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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_5116.vcf.gz --id UKB-b:9099 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5116.txt.gz --cohort_controls 96007 --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-9099/UKB-b-9099_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9099/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:16 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9099/UKB-b-9099_data.vcf.gz ...
Read summary statistics for 8993776 SNPs.
Dropped 8680 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, 1287187 SNPs remain.
After merging with regression SNP LD, 1287187 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0931 (0.0075)
Lambda GC: 1.146
Mean Chi^2: 1.1826
Intercept: 1.0075 (0.0071)
Ratio: 0.0412 (0.0386)
Analysis finished at Thu Oct 17 14:43:46 2019
Total time elapsed: 1.0m:29.51s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9478,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 12,
    "n_p_sig": 510,
    "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": 92908,
    "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": 1287187,
    "ldsc_nsnp_merge_regression_ld": 1287187,
    "ldsc_observed_scale_h2_beta": 0.0931,
    "ldsc_observed_scale_h2_se": 0.0075,
    "ldsc_intercept_beta": 1.0075,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.146,
    "ldsc_mean_chisq": 1.1826,
    "ldsc_ratio": 0.0411
}

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 8985135 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 8993776 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 8.643392e+00 5.758311e+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 7.878744e+07 5.634103e+07 828.0000000 3.242790e+07 6.934861e+07 1.145459e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.290000e-05 1.519750e-02 -0.1630580 -6.158000e-03 -1.400000e-06 6.130000e-03 1.777140e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.202270e-02 8.783700e-03 0.0043836 5.224500e-03 7.988600e-03 1.648600e-02 1.015740e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.830991e-01 2.936036e-01 0.0000000 2.200002e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.830995e-01 2.935790e-01 0.0000000 2.244286e-01 4.772790e-01 7.379032e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.205249e-01 2.585740e-01 0.0036460 2.066900e-02 1.019340e-01 3.478320e-01 9.963540e-01 ▇▂▁▁▁
numeric AF_reference 92908 0.9896697 NA NA NA NA NA NA NA 2.208105e-01 2.504823e-01 0.0000000 1.797120e-02 1.194090e-01 3.460460e-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 -0.0022965 0.0080952 0.7800007 0.7766557 0.623641 0.7821490 NA
1 54676 rs2462492 C T -0.0067372 0.0080412 0.4000000 0.4021243 0.398977 NA NA
1 86028 rs114608975 T C 0.0211474 0.0127524 0.0969996 0.0972554 0.104065 0.0277556 NA
1 91536 rs6702460 G T -0.0156570 0.0079010 0.0479999 0.0475198 0.455685 0.4207270 NA
1 234313 rs8179466 C T -0.0058468 0.0154688 0.7099994 0.7054477 0.074834 NA NA
1 534192 rs6680723 C T 0.0000751 0.0090438 0.9900000 0.9933765 0.240593 NA NA
1 546697 rs12025928 A G 0.0191225 0.0112111 0.0879995 0.0880685 0.912843 NA NA
1 693731 rs12238997 A G 0.0100580 0.0075187 0.1800002 0.1809846 0.117831 0.1417730 NA
1 705882 rs72631875 G A -0.0162944 0.0110153 0.1400000 0.1390731 0.067561 0.0315495 NA
1 706368 rs55727773 A G -0.0114305 0.0055754 0.0400000 0.0403478 0.514323 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0024306 0.0067526 0.7199992 0.7188827 0.137178 0.2052720 NA
22 51219387 rs9616832 T C -0.0032418 0.0087984 0.7099994 0.7125390 0.072666 0.0654952 NA
22 51219704 rs147475742 G A -0.0163870 0.0117076 0.1600000 0.1616064 0.041778 0.0473243 NA
22 51221190 rs369304721 G A -0.0055952 0.0117746 0.6300007 0.6346474 0.049109 NA NA
22 51221731 rs115055839 T C -0.0034888 0.0088009 0.6899999 0.6917981 0.072193 0.0625000 NA
22 51222100 rs114553188 G T 0.0091832 0.0103024 0.3700002 0.3727312 0.054410 0.0880591 NA
22 51223637 rs375798137 G A 0.0079518 0.0103564 0.4400003 0.4425948 0.054024 0.0788738 NA
22 51229805 rs9616985 T C -0.0029647 0.0088328 0.7400005 0.7371384 0.072053 0.0730831 NA
22 51232488 rs376461333 A G 0.0096410 0.0208565 0.6400000 0.6439009 0.019996 NA NA
22 51237063 rs3896457 T C 0.0022741 0.0053691 0.6700003 0.6718850 0.298510 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623641 ES:SE:LP:AF:ID  -0.00229646:0.00809525:0.107905:0.623641:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398977 ES:SE:LP:AF:ID  -0.00673716:0.00804117:0.39794:0.398977:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104065 ES:SE:LP:AF:ID  0.0211474:0.0127524:1.01323:0.104065:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455685 ES:SE:LP:AF:ID  -0.015657:0.00790104:1.31876:0.455685:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074834 ES:SE:LP:AF:ID  -0.00584685:0.0154688:0.148742:0.074834:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240593 ES:SE:LP:AF:ID  7.50766e-05:0.00904384:0.00436481:0.240593:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912843 