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_6150_2.vcf.gz --id UKB-b:8468 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6150_2.txt.gz --cohort_cases 14828 --cohort_controls 447052 --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": "2019-09-13T01:52:38.058886",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-8468/ukb-b-8468.vcf.gz; Date=Sun May 10 11:55:18 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-8468/UKB-b-8468_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8468/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:55 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8468/UKB-b-8468_data.vcf.gz ...
Read summary statistics for 6511674 SNPs.
Dropped 3540 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, 1244254 SNPs remain.
After merging with regression SNP LD, 1244254 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0206 (0.0016)
Lambda GC: 1.1897
Mean Chi^2: 1.226
Intercept: 1.0393 (0.0078)
Ratio: 0.1741 (0.0347)
Analysis finished at Thu Oct 17 14:43:50 2019
Total time elapsed: 1.0m:55.22sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9309,
"inflation_factor": 1.1999,
"mean_EFFECT": 3.8574e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 23,
"n_p_sig": 827,
"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": 59720,
"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": 1244254,
"ldsc_nsnp_merge_regression_ld": 1244254,
"ldsc_observed_scale_h2_beta": 0.0206,
"ldsc_observed_scale_h2_se": 0.0016,
"ldsc_intercept_beta": 1.0393,
"ldsc_intercept_se": 0.0078,
"ldsc_lambda_gc": 1.1897,
"ldsc_mean_chisq": 1.226,
"ldsc_ratio": 0.1739
}
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 | 6508155 | 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 | 6511674 | 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 | 8.666754e+00 | 5.763871e+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 | 7.857329e+07 | 5.647545e+07 | 828.0000000 | 3.204541e+07 | 6.900669e+07 | 1.144675e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.900000e-06 | 6.762000e-04 | -0.0062534 | -3.836000e-04 | 1.700000e-06 | 3.885000e-04 | 8.735900e-03 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.839000e-04 | 2.339000e-04 | 0.0003527 | 3.939000e-04 | 4.890000e-04 | 7.167000e-04 | 3.059200e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.733214e-01 | 2.955801e-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 | 4.733229e-01 | 2.955538e-01 | 0.0000000 | 2.111643e-01 | 4.642048e-01 | 7.288552e-01 | 9.999994e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.927066e-01 | 2.574706e-01 | 0.0236040 | 7.616100e-02 | 2.031870e-01 | 4.543330e-01 | 9.763960e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 59720 | 0.9908288 | NA | NA | NA | NA | NA | NA | NA | 2.902508e-01 | 2.498512e-01 | 0.0000000 | 8.546330e-02 | 2.128590e-01 | 4.460860e-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 | -0.0004763 | 0.0006490 | 0.4600002 | 0.4629924 | 0.623769 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0008745 | 0.0006429 | 0.1700000 | 0.1737785 | 0.400415 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0001816 | 0.0010279 | 0.8600001 | 0.8597805 | 0.103557 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002112 | 0.0006330 | 0.7400005 | 0.7385969 | 0.456849 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0000920 | 0.0012480 | 0.9400001 | 0.9412545 | 0.074513 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0006201 | 0.0007231 | 0.3900004 | 0.3911218 | 0.240956 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0010327 | 0.0009021 | 0.2500000 | 0.2523058 | 0.913480 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0012397 | 0.0006060 | 0.0409996 | 0.0407744 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0005961 | 0.0008880 | 0.5000000 | 0.5020519 | 0.067288 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0003238 | 0.0004489 | 0.4700002 | 0.4707794 | 0.515647 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | -0.0001912 | 0.0007015 | 0.7899998 | 0.7852253 | 0.073631 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0003137 | 0.0005416 | 0.5600000 | 0.5623859 | 0.137960 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0001312 | 0.0007030 | 0.8499999 | 0.8519433 | 0.073753 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0008863 | 0.0009420 | 0.3500000 | 0.3467904 | 0.041960 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0000602 | 0.0009404 | 0.9500000 | 0.9489864 | 0.049736 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0001130 | 0.0007034 | 0.8700001 | 0.8723602 | 0.073244 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0006092 | 0.0008281 | 0.4600002 | 0.4619952 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0006117 | 0.0008322 | 0.4600002 | 0.4622931 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0001198 | 0.0007059 | 0.8700001 | 0.8652320 | 0.073079 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0010598 | 0.0004318 | 0.0140001 | 0.0141156 | 0.297956 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623769 ES:SE:LP:AF:ID -0.000476307:0.000648983:0.337242:0.623769:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400415 ES:SE:LP:AF:ID -0.000874462:0.00064291:0.769551:0.400415:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103557 ES:SE:LP:AF:ID -0.000181584:0.00102791:0.0655015:0.103557:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456849 ES:SE:LP:AF:ID 0.000211243:0.00063301:0.130768:0.456849:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074513 ES:SE:LP:AF:ID 9.19699e-05:0.00124801:0.0268721:0.074513:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240956 ES:SE:LP:AF:ID -0.000620147:0.000723132:0.408935:0.240956:rs6680723
