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_1.vcf.gz --id UKB-b:14371 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20111_1.txt.gz --cohort_cases 37858 --cohort_controls 325684 --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": "2019-09-13T11:52:58.565248",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14371/ukb-b-14371.vcf.gz; Date=Sat May 9 23:29:25 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-14371/UKB-b-14371_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14371/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:25 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14371/UKB-b-14371_data.vcf.gz ...
Read summary statistics for 7844132 SNPs.
Dropped 5817 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, 1280834 SNPs remain.
After merging with regression SNP LD, 1280834 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0181 (0.0019)
Lambda GC: 1.1812
Mean Chi^2: 1.1902
Intercept: 1.0626 (0.0075)
Ratio: 0.3292 (0.0396)
Analysis finished at Thu Oct 17 14:44:50 2019
Total time elapsed: 1.0m:25.81sQC metrics
Metrics
Metrics
{
"af_correlation": 0.942,
"inflation_factor": 1.1474,
"mean_EFFECT": 8.4306e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 8,
"n_p_sig": 271,
"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": 73089,
"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": 1280834,
"ldsc_nsnp_merge_regression_ld": 1280834,
"ldsc_observed_scale_h2_beta": 0.0181,
"ldsc_observed_scale_h2_se": 0.0019,
"ldsc_intercept_beta": 1.0626,
"ldsc_intercept_se": 0.0075,
"ldsc_lambda_gc": 1.1812,
"ldsc_mean_chisq": 1.1902,
"ldsc_ratio": 0.3291
}
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 | 7838342 | 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 | 7844132 | 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 | 8.661153e+00 | 5.763852e+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 | 7.870428e+07 | 5.643033e+07 | 828.0000000 | 3.223711e+07 | 6.917164e+07 | 1.145669e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.400000e-06 | 1.790000e-03 | -0.0154650 | -8.552000e-04 | 6.800000e-06 | 8.704000e-04 | 2.018460e-02 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.470000e-03 | 8.543000e-04 | 0.0006904 | 7.964000e-04 | 1.094600e-03 | 1.921800e-03 | 8.524500e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.765214e-01 | 2.947484e-01 | 0.0000000 | 2.200002e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.765210e-01 | 2.947228e-01 | 0.0000000 | 2.151479e-01 | 4.677850e-01 | 7.320606e-01 | 9.999999e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.498941e-01 | 2.608151e-01 | 0.0092460 | 3.845000e-02 | 1.427090e-01 | 3.954550e-01 | 9.907540e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 73089 | 0.9906823 | NA | NA | NA | NA | NA | NA | NA | 2.490438e-01 | 2.526504e-01 | 0.0000000 | 4.153350e-02 | 1.577480e-01 | 3.903750e-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 | 0.0001723 | 0.0012705 | 0.8900000 | 0.8921339 | 0.623816 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0011431 | 0.0012577 | 0.3599996 | 0.3634097 | 0.400301 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0010444 | 0.0020126 | 0.5999997 | 0.6038070 | 0.103505 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003587 | 0.0012396 | 0.7700005 | 0.7722835 | 0.456723 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0009391 | 0.0024401 | 0.6999999 | 0.7003520 | 0.074527 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0019322 | 0.0014158 | 0.1700000 | 0.1723287 | 0.240878 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0020315 | 0.0017674 | 0.2500000 | 0.2503736 | 0.913522 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0006465 | 0.0011870 | 0.5900000 | 0.5860064 | 0.116089 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0017230 | 0.0017429 | 0.3200000 | 0.3228704 | 0.067079 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0013107 | 0.0008789 | 0.1400000 | 0.1358980 | 0.515719 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0003329 | 0.0010612 | 0.7499995 | 0.7537706 | 0.137853 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0012514 | 0.0013742 | 0.3599996 | 0.3624770 | 0.073997 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0023538 | 0.0018413 | 0.2000000 | 0.2011441 | 0.042106 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0012551 | 0.0018387 | 0.4899999 | 0.4948644 | 0.049845 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0010442 | 0.0013751 | 0.4500005 | 0.4476397 | 0.073476 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0007514 | 0.0016273 | 0.6400000 | 0.6442466 | 0.054157 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0007327 | 0.0016351 | 0.6499995 | 0.6540704 | 0.053791 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0011476 | 0.0013799 | 0.4100001 | 0.4055856 | 0.073327 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0005278 | 0.0032762 | 0.8700001 | 0.8720117 | 0.019864 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0013429 | 0.0008453 | 0.1100001 | 0.1121283 | 0.298104 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623816 ES:SE:LP:AF:ID 0.000172291:0.00127054:0.05061:0.623816:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400301 ES:SE:LP:AF:ID 0.00114308:0.00125767:0.443698:0.400301:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103505 ES:SE:LP:AF:ID 0.00104438:0.00201255:0.221849:0.103505:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456723 