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_2986.vcf.gz --id UKB-b:8388 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2986.txt.gz --cohort_cases 2592 --cohort_controls 18649 --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": "2019-09-12T19:34:09.529052",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-8388/ukb-b-8388.vcf.gz; Date=Sun May 10 08:55:08 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-8388/UKB-b-8388_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8388/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:34 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8388/UKB-b-8388_data.vcf.gz ...
Read summary statistics for 3736159 SNPs.
Dropped 644 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, 902402 SNPs remain.
After merging with regression SNP LD, 902402 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1246 (0.0423)
Lambda GC: 1.0576
Mean Chi^2: 1.0779
Intercept: 1.0175 (0.0112)
Ratio: 0.2244 (0.1442)
Analysis finished at Thu Oct 17 14:42:23 2019
Total time elapsed: 49.18sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8639,
"inflation_factor": 1.0475,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 9,
"n_p_sig": 8738,
"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": 30221,
"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": 902402,
"ldsc_nsnp_merge_regression_ld": 902402,
"ldsc_observed_scale_h2_beta": 0.1246,
"ldsc_observed_scale_h2_se": 0.0423,
"ldsc_intercept_beta": 1.0175,
"ldsc_intercept_se": 0.0112,
"ldsc_lambda_gc": 1.0576,
"ldsc_mean_chisq": 1.0779,
"ldsc_ratio": 0.2246
}
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 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 | 3735518 | 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 | 3736159 | 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 | 8.663840e+00 | 5.770444e+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 | 7.854937e+07 | 5.675336e+07 | 828.0000000 | 3.159218e+07 | 6.890210e+07 | 1.147166e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.260000e-05 | 4.231900e-03 | -0.0661569 | -2.460300e-03 | -6.000000e-06 | 2.476100e-03 | 1.477540e-01 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.590100e-03 | 4.270000e-04 | 0.0030091 | 3.219600e-03 | 3.452900e-03 | 3.887500e-03 | 1.097960e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.892107e-01 | 2.932202e-01 | 0.0000000 | 2.300001e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.892116e-01 | 2.931963e-01 | 0.0000000 | 2.319425e-01 | 4.868317e-01 | 7.439129e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.053167e-01 | 2.040385e-01 | 0.1350310 | 2.268470e-01 | 3.622210e-01 | 5.587495e-01 | 8.649690e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 30221 | 0.9919112 | NA | NA | NA | NA | NA | NA | NA | 3.929471e-01 | 2.108370e-01 | 0.0000000 | 2.200480e-01 | 3.572280e-01 | 5.461260e-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 | 0.0085174 | 0.0055701 | 0.1299999 | 0.1262364 | 0.623063 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0013178 | 0.0054833 | 0.8100000 | 0.8100792 | 0.402242 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0070738 | 0.0054248 | 0.1900002 | 0.1922444 | 0.458495 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0055874 | 0.0061720 | 0.3700002 | 0.3653139 | 0.241507 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0030740 | 0.0038533 | 0.4299995 | 0.4250093 | 0.513571 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0002776 | 0.0045016 | 0.9500000 | 0.9508221 | 0.843741 | 0.639976 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0012962 | 0.0043609 | 0.7700005 | 0.7662985 | 0.839663 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | -0.0011116 | 0.0043507 | 0.8000000 | 0.7983398 | 0.839108 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | -0.0010307 | 0.0043423 | 0.8100000 | 0.8123687 | 0.838654 | 0.663938 | NA |
