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_20110_10.vcf.gz --id UKB-b:14699 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20110_10.txt.gz --cohort_cases 36548 --cohort_controls 387190 --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": "2019-09-13T11:49:15.043578",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14699/ukb-b-14699.vcf.gz; Date=Sat May 9 18:20:03 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-14699/UKB-b-14699_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14699/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14699/UKB-b-14699_data.vcf.gz ...
Read summary statistics for 7793502 SNPs.
Dropped 5738 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, 1280250 SNPs remain.
After merging with regression SNP LD, 1280250 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0073 (0.0015)
Lambda GC: 1.0988
Mean Chi^2: 1.121
Intercept: 1.0626 (0.0078)
Ratio: 0.5177 (0.0647)
Analysis finished at Thu Oct 17 14:45:21 2019
Total time elapsed: 1.0m:34.45sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9417,
"inflation_factor": 1.0966,
"mean_EFFECT": -8.9983e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 7,
"n_p_sig": 529,
"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": 72629,
"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": 1280250,
"ldsc_nsnp_merge_regression_ld": 1280250,
"ldsc_observed_scale_h2_beta": 0.0073,
"ldsc_observed_scale_h2_se": 0.0015,
"ldsc_intercept_beta": 1.0626,
"ldsc_intercept_se": 0.0078,
"ldsc_lambda_gc": 1.0988,
"ldsc_mean_chisq": 1.121,
"ldsc_ratio": 0.5174
}
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 | TRUE |
| 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 | 7787790 | 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 | 7793502 | 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 | 8.661793e+00 | 5.764091e+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 | 7.869546e+07 | 5.642885e+07 | 828.0000000 | 3.222716e+07 | 6.916099e+07 | 1.145622e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -9.000000e-06 | 1.482800e-03 | -0.0181969 | -7.166000e-04 | -7.500000e-06 | 6.988000e-04 | 4.784820e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.239700e-03 | 7.121000e-04 | 0.0005882 | 6.777000e-04 | 9.276000e-04 | 1.617400e-03 | 7.287800e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.867802e-01 | 2.924639e-01 | 0.0000000 | 2.300001e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.867799e-01 | 2.924392e-01 | 0.0000000 | 2.306045e-01 | 4.822381e-01 | 7.403324e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.513335e-01 | 2.608257e-01 | 0.0095770 | 3.950700e-02 | 1.448580e-01 | 3.976440e-01 | 9.904230e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 72629 | 0.9906808 | NA | NA | NA | NA | NA | NA | NA | 2.504414e-01 | 2.526681e-01 | 0.0000000 | 4.273160e-02 | 1.597440e-01 | 3.923720e-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 | -0.0015161 | 0.0010832 | 0.1600000 | 0.1616013 | 0.623795 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001643 | 0.0010721 | 0.8800001 | 0.8781833 | 0.400507 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0023688 | 0.0017144 | 0.1700000 | 0.1670621 | 0.103575 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0009122 | 0.0010560 | 0.3900004 | 0.3876774 | 0.456963 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0048108 | 0.0020835 | 0.0210000 | 0.0209441 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0030004 | 0.0012069 | 0.0129999 | 0.0129155 | 0.240950 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0006823 | 0.0015040 | 0.6499995 | 0.6501096 | 0.913384 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0007080 | 0.0010112 | 0.4799997 | 0.4838242 | 0.116273 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0007709 | 0.0014811 | 0.5999997 | 0.6027078 | 0.067358 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0011250 | 0.0007492 | 0.1299999 | 0.1331996 | 0.515802 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0005136 | 0.0009036 | 0.5700002 | 0.5697777 | 0.137922 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0009054 | 0.0011726 | 0.4400003 | 0.4400404 | 0.073771 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0006199 | 0.0015718 | 0.6899999 | 0.6932848 | 0.041963 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0012174 | 0.0015684 | 0.4400003 | 0.4376049 | 0.049757 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0007878 | 0.0011734 | 0.5000000 | 0.5019748 | 0.073263 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0017087 | 0.0013819 | 0.2200002 | 0.2162832 | 0.054402 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0018412 | 0.0013886 | 0.1800002 | 0.1848438 | 0.054033 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0009315 | 0.0011776 | 0.4299995 | 0.4289488 | 0.073097 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0023879 | 0.0027738 | 0.3900004 | 0.3893011 | 0.020023 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000962 | 0.0007204 | 0.8900000 | 0.8938124 | 0.298071 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623795 ES:SE:LP:AF:ID -0.00151612:0.00108317:0.79588:0.623795:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400507 ES:SE:LP:AF:ID -0.000164322:0.00107209:0.0555173:0.400507:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103575 ES:SE:LP:AF:ID 