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_20111_10.vcf.gz --id UKB-b:14043 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20111_10.txt.gz --cohort_cases 2094 --cohort_controls 359170 --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": "2019-09-13T11:52:18.067223",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14043/ukb-b-14043.vcf.gz; Date=Sun May 10 07:20:51 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-14043/UKB-b-14043_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14043/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:13 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14043/UKB-b-14043_data.vcf.gz ...
Read summary statistics for 3291295 SNPs.
Dropped 472 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, 810810 SNPs remain.
After merging with regression SNP LD, 810810 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0015 (0.0016)
Lambda GC: 1.038
Mean Chi^2: 1.0339
Intercept: 1.0214 (0.0095)
Ratio: 0.6323 (0.2793)
Analysis finished at Thu Oct 17 14:43:53 2019
Total time elapsed: 40.0sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8382,
"inflation_factor": 1.0475,
"mean_EFFECT": 1.0611e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 19,
"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": 26387,
"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": 810810,
"ldsc_nsnp_merge_regression_ld": 810810,
"ldsc_observed_scale_h2_beta": 0.0015,
"ldsc_observed_scale_h2_se": 0.0016,
"ldsc_intercept_beta": 1.0214,
"ldsc_intercept_se": 0.0095,
"ldsc_lambda_gc": 1.038,
"ldsc_mean_chisq": 1.0339,
"ldsc_ratio": 0.6313
}
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 | 3290826 | 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 | 3291295 | 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 | 8.662205e+00 | 5.773241e+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 | 7.853571e+07 | 5.670826e+07 | 828.0000000 | 3.157823e+07 | 6.892322e+07 | 1.147670e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.100000e-06 | 2.029000e-04 | -0.0012105 | -1.353000e-04 | 1.100000e-06 | 1.364000e-04 | 3.148200e-03 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.985000e-04 | 1.820000e-05 | 0.0001723 | 1.826000e-04 | 1.928000e-04 | 2.114000e-04 | 4.263000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.941525e-01 | 2.900573e-01 | 0.0000000 | 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 | 4.941538e-01 | 2.900294e-01 | 0.0000000 | 2.424714e-01 | 4.910489e-01 | 7.460150e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.236301e-01 | 1.874059e-01 | 0.1671450 | 2.593585e-01 | 3.883620e-01 | 5.681180e-01 | 8.328540e-01 | ▇▆▅▃▂ |
| numeric | AF_reference | 26387 | 0.9919828 | NA | NA | NA | NA | NA | NA | NA | 4.090081e-01 | 1.995716e-01 | 0.0000000 | 2.472040e-01 | 3.807910e-01 | 5.551120e-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 | 0.0003370 | 0.0003173 | 0.2900000 | 0.2881658 | 0.623823 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0006537 | 0.0003140 | 0.0369999 | 0.0373681 | 0.400339 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000681 | 0.0003095 | 0.8300000 | 0.8257488 | 0.456754 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002547 | 0.0003535 | 0.4700002 | 0.4712485 | 0.240921 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000061 | 0.0002195 | 0.9800000 | 0.9777564 | 0.515732 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0002424 | 0.0002572 | 0.3500000 | 0.3459880 | 0.706822 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0001462 | 0.0002100 | 0.4899999 | 0.4861885 | 0.761404 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0001559 | 0.0002346 | 0.5099998 | 0.5063061 | 0.265386 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | 0.0001098 | 0.0002242 | 0.6200004 | 0.6243822 | 0.206338 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | 0.0001160 | 0.0002243 | 0.6100002 | 0.6050467 | 0.206161 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51182485 | rs6009961 | A | G | -0.0001339 | 0.0002072 | 0.5199996 | 0.5182797 | 0.715841 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0001923 | 0.0001919 | 0.3200000 | 0.3163886 | 0.382165 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001571 | 0.0001871 | 0.4000000 | 0.4011136 | 0.451144 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0004799 | 0.0002522 | 0.0569994 | 0.0571085 | 0.167705 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | -0.0000092 | 0.0002580 | 0.9699999 | 0.9716761 | 0.168508 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0003356 | 0.0001932 | 0.0819993 | 0.0824080 | 0.386050 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0000078 | 0.0002187 | 0.9699999 | 0.9713871 | 0.254789 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000506 | 0.0002168 | 0.8200001 | 0.8153564 | 0.271798 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000523 | 0.0002060 | 0.8000000 | 0.7997564 | 0.331788 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0004105 | 0.0002110 | 0.0519996 | 0.0517184 | 0.298059 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623823 ES:SE:LP:AF:ID 0.00033698:0.000317261:0.537602:0.623823:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400339 ES:SE:LP:AF:ID -0.000653664:0.000314002:1.4318:0.400339:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456754 ES:SE:LP:AF:ID 