| 1 | 754964 | rs3131966 | C | T | -0.0012782 | 0.0043550 | 0.7700005 | 0.7691424 | 0.839288 | 0.663339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51186143 | rs2879914 | T | C | -0.0051355 | 0.0033960 | 0.1299999 | 0.1304741 | 0.383075 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0042367 | 0.0033007 | 0.2000000 | 0.1992975 | 0.452633 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0009128 | 0.0044435 | 0.8400000 | 0.8372496 | 0.167760 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0050746 | 0.0045277 | 0.2599998 | 0.2623773 | 0.169087 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0043891 | 0.0033888 | 0.2000000 | 0.1952570 | 0.387148 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0054463 | 0.0038581 | 0.1600000 | 0.1580536 | 0.253011 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0065458 | 0.0038141 | 0.0860003 | 0.0861252 | 0.269402 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0056476 | 0.0036392 | 0.1199999 | 0.1206958 | 0.333027 | 0.3724040 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0031669 | 0.0046428 | 0.5000000 | 0.4951667 | 0.138458 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0073614 | 0.0037173 | 0.0479999 | 0.0476716 | 0.298098 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623063 ES:SE:LP:AF:ID 0.00851736:0.00557013:0.886057:0.623063:rs10399793
1 54676 rs2462492 C T . PASS AF=0.402242 ES:SE:LP:AF:ID 0.00131777:0.00548331:0.091515:0.402242:rs2462492
1 91536 rs6702460 G T . PASS AF=0.458495 ES:SE:LP:AF:ID 0.00707378:0.00542481:0.721246:0.458495:rs6702460
1 534192 rs6680723 C T . PASS AF=0.241507 ES:SE:LP:AF:ID 0.00558738:0.00617195:0.431798:0.241507:rs6680723
1 706368 rs12029736 A G . PASS AF=0.513571 ES:SE:LP:AF:ID -0.00307398:0.00385326:0.366532:0.513571:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843741 ES:SE:LP:AF:ID 0.000277632:0.00450157:0.0222764:0.843741:rs4951859
1 752566 rs3094315 G A . PASS AF=0.839663 ES:SE:LP:AF:ID -0.00129615:0.00436091:0.113509:0.839663:rs3094315
1 752721 rs3131972 A G . PASS AF=0.839108 ES:SE:LP:AF:ID -0.00111158:0.00435067:0.09691:0.839108:rs3131972
1 754503 rs3115859 G A . PASS AF=0.838654 ES:SE:LP:AF:ID -0.00103074:0.00434231:0.091515:0.838654:rs3115859
1 754964 rs3131966 C T . PASS AF=0.839288 ES:SE:LP:AF:ID -0.00127817:0.00435497:0.113509:0.839288:rs3131966
1 755775 rs3131965 A G . PASS AF=0.84041 ES:SE:LP:AF:ID -0.00115724:0.00441705:0.102373:0.84041:rs3131965
1 760912 rs1048488 C T . PASS AF=0.839436 ES:SE:LP:AF:ID -0.00115742:0.00434267:0.102373:0.839436:rs1048488
1 761147 rs3115850 T C . PASS AF=0.839517 ES:SE:LP:AF:ID -0.00113565:0.00434536:0.102373:0.839517:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862978 ES:SE:LP:AF:ID -0.0029261:0.00464524:0.275724:0.862978:rs2286139
1 763394 rs3115847 G A . PASS AF=0.708189 ES:SE:LP:AF:ID -0.00149244:0.0045249:0.130768:0.708189:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761243 ES:SE:LP:AF:ID -0.00143619:0.00370259:0.154902:0.761243:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265093 ES:SE:LP:AF:ID -0.00418453:0.00412954:0.508638:0.265093:rs12124819
1 787399 rs2905055 G T . PASS AF=0.861364 ES:SE:LP:AF:ID -0.00170446:0.00466208:0.148742:0.861364:rs2905055
