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_6147_5.vcf.gz --id UKB-b:11465 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6147_5.txt.gz --cohort_cases 1962 --cohort_controls 458574 --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": "2019-09-13T01:53:16.249048",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11465/ukb-b-11465.vcf.gz; Date=Sun May 10 05:27:28 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-11465/UKB-b-11465_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11465/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11465/UKB-b-11465_data.vcf.gz ...
Read summary statistics for 3146742 SNPs.
Dropped 437 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, 779504 SNPs remain.
After merging with regression SNP LD, 779504 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0057 (0.0013)
Lambda GC: 1.0505
Mean Chi^2: 1.0598
Intercept: 0.9998 (0.0098)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:02 2019
Total time elapsed: 43.46sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8283,
"inflation_factor": 1.0475,
"mean_EFFECT": 8.6931e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 6,
"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": 25149,
"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": 779504,
"ldsc_nsnp_merge_regression_ld": 779504,
"ldsc_observed_scale_h2_beta": 0.0057,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 0.9998,
"ldsc_intercept_se": 0.0098,
"ldsc_lambda_gc": 1.0505,
"ldsc_mean_chisq": 1.0598,
"ldsc_ratio": -0.0033
}
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 | 3146308 | 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 | 3146742 | 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 | 8.663104e+00 | 5.772237e+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 | 7.854239e+07 | 5.670111e+07 | 828.0000000 | 3.160638e+07 | 6.892623e+07 | 1.147796e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.000000e-07 | 1.542000e-04 | -0.0009615 | -1.017000e-04 | 5.000000e-07 | 1.041000e-04 | 9.102000e-04 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.494000e-04 | 1.250000e-05 | 0.0001310 | 1.385000e-04 | 1.456000e-04 | 1.583000e-04 | 3.238000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.916070e-01 | 2.914521e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.916044e-01 | 2.914274e-01 | 0.0000000 | 2.366129e-01 | 4.893037e-01 | 7.443347e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.293202e-01 | 1.814332e-01 | 0.1783900 | 2.701010e-01 | 3.963165e-01 | 5.703260e-01 | 8.216100e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 25149 | 0.9920079 | NA | NA | NA | NA | NA | NA | NA | 4.139868e-01 | 1.956153e-01 | 0.0000000 | 2.561900e-01 | 3.883790e-01 | 5.575080e-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 | 0.0000977 | 0.0002410 | 0.6899999 | 0.6852414 | 0.623741 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001095 | 0.0002388 | 0.6499995 | 0.6466685 | 0.400426 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001847 | 0.0002351 | 0.4299995 | 0.4319916 | 0.456883 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0006517 | 0.0002686 | 0.0150000 | 0.0152452 | 0.240976 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000727 | 0.0001667 | 0.6600001 | 0.6626754 | 0.515659 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0002134 | 0.0001955 | 0.2700001 | 0.2749034 | 0.706791 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0000064 | 0.0001595 | 0.9699999 | 0.9678480 | 0.761325 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0002145 | 0.0001782 | 0.2300001 | 0.2286681 | 0.265403 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | -0.0000102 | 0.0001702 | 0.9500000 | 0.9523224 | 0.206565 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | -0.0000084 | 0.0001703 | 0.9599999 | 0.9607292 | 0.206393 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -0.0001541 | 0.0001569 | 0.3300000 | 0.3262043 | 0.713670 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0001014 | 0.0001561 | 0.5199996 | 0.5161009 | 0.695475 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0001782 | 0.0001574 | 0.2599998 | 0.2575635 | 0.715511 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000775 | 0.0001460 | 0.5999997 | 0.5956667 | 0.381789 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0002460 | 0.0001423 | 0.0840001 | 0.0837535 | 0.451031 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0003059 | 0.0001469 | 0.0369999 | 0.0372665 | 0.386331 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000167 | 0.0001662 | 0.9199999 | 0.9200182 | 0.254597 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000926 | 0.0001648 | 0.5700002 | 0.5743419 | 0.271583 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000175 | 0.0001566 | 0.9100000 | 0.9109633 | 0.331417 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001240 | 0.0001603 | 0.4400003 | 0.4391542 | 0.297968 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623741 ES:SE:LP:AF:ID 9.76954e-05:0.000241032:0.161151:0.623741:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400426 ES:SE:LP:AF:ID -0.000109457:0.000238783:0.187087:0.400426:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456883 ES:SE:LP:AF:ID -0.000184749:0.000235113:0.366532:0.456883:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240976 ES:SE:LP:AF:ID 