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_41245_1250.vcf.gz --id UKB-b:3241 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1250.txt.gz --cohort_cases 1929 --cohort_controls 459177 --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": "2019-09-12T19:14:02.024514",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3241/ukb-b-3241.vcf.gz; Date=Sat May 9 19:07:19 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-3241/UKB-b-3241_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3241/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:23 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3241/UKB-b-3241_data.vcf.gz ...
Read summary statistics for 3106819 SNPs.
Dropped 427 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, 770827 SNPs remain.
After merging with regression SNP LD, 770827 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.001 (0.0012)
Lambda GC: 1.0203
Mean Chi^2: 1.0183
Intercept: 1.0084 (0.0099)
Ratio: 0.4611 (0.5422)
Analysis finished at Thu Oct 17 14:44:04 2019
Total time elapsed: 40.1sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8252,
"inflation_factor": 1.0475,
"mean_EFFECT": -6.4041e-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": 24818,
"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": 770827,
"ldsc_nsnp_merge_regression_ld": 770827,
"ldsc_observed_scale_h2_beta": 0.001,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0084,
"ldsc_intercept_se": 0.0099,
"ldsc_lambda_gc": 1.0203,
"ldsc_mean_chisq": 1.0183,
"ldsc_ratio": 0.459
}
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 | 3106395 | 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 | 3106819 | 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 | 8.664737e+00 | 5.772514e+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 | 7.854851e+07 | 5.670614e+07 | 828.0000000 | 3.158997e+07 | 6.894266e+07 | 1.147946e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.000000e-07 | 1.494000e-04 | -0.0008002 | -1.009000e-04 | -3.000000e-07 | 9.960000e-05 | 7.614000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.477000e-04 | 1.210000e-05 | 0.0001297 | 1.371000e-04 | 1.440000e-04 | 1.562000e-04 | 3.136000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.965048e-01 | 2.896488e-01 | 0.0000010 | 2.500000e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.965057e-01 | 2.896233e-01 | 0.0000010 | 2.451702e-01 | 4.944888e-01 | 7.472476e-01 | 9.999992e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.308485e-01 | 1.797283e-01 | 0.1814420 | 2.730560e-01 | 3.984770e-01 | 5.708360e-01 | 8.185580e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 24818 | 0.9920118 | NA | NA | NA | NA | NA | NA | NA | 4.153257e-01 | 1.944610e-01 | 0.0000000 | 2.585860e-01 | 3.903750e-01 | 5.581070e-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 | -0.0000068 | 0.0002387 | 0.9800000 | 0.9771069 | 0.623778 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001180 | 0.0002365 | 0.6200004 | 0.6178121 | 0.400403 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000542 | 0.0002329 | 0.8200001 | 0.8160087 | 0.456860 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0003846 | 0.0002660 | 0.1499999 | 0.1482354 | 0.240946 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000205 | 0.0001651 | 0.9000000 | 0.9012075 | 0.515668 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0003046 | 0.0001936 | 0.1199999 | 0.1156497 | 0.706759 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0002452 | 0.0001580 | 0.1199999 | 0.1207208 | 0.761336 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0001317 | 0.0001765 | 0.4600002 | 0.4554373 | 0.265365 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | 0.0000792 | 0.0001686 | 0.6400000 | 0.6385074 | 0.206550 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | 0.0000788 | 0.0001686 | 0.6400000 | 0.6402601 | 0.206379 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001996 | 0.0001554 | 0.2000000 | 0.1989677 | 0.713667 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001536 | 0.0001546 | 0.3200000 | 0.3204066 | 0.695497 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001916 | 0.0001559 | 0.2200002 | 0.2190414 | 0.715512 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001218 | 0.0001445 | 0.4000000 | 0.3993670 | 0.381822 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001207 | 0.0001409 | 0.3900004 | 0.3915615 | 0.451063 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0001049 | 0.0001455 | 0.4700002 | 0.4709720 | 0.386347 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000133 | 0.0001647 | 0.9400001 | 0.9354167 | 0.254571 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000113 | 0.0001633 | 0.9500000 | 0.9450083 | 0.271568 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000340 | 0.0001551 | 0.8300000 | 0.8266197 | 0.331450 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000410 | 0.0001588 | 0.8000000 | 0.7963365 | 0.297975 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623778 ES:SE:LP:AF:ID -6.84965e-06:0.000238695:0.00877392:0.623778:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400403 ES:SE:LP:AF:ID 0.000118017:0.000236529:0.207608:0.400403:rs2462492
