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_6151_1.vcf.gz --id UKB-b:15582 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6151_1.txt.gz --cohort_cases 6479 --cohort_controls 453861 --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",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-15582/ukb-b-15582.vcf.gz; Date=Sat May 9 21:04:09 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-15582/UKB-b-15582_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15582/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15582/UKB-b-15582_data.vcf.gz ...
Read summary statistics for 5310460 SNPs.
Dropped 1775 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, 1153239 SNPs remain.
After merging with regression SNP LD, 1153239 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0055 (0.0011)
Lambda GC: 1.0601
Mean Chi^2: 1.053
Intercept: 1.0007 (0.0074)
Ratio: 0.0133 (0.1404)
Analysis finished at Thu Oct 17 14:45:16 2019
Total time elapsed: 1.0m:3.94sQC metrics
Metrics
Metrics
{
"af_correlation": 0.913,
"inflation_factor": 1.0475,
"mean_EFFECT": -5.524e-08,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 46,
"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": 46048,
"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": 1153239,
"ldsc_nsnp_merge_regression_ld": 1153239,
"ldsc_observed_scale_h2_beta": 0.0055,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0007,
"ldsc_intercept_se": 0.0074,
"ldsc_lambda_gc": 1.0601,
"ldsc_mean_chisq": 1.053,
"ldsc_ratio": 0.0132
}
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 | 5308698 | 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 | 5310460 | 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 | 8.672856e+00 | 5.763187e+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 | 7.853180e+07 | 5.657477e+07 | 828.0000000 | 3.191090e+07 | 6.893664e+07 | 1.144919e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-07 | 3.470000e-04 | -0.0029113 | -2.186000e-04 | -5.000000e-07 | 2.169000e-04 | 3.215700e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.289000e-04 | 8.400000e-05 | 0.0002369 | 2.583000e-04 | 2.980000e-04 | 3.809000e-04 | 1.120000e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.919660e-01 | 2.906634e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.919680e-01 | 2.906370e-01 | 0.0000000 | 2.381523e-01 | 4.886124e-01 | 7.436944e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.389249e-01 | 2.440210e-01 | 0.0540210 | 1.295480e-01 | 2.681575e-01 | 5.057570e-01 | 9.459790e-01 | ▇▃▂▂▂ |
| numeric | AF_reference | 46048 | 0.9913288 | NA | NA | NA | NA | NA | NA | NA | 3.334193e-01 | 2.393334e-01 | 0.0000000 | 1.361820e-01 | 2.721650e-01 | 4.954070e-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 | 0.0001407 | 0.0004358 | 0.7499995 | 0.7467835 | 0.623788 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000583 | 0.0004318 | 0.8900000 | 0.8925270 | 0.400410 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0008437 | 0.0006904 | 0.2200002 | 0.2216412 | 0.103546 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002716 | 0.0004251 | 0.5199996 | 0.5228853 | 0.456857 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0012990 | 0.0008383 | 0.1199999 | 0.1212277 | 0.074507 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0000053 | 0.0004857 | 0.9900000 | 0.9912682 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0001776 | 0.0006059 | 0.7700005 | 0.7693768 | 0.913490 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0002555 | 0.0004070 | 0.5300002 | 0.5301933 | 0.116342 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0002869 | 0.0005964 | 0.6300007 | 0.6304976 | 0.067264 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000270 | 0.0003015 | 0.9299999 | 0.9285196 | 0.515584 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51217954 | rs9616974 | G | A | 0.0001382 | 0.0004715 | 0.7700005 | 0.7694676 | 0.073312 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | 0.0001366 | 0.0004717 | 0.7700005 | 0.7720814 | 0.073334 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | 0.0001523 | 0.0004711 | 0.7499995 | 0.7464458 | 0.073623 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0004295 | 0.0003637 | 0.2399999 | 0.2376632 | 0.137943 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0001452 | 0.0004721 | 0.7600007 | 0.7584446 | 0.073746 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0001482 | 0.0004724 | 0.7499995 | 0.7537372 | 0.073236 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0005484 | 0.0005562 | 0.3200000 | 0.3241167 | 0.054450 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0005789 | 0.0005589 | 0.2999998 | 0.3002611 | 0.054079 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0001163 | 0.0004741 | 0.8100000 | 0.8061690 | 0.073070 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002344 | 0.0002899 | 0.4199997 | 0.4187613 | 0.298005 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623788 ES:SE:LP:AF:ID 0.000140722:0.000435829:0.124939:0.623788:rs10399793
