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_20088_350.vcf.gz --id UKB-b:4843 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20088_350.txt.gz --cohort_cases 2930 --cohort_controls 62019 --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": "2019-09-13T11:41:55.739356",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4843/ukb-b-4843.vcf.gz; Date=Sat May 9 13:13:51 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-4843/UKB-b-4843_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4843/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4843/UKB-b-4843_data.vcf.gz ...
Read summary statistics for 3978744 SNPs.
Dropped 748 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, 948642 SNPs remain.
After merging with regression SNP LD, 948642 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0069 (0.0081)
Lambda GC: 1.0164
Mean Chi^2: 1.0154
Intercept: 1.0056 (0.0082)
Ratio: 0.3626 (0.5334)
Analysis finished at Thu Oct 17 14:45:32 2019
Total time elapsed: 50.87sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8748,
"inflation_factor": 1,
"mean_EFFECT": -6.404e-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": 32484,
"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": 948642,
"ldsc_nsnp_merge_regression_ld": 948642,
"ldsc_observed_scale_h2_beta": 0.0069,
"ldsc_observed_scale_h2_se": 0.0081,
"ldsc_intercept_beta": 1.0056,
"ldsc_intercept_se": 0.0082,
"ldsc_lambda_gc": 1.0164,
"ldsc_mean_chisq": 1.0154,
"ldsc_ratio": 0.3636
}
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 | 3978000 | 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 | 3978744 | 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 | 8.659047e+00 | 5.766630e+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 | 7.857534e+07 | 5.675383e+07 | 828.0000000 | 3.163092e+07 | 6.891156e+07 | 1.146988e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.000000e-07 | 1.369300e-03 | -0.0088967 | -9.070000e-04 | -5.600000e-06 | 9.032000e-04 | 8.275200e-03 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.345500e-03 | 1.826000e-04 | 0.0011121 | 1.187100e-03 | 1.285800e-03 | 1.471000e-03 | 4.087200e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.965312e-01 | 2.891524e-01 | 0.0000016 | 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 | 4.965328e-01 | 2.891242e-01 | 0.0000016 | 2.452620e-01 | 4.951176e-01 | 7.462643e-01 | 9.999993e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.952100e-01 | 2.121589e-01 | 0.1194540 | 2.101700e-01 | 3.478530e-01 | 5.526440e-01 | 8.805460e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 32484 | 0.9918356 | NA | NA | NA | NA | NA | NA | NA | 3.840517e-01 | 2.164595e-01 | 0.0000000 | 2.060700e-01 | 3.440500e-01 | 5.403350e-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 | 0.0042431 | 0.0020495 | 0.0379997 | 0.0384243 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0032015 | 0.0020436 | 0.1199999 | 0.1172015 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0009165 | 0.0020100 | 0.6499995 | 0.6484254 | 0.455916 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0007747 | 0.0022893 | 0.7400005 | 0.7350514 | 0.242057 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0006978 | 0.0014169 | 0.6200004 | 0.6223586 | 0.513304 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0025511 | 0.0016508 | 0.1199999 | 0.1222615 | 0.841441 | 0.639976 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0028842 | 0.0018132 | 0.1100001 | 0.1116773 | 0.123078 | 0.154353 | NA |
