Summary
Summary {data-width=650}
Manhattan plot
manhattan_plot
QQ plot
qq_plot
AF plot
af_plot
P-Z plot
pz_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_41245_1480.vcf.gz --id UKB-b:15430 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1480.txt.gz --cohort_cases 1688 --cohort_controls 459401 --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",
"file_date": "2019-09-13T13:14:07.065956",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-15430/ukb-b-15430.vcf.gz; Date=Sat May 9 18:06:06 2020"
}
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-15430/UKB-b-15430_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15430/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15430/UKB-b-15430_data.vcf.gz ...
Read summary statistics for 2794075 SNPs.
Dropped 344 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, 700628 SNPs remain.
After merging with regression SNP LD, 700628 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0035 (0.0012)
Lambda GC: 1.0416
Mean Chi^2: 1.0394
Intercept: 1.0025 (0.0092)
Ratio: 0.0646 (0.2338)
Analysis finished at Thu Oct 17 14:44:50 2019
Total time elapsed: 39.85sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7994,
"inflation_factor": 1.0475,
"mean_EFFECT": -2.7551e-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": 22190,
"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": 700628,
"ldsc_nsnp_merge_regression_ld": 700628,
"ldsc_observed_scale_h2_beta": 0.0035,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0025,
"ldsc_intercept_se": 0.0092,
"ldsc_lambda_gc": 1.0416,
"ldsc_mean_chisq": 1.0394,
"ldsc_ratio": 0.0635
}
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 betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.n: Maximum value of reported sample size across all SNPs, \(n\).n_clumped_hits: Number of clumped hits.n_snps: Number of SNPsn_p_sig: Number of SNPs with pvalue below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(\(2 \times N \times MAF\)) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations 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 expectratio_se_nto 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 ofdiffand the mean ofdiff2(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<*>:r2statistics under various assumptions1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).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: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_mean_chisq< 0.7.n_p_sig:n_p_sig> 1000.miss_<*>:n_miss_<*>/n_snps> 0.01.ldsc_ratio:ldsc_ratio> 0.5ldsc_intercept_beta:ldsc_intercept_beta> 1.5n_clumped_hits:n_clumped_hits> 1000r2_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 | 2793734 | 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 | 2794075 | 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 | 8.660030e+00 | 5.770353e+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 | 7.856522e+07 | 5.665125e+07 | 5687.0000000 | 3.170425e+07 | 6.898546e+07 | 1.147402e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.000000e-07 | 1.384000e-04 | -0.0006719 | -9.310000e-05 | -5.000000e-07 | 9.280000e-05 | 7.843000e-04 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.355000e-04 | 9.000000e-06 | 0.0001212 | 1.277000e-04 | 1.329000e-04 | 1.419000e-04 | 2.765000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.938444e-01 | 2.907715e-01 | 0.0000005 | 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 | 4.938451e-01 | 2.907477e-01 | 0.0000005 | 2.396581e-01 | 4.915566e-01 | 7.460772e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.427375e-01 | 1.657954e-01 | 0.2073460 | 2.970250e-01 | 4.153850e-01 | 5.739750e-01 | 7.926540e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 22190 | 0.9920582 | NA | NA | NA | NA | NA | NA | NA | 4.256084e-01 | 1.855084e-01 | 0.0001997 | 2.775560e-01 | 4.057510e-01 | 5.617010e-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 | 0.0000999 | 0.0002230 | 0.6499995 | 0.6541408 | 0.623778 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001930 | 0.0002210 | 0.3800004 | 0.3823925 | 0.400402 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001130 | 0.0002176 | 0.5999997 | 0.6034596 | 0.456860 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0001923 | 0.0002485 | 0.4400003 | 0.4391398 | 0.240948 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0002454 | 0.0001543 | 0.1100001 | 0.1117690 | 0.515669 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0001404 | 0.0001809 | 0.4400003 | 0.4377860 | 0.706758 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0000368 | 0.0001476 | 0.8000000 | 0.8032338 | 0.761333 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0001500 | 0.0001649 | 0.3599996 | 0.3629028 | 0.265364 | 0.0756789 | NA |
| 1 | 808631 | rs11240779 | G | A | -0.0001184 | 0.0001499 | 0.4299995 | 0.4295956 | 0.772664 | 0.4534740 | NA |
| 1 | 808928 | rs11240780 | C | T | -0.0001132 | 0.0001501 | 0.4500005 | 0.4507773 | 0.772892 | 0.4522760 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001205 | 0.0001452 | 0.4100001 | 0.4065830 | 0.713665 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0000987 | 0.0001445 | 0.4899999 | 0.4942922 | 0.695497 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001389 | 0.0001456 | 0.3400001 | 0.3404113 | 0.715510 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000301 | 0.0001351 | 0.8200001 | 0.8237195 | 0.381820 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000217 | 0.0001316 | 0.8700001 | 0.8692485 | 0.451062 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000246 | 0.0001359 | 0.8600001 | 0.8564387 | 0.386349 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0001269 | 0.0001538 | 0.4100001 | 0.4094778 | 0.254570 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000340 | 0.0001525 | 0.8200001 | 0.8237639 | 0.271567 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000181 | 0.0001449 | 0.9000000 | 0.9006592 | 0.331448 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000851 | 0.0001484 | 0.5700002 | 0.5663307 | 0.297975 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623778 ES:SE:LP:AF:ID 9.99132e-05:0.000223012:0.187087:0.623778:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400402 ES:SE:LP:AF:ID -0.000193034:0.00022099:0.420216:0.400402:rs2462492
