:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03772  ES:SE:LP:AF:ID  -5.04874e-05:0.00363239:0.00436481:0.03772:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841452 ES:SE:LP:AF:ID  -0.00221709:0.00189798:0.619789:0.841452:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056323 ES:SE:LP:AF:ID  0.00360962:0.0030837:0.619789:0.056323:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123064 ES:SE:LP:AF:ID  0.00281939:0.00208474:0.744727:0.123064:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025132 ES:SE:LP:AF:ID  0.00305786:0.00519096:0.251812:0.025132:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122317 ES:SE:LP:AF:ID  0.00300581:0.00208541:0.823909:0.122317:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134127 ES:SE:LP:AF:ID  0.0030361:0.00204716:0.853872:0.134127:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  -0.0141136:0.00729515:1.27572:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.00407226:0.00926394:0.180456:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037602 ES:SE:LP:AF:ID  -0.000311233:0.00359873:0.0315171:0.037602:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83704  ES:SE:LP:AF:ID  -0.00252109:0.00183605:0.769551:0.83704:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836744 ES:SE:LP:AF:ID  -0.00262327:0.0018347:0.823909:0.836744:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868575 ES:SE:LP:AF:ID  -0.0031288:0.00197152:0.958607:0.868575:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130991 ES:SE:LP:AF:ID  0.00335645:0.00197659:1.05061:0.130991:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038048 ES:SE:LP:AF:ID  -0.000279116:0.00354227:0.0268721:0.038048:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038295 ES:SE:LP:AF:ID  -0.000402058:0.00352032:0.0409586:0.038295:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867989 ES:SE:LP:AF:ID  -0.00320773:0.00196835:1:0.867989:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868063 ES:SE:LP:AF:ID  -0.00309482:0.00196916:0.920819:0.868063:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038202 ES:SE:LP:AF:ID  -0.000345018:0.00353658:0.0362122:0.038202:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868    ES:SE:LP:AF:ID  -0.00320503:0.0019683:1:0.868:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  8.37698e-05:0.00986379:0.00436481:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.83617  ES:SE:LP:AF:ID  -0.0025204:0.00182933:0.769551:0.83617:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038205 ES:SE:LP:AF:ID  -0.000366957:0.00354174:0.0362122:0.038205:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836804 ES:SE:LP:AF:ID  -0.00249705:0.00183434:0.769551:0.836804:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  -0.00367464:0.00663576:0.236572:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005656 ES:SE:LP:AF:ID  0.00680074:0.00985657:0.309804:0.005656:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.83812  ES:SE:LP:AF:ID  -0.00253614:0.00186017:0.769551:0.83812:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868241 ES:SE:LP:AF:ID  -0.00300662:0.00196577:0.886057:0.868241:rs3115858