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_20004_1557.vcf.gz --id UKB-b:14339 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20004_1557.txt.gz --cohort_cases 2648 --cohort_controls 460285 --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": "2019-09-13T13:32:32.859360",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14339/ukb-b-14339.vcf.gz; Date=Sun May 10 10:21:25 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-14339/UKB-b-14339_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14339/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:24 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14339/UKB-b-14339_data.vcf.gz ...
Read summary statistics for 3777199 SNPs.
Dropped 660 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, 910377 SNPs remain.
After merging with regression SNP LD, 910377 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0029 (0.0011)
Lambda GC: 1.0443
Mean Chi^2: 1.0398
Intercept: 1.0101 (0.0087)
Ratio: 0.2543 (0.2179)
Analysis finished at Thu Oct 17 14:44:15 2019
Total time elapsed: 51.41sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8656,
"inflation_factor": 1.0475,
"mean_EFFECT": -2.7654e-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": 30600,
"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": 910377,
"ldsc_nsnp_merge_regression_ld": 910377,
"ldsc_observed_scale_h2_beta": 0.0029,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0101,
"ldsc_intercept_se": 0.0087,
"ldsc_lambda_gc": 1.0443,
"ldsc_mean_chisq": 1.0398,
"ldsc_ratio": 0.2538
}
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 | 3776542 | 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 | 3777199 | 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 | 8.663215e+00 | 5.769618e+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 | 7.856104e+07 | 5.675568e+07 | 828.0000000 | 3.160706e+07 | 6.889886e+07 | 1.147183e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.000000e-07 | 1.846000e-04 | -0.0011095 | -1.226000e-04 | -4.000000e-07 | 1.223000e-04 | 1.221600e-03 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.802000e-04 | 2.190000e-05 | 0.0001512 | 1.611000e-04 | 1.731000e-04 | 1.954000e-04 | 5.530000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.942185e-01 | 2.905557e-01 | 0.0000019 | 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 | 4.942180e-01 | 2.905304e-01 | 0.0000019 | 2.404451e-01 | 4.924017e-01 | 7.461749e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.034987e-01 | 2.054359e-01 | 0.1321760 | 2.241250e-01 | 3.597050e-01 | 5.575390e-01 | 8.678240e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 30600 | 0.9918988 | NA | NA | NA | NA | NA | NA | NA | 3.913686e-01 | 2.117657e-01 | 0.0000000 | 2.174520e-01 | 3.548320e-01 | 5.451280e-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 | 0.0004035 | 0.0002783 | 0.1499999 | 0.1470429 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0002291 | 0.0002757 | 0.4100001 | 0.4059833 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001137 | 0.0002715 | 0.6800001 | 0.6754418 | 0.456851 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002756 | 0.0003101 | 0.3700002 | 0.3740370 | 0.240960 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001381 | 0.0001925 | 0.4700002 | 0.4730743 | 0.515650 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0000526 | 0.0002252 | 0.8200001 | 0.8153698 | 0.843212 | 0.639976 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0000311 | 0.0002430 | 0.9000000 | 0.8981009 | 0.132330 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0001520 | 0.0002181 | 0.4899999 | 0.4859240 | 0.838951 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | 0.0001396 | 0.0002179 | 0.5199996 | 0.5215331 | 0.838580 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | 0.0001612 | 0.0002172 | 0.4600002 | 0.4580749 | 0.838033 | 0.663938 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51186228 | rs3865766 | C | T | -0.0001674 | 0.0001642 | 0.3100002 | 0.3080978 | 0.451063 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0001589 | 0.0002213 | 0.4700002 | 0.4727733 | 0.167620 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | -0.0003489 | 0.0002262 | 0.1199999 | 0.1229494 | 0.168489 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000389 | 0.0001696 | 0.8200001 | 0.8186246 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0002447 | 0.0001920 | 0.2000000 | 0.2024130 | 0.254557 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0002770 | 0.0001903 | 0.1499999 | 0.1455213 | 0.271547 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0003106 | 0.0002533 | 0.2200002 | 0.2199732 | 0.132635 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001351 | 0.0001808 | 0.4500005 | 0.4549108 | 0.331455 | 0.3724040 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0002252 | 0.0002322 | 0.3300000 | 0.3320662 | 0.137953 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000662 | 0.0001851 | 0.7199992 | 0.7206025 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.000403523:0.000278281:0.823909:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000229092:0.000275688:0.387216:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000113655:0.000271453:0.167491:0.456851:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.00027563:0.000310067:0.431798:0.24096:rs6680723
