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_20111_9.vcf.gz --id UKB-b:18042 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20111_9.txt.gz --cohort_cases 31073 --cohort_controls 331753 --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": "2019-09-13T11:51:00.578873",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-18042/ukb-b-18042.vcf.gz; Date=Sat May 9 15:11:04 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-18042/UKB-b-18042_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18042/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-18042/UKB-b-18042_data.vcf.gz ...
Read summary statistics for 7560850 SNPs.
Dropped 5282 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, 1277020 SNPs remain.
After merging with regression SNP LD, 1277020 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.022 (0.0019)
Lambda GC: 1.1731
Mean Chi^2: 1.2101
Intercept: 1.0529 (0.0074)
Ratio: 0.2519 (0.0354)
Analysis finished at Thu Oct 17 14:41:44 2019
Total time elapsed: 1.0m:26.02sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9402,
"inflation_factor": 1.1474,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 18,
"n_p_sig": 2161,
"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": 70203,
"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": 1277020,
"ldsc_nsnp_merge_regression_ld": 1277020,
"ldsc_observed_scale_h2_beta": 0.022,
"ldsc_observed_scale_h2_se": 0.0019,
"ldsc_intercept_beta": 1.0529,
"ldsc_intercept_se": 0.0074,
"ldsc_lambda_gc": 1.1731,
"ldsc_mean_chisq": 1.2101,
"ldsc_ratio": 0.2518
}
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 | TRUE |
| 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 | 7555591 | 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 | 7560850 | 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 | 8.663160e+00 | 5.764254e+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 | 7.865924e+07 | 5.643892e+07 | 828.0000000 | 3.219190e+07 | 6.909410e+07 | 1.145459e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.030000e-05 | 1.541600e-03 | -0.0159670 | -7.699000e-04 | 3.000000e-06 | 7.767000e-04 | 1.927550e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.272600e-03 | 6.909000e-04 | 0.0006332 | 7.249000e-04 | 9.742000e-04 | 1.642200e-03 | 7.605200e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.748570e-01 | 2.955838e-01 | 0.0000000 | 2.099999e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.748578e-01 | 2.955601e-01 | 0.0000000 | 2.118730e-01 | 4.670908e-01 | 7.309824e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.582266e-01 | 2.608392e-01 | 0.0112640 | 4.475400e-02 | 1.546140e-01 | 4.076250e-01 | 9.887360e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 70203 | 0.9907149 | NA | NA | NA | NA | NA | NA | NA | 2.571219e-01 | 2.526847e-01 | 0.0000000 | 4.952080e-02 | 1.687300e-01 | 4.019570e-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 | 0.0002656 | 0.0011652 | 0.8200001 | 0.8196799 | 0.623805 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0012045 | 0.0011534 | 0.2999998 | 0.2963412 | 0.400343 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0012306 | 0.0018453 | 0.5000000 | 0.5048454 | 0.103534 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0021294 | 0.0011368 | 0.0610000 | 0.0610396 | 0.456728 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0024955 | 0.0022375 | 0.2599998 | 0.2647047 | 0.074549 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0003372 | 0.0012986 | 0.8000000 | 0.7951180 | 0.240877 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0009320 | 0.0016217 | 0.5700002 | 0.5654666 | 0.913571 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0002458 | 0.0010887 | 0.8200001 | 0.8213629 | 0.116084 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0010076 | 0.0015989 | 0.5300002 | 0.5285906 | 0.067032 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000932 | 0.0008061 | 0.9100000 | 0.9079707 | 0.515714 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0012900 | 0.0009733 | 0.1900002 | 0.1850636 | 0.137865 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0016122 | 0.0012604 | 0.2000000 | 0.2008576 | 0.074013 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0000516 | 0.0016895 | 0.9800000 | 0.9756325 | 0.042095 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0006349 | 0.0016867 | 0.7099994 | 0.7066162 | 0.049845 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0015085 | 0.0012612 | 0.2300001 | 0.2316394 | 0.073491 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0006127 | 0.0014925 | 0.6800001 | 0.6814328 | 0.054161 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0004121 | 0.0014996 | 0.7800007 | 0.7834923 | 0.053796 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0014452 | 0.0012656 | 0.2500000 | 0.2534811 | 0.073342 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0012317 | 0.0030042 | 0.6800001 | 0.6818054 | 0.019869 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0009048 | 0.0007754 | 0.2399999 | 0.2432377 | 0.297983 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623805 ES:SE:LP:AF:ID 0.000265622:0.00116523:0.0861861:0.623805:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400343 ES:SE:LP:AF:ID -0.00120453:0.00115342:0.522879:0.400343:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103534 ES:SE:LP:AF:ID 0.00123059:0.00184528:0.30103:0.103534:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456728 