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_20002_1165.vcf.gz --id UKB-b:131 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1165.txt.gz --cohort_cases 1006 --cohort_controls 461927 --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-131/UKB-b-131_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-131/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-131/UKB-b-131_data.vcf.gz ...
Read summary statistics for 1354429 SNPs.
Dropped 110 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, 350799 SNPs remain.
After merging with regression SNP LD, 350799 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0016 (0.0013)
Lambda GC: 1.0064
Mean Chi^2: 1.0114
Intercept: 1.0287 (0.0116)
Ratio: 2.5251 (1.0203)
Analysis finished at Thu Oct 17 14:42:37 2019
Total time elapsed: 27.58s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.5543,
    "inflation_factor": 1,
    "mean_EFFECT": 8.7933e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 10812,
    "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": 350799,
    "ldsc_nsnp_merge_regression_ld": 350799,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0287,
    "ldsc_intercept_se": 0.0116,
    "ldsc_lambda_gc": 1.0064,
    "ldsc_mean_chisq": 1.0114,
    "ldsc_ratio": 2.5175
}
 

Flags

name value
af_correlation TRUE
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 4 58 0 1354321 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 1354429 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 8.681191e+00 5.769216e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.844265e+07 5.650137e+07 1.23330e+04 3.161956e+07 6.889402e+07 1.143316e+08 2.491722e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 9.000000e-07 9.950000e-05 -4.75800e-04 -6.630000e-05 8.000000e-07 6.730000e-05 5.440000e-04 ▁▃▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.880000e-05 2.500000e-06 9.34000e-05 9.740000e-05 9.840000e-05 9.990000e-05 1.827000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.993713e-01 2.898135e-01 1.80000e-06 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 4.993733e-01 2.897835e-01 1.80000e-06 2.480420e-01 4.992188e-01 7.513803e-01 9.999993e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.851504e-01 8.752480e-02 3.47913e-01 4.080270e-01 4.776180e-01 5.579900e-01 6.520870e-01 ▇▇▆▆▅
numeric AF_reference 10812 0.9920173 NA NA NA NA NA NA NA 4.627861e-01 1.412985e-01 1.99700e-04 3.578270e-01 4.574680e-01 5.634980e-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 -0.0001943 0.0001719 0.2599998 0.2583162 0.623763 0.782149 NA
1 54676 rs2462492 C T 0.0001750 0.0001703 0.2999998 0.3039649 0.400401 NA NA
1 91536 rs6702460 G T -0.0000056 0.0001676 0.9699999 0.9733741 0.456851 0.420727 NA
1 706368 rs55727773 A G 0.0001283 0.0001189 0.2800000 0.2805222 0.515650 0.275160 NA
1 840753 rs4970382 T C 0.0001181 0.0000988 0.2300001 0.2319648 0.400106 0.468850 NA
1 843405 rs11516185 A G 0.0001502 0.0001226 0.2200002 0.2206898 0.362599 0.375399 NA
1 850218 rs6664536 T A 0.0000143 0.0000985 0.8800001 0.8847759 0.590333 0.345248 NA
1 850371 rs6679046 G T -0.0000036 0.0000990 0.9699999 0.9710288 0.603726 0.508786 NA
1 850780 rs6657440 C T -0.0000050 0.0000990 0.9599999 0.9595964 0.603944 0.560304 NA
1 852037 rs4970463 G A 0.0000168 0.0000986 0.8600001 0.8645724 0.589688 0.345048 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51158017 rs6010065 G C -0.0001215 0.0000971 0.2099999 0.2107169 0.461423 0.547524 NA
