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_20002_1330.vcf.gz --id UKB-b:10098 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1330.txt.gz --cohort_cases 2577 --cohort_controls 460356 --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": "2019-09-12T22:35:58.501992",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10098/ukb-b-10098.vcf.gz; Date=Sat May 9 20:54:07 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-10098/UKB-b-10098_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10098/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-10098/UKB-b-10098_data.vcf.gz ...
Read summary statistics for 3723287 SNPs.
Dropped 642 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, 899785 SNPs remain.
After merging with regression SNP LD, 899785 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0015 (0.0012)
Lambda GC: 1.0256
Mean Chi^2: 1.023
Intercept: 1.0077 (0.0088)
Ratio: 0.3332 (0.3843)
Analysis finished at Thu Oct 17 14:41:06 2019
Total time elapsed: 48.7sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8631,
"inflation_factor": 1.0475,
"mean_EFFECT": -1.6249e-06,
"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": 30100,
"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": 899785,
"ldsc_nsnp_merge_regression_ld": 899785,
"ldsc_observed_scale_h2_beta": 0.0015,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0077,
"ldsc_intercept_se": 0.0088,
"ldsc_lambda_gc": 1.0256,
"ldsc_mean_chisq": 1.023,
"ldsc_ratio": 0.3348
}
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 | 3 | 58 | 0 | 3722648 | 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 | 3723287 | 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 | 8.663039e+00 | 5.770507e+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 | 7.855705e+07 | 5.675749e+07 | 828.0000000 | 3.159339e+07 | 6.890383e+07 | 1.147212e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.600000e-06 | 1.807000e-04 | -0.0010404 | -1.214000e-04 | -2.500000e-06 | 1.182000e-04 | 1.323500e-03 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.770000e-04 | 2.090000e-05 | 0.0001491 | 1.588000e-04 | 1.703000e-04 | 1.915000e-04 | 5.454000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.953738e-01 | 2.898730e-01 | 0.0000008 | 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 | 4.953749e-01 | 2.898480e-01 | 0.0000008 | 2.432449e-01 | 4.940718e-01 | 7.466852e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.058670e-01 | 2.036216e-01 | 0.1358170 | 2.276965e-01 | 3.629900e-01 | 5.590430e-01 | 8.641830e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 30100 | 0.9919157 | NA | NA | NA | NA | NA | NA | NA | 3.934074e-01 | 2.105739e-01 | 0.0000000 | 2.206470e-01 | 3.578270e-01 | 5.465260e-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 | -0.0003404 | 0.0002744 | 0.2099999 | 0.2148108 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0003039 | 0.0002719 | 0.2599998 | 0.2636918 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0006638 | 0.0002677 | 0.0129999 | 0.0131509 | 0.456851 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0000129 | 0.0003058 | 0.9699999 | 0.9664263 | 0.240960 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000840 | 0.0001898 | 0.6600001 | 0.6580172 | 0.515650 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0000270 | 0.0002221 | 0.9000000 | 0.9031680 | 0.843212 | 0.639976 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0000375 | 0.0002151 | 0.8600001 | 0.8615371 | 0.838951 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | -0.0000251 | 0.0002148 | 0.9100000 | 0.9069842 | 0.838580 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | -0.0000716 | 0.0002142 | 0.7400005 | 0.7383565 | 0.838033 | 0.663938 | NA |
| 1 | 754964 | rs3131966 | C | T | -0.0000724 | 0.0002149 | 0.7400005 | 0.7361684 | 0.838664 | 0.663339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51186143 | rs2879914 | T | C | -0.0001860 | 0.0001662 | 0.2599998 | 0.2631671 | 0.381826 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001982 | 0.0001620 | 0.2200002 | 0.2210386 | 0.451063 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0000865 | 0.0002182 | 0.6899999 | 0.6917588 | 0.167620 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0001644 | 0.0002231 | 0.4600002 | 0.4611225 | 0.168489 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001795 | 0.0001672 | 0.2800000 | 0.2831397 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0002033 | 0.0001893 | 0.2800000 | 0.2828823 | 0.254557 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0002572 | 0.0001877 | 0.1700000 | 0.1706138 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001349 | 0.0001783 | 0.4500005 | 0.4493141 | 0.331455 | 0.3724040 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0001064 | 0.0002290 | 0.6400000 | 0.6422655 | 0.137953 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001564 | 0.0001826 | 0.3900004 | 0.3916838 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.000340429:0.000274441:0.677781:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000303888:0.000271885:0.585027:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -0.000663824:0.000267708:1.88606:0.456851:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -1.28709e-05:0.000305789:0.0132283:0.24096:rs6680723
