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_40006_D051.vcf.gz --id UKB-b:4674 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_40006_D051.txt.gz --cohort_cases 1917 --cohort_controls 461093 --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": "2019-09-13T05:01:46.107769",
"bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4674/ukb-b-4674.vcf.gz; Date=Sat May 9 18:57:35 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-4674/UKB-b-4674_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4674/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:33 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4674/UKB-b-4674_data.vcf.gz ...
Read summary statistics for 3092324 SNPs.
Dropped 422 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, 767770 SNPs remain.
After merging with regression SNP LD, 767770 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0025 (0.0012)
Lambda GC: 1.017
Mean Chi^2: 1.0191
Intercept: 0.9922 (0.0089)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:45:16 2019
Total time elapsed: 42.61sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8241,
"inflation_factor": 1.0475,
"mean_EFFECT": -1.9598e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 30,
"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": 24693,
"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": 767770,
"ldsc_nsnp_merge_regression_ld": 767770,
"ldsc_observed_scale_h2_beta": 0.0025,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 0.9922,
"ldsc_intercept_se": 0.0089,
"ldsc_lambda_gc": 1.017,
"ldsc_mean_chisq": 1.0191,
"ldsc_ratio": -0.4084
}
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 | 3091905 | 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 | 3092324 | 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 | 8.664824e+00 | 5.772831e+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 | 7.856416e+07 | 5.671088e+07 | 828.0000000 | 3.159887e+07 | 6.895863e+07 | 1.148245e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-07 | 1.486000e-04 | -0.0010207 | -9.980000e-05 | 3.000000e-07 | 9.930000e-05 | 9.568000e-04 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.462000e-04 | 1.180000e-05 | 0.0001286 | 1.359000e-04 | 1.426000e-04 | 1.546000e-04 | 3.048000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.959662e-01 | 2.895002e-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 | 4.959667e-01 | 2.894741e-01 | 0.0000000 | 2.448097e-01 | 4.935672e-01 | 7.469526e-01 | 9.999995e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.313799e-01 | 1.790961e-01 | 0.1825770 | 2.741238e-01 | 3.992260e-01 | 5.709810e-01 | 8.174230e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 24693 | 0.9920147 | NA | NA | NA | NA | NA | NA | NA | 4.157765e-01 | 1.940465e-01 | 0.0000000 | 2.593850e-01 | 3.911740e-01 | 5.583070e-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 | 0.0000285 | 0.0002366 | 0.9000000 | 0.9042422 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000652 | 0.0002344 | 0.7800007 | 0.7807593 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000403 | 0.0002308 | 0.8600001 | 0.8613824 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0001023 | 0.0002636 | 0.6999999 | 0.6980782 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002533 | 0.0001636 | 0.1199999 | 0.1216580 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0001808 | 0.0001919 | 0.3500000 | 0.3459138 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0001046 | 0.0001566 | 0.5000000 | 0.5039479 | 0.761297 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0000807 | 0.0001749 | 0.6400000 | 0.6443282 | 0.265385 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | 0.0000722 | 0.0001670 | 0.6700003 | 0.6657982 | 0.206591 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | 0.0000741 | 0.0001671 | 0.6600001 | 0.6572929 | 0.206420 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -0.0003708 | 0.0001540 | 0.0160000 | 0.0160655 | 0.713656 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0003953 | 0.0001532 | 0.0099001 | 0.0098892 | 0.695470 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0003756 | 0.0001545 | 0.0150000 | 0.0150489 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0003785 | 0.0001433 | 0.0081999 | 0.0082461 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0004004 | 0.0001396 | 0.0041000 | 0.0041383 | 0.451061 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0003428 | 0.0001442 | 0.0170000 | 0.0174213 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000883 | 0.0001632 | 0.5900000 | 0.5884581 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000802 | 0.0001618 | 0.6200004 | 0.6202883 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0003457 | 0.0001537 | 0.0250000 | 0.0245391 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0003775 | 0.0001574 | 0.0160000 | 0.0164527 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 2.84618e-05:0.000236582:0.0457575:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 6.52349e-05:0.00023438:0.107905:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -4.02967e-05:0.000230775:0.0655015:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.000102257:0.000263606:0.154902:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000253292:0.000163641:0.920819:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000180831:0.000191854:0.455932:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -0.000104645:0.000156586:0.30103:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -8.07496e-05:0.000174913:0.19382:0.265385:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206591 ES:SE:LP:AF:ID 7.21504e-05:0.000167045:0.173925:0.206591:rs10900604