ES:SE:LP:AF:ID 0.000358724:0.00123959:0.113509:0.456723:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074527 ES:SE:LP:AF:ID 0.000939061:0.0024401:0.154902:0.074527:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240878 ES:SE:LP:AF:ID -0.00193224:0.00141581:0.769551:0.240878:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913522 ES:SE:LP:AF:ID -0.00203149:0.00176737:0.60206:0.913522:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116089 ES:SE:LP:AF:ID -0.000646484:0.00118701:0.229148:0.116089:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067079 ES:SE:LP:AF:ID 0.001723:0.00174291:0.49485:0.067079:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515719 ES:SE:LP:AF:ID -0.00131066:0.000878905:0.853872:0.515719:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032993 ES:SE:LP:AF:ID -3.73246e-05:0.00221611:0.00436481:0.032993:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036638 ES:SE:LP:AF:ID 0.000189205:0.00201179:0.0315171:0.036638:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036754 ES:SE:LP:AF:ID 0.000180702:0.00200421:0.0315171:0.036754:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036463 ES:SE:LP:AF:ID 5.61569e-05:0.0020184:0.00877392:0.036463:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016444 ES:SE:LP:AF:ID -0.00127265:0.00310391:0.167491:0.016444:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036991 ES:SE:LP:AF:ID 0.000349028:0.00199641:0.0655015:0.036991:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037092 ES:SE:LP:AF:ID 6.15621e-05:0.00198945:0.00877392:0.037092:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101253 ES:SE:LP:AF:ID 0.00166295:0.00145007:0.60206:0.101253:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959081 ES:SE:LP:AF:ID -0.000165482:0.00191852:0.0315171:0.959081:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031463 ES:SE:LP:AF:ID 0.00107231:0.00347984:0.119186:0.031463:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053294 ES:SE:LP:AF:ID -0.00436664:0.0027697:0.958607:0.053294:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036615 ES:SE:LP:AF:ID 5.66276e-05:0.00200202:0.00877392:0.036615:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036942 ES:SE:LP:AF:ID 0.000155442:0.00198352:0.0268721:0.036942:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843383 ES:SE:LP:AF:ID 0.000782612:0.00102847:0.346787:0.843383:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055655 ES:SE:LP:AF:ID -0.00227742:0.00166808:0.769551:0.055655:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122065 ES:SE:LP:AF:ID -0.000700619:0.00112602:0.275724:0.122065:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025742 ES:SE:LP:AF:ID -0.0017156:0.00276656:0.267606:0.025742:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121303 ES:SE:LP:AF:ID -0.0007229:0.00112649:0.283997:0.121303:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132115 ES:SE:LP:AF:ID -0.000622769:0.00111021:0.244125:0.132115:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011125 ES:SE:LP:AF:ID -0.0047397:0.00403379:0.619789:0.011125:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036848 ES:SE:LP:AF:ID 0.000228019:0.00196382:0.0409586:0.036848:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839031 ES:SE:LP:AF:ID 0.000817476:0.000995697:0.387216:0.839031:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83867 ES:SE:LP:AF:ID 0.000784102:0.000994662:0.366532:0.83867:rs3131972
1 753405 rs3115860 C A . PASS AF=0.86992 ES:SE:LP:AF:ID 0.000693747:0.00106739:0.283997:0.86992:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129722 ES:SE:LP:AF:ID -0.000734442:0.00106956:0.309804:0.129722:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037356 ES:SE:LP:AF:ID 6.05284e-05:0.00193058:0.0132283:0.037356:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037588 ES:SE:LP:AF:ID 0.00016472:0.00191875:0.0315171:0.037588:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869272 ES:SE:LP:AF:ID 0.000669375:0.00106535:0.275724:0.869272:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869372 ES:SE:LP:AF:ID 0.000629321:0.00106579:0.259637:0.869372:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037569 ES:SE:LP:AF:ID 1.26261e-05:0.00192645:0.00436481:0.037569:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869275 ES:SE:LP:AF:ID 0.000667211:0.00106534:0.275724:0.869275:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838119 ES:SE:LP:AF:ID 0.000810812:0.000991911:0.387216:0.838119:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037577 ES:SE:LP:AF:ID -0.000105736:0.00192938:0.0177288:0.037577:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838743 ES:SE:LP:AF:ID 0.000786571:0.000994706:0.366532:0.838743:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013813 ES:SE:LP:AF:ID 0.00206372:0.00346729:0.259637:0.013813:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839845 ES:SE:LP:AF:ID 0.00058122:0.00100805:0.251812:0.839845:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869556 ES:SE:LP:AF:ID 0.00075346:0.0010641:0.318759:0.869556:rs3115858
1 756604 rs3131962 A G . PASS AF=0.869082 ES:SE:LP:AF:ID 0.000717771:0.00106134:0.30103:0.869082:rs3131962
1 757640 rs3115853 G A . PASS AF=0.86806 ES:SE:LP:AF:ID 0.000725252:0.0010594:0.309804:0.86806:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869241 ES:SE:LP:AF:ID 0.000770097:0.00106226:0.327902:0.869241:rs4951929