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_41204_E780.vcf.gz --id UKB-b:12651 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_E780.txt.gz --cohort_cases 22622 --cohort_controls 440388 --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": "2019-09-13T11:20:15.168224",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12651/ukb-b-12651.vcf.gz; Date=Sun May 10 03:30:13 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-12651/UKB-b-12651_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12651/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12651/UKB-b-12651_data.vcf.gz ...
Read summary statistics for 7108835 SNPs.
Dropped 4467 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, 1266861 SNPs remain.
After merging with regression SNP LD, 1266861 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0235 (0.0017)
Lambda GC: 1.1912
Mean Chi^2: 1.2384
Intercept: 1.022 (0.0086)
Ratio: 0.0921 (0.0361)
Analysis finished at Thu Oct 17 14:43:30 2019
Total time elapsed: 1.0m:25.26sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9367,
"inflation_factor": 1.1999,
"mean_EFFECT": 4.6707e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 20,
"n_p_sig": 1323,
"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": 65489,
"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": 1266861,
"ldsc_nsnp_merge_regression_ld": 1266861,
"ldsc_observed_scale_h2_beta": 0.0235,
"ldsc_observed_scale_h2_se": 0.0017,
"ldsc_intercept_beta": 1.022,
"ldsc_intercept_se": 0.0086,
"ldsc_lambda_gc": 1.1912,
"ldsc_mean_chisq": 1.2384,
"ldsc_ratio": 0.0923
}
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 | 7104390 | 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 | 7108835 | 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 | 8.664427e+00 | 5.764241e+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 | 7.863785e+07 | 5.646340e+07 | 828.0000000 | 3.214450e+07 | 6.906282e+07 | 1.145274e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.700000e-06 | 9.357000e-04 | -0.0132784 | -5.022000e-04 | 3.800000e-06 | 5.056000e-04 | 2.258850e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.933000e-04 | 3.815000e-04 | 0.0004309 | 4.879000e-04 | 6.324000e-04 | 1.001700e-03 | 4.673300e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.736589e-01 | 2.952203e-01 | 0.0000000 | 2.099999e-01 | 4.600002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.736610e-01 | 2.951980e-01 | 0.0000000 | 2.119535e-01 | 4.646842e-01 | 7.283693e-01 | 9.999999e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.723114e-01 | 2.600793e-01 | 0.0154720 | 5.664500e-02 | 1.745400e-01 | 4.278580e-01 | 9.845280e-01 | ▇▃▂▁▁ |
| numeric | AF_reference | 65489 | 0.9907877 | NA | NA | NA | NA | NA | NA | NA | 2.707308e-01 | 2.520398e-01 | 0.0000000 | 6.389780e-02 | 1.869010e-01 | 4.207270e-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 | -0.0001981 | 0.0007929 | 0.8000000 | 0.8027244 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0011878 | 0.0007855 | 0.1299999 | 0.1305305 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0019588 | 0.0012559 | 0.1199999 | 0.1188493 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002975 | 0.0007735 | 0.6999999 | 0.7004651 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0002846 | 0.0015251 | 0.8499999 | 0.8519542 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0005359 | 0.0008835 | 0.5400003 | 0.5441031 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0008425 | 0.0011022 | 0.4400003 | 0.4446291 | 0.913475 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0003768 | 0.0007404 | 0.6100002 | 0.6108366 | 0.116329 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0027022 | 0.0010850 | 0.0129999 | 0.0127550 | 0.067288 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0004750 | 0.0005485 | 0.3900004 | 0.3864709 | 0.515645 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0005791 | 0.0006617 | 0.3800004 | 0.3814995 | 0.137950 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0004102 | 0.0008589 | 0.6300007 | 0.6329830 | 0.073744 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0000036 | 0.0011510 | 1.0000000 | 0.9974774 | 0.041954 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0001390 | 0.0011491 | 0.9000000 | 0.9037319 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0004822 | 0.0008595 | 0.5700002 | 0.5747651 | 0.073235 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0006006 | 0.0010118 | 0.5500004 | 0.5527890 | 0.054460 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0006464 | 0.0010168 | 0.5199996 | 0.5249277 | 0.054089 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0004411 | 0.0008626 | 0.6100002 | 0.6090900 | 0.073071 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0007786 | 0.0020321 | 0.6999999 | 0.7016164 | 0.020042 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0020171 | 0.0005276 | 0.0001300 | 0.0001316 