ES:SE:LP:AF:ID -0.00212937:0.00113675:1.21467:0.456728:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074549 ES:SE:LP:AF:ID 0.00249554:0.00223747:0.585027:0.074549:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240877 ES:SE:LP:AF:ID -0.000337195:0.00129855:0.09691:0.240877:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913571 ES:SE:LP:AF:ID 0.00093204:0.00162167:0.244125:0.913571:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116084 ES:SE:LP:AF:ID -0.000245827:0.00108873:0.0861861:0.116084:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067032 ES:SE:LP:AF:ID -0.00100758:0.00159893:0.275724:0.067032:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515714 ES:SE:LP:AF:ID 9.31795e-05:0.000806061:0.0409586:0.515714:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032999 ES:SE:LP:AF:ID -0.00162395:0.00203225:0.376751:0.032999:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036645 ES:SE:LP:AF:ID -0.00162371:0.00184489:0.420216:0.036645:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036762 ES:SE:LP:AF:ID -0.00163759:0.00183791:0.431798:0.036762:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036471 ES:SE:LP:AF:ID -0.00158476:0.00185092:0.408935:0.036471:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016438 ES:SE:LP:AF:ID -0.00187136:0.002847:0.29243:0.016438:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037001 ES:SE:LP:AF:ID -0.00147366:0.00183073:0.376751:0.037001:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037102 ES:SE:LP:AF:ID -0.00173928:0.00182436:0.468521:0.037102:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101201 ES:SE:LP:AF:ID 0.000757509:0.00133036:0.244125:0.101201:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959071 ES:SE:LP:AF:ID 0.001104:0.00175927:0.275724:0.959071:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031463 ES:SE:LP:AF:ID 0.00254631:0.00319071:0.376751:0.031463:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053289 ES:SE:LP:AF:ID -0.00192051:0.00254002:0.346787:0.053289:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036624 ES:SE:LP:AF:ID -0.00152341:0.00183588:0.387216:0.036624:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03695 ES:SE:LP:AF:ID -0.00114724:0.00181896:0.275724:0.03695:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843378 ES:SE:LP:AF:ID 0.00051149:0.000943358:0.229148:0.843378:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055664 ES:SE:LP:AF:ID -0.00191317:0.00152979:0.677781:0.055664:rs148120343
1 731718 rs58276399 T C . PASS AF=0.12207 ES:SE:LP:AF:ID -9.99766e-05:0.00103276:0.0362122:0.12207:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025757 ES:SE:LP:AF:ID 0.00217448:0.00253627:0.408935:0.025757:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121306 ES:SE:LP:AF:ID -0.000140578:0.0010332:0.05061:0.121306:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132119 ES:SE:LP:AF:ID -0.000645863:0.0010183:0.275724:0.132119:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036855 ES:SE:LP:AF:ID -0.00155255:0.00180091:0.408935:0.036855:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839028 ES:SE:LP:AF:ID 0.000433597:0.000913324:0.200659:0.839028:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838666 ES:SE:LP:AF:ID 0.000406619:0.00091237:0.180456:0.838666:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869925 ES:SE:LP:AF:ID 4.20543e-05:0.00097911:0.0132283:0.869925:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129724 ES:SE:LP:AF:ID -7.64955e-05:0.000981055:0.0268721:0.129724:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037364 ES:SE:LP:AF:ID -0.00180745:0.00177044:0.508638:0.037364:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037596 ES:SE:LP:AF:ID -0.00159362:0.00175957:0.431798:0.037596:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869275 ES:SE:LP:AF:ID 3.46005e-05:0.000977232:0.0132283:0.869275:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869376 ES:SE:LP:AF:ID 9.01202e-06:0.000977639:0.00436481:0.869376:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037576 ES:SE:LP:AF:ID -0.00160837:0.00176668:0.443698:0.037576:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869278 ES:SE:LP:AF:ID 2.84429e-05:0.000977225:0.00877392:0.869278:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838114 ES:SE:LP:AF:ID 0.00050663:0.000909848:0.236572:0.838114:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037584 ES:SE:LP:AF:ID -0.0016454:0.00176936:0.455932:0.037584:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83874 ES:SE:LP:AF:ID 0.00053141:0.00091243:0.251812:0.83874:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013781 ES:SE:LP:AF:ID 0.0034669:0.0031838:0.552842:0.013781:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839839 ES:SE:LP:AF:ID 0.000640951:0.00092465:0.309804:0.839839:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869553 ES:SE:LP:AF:ID 5.30273e-05:0.00097606:0.0177288:0.869553:rs3115858
1 756604 rs3131962 A G . PASS AF=0.86908 ES:SE:LP:AF:ID 7.41755e-05:0.000973531:0.0268721:0.86908:rs3131962
1 757640 rs3115853 G A . PASS AF=0.868053 ES:SE:LP:AF:ID -8.7699e-05:0.000971727:0.0315171:0.868053:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869238 ES:SE:LP:AF:ID 7.64585e-05:0.000974371:0.0268721:0.869238:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869247 ES:SE:LP:AF:ID 7.67904e-05:0.000974447:0.0268721:0.869247:rs4951862