1 91536 rs6702460 G T . PASS AF=0.45686 ES:SE:LP:AF:ID -0.000113005:0.000217555:0.221849:0.45686:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240948 ES:SE:LP:AF:ID 0.000192262:0.000248514:0.356547:0.240948:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515669 ES:SE:LP:AF:ID 0.000245355:0.000154283:0.958607:0.515669:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706758 ES:SE:LP:AF:ID 0.00014035:0.000180878:0.356547:0.706758:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761333 ES:SE:LP:AF:ID 3.67861e-05:0.000147638:0.09691:0.761333:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265364 ES:SE:LP:AF:ID 0.000150033:0.000164899:0.443698:0.265364:rs12124819
1 808631 rs11240779 G A . PASS AF=0.772664 ES:SE:LP:AF:ID -0.000118387:0.000149879:0.366532:0.772664:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772892 ES:SE:LP:AF:ID -0.000113216:0.00015013:0.346787:0.772892:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340388 ES:SE:LP:AF:ID 9.53194e-05:0.000211542:0.187087:0.340388:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697217 ES:SE:LP:AF:ID 0.000149147:0.000141522:0.537602:0.697217:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705355 ES:SE:LP:AF:ID 8.05692e-05:0.000138962:0.251812:0.705355:rs4970385
1 831909 rs9697642 C T . PASS AF=0.7054 ES:SE:LP:AF:ID 8.20744e-05:0.000138958:0.259637:0.7054:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705585 ES:SE:LP:AF:ID 8.50277e-05:0.000138964:0.267606:0.705585:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705614 ES:SE:LP:AF:ID 8.50286e-05:0.000138978:0.267606:0.705614:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730081 ES:SE:LP:AF:ID 8.25545e-05:0.000142766:0.251812:0.730081:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294419 ES:SE:LP:AF:ID -8.59796e-05:0.000138972:0.267606:0.294419:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236737 ES:SE:LP:AF:ID -0.000121467:0.000147951:0.387216:0.236737:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236726 ES:SE:LP:AF:ID -0.000115347:0.000147952:0.356547:0.236726:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239783 ES:SE:LP:AF:ID -0.000134626:0.000147476:0.443698:0.239783:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236727 ES:SE:LP:AF:ID -0.000121509:0.000147951:0.387216:0.236727:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212461 ES:SE:LP:AF:ID -0.00011554:0.000153774:0.346787:0.212461:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212357 ES:SE:LP:AF:ID -0.000119343:0.000153801:0.356547:0.212357:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237211 ES:SE:LP:AF:ID -0.000124096:0.000147838:0.39794:0.237211:rs4411087
1 834928 rs4422949 A G . PASS AF=0.213002 ES:SE:LP:AF:ID -0.000113:0.000153583:0.337242:0.213002:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212963 ES:SE:LP:AF:ID -0.000115521:0.000153614:0.346787:0.212963:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241194 ES:SE:LP:AF:ID -0.000138728:0.000146806:0.468521:0.241194:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213585 ES:SE:LP:AF:ID -0.000110339:0.000153387:0.327902:0.213585:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269531 ES:SE:LP:AF:ID -0.000218773:0.000141658:0.920819:0.269531:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213555 ES:SE:LP:AF:ID -0.000111783:0.000153406:0.327902:0.213555:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214629 ES:SE:LP:AF:ID -0.000106397:0.00015311:0.309804:0.214629:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246231 ES:SE:LP:AF:ID -0.000203036:0.000145798:0.79588:0.246231:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270034 ES:SE:LP:AF:ID -0.00022285:0.000141758:0.920819:0.270034:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400164 ES:SE:LP:AF:ID -7.93751e-06:0.000128182:0.0222764:0.400164:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237128 ES:SE:LP:AF:ID -0.000260027:0.000148883:1.09151:0.237128:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215435 ES:SE:LP:AF:ID -0.00010114:0.000153212:0.29243:0.215435:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235372 ES:SE:LP:AF:ID -0.000218846:0.000151107:0.823909:0.235372:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362586 ES:SE:LP:AF:ID 0.00022514:0.000159114:0.79588:0.362586:rs11516185
1 845938 rs57760052 G A . PASS AF=0.210874 ES:SE:LP:AF:ID -5.89381e-05:0.000154327:0.154902:0.210874:rs57760052
1 847491 rs28407778 G A . PASS AF=0.214219 ES:SE:LP:AF:ID -5.30165e-05:0.000153298:0.136677:0.214219:rs28407778
1 848090 rs4246505 G A . PASS AF=0.212531 ES:SE:LP:AF:ID -7.19715e-05:0.000153702:0.19382:0.212531:rs4246505
1 848445 rs4626817 G A . PASS AF=0.20931 ES:SE:LP:AF:ID -1.73601e-05:0.00015522:0.0409586:0.20931:rs4626817
1 848456 rs11507767 A G . PASS AF=0.209259 ES:SE:LP:AF:ID -1.68527e-05:0.000155248:0.0409586:0.209259:rs11507767
1 848738 rs3829741 C T . PASS AF=0.212355 ES:SE:LP:AF:ID -7.25286e-05:0.00015384:0.19382:0.212355:rs3829741
1 850062 rs28723578 A T . PASS AF=0.214427 ES:SE:LP:AF:ID -4.89152e-05:0.000153156:0.124939:0.214427:rs28723578
1 850123 rs28622257 C T . PASS AF=0.21279 ES:SE:LP:AF:ID -7.09441e-05:0.000153547:0.19382:0.21279:rs28622257
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID 1.51081e-05:0.000127806:0.0409586:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603725 ES:SE:LP:AF:ID 1.97589e-05:0.000128524:0.0555173:0.603725:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603949 ES:SE:LP:AF:ID 1.11562e-05:0.000128506:0.0315171:0.603949:rs6657440