0.000651724:0.000268586:1.82391:0.240976:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515659 ES:SE:LP:AF:ID 7.27222e-05:0.000166709:0.180456:0.515659:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706791 ES:SE:LP:AF:ID 0.000213446:0.000195492:0.568636:0.706791:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761325 ES:SE:LP:AF:ID -6.43017e-06:0.000159528:0.0132283:0.761325:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265403 ES:SE:LP:AF:ID 0.000214519:0.000178202:0.638272:0.265403:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206565 ES:SE:LP:AF:ID -1.01754e-05:0.000170184:0.0222764:0.206565:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206393 ES:SE:LP:AF:ID -8.3832e-06:0.000170257:0.0177288:0.206393:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772651 ES:SE:LP:AF:ID 0.000193697:0.00016196:0.638272:0.772651:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772876 ES:SE:LP:AF:ID 0.000192062:0.000162233:0.619789:0.772876:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340418 ES:SE:LP:AF:ID -0.000409745:0.000228638:1.13668:0.340418:rs74461805
1 830181 rs28444699 A G . PASS AF=0.69728 ES:SE:LP:AF:ID 0.000107506:0.000152936:0.318759:0.69728:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705437 ES:SE:LP:AF:ID 9.58571e-06:0.000150172:0.0222764:0.705437:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705482 ES:SE:LP:AF:ID 8.9466e-06:0.000150168:0.0222764:0.705482:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705668 ES:SE:LP:AF:ID 9.59008e-06:0.000150175:0.0222764:0.705668:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705697 ES:SE:LP:AF:ID 8.99099e-06:0.00015019:0.0222764:0.705697:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730165 ES:SE:LP:AF:ID -1.90763e-05:0.000154284:0.0457575:0.730165:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294336 ES:SE:LP:AF:ID -9.74689e-06:0.000150183:0.0222764:0.294336:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236672 ES:SE:LP:AF:ID 2.82897e-05:0.000159895:0.0655015:0.236672:rs13303211
1 833302 rs28752186 C T . PASS AF=0.23666 ES:SE:LP:AF:ID 2.85814e-05:0.000159897:0.0655015:0.23666:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239718 ES:SE:LP:AF:ID 2.99292e-05:0.000159383:0.0705811:0.239718:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236662 ES:SE:LP:AF:ID 2.85768e-05:0.000159895:0.0655015:0.236662:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212395 ES:SE:LP:AF:ID 5.84637e-05:0.00016619:0.142668:0.212395:rs28593608
1 834198 rs28385272 T C . PASS AF=0.21229 ES:SE:LP:AF:ID 6.13433e-05:0.000166219:0.148742:0.21229:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237146 ES:SE:LP:AF:ID 2.49086e-05:0.000159772:0.0555173:0.237146:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212935 ES:SE:LP:AF:ID 5.33713e-05:0.000165983:0.124939:0.212935:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212897 ES:SE:LP:AF:ID 5.44262e-05:0.000166017:0.130768:0.212897:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241121 ES:SE:LP:AF:ID -9.11101e-06:0.000158665:0.0222764:0.241121:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213516 ES:SE:LP:AF:ID 6.089e-05:0.000165772:0.148742:0.213516:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269473 ES:SE:LP:AF:ID -3.49421e-05:0.000153095:0.0861861:0.269473:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213485 ES:SE:LP:AF:ID 6.19163e-05:0.000165793:0.148742:0.213485:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214554 ES:SE:LP:AF:ID 4.24327e-05:0.000165474:0.09691:0.214554:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246169 ES:SE:LP:AF:ID -7.28495e-06:0.000157572:0.0177288:0.246169:rs4970383
1 839103 rs28562941 A G . PASS AF=0.269984 ES:SE:LP:AF:ID -2.30597e-05:0.000153203:0.0555173:0.269984:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400109 ES:SE:LP:AF:ID 2.35956e-05:0.000138518:0.0655015:0.400109:rs4970382
1 841085 rs1574243 C G . PASS AF=0.23708 ES:SE:LP:AF:ID -3.82036e-05:0.000160899:0.091515:0.23708:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215366 ES:SE:LP:AF:ID 5.08754e-05:0.000165584:0.119186:0.215366:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235298 ES:SE:LP:AF:ID 1.02757e-05:0.000163304:0.0222764:0.235298:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362604 ES:SE:LP:AF:ID -0.000176883:0.000171966:0.522879:0.362604:rs11516185
1 844300 rs61769713 C G . PASS AF=0.818864 ES:SE:LP:AF:ID 0.000215433:0.000176752:0.657577:0.818864:rs61769713
1 845283 rs7366404 G T . PASS AF=0.814556 ES:SE:LP:AF:ID 0.000173722:0.000175197:0.49485:0.814556:rs7366404
1 845635 rs117086422 C T . PASS AF=0.205407 ES:SE:LP:AF:ID -0.000242094:0.000168399:0.823909:0.205407:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210831 ES:SE:LP:AF:ID -0.000262369:0.000166783:0.920819:0.210831:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196767 ES:SE:LP:AF:ID -0.000169604:0.000170975:0.49485:0.196767:rs28612348
1 846338 rs4970334 A G . PASS AF=0.813888 ES:SE:LP:AF:ID 0.000179333:0.000175048:0.508638:0.813888:rs4970334
1 846398 rs58781670 G A . PASS AF=0.204413 ES:SE:LP:AF:ID -0.000257748:0.000168977:0.886057:0.204413:rs58781670
1 846489 rs4970333 T C . PASS AF=0.813978 ES:SE:LP:AF:ID 0.000171669:0.000175168:0.481486:0.813978:rs4970333
1 846808 rs4475691 C T . PASS AF=0.198361 ES:SE:LP:AF:ID -0.000198068:0.000170489:0.60206:0.198361:rs4475691