| 1 | 734349 | rs141242758 | T | C | 0.0028624 | 0.0018138 | 0.1100001 | 0.1145384 | 0.122330 | 0.152556 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0021711 | 0.0017805 | 0.2200002 | 0.2227065 | 0.134139 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0026616 | 0.0015970 | 0.0959997 | 0.0955757 | 0.837029 | 0.718251 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | 0.0010864 | 0.0014123 | 0.4400003 | 0.4417741 | 0.254586 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | -0.0018357 | 0.0018873 | 0.3300000 | 0.3307280 | 0.120936 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0004085 | 0.0018823 | 0.8300000 | 0.8282019 | 0.129762 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0016264 | 0.0014010 | 0.2500000 | 0.2456756 | 0.271468 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0003608 | 0.0018655 | 0.8499999 | 0.8466260 | 0.132383 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0018267 | 0.0013317 | 0.1700000 | 0.1701686 | 0.331351 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0005475 | 0.0017673 | 0.7600007 | 0.7567252 | 0.126422 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0008155 | 0.0017603 | 0.6400000 | 0.6431859 | 0.126887 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0003145 | 0.0017234 | 0.8600001 | 0.8551882 | 0.136315 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0012634 | 0.0013605 | 0.3500000 | 0.3530734 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID 0.00424312:0.00204952:1.42022:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID -0.0032015:0.00204356:0.920819:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID 0.000916452:0.00200998:0.187087:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID 0.000774717:0.00228926:0.130768:0.242057:rs6680723
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.000697833:0.00141689:0.207608:0.513304:rs12029736
1 729679 rs4951859 C G . PASS AF=0.841441 ES:SE:LP:AF:ID -0.00255108:0.00165081:0.920819:0.841441:rs4951859
1 731718 rs58276399 T C . PASS AF=0.123078 ES:SE:LP:AF:ID 0.00288424:0.00181319:0.958607:0.123078:rs58276399
1 734349 rs141242758 T C . PASS AF=0.12233 ES:SE:LP:AF:ID 0.00286236:0.00181378:0.958607:0.12233:rs141242758
1 736289 rs79010578 T A . PASS AF=0.134139 ES:SE:LP:AF:ID 0.0021711:0.00178052:0.657577:0.134139:rs79010578
1 752566 rs3094315 G A . PASS AF=0.837029 ES:SE:LP:AF:ID -0.00266164:0.00159696:1.01773:0.837029:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836733 ES:SE:LP:AF:ID -0.00268465:0.00159579:1.03152:0.836733:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868562 ES:SE:LP:AF:ID -0.00306374:0.00171475:1.13077:0.868562:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131004 ES:SE:LP:AF:ID 0.00301257:0.00171916:1.09691:0.131004:rs2073813
1 754182 rs3131969 A G . PASS AF=0.867976 ES:SE:LP:AF:ID -0.00311072:0.001712:1.16115:0.867976:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86805 ES:SE:LP:AF:ID -0.00314112:0.0017127:1.17393:0.86805:rs3131968
1 754334 rs3131967 T C . PASS AF=0.867987 ES:SE:LP:AF:ID -0.00312083:0.00171195:1.16749:0.867987:rs3131967
1 754503 rs3115859 G A . PASS AF=0.836159 ES:SE:LP:AF:ID -0.00272991:0.00159112:1.0655:0.836159:rs3115859
1 754964 rs3131966 C T . PASS AF=0.836793 ES:SE:LP:AF:ID -0.00280175:0.00159547:1.10237:0.836793:rs3131966