0.00236881:0.00171441:0.769551:0.103575:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456963 ES:SE:LP:AF:ID 0.000912212:0.001056:0.408935:0.456963:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID -0.00481085:0.00208353:1.67778:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24095 ES:SE:LP:AF:ID 0.00300039:0.00120687:1.88606:0.24095:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913384 ES:SE:LP:AF:ID 0.000682252:0.00150405:0.187087:0.913384:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116273 ES:SE:LP:AF:ID 0.000708042:0.00101125:0.318759:0.116273:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067358 ES:SE:LP:AF:ID -0.000770923:0.00148109:0.221849:0.067358:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515802 ES:SE:LP:AF:ID -0.00112498:0.000749186:0.886057:0.515802:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033002 ES:SE:LP:AF:ID 0.00275993:0.00188808:0.853872:0.033002:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036638 ES:SE:LP:AF:ID 0.00244675:0.00171433:0.823909:0.036638:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036747 ES:SE:LP:AF:ID 0.00252024:0.00170806:0.853872:0.036747:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036448 ES:SE:LP:AF:ID 0.00261542:0.00172037:0.886057:0.036448:rs12184279
1 717587 rs144155419 G A . PASS AF=0.01643 ES:SE:LP:AF:ID 0.00320452:0.00264795:0.638272:0.01643:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036986 ES:SE:LP:AF:ID 0.00240632:0.0017013:0.79588:0.036986:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037081 ES:SE:LP:AF:ID 0.00251812:0.00169554:0.853872:0.037081:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101272 ES:SE:LP:AF:ID -0.000625114:0.00123526:0.21467:0.101272:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959101 ES:SE:LP:AF:ID -0.00223707:0.00163573:0.769551:0.959101:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031488 ES:SE:LP:AF:ID -0.000103459:0.0029652:0.0132283:0.031488:rs12126395
1 725060 rs865924913 A T . PASS AF=0.0533 ES:SE:LP:AF:ID -0.00376407:0.0023593:0.958607:0.0533:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036599 ES:SE:LP:AF:ID 0.00271253:0.00170653:0.958607:0.036599:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03691 ES:SE:LP:AF:ID 0.00251466:0.00169117:0.853872:0.03691:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843239 ES:SE:LP:AF:ID -0.00108768:0.000876186:0.677781:0.843239:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID -0.000714421:0.00141873:0.21467:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122264 ES:SE:LP:AF:ID 0.000815186:0.000959219:0.39794:0.122264:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025718 ES:SE:LP:AF:ID -0.00104652:0.00235714:0.180456:0.025718:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121513 ES:SE:LP:AF:ID 0.000924309:0.000959613:0.468521:0.121513:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132316 ES:SE:LP:AF:ID 0.00112386:0.000945666:0.638272:0.132316:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011122 ES:SE:LP:AF:ID -0.00070621:0.00343986:0.0757207:0.011122:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036838 ES:SE:LP:AF:ID 0.00236938:0.00167371:0.79588:0.036838:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -0.00129819:0.000848541:0.886057:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838569 ES:SE:LP:AF:ID -0.00130093:0.000847578:0.920819:0.838569:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869819 ES:SE:LP:AF:ID -0.00125579:0.000909721:0.769551:0.869819:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129837 ES:SE:LP:AF:ID 0.0012045:0.000911564:0.721246:0.129837:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037339 ES:SE:LP:AF:ID 0.00227295:0.00164562:0.769551:0.037339:rs12184312
1 754105 rs12184325 C T . PASS AF=0.03758 ES:SE:LP:AF:ID 0.00229734:0.0016353:0.79588:0.03758:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869159 ES:SE:LP:AF:ID -0.00125894:0.000907905:0.769551:0.869159:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869255 ES:SE:LP:AF:ID -0.00127737:0.000908256:0.79588:0.869255:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037541 ES:SE:LP:AF:ID 0.00223205:0.00164226:0.769551:0.037541:rs12184313
1 754334 rs3131967 T C . PASS AF=0.86916 ES:SE:LP:AF:ID -0.00126684:0.000907887:0.79588:0.86916:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838018 ES:SE:LP:AF:ID -0.00134089:0.000845212:0.958607:0.838018:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037552 ES:SE:LP:AF:ID 0.00228938:0.00164465:0.79588:0.037552:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838654 ES:SE:LP:AF:ID -0.00139283:0.000847608:1:0.838654:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013746 ES:SE:LP:AF:ID -0.00208533:0.00296141:0.318759:0.013746:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839764 ES:SE:LP:AF:ID -0.00118225:0.00085906:0.769551:0.839764:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869447 ES:SE:LP:AF:ID -0.0011768:0.000906891:0.721246:0.869447:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868995 ES:SE:LP:AF:ID -0.00115185:0.00090461:0.69897:0.868995:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867939 ES:SE:LP:AF:ID -0.00119513:0.000902826:0.721246:0.867939:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869138 ES:SE:LP:AF:ID -0.0011768:0.00090535:0.721246:0.869138:rs4951929