1 54676 rs2462492 C T . PASS AF=0.40041 ES:SE:LP:AF:ID -5.83342e-05:0.000431762:0.05061:0.40041:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103546 ES:SE:LP:AF:ID -0.000843735:0.000690355:0.657577:0.103546:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456857 ES:SE:LP:AF:ID 0.000271602:0.000425106:0.283997:0.456857:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074507 ES:SE:LP:AF:ID 0.00129903:0.000838278:0.920819:0.074507:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 5.315e-06:0.000485658:0.00436481:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.91349 ES:SE:LP:AF:ID 0.000177648:0.000605914:0.113509:0.91349:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116342 ES:SE:LP:AF:ID -0.000255452:0.000406958:0.275724:0.116342:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067264 ES:SE:LP:AF:ID -0.000286903:0.000596439:0.200659:0.067264:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515584 ES:SE:LP:AF:ID 2.70429e-05:0.000301456:0.0315171:0.515584:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101206 ES:SE:LP:AF:ID 0.00023431:0.000497381:0.19382:0.101206:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843178 ES:SE:LP:AF:ID -0.00037621:0.000352695:0.537602:0.843178:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055936 ES:SE:LP:AF:ID -0.000105122:0.000570975:0.0705811:0.055936:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122324 ES:SE:LP:AF:ID -5.15939e-05:0.000386045:0.05061:0.122324:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121567 ES:SE:LP:AF:ID -5.81952e-05:0.0003862:0.0555173:0.121567:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132371 ES:SE:LP:AF:ID 0.00050472:0.000380641:0.744727:0.132371:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838923 ES:SE:LP:AF:ID -0.000320919:0.000341563:0.455932:0.838923:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838551 ES:SE:LP:AF:ID -0.000320039:0.000341195:0.455932:0.838551:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869773 ES:SE:LP:AF:ID 7.77655e-05:0.000366116:0.0809219:0.869773:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID -7.40478e-05:0.000366864:0.0757207:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869115 ES:SE:LP:AF:ID 8.47961e-05:0.000365401:0.0861861:0.869115:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869212 ES:SE:LP:AF:ID 9.26165e-05:0.000365546:0.09691:0.869212:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869117 ES:SE:LP:AF:ID 8.27986e-05:0.000365393:0.0861861:0.869117:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838004 ES:SE:LP:AF:ID -0.000299335:0.000340252:0.420216:0.838004:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838637 ES:SE:LP:AF:ID -0.000298405:0.000341211:0.420216:0.838637:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839752 ES:SE:LP:AF:ID -0.000319855:0.000345818:0.443698:0.839752:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869397 ES:SE:LP:AF:ID 8.09404e-05:0.000364969:0.0861861:0.869397:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868944 ES:SE:LP:AF:ID 9.05477e-05:0.000364051:0.09691:0.868944:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867895 ES:SE:LP:AF:ID 9.80646e-05:0.000363353:0.102373:0.867895:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869087 ES:SE:LP:AF:ID 8.58414e-05:0.000364348:0.091515:0.869087:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869095 ES:SE:LP:AF:ID 8.56401e-05:0.000364376:0.091515:0.869095:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869103 ES:SE:LP:AF:ID 8.51244e-05:0.000364385:0.0861861:0.869103:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869581 ES:SE:LP:AF:ID 8.38462e-05:0.000365385:0.0861861:0.869581:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838285 ES:SE:LP:AF:ID -0.000319418:0.000339604:0.455932:0.838285:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838406 ES:SE:LP:AF:ID -0.000317955:0.000339844:0.455932:0.838406:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862249 ES:SE:LP:AF:ID 3.48667e-05:0.000363076:0.0362122:0.862249:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706771 ES:SE:LP:AF:ID -0.00011248:0.000353447:0.124939:0.706771:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105159 ES:SE:LP:AF:ID -5.73866e-05:0.00040715:0.05061:0.105159:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761255 ES:SE:LP:AF:ID -0.000135439:0.000288474:0.19382:0.761255:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106528 ES:SE:LP:AF:ID 0.000378907:0.00039759:0.468521:0.106528:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129582 ES:SE:LP:AF:ID -4.68706e-05:0.000366642:0.0457575:0.129582:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868903 ES:SE:LP:AF:ID 9.16673e-05:0.000364685:0.09691:0.868903:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129683 ES:SE:LP:AF:ID -5.6046e-05:0.000366404:0.0555173:0.129683:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868913 ES:SE:LP:AF:ID 0.000105643:0.000364692:0.113509:0.868913:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265374 ES:SE:LP:AF:ID 0.000441915:0.000322257:0.769551:0.265374:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870033 ES:SE:LP:AF:ID 8.2525e-05:0.000365436:0.0861861:0.870033:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095179 ES:SE:LP:AF:ID 0.000384063:0.000423504:0.443698:0.095179:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128585 ES:SE:LP:AF:ID -6.58772e-05:0.000366876:0.0655015:0.128585:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128883 ES:SE:LP:AF:ID -8.18683e-05:0.000366253:0.0861861:0.128883:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868775 ES:SE:LP:AF:ID 7.52779e-05:0.000364465:0.0757207:0.868775:rs2977612