22 51158499 rs8136930 T G -0.0001171 0.0000972 0.2300001 0.2282395 0.461648 0.544728 NA
22 51161019 rs5770994 C T 0.0000879 0.0000969 0.3599996 0.3642917 0.482193 0.425719 NA
22 51163039 rs715584 G T -0.0000995 0.0000984 0.3100002 0.3120088 0.426984 0.473642 NA
22 51164109 rs5770995 G C -0.0001029 0.0000978 0.2900000 0.2927688 0.452716 0.510982 NA
22 51164115 rs5770996 C T -0.0001212 0.0000977 0.2200002 0.2150092 0.456928 0.514776 NA
22 51174048 rs9628245 G C 0.0000847 0.0001104 0.4400003 0.4432812 0.380130 0.433107 NA
22 51186143 rs2879914 T C 0.0000290 0.0001041 0.7800007 0.7804480 0.381826 0.273363 NA
22 51186228 rs3865766 C T 0.0000255 0.0001014 0.8000000 0.8012626 0.451063 0.453275 NA
22 51197266 rs61290853 A G -0.0000139 0.0001047 0.8900000 0.8945112 0.386333 0.422923 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  -0.000194275:0.000171867:0.585027:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000175028:0.000170266:0.522879:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  -5.59562e-06:0.00016765:0.0132283:0.456851:rs6702460
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  0.000128289:0.00011888:0.552842:0.51565:rs12029736
1   840753  rs4970382   T   C   .   PASS    AF=0.400106 ES:SE:LP:AF:ID  0.000118068:9.87758e-05:0.638272:0.400106:rs4970382
1   843405  rs11516185  A   G   .   PASS    AF=0.362599 ES:SE:LP:AF:ID  0.000150171:0.000122619:0.657577:0.362599:rs11516185
1   850218  rs6664536   T   A   .   PASS    AF=0.590333 ES:SE:LP:AF:ID  1.42724e-05:9.84863e-05:0.0555173:0.590333:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603726 ES:SE:LP:AF:ID  -3.5969e-06:9.90392e-05:0.0132283:0.603726:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603944 ES:SE:LP:AF:ID  -5.01661e-06:9.9025e-05:0.0177288:0.603944:rs6657440
1   852037  rs4970463   G   A   .   PASS    AF=0.589688 ES:SE:LP:AF:ID  1.68248e-05:9.86464e-05:0.0655015:0.589688:rs4970463
1   852063  rs28436996  G   A   .   PASS    AF=0.589667 ES:SE:LP:AF:ID  1.62542e-05:9.86021e-05:0.0604807:0.589667:rs28436996
1   852875  rs13303369  C   T   .   PASS    AF=0.607674 ES:SE:LP:AF:ID  -1.43352e-05:9.92466e-05:0.05061:0.607674:rs13303369
1   853954  rs1806509   C   A   .   PASS    AF=0.607833 ES:SE:LP:AF:ID  -1.49702e-05:9.92601e-05:0.0555173:0.607833:rs1806509
1   854777  rs13303019  A   G   .   PASS    AF=0.610318 ES:SE:LP:AF:ID  -3.16917e-06:9.93575e-05:0.0132283:0.610318:rs13303019
1   854978  rs13303057  A   C   .   PASS    AF=0.603286 ES:SE:LP:AF:ID  -5.29223e-06:9.90634e-05:0.0177288:0.603286:rs13303057
1   855075  rs6673914   C   G   .   PASS    AF=0.610338 ES:SE:LP:AF:ID  -3.45394e-06:9.93594e-05:0.0132283:0.610338:rs6673914
1   856099  rs28534711  T   G   .   PASS    AF=0.389935 ES:SE:LP:AF:ID  9.71436e-06:9.93783e-05:0.0362122:0.389935:rs28534711
1   856108  rs28742275  A   G   .   PASS    AF=0.389918 ES:SE:LP:AF:ID  9.6288e-06:9.93833e-05:0.0362122:0.389918:rs28742275
1   856476  rs4040605   A   G   .   PASS    AF=0.350351 ES:SE:LP:AF:ID  -6.43049e-06:0.000102095:0.0222764:0.350351:rs4040605
1   866893  rs2880024   T   C   .   PASS    AF=0.610554 ES:SE:LP:AF:ID  3.2474e-05:9.99176e-05:0.124939:0.610554:rs2880024
1   875770  rs4970379   A   G   .   PASS    AF=0.600084 ES:SE:LP:AF:ID  0.000105169:0.000100748:0.522879:0.600084:rs4970379
1   903245  rs28690976  A   G   .   PASS    AF=0.566937 ES:SE:LP:AF:ID  0.00014045:0.000101185:0.769551:0.566937:rs28690976