6.81371e-05:0.000309493:0.0809219:0.456754:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240921 ES:SE:LP:AF:ID -0.000254691:0.000353517:0.327902:0.240921:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515732 ES:SE:LP:AF:ID 6.11875e-06:0.000219453:0.00877392:0.515732:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706822 ES:SE:LP:AF:ID -0.000242389:0.000257204:0.455932:0.706822:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761404 ES:SE:LP:AF:ID -0.000146242:0.000210002:0.309804:0.761404:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265386 ES:SE:LP:AF:ID 0.000155892:0.000234565:0.29243:0.265386:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206338 ES:SE:LP:AF:ID 0.000109772:0.000224185:0.207608:0.206338:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206161 ES:SE:LP:AF:ID 0.00011599:0.000224284:0.21467:0.206161:rs11240777
1 808631 rs11240779 G A . PASS AF=0.773002 ES:SE:LP:AF:ID -0.000231493:0.000213333:0.552842:0.773002:rs11240779
1 808928 rs11240780 C T . PASS AF=0.773213 ES:SE:LP:AF:ID -0.000234027:0.000213693:0.568636:0.773213:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340515 ES:SE:LP:AF:ID 0.000411383:0.00030074:0.769551:0.340515:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697001 ES:SE:LP:AF:ID -7.08786e-06:0.000201354:0.0132283:0.697001:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705313 ES:SE:LP:AF:ID -0.000133218:0.000197741:0.30103:0.705313:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705358 ES:SE:LP:AF:ID -0.000138309:0.000197736:0.318759:0.705358:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705546 ES:SE:LP:AF:ID -0.000132007:0.000197746:0.30103:0.705546:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705574 ES:SE:LP:AF:ID -0.000129871:0.000197768:0.29243:0.705574:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730054 ES:SE:LP:AF:ID -0.000142059:0.000203158:0.318759:0.730054:rs4553118
1 832918 rs28765502 T C . PASS AF=0.29446 ES:SE:LP:AF:ID 0.000134281:0.000197757:0.30103:0.29446:rs28765502
1 833223 rs13303211 C T . PASS AF=0.237063 ES:SE:LP:AF:ID 0.000144178:0.000210433:0.309804:0.237063:rs13303211
1 833302 rs28752186 C T . PASS AF=0.23705 ES:SE:LP:AF:ID 0.000137254:0.000210434:0.29243:0.23705:rs28752186
1 833641 rs28594623 T C . PASS AF=0.240139 ES:SE:LP:AF:ID 8.81574e-05:0.000209757:0.173925:0.240139:rs28594623
1 833824 rs28484835 T C . PASS AF=0.237051 ES:SE:LP:AF:ID 0.000137247:0.000210433:0.29243:0.237051:rs28484835
1 833927 rs28593608 T C . PASS AF=0.21277 ES:SE:LP:AF:ID 0.000144062:0.000218696:0.29243:0.21277:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212675 ES:SE:LP:AF:ID 0.000153804:0.000218732:0.318759:0.212675:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237555 ES:SE:LP:AF:ID 0.000152136:0.00021026:0.327902:0.237555:rs4411087
1 834928 rs4422949 A G . PASS AF=0.213328 ES:SE:LP:AF:ID 0.000158995:0.000218415:0.327902:0.213328:rs4422949
1 834999 rs28570054 G A . PASS AF=0.2133 ES:SE:LP:AF:ID 0.000159525:0.000218455:0.327902:0.2133:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241487 ES:SE:LP:AF:ID 0.000120676:0.000208819:0.251812:0.241487:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213905 ES:SE:LP:AF:ID 0.000146948:0.000218139:0.30103:0.213905:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269788 ES:SE:LP:AF:ID 0.00018423:0.0002015:0.443698:0.269788:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213872 ES:SE:LP:AF:ID 0.00014262:0.000218167:0.29243:0.213872:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214946 ES:SE:LP:AF:ID 0.000130133:0.000217733:0.259637:0.214946:rs4970384
1 838555 rs4970383 C A . PASS AF=0.24651 ES:SE:LP:AF:ID 0.000211278:0.000207392:0.508638:0.24651:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270301 ES:SE:LP:AF:ID 0.000193131:0.000201638:0.468521:0.270301:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400051 ES:SE:LP:AF:ID 0.000183694:0.000182244:0.508638:0.400051:rs4970382
1 841085 rs1574243 C G . PASS AF=0.23739 ES:SE:LP:AF:ID 0.00017277:0.000211842:0.387216:0.23739:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215742 ES:SE:LP:AF:ID 0.000165393:0.000217871:0.346787:0.215742:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235701 ES:SE:LP:AF:ID -1.51366e-05:0.00021487:0.0268721:0.235701:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362375 ES:SE:LP:AF:ID 0.000188588:0.000226312:0.39794:0.362375:rs11516185
1 844300 rs61769713 C G . PASS AF=0.819141 ES:SE:LP:AF:ID 0.000183797:0.000232729:0.366532:0.819141:rs61769713
1 845283 rs7366404 G T . PASS AF=0.814821 ES:SE:LP:AF:ID 0.000233364:0.000230667:0.508638:0.814821:rs7366404
1 845635 rs117086422 C T . PASS AF=0.205871 ES:SE:LP:AF:ID -5.60807e-06:0.000221551:0.00877392:0.205871:rs117086422
1 845938 rs57760052 G A . PASS AF=0.21127 ES:SE:LP:AF:ID -1.53217e-05:0.000219439:0.0268721:0.21127:rs57760052
1 846078 rs28612348 C T . PASS AF=0.197235 ES:SE:LP:AF:ID -8.56044e-05:0.000224937:0.154902:0.197235:rs28612348
1 846338 rs4970334 A G . PASS AF=0.814141 ES:SE:LP:AF:ID 0.000253:0.000230469:0.568636:0.814141:rs4970334
1 846398 rs58781670 G A . PASS AF=0.204811 ES:SE:LP:AF:ID -5.94767e-06:0.000222335:0.00877392:0.204811:rs58781670
1 846489 rs4970333 T C . PASS AF=0.81423 ES:SE:LP:AF:ID 0.000257439:0.000230618:0.585027:0.81423:rs4970333
1 846808 rs4475691 C T . PASS AF=0.198817 ES:SE:LP:AF:ID -0.000104878:0.000224309:0.19382:0.198817:rs4475691