1 787685 rs2905054 G T . PASS AF=0.861663 ES:SE:LP:AF:ID -0.00167129:0.00466053:0.142668:0.861663:rs2905054
1 795988 rs59380221 C T . PASS AF=0.14387 ES:SE:LP:AF:ID 0.00571559:0.00482368:0.619789:0.14387:rs59380221
1 798400 rs10900604 A G . PASS AF=0.206542 ES:SE:LP:AF:ID -0.00028843:0.00393445:0.0268721:0.206542:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206384 ES:SE:LP:AF:ID -0.000324565:0.00393657:0.0315171:0.206384:rs11240777
1 808631 rs11240779 G A . PASS AF=0.773834 ES:SE:LP:AF:ID 0.000291747:0.00376498:0.0268721:0.773834:rs11240779
1 808928 rs11240780 C T . PASS AF=0.773882 ES:SE:LP:AF:ID 4.55908e-05:0.00376968:0.00436481:0.773882:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340304 ES:SE:LP:AF:ID 0.00040323:0.00530675:0.0268721:0.340304:rs74461805
1 824398 rs7538305 A C . PASS AF=0.139097 ES:SE:LP:AF:ID 0.00089379:0.00466637:0.0705811:0.139097:rs7538305
1 830181 rs28444699 A G . PASS AF=0.696493 ES:SE:LP:AF:ID -0.00616187:0.00351055:1.10237:0.696493:rs28444699
1 831489 rs4970385 C T . PASS AF=0.704756 ES:SE:LP:AF:ID -0.00473071:0.00345:0.769551:0.704756:rs4970385
1 831909 rs9697642 C T . PASS AF=0.704844 ES:SE:LP:AF:ID -0.00465232:0.00344991:0.744727:0.704844:rs9697642
1 832066 rs9697380 G C . PASS AF=0.704978 ES:SE:LP:AF:ID -0.00470756:0.00344962:0.769551:0.704978:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705003 ES:SE:LP:AF:ID -0.004704:0.00344973:0.769551:0.705003:rs4500250
1 832398 rs4553118 T C . PASS AF=0.72883 ES:SE:LP:AF:ID -0.00636576:0.00354031:1.14267:0.72883:rs4553118
1 832918 rs28765502 T C . PASS AF=0.295023 ES:SE:LP:AF:ID 0.00468528:0.00344975:0.769551:0.295023:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236977 ES:SE:LP:AF:ID 0.00266496:0.00368305:0.327902:0.236977:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236973 ES:SE:LP:AF:ID 0.00264718:0.003683:0.327902:0.236973:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239732 ES:SE:LP:AF:ID 0.00285262:0.00367177:0.356547:0.239732:rs28594623
1 833824 rs28484835 T C . PASS AF=0.23702 ES:SE:LP:AF:ID 0.00259069:0.0036825:0.318759:0.23702:rs28484835
1 833927 rs28593608 T C . PASS AF=0.213211 ES:SE:LP:AF:ID 0.00447134:0.0038168:0.619789:0.213211:rs28593608
1 834198 rs28385272 T C . PASS AF=0.213129 ES:SE:LP:AF:ID 0.00448272:0.00381692:0.619789:0.213129:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237454 ES:SE:LP:AF:ID 0.00256429:0.00368072:0.309804:0.237454:rs4411087
1 834928 rs4422949 A G . PASS AF=0.213802 ES:SE:LP:AF:ID 0.00441191:0.00381205:0.60206:0.213802:rs4422949
1 834999 rs28570054 G A . PASS AF=0.213773 ES:SE:LP:AF:ID 0.00444098:0.00381273:0.619789:0.213773:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241985 ES:SE:LP:AF:ID 0.00266428:0.00364957:0.327902:0.241985:rs4422948
1 836529 rs28731045 C G . PASS AF=0.214692 ES:SE:LP:AF:ID 0.00471826:0.00380606:0.657577:0.214692:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269664 ES:SE:LP:AF:ID 0.00256083:0.00354273:0.327902:0.269664:rs28705752
1 836924 rs72890788 G A . PASS AF=0.214603 ES:SE:LP:AF:ID 0.00480125:0.00380681:0.677781:0.214603:rs72890788
1 838387 rs4970384 T C . PASS AF=0.21581 ES:SE:LP:AF:ID 0.00473186:0.00379812:0.677781:0.21581:rs4970384
1 838555 rs4970383 C A . PASS AF=0.247046 ES:SE:LP:AF:ID 0.00449913:0.0036364:0.657577:0.247046:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270302 ES:SE:LP:AF:ID 0.0026198:0.00354417:0.337242:0.270302:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400334 ES:SE:LP:AF:ID 0.00231889:0.00317945:0.327902:0.400334:rs4970382