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_20086_8.vcf.gz --id UKB-b:11189 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20086_8.txt.gz --cohort_cases 1376 --cohort_controls 63573 --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-11189/UKB-b-11189_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11189/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11189/UKB-b-11189_data.vcf.gz ...
Read summary statistics for 2273867 SNPs.
Dropped 247 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, 578933 SNPs remain.
After merging with regression SNP LD, 578933 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0122 (0.0085)
Lambda GC: 1.017
Mean Chi^2: 1.0145
Intercept: 0.9955 (0.0095)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:40:51 2019
Total time elapsed: 32.79s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.7392,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -1.7564e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 1505,
    "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": 17997,
    "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": 578933,
    "ldsc_nsnp_merge_regression_ld": 578933,
    "ldsc_observed_scale_h2_beta": 0.0122,
    "ldsc_observed_scale_h2_se": 0.0085,
    "ldsc_intercept_beta": 0.9955,
    "ldsc_intercept_se": 0.0095,
    "ldsc_lambda_gc": 1.017,
    "ldsc_mean_chisq": 1.0145,
    "ldsc_ratio": -0.3103
}
 

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 TRUE
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 4 58 0 2273622 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 2273867 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 8.650877e+00 5.767783e+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 7.862013e+07 5.664016e+07 5687.0000000 3.175002e+07 6.909904e+07 1.147360e+08 2.491722e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.800000e-06 8.737000e-04 -0.0072723 -5.774000e-04 -5.200000e-06 5.722000e-04 1.048430e-02 ▁▆▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.397000e-04 3.870000e-05 0.0007703 8.076000e-04 8.289000e-04 8.656000e-04 1.666500e-03 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.946306e-01 2.901840e-01 0.0000000 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.946279e-01 2.901608e-01 0.0000000 2.423893e-01 4.927251e-01 7.456814e-01 9.999991e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.608365e-01 1.400151e-01 0.2543610 3.377245e-01 4.419870e-01 5.743365e-01 7.456390e-01 ▇▆▅▅▃
numeric AF_reference 17997 0.9920853 NA NA NA NA NA NA NA 4.416152e-01 1.692640e-01 0.0001997 3.095050e-01 4.291130e-01 5.648960e-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 -0.0008073 0.0014196 0.5700002 0.5695622 0.623812 0.7821490 NA
1 54676 rs2462492 C T -0.0005104 0.0014155 0.7199992 0.7184102 0.399144 NA NA
1 91536 rs6702460 G T 0.0014980 0.0013922 0.2800000 0.2819236 0.455916 0.4207270 NA
1 706368 rs55727773 A G 0.0010394 0.0009814 0.2900000 0.2895661 0.513304 0.2751600 NA
1 763394 rs369924889 G A 0.0016040 0.0011498 0.1600000 0.1630035 0.705804 0.6176120 NA
1 776546 rs12124819 A G 0.0003206 0.0010530 0.7600007 0.7607452 0.263729 0.0756789 NA
1 814495 rs74461805 C A 0.0006990 0.0013439 0.5999997 0.6030101 0.340108 NA NA
1 830181 rs28444699 A G -0.0015012 0.0008991 0.0949992 0.0949921 0.696612 0.6912940 NA
1 831489 rs4970385 C T -0.0010863 0.0008828 0.2200002 0.2185097 0.705031 0.6491610 NA
