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_41200_S472.vcf.gz --id UKB-b:13276 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41200_S472.txt.gz --cohort_cases 2283 --cohort_controls 460727 --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": "2019-09-13T00:57:05.775204",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13276/ukb-b-13276.vcf.gz; Date=Sun May 10 12:50:43 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-13276/UKB-b-13276_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13276/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13276/UKB-b-13276_data.vcf.gz ...
Read summary statistics for 3476235 SNPs.
Dropped 530 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, 849836 SNPs remain.
After merging with regression SNP LD, 849836 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0009 (0.0011)
Lambda GC: 1.0323
Mean Chi^2: 1.0188
Intercept: 1.0093 (0.0086)
Ratio: 0.495 (0.4566)
Analysis finished at Thu Oct 17 14:43:03 2019
Total time elapsed: 44.47sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8498,
"inflation_factor": 1.0475,
"mean_EFFECT": 1.2137e-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": 27938,
"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": 849836,
"ldsc_nsnp_merge_regression_ld": 849836,
"ldsc_observed_scale_h2_beta": 0.0009,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0093,
"ldsc_intercept_se": 0.0086,
"ldsc_lambda_gc": 1.0323,
"ldsc_mean_chisq": 1.0188,
"ldsc_ratio": 0.4947
}
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 | 3475708 | 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 | 3476235 | 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 | 8.663760e+00 | 5.773507e+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 | 7.854255e+07 | 5.672127e+07 | 828.0000000 | 3.160538e+07 | 6.891410e+07 | 1.146936e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.000000e-07 | 1.663000e-04 | -0.0011913 | -1.111000e-04 | -3.000000e-07 | 1.114000e-04 | 1.277700e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.638000e-04 | 1.680000e-05 | 0.0001405 | 1.491000e-04 | 1.585000e-04 | 1.756000e-04 | 5.138000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.957464e-01 | 2.894269e-01 | 0.0000007 | 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 | 4.957453e-01 | 2.894019e-01 | 0.0000007 | 2.441830e-01 | 4.936055e-01 | 7.465322e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.160360e-01 | 1.945915e-01 | 0.1533080 | 2.453860e-01 | 3.774180e-01 | 5.644355e-01 | 8.466920e-01 | ▇▆▅▃▂ |
| numeric | AF_reference | 27938 | 0.9919631 | NA | NA | NA | NA | NA | NA | NA | 4.023234e-01 | 2.044050e-01 | 0.0000000 | 2.356230e-01 | 3.712060e-01 | 5.517170e-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 | 0.0001889 | 0.0002585 | 0.4700002 | 0.4650662 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000226 | 0.0002561 | 0.9299999 | 0.9297901 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002395 | 0.0002522 | 0.3400001 | 0.3423250 | 0.456846 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0001140 | 0.0002880 | 0.6899999 | 0.6923146 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0002516 | 0.0001788 | 0.1600000 | 0.1593751 | 0.515645 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0003062 | 0.0002092 | 0.1400000 | 0.1432932 | 0.843204 | 0.639976 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0003830 | 0.0002026 | 0.0589997 | 0.0586708 | 0.838945 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | 0.0003701 | 0.0002024 | 0.0669993 | 0.0674189 | 0.838573 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | 0.0003797 | 0.0002018 | 0.0599998 | 0.0598859 | 0.838026 | 0.663938 | NA |
| 1 | 754964 | rs3131966 | C | T | 0.0004000 | 0.0002024 | 0.0479999 | 0.0480859 | 0.838657 | 0.663339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51182485 | rs6009961 | A | G | -0.0002541 | 0.0001688 | 0.1299999 | 0.1323334 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0003782 | 0.0001565 | 0.0160000 | 0.0156916 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0003324 | 0.0001526 | 0.0290001 | 0.0293817 | 0.451061 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0002637 | 0.0002056 | 0.2000000 | 0.1994698 | 0.167627 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0001343 | 0.0002101 | 0.5199996 | 0.5226935 | 0.168490 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0002087 | 0.0001575 | 0.1900002 | 0.1853009 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0001934 | 0.0001783 | 0.2800000 | 0.2781039 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000900 | 0.0001768 | 0.6100002 | 0.6108857 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0003590 | 0.0001680 | 0.0329997 | 0.0326075 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0003154 | 0.0001720 | 0.0669993 | 0.0666151 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 0.000188853:0.000258515:0.327902:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 2.25655e-05:0.000256109:0.0315171:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000239455:0.000252169:0.468521:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.000113984:0.000288045:0.161151:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 0.00025162:0.000178812:0.79588:0.515645:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID 0.000306187:0.000209196:0.853872:0.843204:rs4951859
