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_101160.vcf.gz --id UKB-b:6246 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_101160.txt.gz --cohort_controls 64940 --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-6246/UKB-b-6246_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6246/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-6246/UKB-b-6246_data.vcf.gz ...
Read summary statistics for 8624993 SNPs.
Dropped 7372 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, 1285750 SNPs remain.
After merging with regression SNP LD, 1285750 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0014 (0.0066)
Lambda GC: 1.0043
Mean Chi^2: 1.0093
Intercept: 1.0111 (0.0061)
Ratio: 1.1959 (0.6568)
Analysis finished at Thu Oct 17 14:41:55 2019
Total time elapsed: 1.0m:36.46s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 2,
    "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": 83096,
    "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": 1285750,
    "ldsc_nsnp_merge_regression_ld": 1285750,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0111,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0043,
    "ldsc_mean_chisq": 1.0093,
    "ldsc_ratio": 1.1935
}

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 8617655 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 8624993 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 5.760987e+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 5.637158e+07 828.0000000 3.238545e+07 6.929207e+07 1.145687e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -6.600000e-05 1.377980e-02 -0.1300430 -5.765900e-03 -6.390000e-05 5.608700e-03 1.479210e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.128800e-02 7.705300e-03 0.0044833 5.279400e-03 7.784200e-03 1.525900e-02 7.727920e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.979626e-01 2.893985e-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 4.979599e-01 2.893722e-01 0.0000000 2.465798e-01 4.972955e-01 7.486510e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291171e-01 2.594883e-01 0.0053900 2.519000e-02 1.138970e-01 3.626190e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83096 0.9903657 NA NA NA NA NA NA NA 2.288662e-01 2.514591e-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 -0.0143363 0.0082623 0.0830004 0.0827164 0.623804 0.7821490 NA
1 54676 rs2462492 C T 0.0125071 0.0082380 0.1299999 0.1289593 0.399143 NA NA
1 86028 rs114608975 T C -0.0138274 0.0131136 0.2900000 0.2916852 0.103537 0.0277556 NA
1 91536 rs6702460 G T -0.0074772 0.0081029 0.3599996 0.3561208 0.455920 0.4207270 NA
1 234313 rs8179466 C T -0.0089638 0.0160257 0.5800000 0.5759320 0.074453 NA NA
1 534192 rs6680723 C T 0.0025523 0.0092286 0.7800007 0.7821179 0.242064 NA NA
1 546697 rs12025928 A G -0.0155513 0.0114512 0.1700000 0.1744479 0.912863 NA NA
1 693731 rs12238997 A G -0.0002029 0.0076951 0.9800000 0.9789615 0.117317 0.1417730 NA
1 705882 rs72631875 G A 0.0181601 0.0112169 0.1100001 0.1054484 0.067697 0.0315495 NA
1 706368 rs55727773 A G -0.0015369 0.0057122 0.7899998 0.7878879 0.513300 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0019082 0.0069472 0.7800007 0.7835717 0.136316 0.2052720 NA
22 51219387 rs9616832 T C 0.0036908 0.0090548 0.6800001 0.6835663 0.071799 0.0654952 NA
22 51219704 rs147475742 G A 0.0098904 0.0120459 0.4100001 0.4116129 0.041188 0.0473243 NA
22 51221190 rs369304721 G A 0.0025328 0.0121313 0.8300000 0.8346163 0.048371 NA NA
22 51221731 rs115055839 T C 0.0044058 0.0090568 0.6300007 0.6266442 0.071350 0.0625000 NA
22 51222100 rs114553188 G T -0.0030108 0.0104950 0.7700005 0.7742046 0.054851 0.0880591 NA
22 51223637 rs375798137 G A -0.0029466 0.0105498 0.7800007 0.7800109 0.054471 0.0788738 NA
22 51229805 rs9616985 T C 0.0048871 0.0090848 0.5900000 0.5906145 0.071255 0.0730831 NA
22 51232488 rs376461333 A G -0.0271524 0.0209234 0.1900002 0.1943887 0.020461 NA NA
22 51237063 rs3896457 T C 0.0046267 0.0054846 0.4000000 0.3989036 0.298397 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623804 ES:SE:LP:AF:ID  -0.0143363:0.00826234:1.08092:0.623804:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399143 ES:SE:LP:AF:ID  0.0125071:0.00823801:0.886057:0.399143:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103537 ES:SE:LP:AF:ID  -0.0138274:0.0131136:0.537602:0.103537:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45592  ES:SE:LP:AF:ID  -0.00747721:0.0081029:0.443698:0.45592:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074453 ES:SE:LP:AF:ID  -0.00896376:0.0160257:0.236572:0.074453:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242064 ES:SE:LP:AF:ID  0.00255226:0.00922859:0.107905:0.242064:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912863 ES:SE:LP:AF:ID  -0.0155513:0.0114512:0.769551:0.912863:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117317 