1   909073  rs3892467   C   T   .   PASS    AF=0.386683 ES:SE:LP:AF:ID  -1.16367e-05:0.00010091:0.0409586:0.386683:rs3892467
1   909238  rs3829740   G   C   .   PASS    AF=0.571412 ES:SE:LP:AF:ID  -0.000171528:9.77277e-05:1.10237:0.571412:rs3829740
1   912049  rs7367995   T   C   .   PASS    AF=0.58525  ES:SE:LP:AF:ID  7.91325e-05:9.87186e-05:0.376751:0.58525:rs7367995
1   913889  rs2340596   G   A   .   PASS    AF=0.599213 ES:SE:LP:AF:ID  7.30316e-05:9.88793e-05:0.337242:0.599213:rs2340596
1   914333  rs13302979  C   G   .   PASS    AF=0.602521 ES:SE:LP:AF:ID  5.66639e-05:9.9179e-05:0.244125:0.602521:rs13302979
1   914852  rs13303368  G   C   .   PASS    AF=0.600078 ES:SE:LP:AF:ID  7.43474e-05:9.89884e-05:0.346787:0.600078:rs13303368
1   914940  rs13303033  T   C   .   PASS    AF=0.584292 ES:SE:LP:AF:ID  7.41771e-05:9.84332e-05:0.346787:0.584292:rs13303033
1   916834  rs6694632   G   A   .   PASS    AF=0.589106 ES:SE:LP:AF:ID  6.87966e-05:9.85835e-05:0.309804:0.589106:rs6694632
1   918384  rs13303118  G   T   .   PASS    AF=0.584205 ES:SE:LP:AF:ID  7.66725e-05:9.8387e-05:0.356547:0.584205:rs13303118
1   918573  rs2341354   A   G   .   PASS    AF=0.589331 ES:SE:LP:AF:ID  6.77661e-05:9.85078e-05:0.309804:0.589331:rs2341354
1   919501  rs4970414   G   T   .   PASS    AF=0.583932 ES:SE:LP:AF:ID  0.000164228:0.000101874:0.958607:0.583932:rs4970414
1   921716  rs13303278  C   A   .   PASS    AF=0.567888 ES:SE:LP:AF:ID  0.000128544:9.83053e-05:0.721246:0.567888:rs13303278
1   924528  rs34712273  C   A   .   PASS    AF=0.578492 ES:SE:LP:AF:ID  9.4452e-05:9.85876e-05:0.468521:0.578492:rs34712273
1   930533  rs3128110   C   G   .   PASS    AF=0.386078 ES:SE:LP:AF:ID  -0.000104223:9.98472e-05:0.522879:0.386078:rs3128110
1   930567  rs3121574   A   G   .   PASS    AF=0.38612  ES:SE:LP:AF:ID  -0.000104569:9.98497e-05:0.537602:0.38612:rs3121574
1   930751  rs3128111   C   G   .   PASS    AF=0.385075 ES:SE:LP:AF:ID  -0.000109323:9.99103e-05:0.568636:0.385075:rs3128111
1   931166  rs2710880   A   G   .   PASS    AF=0.386762 ES:SE:LP:AF:ID  -0.000104405:9.9866e-05:0.522879:0.386762:rs2710880
1   931362  rs2799060   G   A   .   PASS    AF=0.385568 ES:SE:LP:AF:ID  -0.000111474:9.99146e-05:0.585027:0.385568:rs2799060
1   933790  rs9442392   G   A   .   PASS    AF=0.578607 ES:SE:LP:AF:ID  0.000114325:9.85543e-05:0.60206:0.578607:rs9442392
1   936111  rs1936360   C   T   .   PASS    AF=0.573546 ES:SE:LP:AF:ID  0.000115407:9.85306e-05:0.619789:0.573546:rs1936360
1   940005  rs2799056   A   G   .   PASS    AF=0.399259 ES:SE:LP:AF:ID  -0.000152766:9.95012e-05:0.920819:0.399259:rs2799056
1   940096  rs4503294   C   T   .   PASS    AF=0.565311 ES:SE:LP:AF:ID  0.000152612:9.82487e-05:0.920819:0.565311:rs4503294
1   941284  rs3128116   C   T   .   PASS    AF=0.397393 ES:SE:LP:AF:ID  -0.000149309:9.95227e-05:0.886057:0.397393:rs3128116
1   941334  rs57683598  G   A   .   PASS    AF=0.397396 ES:SE:LP:AF:ID  -0.000149429:9.95289e-05:0.886057:0.397396:rs57683598
1   941539  rs9778087   C   T   .   PASS    AF=0.399588 ES:SE:LP:AF:ID  -0.000146304:9.95324e-05:0.853872:0.399588:rs9778087
1   943687  rs2465140   G   C   .   PASS    AF=0.395208 ES:SE:LP:AF:ID  -0.000114715:9.95739e-05:0.60206:0.395208:rs2465140
1   943968  rs13303313  C   T   .   PASS    AF=0.569677 ES:SE:LP:AF:ID  0.000118882:9.8299e-05:0.638272:0.569677:rs13303313
1   944564  rs3128117   T   C   .   PASS    AF=0.396438 ES:SE:LP:AF:ID  -0.000113825:9.94244e-05:0.60206:0.396438:rs3128117