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_41204_N736.vcf.gz --id UKB-b:6413 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_N736.txt.gz --cohort_cases 1790 --cohort_controls 461220 --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": "2019-09-12T22:59:32.533148",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6413/ukb-b-6413.vcf.gz; Date=Sun May 10 08:06:25 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-6413/UKB-b-6413_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6413/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-6413/UKB-b-6413_data.vcf.gz ...
Read summary statistics for 2934348 SNPs.
Dropped 381 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, 732521 SNPs remain.
After merging with regression SNP LD, 732521 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 4.7732e-05 (0.0011)
Lambda GC: 1.0206
Mean Chi^2: 1.0068
Intercept: 1.0063 (0.0085)
Ratio: 0.9265 (1.2501)
Analysis finished at Thu Oct 17 14:40:58 2019
Total time elapsed: 38.85sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8115,
"inflation_factor": 1,
"mean_EFFECT": 1.4058e-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": 23370,
"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": 732521,
"ldsc_nsnp_merge_regression_ld": 732521,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0063,
"ldsc_intercept_se": 0.0085,
"ldsc_lambda_gc": 1.0206,
"ldsc_mean_chisq": 1.0068,
"ldsc_ratio": 0.9265
}
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 | TRUE |
| 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 | 2933970 | 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 | 2934348 | 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 | 8.658132e+00 | 5.770178e+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 | 7.856571e+07 | 5.666901e+07 | 828.0000000 | 3.167629e+07 | 6.897726e+07 | 1.147816e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.000000e-07 | 1.406000e-04 | -0.0007251 | -9.480000e-05 | 0.000000e+00 | 9.510000e-05 | 6.809000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.400000e-04 | 1.020000e-05 | 0.0001242 | 1.311000e-04 | 1.369000e-04 | 1.472000e-04 | 2.902000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.988593e-01 | 2.885707e-01 | 0.0000007 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.988621e-01 | 2.885426e-01 | 0.0000007 | 2.495247e-01 | 4.961890e-01 | 7.484287e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.373582e-01 | 1.721456e-01 | 0.1955310 | 2.861180e-01 | 4.077080e-01 | 5.726450e-01 | 8.044670e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 23370 | 0.9920357 | NA | NA | NA | NA | NA | NA | NA | 4.210196e-01 | 1.895163e-01 | 0.0001997 | 2.689700e-01 | 3.987620e-01 | 5.601040e-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 | -0.0000488 | 0.0002286 | 0.8300000 | 0.8310670 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0002786 | 0.0002265 | 0.2200002 | 0.2185662 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000508 | 0.0002230 | 0.8200001 | 0.8197635 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0006442 | 0.0002547 | 0.0109999 | 0.0114344 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001977 | 0.0001581 | 0.2099999 | 0.2110854 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000462 | 0.0001854 | 0.8000000 | 0.8030550 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0000664 | 0.0001513 | 0.6600001 | 0.6609573 | 0.761297 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0001149 | 0.0001690 | 0.5000000 | 0.4964301 | 0.265385 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | -0.0000560 | 0.0001614 | 0.7300002 | 0.7284664 | 0.206591 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | -0.0000571 | 0.0001615 | 0.7199992 | 0.7235799 | 0.206420 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0000206 | 0.0001488 | 0.8900000 | 0.8897163 | 0.713656 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000085 | 0.0001481 | 0.9500000 | 0.9540129 | 0.695470 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000232 | 0.0001493 | 0.8800001 | 0.8767501 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001502 | 0.0001384 | 0.2800000 | 0.2777501 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001733 | 0.0001349 | 0.2000000 | 0.1988627 | 0.451061 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0002851 | 0.0001393 | 0.0409996 | 0.0407060 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0002440 | 0.0001577 | 0.1199999 | 0.1217269 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0001967 | 0.0001563 | 0.2099999 | 0.2082650 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0003186 | 0.0001486 | 0.0320000 | 0.0319738 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0001825 | 0.0001521 | 0.2300001 | 0.2299943 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -4.87682e-05:0.000228601:0.0809219:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000278641:0.000226473:0.657577:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 5.08081e-05:0.00022299:0.0861861:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.000644206:0.000254714:1.95861:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 0.000197744:0.000158121:0.677781:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -4.62334e-05:0.000185382:0.09691:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID 6.63607e-05:0.000151304:0.180456:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 0.000114948:0.000169012:0.30103:0.265385:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206591 ES:SE:LP:AF:ID -5.60361e-05:0.00016141:0.136677:0.206591:rs10900604