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID 0.000383031:0.000202592:1.22915:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID 0.000370116:0.000202374:1.17393:0.838573:rs3131972
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID 0.000379736:0.000201812:1.22185:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID 0.000400024:0.000202379:1.31876:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID 0.000410214:0.000205116:1.33724:0.83977:rs3131965
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID 0.000397714:0.000201429:1.31876:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID 0.000394998:0.000201572:1.30103:0.838427:rs3115850
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.000314956:0.00020964:0.886057:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID 0.000132809:0.000171103:0.356547:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -0.000120481:0.000191129:0.275724:0.265385:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206591 ES:SE:LP:AF:ID -0.000226622:0.000182532:0.677781:0.206591:rs10900604
1 798959 rs11240777 G A . PASS AF=0.20642 ES:SE:LP:AF:ID -0.000222351:0.00018261:0.657577:0.20642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772619 ES:SE:LP:AF:ID 0.000202359:0.000173713:0.619789:0.772619:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772847 ES:SE:LP:AF:ID 0.00020716:0.000174006:0.638272:0.772847:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID -0.000247912:0.000245182:0.508638:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID -3.96685e-05:0.00016404:0.091515:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID -0.000144782:0.000161071:0.431798:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID -0.000145747:0.000161066:0.431798:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID -0.000136326:0.000161073:0.39794:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID -0.000137248:0.00016109:0.408935:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID -9.95617e-05:0.000165479:0.259637:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID 0.000146159:0.000161083:0.443698:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID 0.000264823:0.000171497:0.920819:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID 0.000276974:0.000171499:0.958607:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID 0.000242678:0.000170949:0.79588:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID 0.000270836:0.000171498:0.958607:0.236693:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212421 ES:SE:LP:AF:ID 0.000249215:0.000178249:0.79588:0.212421:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212316 ES:SE:LP:AF:ID 0.000240102:0.00017828:0.744727:0.212316:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID 0.000257537:0.000171366:0.886057:0.237178:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212962 ES:SE:LP:AF:ID 0.000236537:0.000178026:0.744727:0.212962:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212924 ES:SE:LP:AF:ID 0.00023086:0.000178063:0.721246:0.212924:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID 0.000290397:0.00017017:1.05552:0.241162:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213543 ES:SE:LP:AF:ID 0.000227793:0.0001778:0.69897:0.213543:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID 0.000158822:0.000164201:0.481486:0.269511:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213513 ES:SE:LP:AF:ID 0.000228853:0.000177822:0.69897:0.213513:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214587 ES:SE:LP:AF:ID 0.000248615:0.00017748:0.79588:0.214587:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID 0.000126044:0.000169:0.337242:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID 0.000147342:0.000164317:0.431798:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 0.000219537:0.000148574:0.853872:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID 0.000109765:0.000172575:0.283997:0.237102:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215391 ES:SE:LP:AF:ID 0.000238891:0.000177598:0.744727:0.215391:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID 0.000287872:0.000175159:1:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -2.8901e-06:0.000184434:0.00436481:0.362606:rs11516185
1 844300 rs61769713 C G . PASS AF=0.818811 ES:SE:LP:AF:ID 1.7102e-05:0.000189553:0.0315171:0.818811:rs61769713