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -0.000138106:0.000192486:0.327902:0.51565:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -5.2584e-05:0.000225195:0.0861861:0.843212:rs4951859
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 3.11249e-05:0.000243048:0.0457575:0.13233:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID 0.000151963:0.000218085:0.309804:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID 0.000139639:0.000217851:0.283997:0.83858:rs3131972
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID 0.000161201:0.000217246:0.337242:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID 0.000153859:0.000217856:0.318759:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID 0.000174386:0.000220803:0.366532:0.839777:rs3131965
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID 0.000174949:0.000216834:0.376751:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID 0.000175137:0.000216987:0.376751:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID 0.000203474:0.000231816:0.420216:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.000312845:0.000225671:0.769551:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID 7.30868e-06:0.000184188:0.0132283:0.761304:rs2977608
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID -7.79427e-05:0.000205741:0.154902:0.26539:rs12124819
1 787399 rs2905055 G T . PASS AF=0.86078 ES:SE:LP:AF:ID 0.000159802:0.000232639:0.309804:0.86078:rs2905055
1 787685 rs2905054 G T . PASS AF=0.861493 ES:SE:LP:AF:ID 0.000156874:0.000232793:0.30103:0.861493:rs2905054
1 795988 rs59380221 C T . PASS AF=0.143051 ES:SE:LP:AF:ID 7.41281e-05:0.000241298:0.119186:0.143051:rs59380221
1 798400 rs10900604 A G . PASS AF=0.20658 ES:SE:LP:AF:ID 0.000114564:0.000196494:0.251812:0.20658:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206409 ES:SE:LP:AF:ID 0.000115445:0.000196578:0.251812:0.206409:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772626 ES:SE:LP:AF:ID -1.44592e-05:0.000186998:0.0268721:0.772626:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772854 ES:SE:LP:AF:ID -2.38885e-05:0.000187314:0.0457575:0.772854:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID -0.000120005:0.000263936:0.187087:0.340397:rs74461805
1 824398 rs7538305 A C . PASS AF=0.138697 ES:SE:LP:AF:ID 0.000326173:0.000233081:0.79588:0.138697:rs7538305
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID -0.000306573:0.000176581:1.08092:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705403 ES:SE:LP:AF:ID -0.000301291:0.000173386:1.08619:0.705403:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID -0.00030249:0.00017338:1.09151:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID -0.000301482:0.000173388:1.08619:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705662 ES:SE:LP:AF:ID -0.000303025:0.000173406:1.09151:0.705662:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730134 ES:SE:LP:AF:ID -0.000352385:0.000178132:1.31876:0.730134:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294371 ES:SE:LP:AF:ID 0.000301356:0.000173398:1.08619:0.294371:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236696 ES:SE:LP:AF:ID 0.000218297:0.00018461:0.619789:0.236696:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236684 ES:SE:LP:AF:ID 0.000218592:0.000184612:0.619789:0.236684:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239743 ES:SE:LP:AF:ID 0.000189869:0.00018402:0.522879:0.239743:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236686 ES:SE:LP:AF:ID 0.000218617:0.000184611:0.619789:0.236686:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212416 ES:SE:LP:AF:ID 0.000270805:0.000191878:0.79588:0.212416:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212311 ES:SE:LP:AF:ID 0.000274187:0.000191911:0.823909:0.212311:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237171 ES:SE:LP:AF:ID 0.000225357:0.000184469:0.657577:0.237171:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212956 ES:SE:LP:AF:ID 0.000273337:0.000191638:0.823909:0.212956:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212918 ES:SE:LP:AF:ID 0.000269629:0.000191678:0.79588:0.212918:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241155 ES:SE:LP:AF:ID 0.000240207:0.000183182:0.721246:0.241155:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213538 ES:SE:LP:AF:ID 0.00026744:0.000191395:0.79588:0.213538:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269503 ES:SE:LP:AF:ID 0.000129916:0.000176756:0.337242:0.269503:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213508 ES:SE:LP:AF:ID 0.000267403:0.000191418:0.79588:0.213508:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214583 ES:SE:LP:AF:ID 0.000276622:0.00019105:0.823909:0.214583:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246197 ES:SE:LP:AF:ID 0.000190045:0.000181922:0.522879:0.246197:rs4970383
1 839103 rs28562941 A G . PASS AF=0.27001 ES:SE:LP:AF:ID 0.000122799:0.000176881:0.309804:0.27001:rs28562941