1 546697 rs12025928 A G . PASS AF=0.91348 ES:SE:LP:AF:ID 0.00103267:0.000902079:0.60206:0.91348:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID -0.00123973:0.000605983:1.38722:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067288 ES:SE:LP:AF:ID -0.000596077:0.000887991:0.30103:0.067288:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515647 ES:SE:LP:AF:ID 0.000323754:0.000448903:0.327902:0.515647:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID 0.000591918:0.00113171:0.221849:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036621 ES:SE:LP:AF:ID 0.000704141:0.00102794:0.309804:0.036621:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID 0.000761753:0.00102405:0.337242:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036437 ES:SE:LP:AF:ID 0.000555077:0.00103143:0.229148:0.036437:rs12184279
1 720381 rs116801199 G T . PASS AF=0.036975 ES:SE:LP:AF:ID 0.000644845:0.00102002:0.275724:0.036975:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037071 ES:SE:LP:AF:ID 0.000622104:0.00101653:0.267606:0.037071:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101212 ES:SE:LP:AF:ID 0.000193831:0.000740544:0.102373:0.101212:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959101 ES:SE:LP:AF:ID -0.000495239:0.000980467:0.21467:0.959101:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031444 ES:SE:LP:AF:ID 0.000270785:0.00178014:0.0555173:0.031444:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053258 ES:SE:LP:AF:ID -0.00158917:0.00141571:0.585027:0.053258:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036591 ES:SE:LP:AF:ID 0.000631733:0.00102308:0.267606:0.036591:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID 0.00047091:0.00101376:0.19382:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843223 ES:SE:LP:AF:ID 0.000800581:0.000525188:0.886057:0.843223:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055914 ES:SE:LP:AF:ID -0.00173802:0.000850289:1.38722:0.055914:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122303 ES:SE:LP:AF:ID -0.0011131:0.000574847:1.27572:0.122303:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025718 ES:SE:LP:AF:ID -0.000802422:0.00141373:0.244125:0.025718:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121546 ES:SE:LP:AF:ID -0.00108766:0.000575089:1.22915:0.121546:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132322 ES:SE:LP:AF:ID -0.000878688:0.000566811:0.920819:0.132322:rs79010578
1 752478 rs146277091 G A . PASS AF=0.03682 ES:SE:LP:AF:ID 0.000452457:0.00100355:0.187087:0.03682:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838958 ES:SE:LP:AF:ID 0.000803305:0.000508602:0.958607:0.838958:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838588 ES:SE:LP:AF:ID 0.000825143:0.000508054:1:0.838588:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869784 ES:SE:LP:AF:ID 0.0011142:0.000545174:1.38722:0.869784:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129864 ES:SE:LP:AF:ID -0.00116191:0.000546288:1.48149:0.129864:rs2073813
1 754063 rs12184312 G T . PASS AF=0.03733 ES:SE:LP:AF:ID 0.000500649:0.000986551:0.21467:0.03733:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037574 ES:SE:LP:AF:ID 0.000521328:0.000980313:0.229148:0.037574:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869128 ES:SE:LP:AF:ID 0.0011518:0.000544106:1.46852:0.869128:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869226 ES:SE:LP:AF:ID 0.00113319:0.000544323:1.4318:0.869226:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037532 ES:SE:LP:AF:ID 0.000474053:0.000984558:0.200659:0.037532:rs12184313
1 754334 rs3131967 T C . PASS AF=0.86913 ES:SE:LP:AF:ID 0.00116153:0.000544095:1.48149:0.86913:rs3131967
1 754503 rs3115859 G A . PASS AF=0.83804 ES:SE:LP:AF:ID 0.000850732:0.000506643:1.03152:0.83804:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037545 ES:SE:LP:AF:ID 0.000481847:0.000985947:0.200659:0.037545:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83867 ES:SE:LP:AF:ID 0.000829444:0.000508067:1:0.83867:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839783 ES:SE:LP:AF:ID 0.000876762:0.000514937:1.05061:0.839783:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869408 ES:SE:LP:AF:ID 0.00111962:0.000543465:1.40894:0.869408:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868954 ES:SE:LP:AF:ID 0.00108085:0.000542095:1.33724:0.868954:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867909 ES:SE:LP:AF:ID 0.00108299:0.000541062:1.34679:0.867909:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869098 ES:SE:LP:AF:ID 0.00110779:0.000542539:1.38722:0.869098:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869106 ES:SE:LP:AF:ID 0.00110759:0.000542581:1.38722:0.869106:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869114 ES:SE:LP:AF:ID 0.00110572:0.000542593:1.37675:0.869114:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869592 ES:SE:LP:AF:ID 0.00113652:0.000544085:1.4318:0.869592:rs3131954