1 798959 rs11240777 G A . PASS AF=0.20642 ES:SE:LP:AF:ID -5.7112e-05:0.00016148:0.142668:0.20642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772619 ES:SE:LP:AF:ID -3.76578e-06:0.000153612:0.00877392:0.772619:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772847 ES:SE:LP:AF:ID -6.2122e-06:0.000153871:0.0132283:0.772847:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID 3.70281e-05:0.000216811:0.0655015:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID 7.77259e-05:0.000145058:0.229148:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID 9.73897e-05:0.000142433:0.309804:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID 9.23364e-05:0.000142428:0.283997:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID 9.58392e-05:0.000142435:0.30103:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID 9.53635e-05:0.00014245:0.30103:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID 0.000136001:0.000146331:0.455932:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID -9.62059e-05:0.000142443:0.30103:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID 5.55896e-06:0.000151653:0.0132283:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID 5.8848e-06:0.000151654:0.0132283:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID 2.5041e-05:0.000151168:0.0604807:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID 5.71887e-06:0.000151653:0.0132283:0.236693:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212421 ES:SE:LP:AF:ID -3.21717e-05:0.000157623:0.0757207:0.212421:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212316 ES:SE:LP:AF:ID -3.21218e-05:0.00015765:0.0757207:0.212316:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID -1.37096e-06:0.000151537:0.00436481:0.237178:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212962 ES:SE:LP:AF:ID -4.30916e-05:0.000157426:0.107905:0.212962:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212924 ES:SE:LP:AF:ID -4.18769e-05:0.000157458:0.102373:0.212924:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID -3.44718e-05:0.000150479:0.0861861:0.241162:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213543 ES:SE:LP:AF:ID -5.55658e-05:0.000157226:0.142668:0.213543:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID 5.26392e-06:0.0001452:0.0132283:0.269511:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213513 ES:SE:LP:AF:ID -5.79222e-05:0.000157245:0.148742:0.213513:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214587 ES:SE:LP:AF:ID -6.25285e-05:0.000156943:0.161151:0.214587:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID -3.37905e-05:0.000149444:0.0861861:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID -9.23439e-06:0.000145303:0.0222764:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 6.64404e-05:0.000131382:0.21467:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID -5.37608e-05:0.000152605:0.142668:0.237102:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215391 ES:SE:LP:AF:ID -6.31568e-05:0.000157048:0.161151:0.215391:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID -4.65416e-05:0.000154891:0.119186:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -0.000250766:0.000163092:0.920819:0.362606:rs11516185
1 845635 rs117086422 C T . PASS AF=0.205429 ES:SE:LP:AF:ID 0.00012892:0.000159718:0.376751:0.205429:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210864 ES:SE:LP:AF:ID 0.000164192:0.000158182:0.522879:0.210864:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196785 ES:SE:LP:AF:ID 0.000141134:0.000162166:0.420216:0.196785:rs28612348
1 846398 rs58781670 G A . PASS AF=0.204447 ES:SE:LP:AF:ID 0.000161928:0.000160264:0.508638:0.204447:rs58781670
1 846808 rs4475691 C T . PASS AF=0.198378 ES:SE:LP:AF:ID 0.000141607:0.000161705:0.420216:0.198378:rs4475691
1 846864 rs950122 G C . PASS AF=0.198106 ES:SE:LP:AF:ID 0.000141233:0.000161613:0.420216:0.198106:rs950122
1 847228 rs3905286 C T . PASS AF=0.203588 ES:SE:LP:AF:ID 0.000187542:0.000160405:0.619789:0.203588:rs3905286
1 847491 rs28407778 G A . PASS AF=0.214198 ES:SE:LP:AF:ID 0.000169077:0.000157126:0.552842:0.214198:rs28407778
1 848090 rs4246505 G A . PASS AF=0.212513 ES:SE:LP:AF:ID 0.000150033:0.000157539:0.468521:0.212513:rs4246505