1 798959 rs11240777 G A . PASS AF=0.20642 ES:SE:LP:AF:ID 7.41421e-05:0.000167117:0.180456:0.20642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772619 ES:SE:LP:AF:ID -7.82739e-05:0.000158975:0.207608:0.772619:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772847 ES:SE:LP:AF:ID -6.42236e-05:0.000159243:0.161151:0.772847:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID 0.000116322:0.00022438:0.221849:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID 3.29205e-05:0.000150122:0.0809219:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID -1.43114e-05:0.000147405:0.0362122:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID -1.29947e-05:0.0001474:0.0315171:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID -1.01881e-05:0.000147407:0.0268721:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID -8.2037e-06:0.000147423:0.0177288:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID 1.14943e-06:0.00015144:0.00436481:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID 7.66866e-06:0.000147416:0.0177288:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID -9.70625e-05:0.000156947:0.267606:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID -0.000102761:0.000156949:0.29243:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID -0.00010945:0.000156446:0.318759:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID -0.000102955:0.000156947:0.29243:0.236693:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212421 ES:SE:LP:AF:ID -0.000130396:0.000163125:0.376751:0.212421:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212316 ES:SE:LP:AF:ID -0.000127593:0.000163154:0.366532:0.212316:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID -9.00619e-05:0.000156827:0.244125:0.237178:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212962 ES:SE:LP:AF:ID -0.000119296:0.000162922:0.337242:0.212962:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212924 ES:SE:LP:AF:ID -0.000120498:0.000162955:0.337242:0.212924:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID -9.94203e-05:0.000155733:0.283997:0.241162:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213543 ES:SE:LP:AF:ID -0.000106878:0.000162715:0.29243:0.213543:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID -7.9431e-05:0.00015027:0.221849:0.269511:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213513 ES:SE:LP:AF:ID -0.000109361:0.000162735:0.30103:0.213513:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214587 ES:SE:LP:AF:ID -9.33938e-05:0.000162422:0.244125:0.214587:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID -8.81969e-05:0.000154661:0.244125:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID -7.04524e-05:0.000150376:0.19382:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID -9.87216e-05:0.000135969:0.327902:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID -3.68866e-05:0.000157933:0.0861861:0.237102:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215391 ES:SE:LP:AF:ID -7.86125e-05:0.00016253:0.200659:0.215391:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID -0.000148817:0.000160298:0.455932:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -5.20661e-05:0.000168786:0.119186:0.362606:rs11516185
1 845283 rs7366404 G T . PASS AF=0.814497 ES:SE:LP:AF:ID -9.49657e-05:0.000171942:0.236572:0.814497:rs7366404
1 845635 rs117086422 C T . PASS AF=0.205429 ES:SE:LP:AF:ID -0.000207448:0.000165294:0.677781:0.205429:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210864 ES:SE:LP:AF:ID -0.000205572:0.000163704:0.677781:0.210864:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196785 ES:SE:LP:AF:ID -0.000266998:0.000167827:0.958607:0.196785:rs28612348
1 846338 rs4970334 A G . PASS AF=0.813828 ES:SE:LP:AF:ID -0.000110916:0.000171795:0.283997:0.813828:rs4970334
1 846398 rs58781670 G A . PASS AF=0.204447 ES:SE:LP:AF:ID -0.0001859:0.000165859:0.585027:0.204447:rs58781670
1 846489 rs4970333 T C . PASS AF=0.81392 ES:SE:LP:AF:ID -0.00011077:0.000171914:0.283997:0.81392:rs4970333
1 846808 rs4475691 C T . PASS AF=0.198378 ES:SE:LP:AF:ID -0.000256057:0.00016735:0.886057:0.198378:rs4475691
1 846864 rs950122 G C . PASS AF=0.198106 ES:SE:LP:AF:ID -0.000292704:0.000167255:1.09691:0.198106:rs950122