ES:SE:LP:AF:ID  -0.000202926:0.00769506:0.00877392:0.117317:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067697 ES:SE:LP:AF:ID  0.0181601:0.0112169:0.958607:0.067697:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.5133   ES:SE:LP:AF:ID  -0.0015369:0.00571223:0.102373:0.5133:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033675 ES:SE:LP:AF:ID  0.000993523:0.0142376:0.0268721:0.033675:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037455 ES:SE:LP:AF:ID  -0.000154131:0.0129129:0.00436481:0.037455:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037642 ES:SE:LP:AF:ID  -0.000221622:0.0128453:0.00436481:0.037642:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037218 ES:SE:LP:AF:ID  0.00113791:0.0129627:0.0315171:0.037218:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016275 ES:SE:LP:AF:ID  0.0364813:0.0203367:1.13668:0.016275:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037858 ES:SE:LP:AF:ID  -0.000445503:0.0128014:0.0132283:0.037858:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037952 ES:SE:LP:AF:ID  -5.91017e-05:0.0127608:-0:0.037952:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102737 ES:SE:LP:AF:ID  -0.0019561:0.00932084:0.0809219:0.102737:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958091 ES:SE:LP:AF:ID  0.00165967:0.0123249:0.05061:0.958091:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031693 ES:SE:LP:AF:ID  -0.0127851:0.022567:0.244125:0.031693:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052722 ES:SE:LP:AF:ID  -0.0167729:0.0181775:0.443698:0.052722:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037446 ES:SE:LP:AF:ID  -0.000810109:0.0128446:0.0222764:0.037446:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037715 ES:SE:LP:AF:ID  -0.00153711:0.0127382:0.0457575:0.037715:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841439 ES:SE:LP:AF:ID  0.00141028:0.00665483:0.0809219:0.841439:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056341 ES:SE:LP:AF:ID  -0.0120075:0.01081:0.568636:0.056341:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123082 ES:SE:LP:AF:ID  -0.0012173:0.0073094:0.0604807:0.123082:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025133 ES:SE:LP:AF:ID  0.0167386:0.0182022:0.443698:0.025133:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122335 ES:SE:LP:AF:ID  -0.00107518:0.00731175:0.0555173:0.122335:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134148 ES:SE:LP:AF:ID  -0.00623281:0.00717764:0.408935:0.134148:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  0.00277791:0.0255805:0.0409586:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  -0.0212837:0.0324841:0.29243:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037597 ES:SE:LP:AF:ID  -0.00164047:0.0126202:0.0457575:0.037597:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83703  ES:SE:LP:AF:ID  0.00357357:0.00643781:0.236572:0.83703:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836734 ES:SE:LP:AF:ID  0.00388494:0.00643309:0.259637:0.836734:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86856  ES:SE:LP:AF:ID  0.00415765:0.00691265:0.259637:0.86856:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131007 ES:SE:LP:AF:ID  -0.00410494:0.0069304:0.259637:0.131007:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038043 ES:SE:LP:AF:ID  7.45857e-05:0.0124222:-0:0.038043:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038289 ES:SE:LP:AF:ID  0.000886134:0.0123452:0.0268721:0.038289:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867973 ES:SE:LP:AF:ID  0.00429361:0.00690153:0.275724:0.867973:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868047 ES:SE:LP:AF:ID  0.00435401:0.00690435:0.275724:0.868047:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038197 ES:SE:LP:AF:ID  -0.00125079:0.0124022:0.0362122:0.038197:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867984 ES:SE:LP:AF:ID  0.00446448:0.00690136:0.283997:0.867984:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005394 ES:SE:LP:AF:ID  -0.0244067:0.034592:0.318759:0.005394:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836159 ES:SE:LP:AF:ID  0.00378537:0.00641426:0.251812:0.836159:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.0382   ES:SE:LP:AF:ID  -0.00159725:0.0124203:0.0457575:0.0382:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836793 ES:SE:LP:AF:ID  0.00406487:0.00643183:0.275724:0.836793:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  -0.0182588:0.0232684:0.366532:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005663 ES:SE:LP:AF:ID  0.01027:0.0345422:0.113509:0.005663:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838109 ES:SE:LP:AF:ID  0.00308997:0.00652236:0.19382:0.838109:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868225 ES:SE:LP:AF:ID  0.00420569:0.00689247:0.267606:0.868225:rs3115858