1 91536 rs6702460 G T . PASS AF=0.45686 ES:SE:LP:AF:ID -5.41806e-05:0.000232853:0.0861861:0.45686:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240946 ES:SE:LP:AF:ID 0.000384562:0.000265987:0.823909:0.240946:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515668 ES:SE:LP:AF:ID -2.04991e-05:0.000165134:0.0457575:0.515668:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706759 ES:SE:LP:AF:ID -0.000304589:0.000193599:0.920819:0.706759:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761336 ES:SE:LP:AF:ID -0.000245213:0.000158023:0.920819:0.761336:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265365 ES:SE:LP:AF:ID 0.000131734:0.000176497:0.337242:0.265365:rs12124819
1 798400 rs10900604 A G . PASS AF=0.20655 ES:SE:LP:AF:ID 7.91896e-05:0.000168565:0.19382:0.20655:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206379 ES:SE:LP:AF:ID 7.881e-05:0.000168637:0.19382:0.206379:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772663 ES:SE:LP:AF:ID -5.47206e-05:0.000160421:0.136677:0.772663:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772891 ES:SE:LP:AF:ID -5.6694e-05:0.000160689:0.142668:0.772891:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340388 ES:SE:LP:AF:ID 0.000121545:0.000226419:0.229148:0.340388:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697219 ES:SE:LP:AF:ID -1.01419e-05:0.000151477:0.0222764:0.697219:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705359 ES:SE:LP:AF:ID -2.64541e-05:0.000148737:0.0655015:0.705359:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705405 ES:SE:LP:AF:ID -2.50608e-05:0.000148732:0.0604807:0.705405:rs9697642
1 832066 rs9697380 G C . PASS AF=0.70559 ES:SE:LP:AF:ID -2.72403e-05:0.000148738:0.0705811:0.70559:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705618 ES:SE:LP:AF:ID -2.76461e-05:0.000148754:0.0705811:0.705618:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730085 ES:SE:LP:AF:ID -8.65895e-05:0.000152809:0.244125:0.730085:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294415 ES:SE:LP:AF:ID 2.60354e-05:0.000148747:0.0655015:0.294415:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236736 ES:SE:LP:AF:ID -5.83974e-05:0.000158358:0.148742:0.236736:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236725 ES:SE:LP:AF:ID -5.81384e-05:0.000158359:0.148742:0.236725:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239782 ES:SE:LP:AF:ID -6.84705e-05:0.00015785:0.180456:0.239782:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236726 ES:SE:LP:AF:ID -6.03774e-05:0.000158358:0.154902:0.236726:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212461 ES:SE:LP:AF:ID -4.31385e-06:0.00016459:0.00877392:0.212461:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212357 ES:SE:LP:AF:ID -1.21074e-06:0.000164619:0.00436481:0.212357:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237211 ES:SE:LP:AF:ID -5.01464e-05:0.000158237:0.124939:0.237211:rs4411087
1 834928 rs4422949 A G . PASS AF=0.213002 ES:SE:LP:AF:ID 4.6969e-06:0.000164386:0.00877392:0.213002:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212964 ES:SE:LP:AF:ID 5.27299e-06:0.00016442:0.0132283:0.212964:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241194 ES:SE:LP:AF:ID 3.94167e-05:0.000157133:0.09691:0.241194:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213586 ES:SE:LP:AF:ID 3.34652e-05:0.000164177:0.0757207:0.213586:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269531 ES:SE:LP:AF:ID -3.48693e-06:0.000151621:0.00877392:0.269531:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213555 ES:SE:LP:AF:ID 4.17472e-05:0.000164197:0.09691:0.213555:rs72890788
1 838387 rs4970384 T C . PASS AF=0.21463 ES:SE:LP:AF:ID 5.46386e-05:0.00016388:0.130768:0.21463:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246231 ES:SE:LP:AF:ID 8.05267e-05:0.000156053:0.21467:0.246231:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270034 ES:SE:LP:AF:ID -1.4948e-05:0.000151728:0.0362122:0.270034:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400165 ES:SE:LP:AF:ID -8.08814e-05:0.000137198:0.251812:0.400165:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237129 ES:SE:LP:AF:ID 9.28893e-06:0.000159355:0.0222764:0.237129:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215436 ES:SE:LP:AF:ID 5.96962e-05:0.000163989:0.142668:0.215436:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235373 ES:SE:LP:AF:ID 3.35568e-05:0.000161736:0.0757207:0.235373:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362584 ES:SE:LP:AF:ID -9.48376e-05:0.000170305:0.236572:0.362584:rs11516185
1 845283 rs7366404 G T . PASS AF=0.814512 ES:SE:LP:AF:ID 0.000118481:0.000173496:0.309804:0.814512:rs7366404
1 845635 rs117086422 C T . PASS AF=0.205435 ES:SE:LP:AF:ID 0.000209698:0.000166785:0.677781:0.205435:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210874 ES:SE:LP:AF:ID 0.000215069:0.000165181:0.721246:0.210874:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196794 ES:SE:LP:AF:ID 0.000147403:0.000169344:0.420216:0.196794:rs28612348
1 846338 rs4970334 A G . PASS AF=0.813844 ES:SE:LP:AF:ID 0.000125242:0.000173349:0.327902:0.813844:rs4970334
1 846398 rs58781670 G A . PASS AF=0.204461 ES:SE:LP:AF:ID 0.000247528:0.000167354:0.853872:0.204461:rs58781670
1 846489 rs4970333 T C . PASS AF=0.813936 ES:SE:LP:AF:ID 0.000118262:0.000173468:0.30103:0.813936:rs4970333
1 846808 rs4475691 C T . PASS AF=0.198388 ES:SE:LP:AF:ID 0.000149105:0.000168863:0.420216:0.198388:rs4475691
1 846864 rs950122 G C . PASS AF=0.198114 ES:SE:LP:AF:ID 0.000119449:0.000168767:0.318759:0.198114:rs950122