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_41210_Z675.vcf.gz --id UKB-b:19054 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41210_Z675.txt.gz --cohort_cases 1838 --cohort_controls 461172 --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": "2019-09-13T10:01:33.217843",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19054/ukb-b-19054.vcf.gz; Date=Sun May 10 08:49:07 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-19054/UKB-b-19054_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19054/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19054/UKB-b-19054_data.vcf.gz ...
Read summary statistics for 2996148 SNPs.
Dropped 400 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, 746303 SNPs remain.
After merging with regression SNP LD, 746303 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0027 (0.0013)
Lambda GC: 1.0293
Mean Chi^2: 1.0298
Intercept: 1.0013 (0.01)
Ratio: 0.0428 (0.3365)
Analysis finished at Thu Oct 17 14:41:34 2019
Total time elapsed: 39.53sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8167,
"inflation_factor": 1.0475,
"mean_EFFECT": 7.4899e-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": 23915,
"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": 746303,
"ldsc_nsnp_merge_regression_ld": 746303,
"ldsc_observed_scale_h2_beta": 0.0027,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 1.0013,
"ldsc_intercept_se": 0.01,
"ldsc_lambda_gc": 1.0293,
"ldsc_mean_chisq": 1.0298,
"ldsc_ratio": 0.0436
}
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 | 2995751 | 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 | 2996148 | 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 | 8.662731e+00 | 5.772731e+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 | 7.855899e+07 | 5.669682e+07 | 828.0000000 | 3.162890e+07 | 6.893825e+07 | 1.147938e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.000000e-07 | 1.449000e-04 | -0.0007651 | -9.630000e-05 | 2.000000e-07 | 9.790000e-05 | 8.240000e-04 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.426000e-04 | 1.090000e-05 | 0.0001261 | 1.332000e-04 | 1.394000e-04 | 1.503000e-04 | 2.990000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.953627e-01 | 2.897878e-01 | 0.0000007 | 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 | 4.953639e-01 | 2.897613e-01 | 0.0000007 | 2.431625e-01 | 4.943296e-01 | 7.460678e-01 | 9.999993e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.351108e-01 | 1.749268e-01 | 0.1904250 | 2.814808e-01 | 4.045185e-01 | 5.721773e-01 | 8.095750e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 23915 | 0.9920181 | NA | NA | NA | NA | NA | NA | NA | 4.190187e-01 | 1.913345e-01 | 0.0001997 | 2.651760e-01 | 3.959660e-01 | 5.595050e-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 | 0.0000029 | 0.0002321 | 0.9900000 | 0.9898932 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002270 | 0.0002299 | 0.3200000 | 0.3235806 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0004435 | 0.0002264 | 0.0500000 | 0.0500977 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0001103 | 0.0002586 | 0.6700003 | 0.6697247 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0003005 | 0.0001605 | 0.0610000 | 0.0612244 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0002787 | 0.0001882 | 0.1400000 | 0.1386696 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0002517 | 0.0001536 | 0.1000000 | 0.1013539 | 0.761297 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0001888 | 0.0001716 | 0.2700001 | 0.2712638 | 0.265385 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | -0.0002649 | 0.0001639 | 0.1100001 | 0.1060051 | 0.206591 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | -0.0002646 | 0.0001639 | 0.1100001 | 0.1065600 | 0.206420 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0000530 | 0.0001511 | 0.7300002 | 0.7257025 | 0.713656 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001571 | 0.0001503 | 0.2999998 | 0.2960361 | 0.695470 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000886 | 0.0001516 | 0.5600000 | 0.5590032 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0003119 | 0.0001405 | 0.0259998 | 0.0264793 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001362 | 0.0001370 | 0.3200000 | 0.3199994 | 0.451061 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0001040 | 0.0001414 | 0.4600002 | 0.4620968 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0000448 | 0.0001601 | 0.7800007 | 0.7798298 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0000724 | 0.0001587 | 0.6499995 | 0.6480688 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0003771 | 0.0001508 | 0.0120000 | 0.0124061 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0003552 | 0.0001544 | 0.0210000 | 0.0214291 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 2.93996e-06:0.000232089:0.00436481:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000226969:0.000229929:0.49485:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000443531:0.000226392:1.30103:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.0001103:0.000258601:0.173925:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 0.000300499:0.000160534:1.21467:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.000278696:0.000188211:0.853872:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID 0.000251668:0.000153613:1:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 0.000188777:0.000171591:0.568636:0.265385:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206591 ES:SE:LP:AF:ID -0.000264888:0.000163874:0.958607:0.206591:rs10900604