1 831909 rs9697642 C T -0.0010703 0.0008828 0.2300001 0.2253675 0.705083 0.6485620 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51180501 rs5770999 T C 0.0009351 0.0009197 0.3100002 0.3092715 0.712571 0.6369810 NA
22 51181919 rs9616825 G C 0.0006033 0.0009159 0.5099998 0.5100878 0.695031 0.6194090 NA
22 51182485 rs6009961 A G 0.0010532 0.0009227 0.2500000 0.2537186 0.714237 0.6383790 NA
22 51186143 rs2879914 T C 0.0007562 0.0008593 0.3800004 0.3788474 0.380077 0.2733630 NA
22 51186228 rs3865766 C T 0.0011972 0.0008370 0.1499999 0.1526259 0.449547 0.4532750 NA
22 51197266 rs61290853 A G 0.0012130 0.0008626 0.1600000 0.1596226 0.386693 0.4229230 NA
22 51198027 rs34939255 A G -0.0004758 0.0009783 0.6300007 0.6267108 0.254586 0.0984425 NA
22 51211106 rs9628250 T C -0.0004509 0.0009704 0.6400000 0.6421925 0.271468 0.1671330 NA
22 51212875 rs2238837 A C 0.0013616 0.0009224 0.1400000 0.1399360 0.331351 0.3724040 NA
22 51237063 rs3896457 T C 0.0002124 0.0009424 0.8200001 0.8216742 0.298393 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623812 ES:SE:LP:AF:ID  -0.000807327:0.00141961:0.244125:0.623812:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399144 ES:SE:LP:AF:ID  -0.0005104:0.00141548:0.142668:0.399144:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.455916 ES:SE:LP:AF:ID  0.00149804:0.00139222:0.552842:0.455916:rs6702460
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  0.00103939:0.000981414:0.537602:0.513304:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.705804 ES:SE:LP:AF:ID  0.00160399:0.00114978:0.79588:0.705804:rs3115847
1   776546  rs12124819  A   G   .   PASS    AF=0.263729 ES:SE:LP:AF:ID  0.000320644:0.00105301:0.119186:0.263729:rs12124819
1   814495  rs74461805  C   A   .   PASS    AF=0.340108 ES:SE:LP:AF:ID  0.000698952:0.00134394:0.221849:0.340108:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.696612 ES:SE:LP:AF:ID  -0.00150122:0.000899132:1.02228:0.696612:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705031 ES:SE:LP:AF:ID  -0.00108634:0.000882844:0.657577:0.705031:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705083 ES:SE:LP:AF:ID  -0.00107034:0.000882843:0.638272:0.705083:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705261 ES:SE:LP:AF:ID  -0.00107535:0.000882802:0.657577:0.705261:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705286 ES:SE:LP:AF:ID  -0.00108428:0.000882925:0.657577:0.705286:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730154 ES:SE:LP:AF:ID  -0.00101465:0.000907281:0.585027:0.730154:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294729 ES:SE:LP:AF:ID  0.00107555:0.000882897:0.657577:0.294729:rs28765502
1   836896  rs28705752  T   C   .   PASS    AF=0.269687 ES:SE:LP:AF:ID  0.000796975:0.000900826:0.420216:0.269687:rs28705752
1   839103  rs28562941  A   G   .   PASS    AF=0.270067 ES:SE:LP:AF:ID  0.000752739:0.000901565:0.39794:0.270067:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400406 ES:SE:LP:AF:ID  0.000417142:0.000815002:0.21467:0.400406:rs4970382
1   843405  rs11516185  A   G   .   PASS    AF=0.362367 ES:SE:LP:AF:ID  -0.00121037:0.001014:0.638272:0.362367:rs11516185
1   850218  rs6664536   T   A   .   PASS    AF=0.589315 ES:SE:LP:AF:ID  0.000168442:0.000810583:0.0757207:0.589315:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603035 ES:SE:LP:AF:ID  -0.000133994:0.000814461:0.0604807:0.603035:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603381 ES:SE:LP:AF:ID  -0.000149582:0.000814475:0.0705811:0.603381:rs6657440
1   852037  rs4970463   G   A   .   PASS    AF=0.588673 ES:SE:LP:AF:ID  0.000174858:0.00081174:0.0809219:0.588673:rs4970463