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.00019809:0.000792922:0.09691:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.00118775:0.000785542:0.886057:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.00195876:0.00125592:0.920819:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000297544:0.000773459:0.154902:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID -0.000284619:0.00152508:0.0705811:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.000535947:0.000883497:0.267606:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913475 ES:SE:LP:AF:ID 0.000842529:0.00110221:0.356547:0.913475:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID 0.000376773:0.000740397:0.21467:0.116329:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067288 ES:SE:LP:AF:ID -0.00270216:0.00108497:1.88606:0.067288:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000474983:0.000548457:0.408935:0.515645:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033009 ES:SE:LP:AF:ID -0.00092558:0.00138261:0.30103:0.033009:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036624 ES:SE:LP:AF:ID -0.000212546:0.00125589:0.0604807:0.036624:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036741 ES:SE:LP:AF:ID -0.000266742:0.00125114:0.0809219:0.036741:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036439 ES:SE:LP:AF:ID -0.000498794:0.00126017:0.161151:0.036439:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016409 ES:SE:LP:AF:ID 0.00217992:0.00194031:0.585027:0.016409:rs144155419
1 720381 rs116801199 G T . PASS AF=0.03698 ES:SE:LP:AF:ID -0.000293844:0.00124618:0.091515:0.03698:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037077 ES:SE:LP:AF:ID -0.000205364:0.00124191:0.0604807:0.037077:rs12565286
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID 0.00135883:0.000904921:0.886057:0.1012:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959091 ES:SE:LP:AF:ID 0.000320169:0.00119779:0.102373:0.959091:rs2977670
1 724849 rs12126395 C A . PASS AF=0.03145 ES:SE:LP:AF:ID 0.00111982:0.00217459:0.21467:0.03145:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053255 ES:SE:LP:AF:ID -4.15183e-05:0.00172984:0.00877392:0.053255:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036594 ES:SE:LP:AF:ID -0.000296906:0.00124997:0.091515:0.036594:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03691 ES:SE:LP:AF:ID -0.000175396:0.00123859:0.05061:0.03691:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID -0.00038535:0.00064165:0.259637:0.843204:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055912 ES:SE:LP:AF:ID -0.000998999:0.00103893:0.468521:0.055912:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID 0.000605404:0.00070234:0.408935:0.122312:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025711 ES:SE:LP:AF:ID 0.00216227:0.0017277:0.677781:0.025711:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID 0.000647461:0.000702635:0.443698:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID 0.000186197:0.000692518:0.102373:0.132335:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036825 ES:SE:LP:AF:ID -0.000366199:0.00122606:0.113509:0.036825:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -0.000270279:0.000621394:0.180456:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID -0.000263682:0.000620726:0.173925:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID -0.000271783:0.000666061:0.167491:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID 0.000224142:0.000667424:0.130768:0.129876:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037336 ES:SE:LP:AF:ID -0.000478017:0.00120527:0.161151:0.037336:rs12184312
1 754105 rs12184325 C T . PASS AF=0.03758 ES:SE:LP:AF:ID -0.000386792:0.00119765:0.124939:0.03758:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID -0.000267792:0.000664755:0.161151:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID -0.000296387:0.000665019:0.180456:0.869215:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037538 ES:SE:LP:AF:ID -0.000443026:0.00120284:0.148742:0.037538:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID -0.00027028:0.000664743:0.167491:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID -0.000210885:0.000619003:0.136677:0.838026:rs3115859
1 754629 rs10454459 A G . PASS AF=0.03755 ES:SE:LP:AF:ID -0.000426517:0.00120453:0.142668:0.03755:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID -0.000234171:0.000620743:0.148742:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID -0.000304427:0.000629138:0.200659:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID -0.000256313:0.000663974:0.154902:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID -0.000317553:0.000662303:0.200659:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID -0.000350056:0.000661033:0.221849:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID -0.000271317:0.000662845:0.167491:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID -0.000270822:0.000662896:0.167491:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID -0.000270367:0.000662912:0.167491:0.869106:rs3131956