ES:SE:LP:AF:ID  0.0191225:0.0112111:1.05552:0.912843:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117831 ES:SE:LP:AF:ID  0.010058:0.00751871:0.744727:0.117831:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067561 ES:SE:LP:AF:ID  -0.0162944:0.0110153:0.853872:0.067561:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514323 ES:SE:LP:AF:ID  -0.0114305:0.00557539:1.39794:0.514323:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033622 ES:SE:LP:AF:ID  -0.00497918:0.013927:0.142668:0.033622:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037269 ES:SE:LP:AF:ID  -0.0053285:0.0126667:0.173925:0.037269:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03736  ES:SE:LP:AF:ID  -0.00414523:0.0126244:0.130768:0.03736:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037059 ES:SE:LP:AF:ID  -0.00502361:0.012711:0.161151:0.037059:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016714 ES:SE:LP:AF:ID  0.0157564:0.0196088:0.376751:0.016714:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037618 ES:SE:LP:AF:ID  -0.00546422:0.0125707:0.180456:0.037618:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037715 ES:SE:LP:AF:ID  -0.00605235:0.0125298:0.200659:0.037715:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101108 ES:SE:LP:AF:ID  0.00107028:0.00922961:0.0409586:0.101108:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958088 ES:SE:LP:AF:ID  0.000803136:0.0120526:0.0222764:0.958088:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031875 ES:SE:LP:AF:ID  -0.00633279:0.0219707:0.113509:0.031875:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052626 ES:SE:LP:AF:ID  -0.00559025:0.0177776:0.124939:0.052626:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03719  ES:SE:LP:AF:ID  -0.00551054:0.0126147:0.180456:0.03719:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037503 ES:SE:LP:AF:ID  -0.00657625:0.0125078:0.221849:0.037503:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840885 ES:SE:LP:AF:ID  -0.00767042:0.00651383:0.619789:0.840885:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056136 ES:SE:LP:AF:ID  0.0133529:0.0106012:0.677781:0.056136:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123708 ES:SE:LP:AF:ID  0.0106598:0.00714013:0.853872:0.123708:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025885 ES:SE:LP:AF:ID  0.00983643:0.0175235:0.244125:0.025885:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122907 ES:SE:LP:AF:ID  0.0111456:0.0071438:0.920819:0.122907:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133739 ES:SE:LP:AF:ID  0.00577671:0.00704064:0.387216:0.133739:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011242 ES:SE:LP:AF:ID  -0.0114202:0.0255262:0.187087:0.011242:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.00609  ES:SE:LP:AF:ID  -0.00653778:0.0319013:0.0757207:0.00609:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037482 ES:SE:LP:AF:ID  -0.00618299:0.0123687:0.207608:0.037482:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836693 ES:SE:LP:AF:ID  -0.00480441:0.00630425:0.346787:0.836693:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836262 ES:SE:LP:AF:ID  -0.00434027:0.00629718:0.309804:0.836262:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868027 ES:SE:LP:AF:ID  -0.00923417:0.00675566:0.769551:0.868027:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131634 ES:SE:LP:AF:ID  0.00936313:0.00676984:0.769551:0.131634:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037923 ES:SE:LP:AF:ID  -0.00230044:0.0121743:0.0705811:0.037923:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038169 ES:SE:LP:AF:ID  -0.00186566:0.0120991:0.0555173:0.038169:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867327 ES:SE:LP:AF:ID  -0.00906621:0.00674209:0.744727:0.867327:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867425 ES:SE:LP:AF:ID  -0.00896256:0.00674527:0.744727:0.867425:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038117 ES:SE:LP:AF:ID  -0.00271901:0.0121476:0.0861861:0.038117:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867325 ES:SE:LP:AF:ID  -0.00909862:0.00674166:0.744727:0.867325:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005085 ES:SE:LP:AF:ID  0.0064473:0.0349446:0.0705811:0.005085:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005052 ES:SE:LP:AF:ID  0.00765737:0.0350421:0.0809219:0.005052:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835803 ES:SE:LP:AF:ID  -0.00450084:0.00628368:0.327902:0.835803:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038133 ES:SE:LP:AF:ID  -0.00260781:0.0121642:0.0809219:0.038133:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836411 ES:SE:LP:AF:ID  -0.00479593:0.00630066:0.346787:0.836411:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013157 ES:SE:LP:AF:ID  -0.0194833:0.0226326:0.408935:0.013157:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005536 ES:SE:LP:AF:ID  -0.0131816:0.0340779:0.154902:0.005536:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837723 ES:SE:LP:AF:ID  -0.00506869:0.00638709:0.366532:0.837723:rs3131965