1   852063  rs28436996  G   A   .   PASS    AF=0.588649 ES:SE:LP:AF:ID  0.000203829:0.000811374:0.09691:0.588649:rs28436996
1   852875  rs13303369  C   T   .   PASS    AF=0.606715 ES:SE:LP:AF:ID  -0.000114022:0.000815815:0.05061:0.606715:rs13303369
1   853954  rs1806509   C   A   .   PASS    AF=0.60688  ES:SE:LP:AF:ID  -0.000132033:0.0008159:0.0604807:0.60688:rs1806509
1   854777  rs13303019  A   G   .   PASS    AF=0.609504 ES:SE:LP:AF:ID  -6.23117e-05:0.000816716:0.0268721:0.609504:rs13303019
1   854978  rs13303057  A   C   .   PASS    AF=0.602505 ES:SE:LP:AF:ID  -0.000124801:0.000814603:0.0555173:0.602505:rs13303057
1   855075  rs6673914   C   G   .   PASS    AF=0.609473 ES:SE:LP:AF:ID  -4.41143e-05:0.000816608:0.0177288:0.609473:rs6673914
1   856099  rs28534711  T   G   .   PASS    AF=0.390835 ES:SE:LP:AF:ID  5.56984e-05:0.00081694:0.0222764:0.390835:rs28534711
1   856108  rs28742275  A   G   .   PASS    AF=0.390777 ES:SE:LP:AF:ID  5.94923e-05:0.000817024:0.0268721:0.390777:rs28742275
1   856476  rs4040605   A   G   .   PASS    AF=0.352504 ES:SE:LP:AF:ID  -0.000285729:0.000838651:0.136677:0.352504:rs4040605
1   866893  rs2880024   T   C   .   PASS    AF=0.608083 ES:SE:LP:AF:ID  -0.000214284:0.000824165:0.102373:0.608083:rs2880024
1   868418  rs28546443  C   T   .   PASS    AF=0.301063 ES:SE:LP:AF:ID  0.00149276:0.000902658:1.00877:0.301063:rs28546443
1   870645  rs28576697  T   C   .   PASS    AF=0.293389 ES:SE:LP:AF:ID  0.000587494:0.000892791:0.29243:0.293389:rs28576697
1   871334  rs4072383   G   T   .   PASS    AF=0.717959 ES:SE:LP:AF:ID  -0.000583777:0.000888157:0.29243:0.717959:rs4072383
1   872352  rs1806780   G   C   .   PASS    AF=0.269716 ES:SE:LP:AF:ID  0.000968756:0.000899184:0.552842:0.269716:rs1806780
1   873558  rs1110052   G   T   .   PASS    AF=0.71249  ES:SE:LP:AF:ID  -0.000785389:0.000881508:0.431798:0.71249:rs1110052
1   875770  rs4970379   A   G   .   PASS    AF=0.598464 ES:SE:LP:AF:ID  -0.000454027:0.000830329:0.236572:0.598464:rs4970379
1   881627  rs2272757   G   A   .   PASS    AF=0.65048  ES:SE:LP:AF:ID  -0.000436406:0.000838783:0.221849:0.65048:rs2272757
1   891059  rs13303065  C   T   .   PASS    AF=0.650626 ES:SE:LP:AF:ID  -0.000445328:0.000838702:0.221849:0.650626:rs13303065
1   891945  rs13303106  A   G   .   PASS    AF=0.650689 ES:SE:LP:AF:ID  -0.00045557:0.000839661:0.229148:0.650689:rs13303106
1   900505  rs28705211  G   C   .   PASS    AF=0.273272 ES:SE:LP:AF:ID  0.00117238:0.000905179:0.69897:0.273272:rs28705211
1   903245  rs28690976  A   G   .   PASS    AF=0.565133 ES:SE:LP:AF:ID  -0.000677946:0.000833773:0.376751:0.565133:rs28690976
1   909073  rs3892467   C   T   .   PASS    AF=0.386217 ES:SE:LP:AF:ID  0.000340624:0.000832031:0.167491:0.386217:rs3892467
1   909238  rs3829740   G   C   .   PASS    AF=0.572164 ES:SE:LP:AF:ID  0.00084245:0.000804945:0.522879:0.572164:rs3829740
1   910394  rs28477686  C   T   .   PASS    AF=0.322663 ES:SE:LP:AF:ID  -0.000347085:0.00087433:0.161151:0.322663:rs28477686
1   912049  rs7367995   T   C   .   PASS    AF=0.585242 ES:SE:LP:AF:ID  -0.000483807:0.000812749:0.259637:0.585242:rs7367995
1   913889  rs2340596   G   A   .   PASS    AF=0.600025 ES:SE:LP:AF:ID  -0.000347015:0.000814365:0.173925:0.600025:rs2340596
1   914333  rs13302979  C   G   .   PASS    AF=0.60338  ES:SE:LP:AF:ID  -0.000432837:0.000817128:0.221849:0.60338:rs13302979
1   914852  rs13303368  G   C   .   PASS    AF=0.60101  ES:SE:LP:AF:ID  -0.000290982:0.000815624:0.142668:0.60101:rs13303368