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -8.40287e-05:0.00018983:0.180456:0.51565:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -2.70193e-05:0.000222088:0.0457575:0.843212:rs4951859
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -3.75131e-05:0.000215076:0.0655015:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -2.51032e-05:0.000214845:0.0409586:0.83858:rs3131972
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -7.15657e-05:0.000214249:0.130768:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -7.23901e-05:0.000214851:0.130768:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -6.8817e-05:0.000217756:0.124939:0.839777:rs3131965
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID -9.81695e-05:0.000213842:0.187087:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID -0.000103656:0.000213993:0.200659:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID -0.000141844:0.000228618:0.275724:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000264714:0.000222557:0.638272:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID -0.000123445:0.000181647:0.30103:0.761304:rs2977608
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID 6.82874e-05:0.000202902:0.130768:0.26539:rs12124819
1 787399 rs2905055 G T . PASS AF=0.86078 ES:SE:LP:AF:ID -0.00015551:0.000229429:0.30103:0.86078:rs2905055
1 787685 rs2905054 G T . PASS AF=0.861493 ES:SE:LP:AF:ID -0.00016866:0.000229581:0.337242:0.861493:rs2905054
1 795988 rs59380221 C T . PASS AF=0.143051 ES:SE:LP:AF:ID -7.55445e-05:0.000237968:0.124939:0.143051:rs59380221
1 798400 rs10900604 A G . PASS AF=0.20658 ES:SE:LP:AF:ID 2.19783e-06:0.000193783:0.00436481:0.20658:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206409 ES:SE:LP:AF:ID 2.50654e-06:0.000193866:0.00436481:0.206409:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772626 ES:SE:LP:AF:ID 7.49102e-05:0.000184418:0.167491:0.772626:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772854 ES:SE:LP:AF:ID 5.97255e-05:0.000184729:0.124939:0.772854:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID -6.0148e-05:0.000260294:0.0861861:0.340397:rs74461805
1 824398 rs7538305 A C . PASS AF=0.138697 ES:SE:LP:AF:ID 0.000299497:0.000229865:0.721246:0.138697:rs7538305
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID -5.92878e-06:0.000174144:0.0132283:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705403 ES:SE:LP:AF:ID -3.88854e-05:0.000170994:0.0861861:0.705403:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID -3.96393e-05:0.000170988:0.0861861:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID -3.8922e-05:0.000170996:0.0861861:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705662 ES:SE:LP:AF:ID -4.49087e-05:0.000171014:0.102373:0.705662:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730134 ES:SE:LP:AF:ID -1.82767e-05:0.000175674:0.0362122:0.730134:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294371 ES:SE:LP:AF:ID 3.85403e-05:0.000171006:0.0861861:0.294371:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236696 ES:SE:LP:AF:ID 7.33497e-05:0.000182063:0.161151:0.236696:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236684 ES:SE:LP:AF:ID 7.38809e-05:0.000182065:0.167491:0.236684:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239743 ES:SE:LP:AF:ID 9.35216e-05:0.000181481:0.21467:0.239743:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236686 ES:SE:LP:AF:ID 7.36618e-05:0.000182063:0.161151:0.236686:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212416 ES:SE:LP:AF:ID 4.66659e-05:0.00018923:0.091515:0.212416:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212311 ES:SE:LP:AF:ID 4.189e-05:0.000189263:0.0861861:0.212311:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237171 ES:SE:LP:AF:ID 9.01305e-05:0.000181924:0.207608:0.237171:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212956 ES:SE:LP:AF:ID 7.58198e-05:0.000188994:0.161151:0.212956:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212918 ES:SE:LP:AF:ID 7.78688e-05:0.000189033:0.167491:0.212918:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241155 ES:SE:LP:AF:ID 0.000103724:0.000180654:0.244125:0.241155:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213538 ES:SE:LP:AF:ID 7.10282e-05:0.000188754:0.148742:0.213538:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269503 ES:SE:LP:AF:ID 2.6024e-05:0.000174317:0.0555173:0.269503:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213508 ES:SE:LP:AF:ID 7.56076e-05:0.000188777:0.161151:0.213508:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214583 ES:SE:LP:AF:ID 7.66364e-05:0.000188414:0.167491:0.214583:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246197 ES:SE:LP:AF:ID 3.11331e-05:0.000179412:0.0655015:0.246197:rs4970383
1 839103 rs28562941 A G . PASS AF=0.27001 ES:SE:LP:AF:ID 5.32209e-06:0.000174441:0.00877392:0.27001:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID 0.000216184:0.000157728:0.769551:0.400106:rs4970382