1 755775 rs3131965 A G . PASS AF=0.838109 ES:SE:LP:AF:ID -0.00299905:0.00161793:1.19382:0.838109:rs3131965
1 755890 rs3115858 A T . PASS AF=0.868228 ES:SE:LP:AF:ID -0.00308322:0.00170975:1.14874:0.868228:rs3115858
1 756604 rs3131962 A G . PASS AF=0.867742 ES:SE:LP:AF:ID -0.00303521:0.00170522:1.12494:0.867742:rs3131962
1 757640 rs3115853 G A . PASS AF=0.866644 ES:SE:LP:AF:ID -0.00301584:0.0017021:1.11919:0.866644:rs3115853
1 757734 rs4951929 C T . PASS AF=0.867911 ES:SE:LP:AF:ID -0.00310581:0.00170695:1.16115:0.867911:rs4951929
1 757936 rs4951862 C A . PASS AF=0.867923 ES:SE:LP:AF:ID -0.00310979:0.00170707:1.16749:0.867923:rs4951862
1 758144 rs3131956 A G . PASS AF=0.867932 ES:SE:LP:AF:ID -0.00311466:0.00170713:1.16749:0.867932:rs3131956
1 758626 rs3131954 C T . PASS AF=0.868398 ES:SE:LP:AF:ID -0.00316448:0.00171162:1.19382:0.868398:rs3131954
1 760912 rs1048488 C T . PASS AF=0.836369 ES:SE:LP:AF:ID -0.00289825:0.00158715:1.16749:0.836369:rs1048488
1 761147 rs3115850 T C . PASS AF=0.836495 ES:SE:LP:AF:ID -0.00291657:0.00158823:1.18046:0.836495:rs3115850
1 761732 rs2286139 C T . PASS AF=0.860851 ES:SE:LP:AF:ID -0.0033044:0.00170108:1.284:0.860851:rs2286139
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID -0.00307:0.00165996:1.19382:0.705804:rs3115847
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID -0.00147411:0.0013454:0.568636:0.758252:rs2977608
1 769223 rs60320384 C G . PASS AF=0.13066 ES:SE:LP:AF:ID 0.00297556:0.0017185:1.08092:0.13066:rs60320384
1 771823 rs2977605 T C . PASS AF=0.867852 ES:SE:LP:AF:ID -0.00323989:0.00170911:1.23657:0.867852:rs2977605
1 771967 rs59066358 G A . PASS AF=0.130756 ES:SE:LP:AF:ID 0.00304675:0.00171735:1.11919:0.130756:rs59066358
1 772755 rs2905039 A C . PASS AF=0.867878 ES:SE:LP:AF:ID -0.00325996:0.00170922:1.25181:0.867878:rs2905039
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID -0.00199148:0.00152026:0.721246:0.263729:rs12124819
1 777122 rs2980319 A T . PASS AF=0.869067 ES:SE:LP:AF:ID -0.00311444:0.00171371:1.16115:0.869067:rs2980319
1 778745 rs1055606 A G . PASS AF=0.129594 ES:SE:LP:AF:ID 0.00287782:0.00172025:1.02687:0.129594:rs1055606
1 779322 rs4040617 A G . PASS AF=0.129863 ES:SE:LP:AF:ID 0.00294601:0.00171732:1.0655:0.129863:rs4040617
1 780785 rs2977612 T A . PASS AF=0.867843 ES:SE:LP:AF:ID -0.00309285:0.00170946:1.1549:0.867843:rs2977612
1 782981 rs6594026 C T . PASS AF=0.130424 ES:SE:LP:AF:ID 0.00298818:0.00171765:1.08619:0.130424:rs6594026
1 785050 rs2905062 G A . PASS AF=0.867493 ES:SE:LP:AF:ID -0.00292954:0.00170895:1.0655:0.867493:rs2905062
1 785989 rs2980300 T C . PASS AF=0.867448 ES:SE:LP:AF:ID -0.00290305:0.00171014:1.04576:0.867448:rs2980300
1 787399 rs2905055 G T . PASS AF=0.859749 ES:SE:LP:AF:ID -0.00269386:0.00170961:0.920819:0.859749:rs2905055
1 787606 rs3863622 G T . PASS AF=0.129403 ES:SE:LP:AF:ID 0.00295028:0.00172654:1.06048:0.129403:rs3863622
1 787685 rs2905054 G T . PASS AF=0.860334 ES:SE:LP:AF:ID -0.003029:0.00171057:1.11351:0.860334:rs2905054
1 787844 rs2905053 C T . PASS AF=0.868172 ES:SE:LP:AF:ID -0.00286682:0.00171593:1.02228:0.868172:rs2905053
1 791191 rs111818025 G A . PASS AF=0.128878 ES:SE:LP:AF:ID 0.00257837:0.00173704:0.853872:0.128878:rs111818025
1 795988 rs59380221 C T . PASS AF=0.144405 ES:SE:LP:AF:ID 0.00295411:0.00176827:1.02228:0.144405:rs59380221
1 796375 rs12083781 T C . PASS AF=0.125094 ES:SE:LP:AF:ID 0.00307193:0.00178066:1.07572:0.125094:rs12083781