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_41246_1810.vcf.gz --id UKB-b:10212 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1810.txt.gz --cohort_cases 5524 --cohort_controls 438797 --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": "2019-09-13T13:20:05.941922",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10212/ukb-b-10212.vcf.gz; Date=Sat May 9 17:33:12 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-10212/UKB-b-10212_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10212/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10212/UKB-b-10212_data.vcf.gz ...
Read summary statistics for 5062364 SNPs.
Dropped 1503 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, 1123771 SNPs remain.
After merging with regression SNP LD, 1123771 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0022 (0.0014)
Lambda GC: 1.0296
Mean Chi^2: 1.0341
Intercept: 1.0137 (0.0081)
Ratio: 0.4011 (0.2385)
Analysis finished at Thu Oct 17 14:41:21 2019
Total time elapsed: 1.0m:3.51sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9078,
"inflation_factor": 1.0475,
"mean_EFFECT": -2.0089e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 157,
"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": 43347,
"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": 1123771,
"ldsc_nsnp_merge_regression_ld": 1123771,
"ldsc_observed_scale_h2_beta": 0.0022,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 1.0137,
"ldsc_intercept_se": 0.0081,
"ldsc_lambda_gc": 1.0296,
"ldsc_mean_chisq": 1.0341,
"ldsc_ratio": 0.4018
}
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 | 5060872 | 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 | 5062364 | 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 | 8.671045e+00 | 5.766889e+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 | 7.855512e+07 | 5.662707e+07 | 828.0000000 | 3.188029e+07 | 6.895627e+07 | 1.145678e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-06 | 3.200000e-04 | -0.0022084 | -2.037000e-04 | -1.500000e-06 | 1.998000e-04 | 2.497400e-03 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.057000e-04 | 7.020000e-05 | 0.0002268 | 2.463000e-04 | 2.805000e-04 | 3.504000e-04 | 8.709000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.944335e-01 | 2.905465e-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 | 4.944333e-01 | 2.905231e-01 | 0.0000000 | 2.412137e-01 | 4.927309e-01 | 7.467282e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.491940e-01 | 2.395729e-01 | 0.0633600 | 1.428720e-01 | 2.826830e-01 | 5.158190e-01 | 9.366400e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 43347 | 0.9914374 | NA | NA | NA | NA | NA | NA | NA | 3.427924e-01 | 2.360129e-01 | 0.0000000 | 1.479630e-01 | 2.851440e-01 | 5.051920e-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 | -0.0000503 | 0.0004174 | 0.9000000 | 0.9040437 | 0.623759 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001772 | 0.0004135 | 0.6700003 | 0.6683229 | 0.400418 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0000326 | 0.0006612 | 0.9599999 | 0.9606281 | 0.103533 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0007815 | 0.0004071 | 0.0549997 | 0.0549120 | 0.456858 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0007789 | 0.0008033 | 0.3300000 | 0.3322625 | 0.074435 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0011813 | 0.0004651 | 0.0109999 | 0.0110941 | 0.240913 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0001769 | 0.0005802 | 0.7600007 | 0.7604195 | 0.913477 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0003412 | 0.0003899 | 0.3800004 | 0.3814214 | 0.116350 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0005141 | 0.0005712 | 0.3700002 | 0.3681369 | 0.067333 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0003927 | 0.0002888 | 0.1700000 | 0.1738701 | 0.515704 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | -0.0005516 | 0.0003573 | 0.1199999 | 0.1226012 | 0.127885 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0005377 | 0.0003557 | 0.1299999 | 0.1306589 | 0.128408 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | -0.0000586 | 0.0004513 | 0.9000000 | 0.8967096 | 0.073356 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0000623 | 0.0004515 | 0.8900000 | 0.8903323 | 0.073377 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0000461 | 0.0004510 | 0.9199999 | 0.9186383 | 0.073658 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0004331 | 0.0003482 | 0.2099999 | 0.2135931 | 0.138011 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0000409 | 0.0004519 | 0.9299999 | 0.9278108 | 0.073786 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0000519 | 0.0004522 | 0.9100000 | 0.9085925 | 0.073282 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0000428 | 0.0004538 | 0.9199999 | 0.9249119 | 0.073119 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001328 | 0.0002777 | 0.6300007 | 0.6323358 | 0.298138 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623759 ES:SE:LP:AF:ID -5.03181e-05:0.000417388:0.0457575:0.623759:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400418 ES:SE:LP:AF:ID -0.000177167:0.000413506:0.173925:0.400418:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103533 ES:SE:LP:AF:ID -3.26391e-05:0.000661174:0.0177288:0.103533:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456858 ES:SE:LP:AF:ID 0.000781452:0.000407097:1.25964:0.456858:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074435 ES:SE:LP:AF:ID 0.000778886:0.000803334:0.481486:0.074435:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240913 ES:SE:LP:AF:ID 0.00118132:0.000465138:1.95861:0.240913:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913477 ES:SE:LP:AF:ID -0.000176931:0.000580235:0.119186:0.913477:rs12025928