1 798959 rs11240777 G A . PASS AF=0.20642 ES:SE:LP:AF:ID -0.000264581:0.000163944:0.958607:0.20642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772619 ES:SE:LP:AF:ID 0.000217009:0.000155956:0.79588:0.772619:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772847 ES:SE:LP:AF:ID 0.000223315:0.000156219:0.823909:0.772847:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID 7.74387e-05:0.00022012:0.142668:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID 0.000180636:0.000147272:0.657577:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID 0.000185096:0.000144606:0.69897:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID 0.000182006:0.000144601:0.677781:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID 0.000179355:0.000144608:0.677781:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID 0.000183895:0.000144623:0.69897:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID 0.000105824:0.000148564:0.318759:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID -0.000184652:0.000144617:0.69897:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID -0.00019893:0.000153967:0.69897:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID -0.000192821:0.000153968:0.677781:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID -0.000213512:0.000153475:0.79588:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID -0.000198518:0.000153967:0.69897:0.236693:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212421 ES:SE:LP:AF:ID -0.000111759:0.000160028:0.318759:0.212421:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212316 ES:SE:LP:AF:ID -0.00011687:0.000160056:0.327902:0.212316:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID -0.000199218:0.000153849:0.69897:0.237178:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212962 ES:SE:LP:AF:ID -0.000122264:0.000159828:0.356547:0.212962:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212924 ES:SE:LP:AF:ID -0.000115645:0.000159861:0.327902:0.212924:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID -0.000191838:0.000152775:0.677781:0.241162:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213543 ES:SE:LP:AF:ID -0.000129307:0.000159626:0.376751:0.213543:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID -0.000206964:0.000147416:0.79588:0.269511:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213513 ES:SE:LP:AF:ID -0.000128383:0.000159645:0.376751:0.213513:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214587 ES:SE:LP:AF:ID -0.00013169:0.000159338:0.387216:0.214587:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID -0.000139682:0.000151725:0.443698:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID -0.00020272:0.000147521:0.769551:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID -0.000138376:0.000133387:0.522879:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID -0.000105286:0.000154934:0.30103:0.237102:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215391 ES:SE:LP:AF:ID -0.000141881:0.000159444:0.431798:0.215391:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID -0.000112616:0.000157255:0.327902:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -8.19823e-06:0.000165581:0.0177288:0.362606:rs11516185
1 845635 rs117086422 C T . PASS AF=0.205429 ES:SE:LP:AF:ID -0.000162938:0.000162156:0.508638:0.205429:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210864 ES:SE:LP:AF:ID -0.000149002:0.000160595:0.455932:0.210864:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196785 ES:SE:LP:AF:ID -0.000179363:0.00016464:0.552842:0.196785:rs28612348
1 846398 rs58781670 G A . PASS AF=0.204447 ES:SE:LP:AF:ID -0.000120864:0.00016271:0.337242:0.204447:rs58781670
1 846808 rs4475691 C T . PASS AF=0.198378 ES:SE:LP:AF:ID -0.000167462:0.000164172:0.508638:0.198378:rs4475691
1 846864 rs950122 G C . PASS AF=0.198106 ES:SE:LP:AF:ID -0.000169446:0.00016408:0.522879:0.198106:rs950122
1 847228 rs3905286 C T . PASS AF=0.203588 ES:SE:LP:AF:ID -0.000232012:0.000162853:0.823909:0.203588:rs3905286
1 847491 rs28407778 G A . PASS AF=0.214198 ES:SE:LP:AF:ID -0.000157152:0.000159524:0.49485:0.214198:rs28407778
1 848090 rs4246505 G A . PASS AF=0.212513 ES:SE:LP:AF:ID -0.000150159:0.000159943:0.455932:0.212513:rs4246505