1 693731 rs12238997 A G . PASS AF=0.11635 ES:SE:LP:AF:ID -0.000341235:0.000389858:0.420216:0.11635:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067333 ES:SE:LP:AF:ID -0.000514063:0.000571201:0.431798:0.067333:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515704 ES:SE:LP:AF:ID -0.000392681:0.000288763:0.769551:0.515704:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101243 ES:SE:LP:AF:ID 0.000102072:0.000476378:0.0809219:0.101243:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843133 ES:SE:LP:AF:ID 0.000307579:0.000337831:0.443698:0.843133:rs4951859
1 731718 rs58276399 T C . PASS AF=0.12234 ES:SE:LP:AF:ID -0.000365192:0.000369769:0.49485:0.12234:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121582 ES:SE:LP:AF:ID -0.0003451:0.000369922:0.455932:0.121582:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132408 ES:SE:LP:AF:ID -0.000381332:0.000364543:0.522879:0.132408:rs79010578
1 752566 rs3094315 G A . PASS AF=0.83883 ES:SE:LP:AF:ID 0.000337788:0.000327089:0.522879:0.83883:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83845 ES:SE:LP:AF:ID 0.000341549:0.000326723:0.522879:0.83845:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869712 ES:SE:LP:AF:ID 0.000385674:0.000350608:0.568636:0.869712:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129953 ES:SE:LP:AF:ID -0.000417631:0.000351305:0.638272:0.129953:rs2073813
1 754182 rs3131969 A G . PASS AF=0.86905 ES:SE:LP:AF:ID 0.000387153:0.00034991:0.568636:0.86905:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869151 ES:SE:LP:AF:ID 0.000378114:0.000350053:0.552842:0.869151:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869054 ES:SE:LP:AF:ID 0.000386094:0.000349904:0.568636:0.869054:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837899 ES:SE:LP:AF:ID 0.000340318:0.000325814:0.522879:0.837899:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838531 ES:SE:LP:AF:ID 0.00034953:0.000326729:0.552842:0.838531:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839649 ES:SE:LP:AF:ID 0.00035684:0.000331166:0.552842:0.839649:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869341 ES:SE:LP:AF:ID 0.000373905:0.000349519:0.552842:0.869341:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868893 ES:SE:LP:AF:ID 0.000379681:0.000348656:0.552842:0.868893:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867834 ES:SE:LP:AF:ID 0.000386367:0.000347965:0.568636:0.867834:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869032 ES:SE:LP:AF:ID 0.000388178:0.000348935:0.568636:0.869032:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869041 ES:SE:LP:AF:ID 0.000388208:0.000348961:0.568636:0.869041:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869048 ES:SE:LP:AF:ID 0.000387254:0.000348969:0.568636:0.869048:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869524 ES:SE:LP:AF:ID 0.000383141:0.000349916:0.568636:0.869524:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838186 ES:SE:LP:AF:ID 0.000333034:0.000325209:0.508638:0.838186:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838305 ES:SE:LP:AF:ID 0.000326347:0.00032544:0.49485:0.838305:rs3115850
1 761732 rs2286139 C T . PASS AF=0.86219 ES:SE:LP:AF:ID 0.000352881:0.000347719:0.508638:0.86219:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706671 ES:SE:LP:AF:ID 6.5519e-05:0.000338525:0.0705811:0.706671:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105157 ES:SE:LP:AF:ID -0.00052599:0.00038999:0.744727:0.105157:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761326 ES:SE:LP:AF:ID 0.000280408:0.000276411:0.508638:0.761326:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106413 ES:SE:LP:AF:ID -7.24797e-05:0.000381029:0.0705811:0.106413:rs12562034
1 769223 rs60320384 C G . PASS AF=0.12964 ES:SE:LP:AF:ID -0.000399354:0.000351138:0.585027:0.12964:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868855 ES:SE:LP:AF:ID 0.000387341:0.000349266:0.568636:0.868855:rs2977605
1 771967 rs59066358 G A . PASS AF=0.12974 ES:SE:LP:AF:ID -0.000394144:0.00035091:0.585027:0.12974:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868865 ES:SE:LP:AF:ID 0.000373335:0.000349273:0.537602:0.868865:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265298 ES:SE:LP:AF:ID 0.00025639:0.000308687:0.387216:0.265298:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870011 ES:SE:LP:AF:ID 0.00038748:0.000350009:0.568636:0.870011:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095092 ES:SE:LP:AF:ID -0.000553813:0.000405811:0.769551:0.095092:rs112618790
1 778745 rs1055606 A G . PASS AF=0.12861 ES:SE:LP:AF:ID -0.000380583:0.00035139:0.552842:0.12861:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128905 ES:SE:LP:AF:ID -0.000400627:0.0003508:0.60206:0.128905:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86875 ES:SE:LP:AF:ID 0.000396044:0.000349077:0.585027:0.86875:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101861 ES:SE:LP:AF:ID -0.000506242:0.00039559:0.69897:0.101861:rs61768199