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_1558.vcf.gz --id UKB-b:5779 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1558.txt.gz --cohort_controls 462346 --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": "2019-09-13T13:40:53.454882",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-5779/ukb-b-5779.vcf.gz; Date=Sun May 10 11:49:04 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-5779/UKB-b-5779_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5779/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:34 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5779/UKB-b-5779_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0785 (0.0032)
Lambda GC: 1.6236
Mean Chi^2: 1.8041
Intercept: 1.095 (0.0114)
Ratio: 0.1181 (0.0141)
Analysis finished at Thu Oct 17 14:47:04 2019
Total time elapsed: 1.0m:30.27sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.4295,
"mean_EFFECT": 0.0006,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 99,
"n_p_sig": 8467,
"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": 184849,
"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": 1289166,
"ldsc_nsnp_merge_regression_ld": 1289166,
"ldsc_observed_scale_h2_beta": 0.0785,
"ldsc_observed_scale_h2_se": 0.0032,
"ldsc_intercept_beta": 1.095,
"ldsc_intercept_se": 0.0114,
"ldsc_lambda_gc": 1.6236,
"ldsc_mean_chisq": 1.8041,
"ldsc_ratio": 0.1181
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| 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 | 9837196 | 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 | 9851866 | 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 | 8.622825e+00 | 5.748290e+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 | 7.886027e+07 | 5.628334e+07 | 828.0000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.634000e-04 | 1.562520e-02 | -0.3281100 | -5.017400e-03 | 1.571000e-04 | 5.528400e-03 | 2.954310e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.043580e-02 | 9.877600e-03 | 0.0029213 | 3.574900e-03 | 5.994700e-03 | 1.383200e-02 | 1.535060e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.429023e-01 | 3.031334e-01 | 0.0000000 | 1.700000e-01 | 4.199997e-01 | 6.999999e-01 | 1.000000e+00 | ▇▆▆▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.429027e-01 | 3.031099e-01 | 0.0000000 | 1.656826e-01 | 4.220009e-01 | 7.047541e-01 | 9.999996e-01 | ▇▆▅▅▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035076e-01 | 2.568616e-01 | 0.0009920 | 1.316900e-02 | 7.791400e-02 | 3.164558e-01 | 9.990040e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.0000000 | 1.198080e-02 | 9.984030e-02 | 3.202880e-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 | -0.0023672 | 0.0053711 | 0.6600001 | 0.6594030 | 0.623754 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0004794 | 0.0053208 | 0.9299999 | 0.9282027 | 0.400415 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0004140 | 0.0085071 | 0.9599999 | 0.9611865 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0032908 | 0.0052394 | 0.5300002 | 0.5299440 | 0.456858 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0119822 | 0.0103301 | 0.2500000 | 0.2460770 | 0.074509 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0086805 | 0.0059850 | 0.1499999 | 0.1469558 | 0.240954 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0033651 | 0.0074652 | 0.6499995 | 0.6521588 | 0.913468 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0018021 | 0.0050150 | 0.7199992 | 0.7193429 | 0.116339 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0128892 | 0.0073492 | 0.0790005 | 0.0794603 | 0.067290 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0054931 | 0.0037152 | 0.1400000 | 0.1392662 | 0.515633 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0074533 | 0.0078052 | 0.3400001 | 0.3396216 | 0.041950 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | 0.0249217 | 0.0363900 | 0.4899999 | 0.4934384 | 0.001935 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | 0.0225905 | 0.0360418 | 0.5300002 | 0.5307994 | 0.001984 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0083657 | 0.0077930 | 0.2800000 | 0.2830518 | 0.049717 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0096449 | 0.0058286 | 0.0980009 | 0.0979759 | 0.073216 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0058624 | 0.0068614 | 0.3900004 | 0.3928812 | 0.054457 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0056414 | 0.0068946 | 0.4100001 | 0.4132198 | 0.054086 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0094957 | 0.0058497 | 0.1000000 | 0.1045321 | 0.073053 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0271074 | 0.0137792 | 0.0490004 | 0.0491521 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0036112 | 0.0035775 | 0.3100002 | 0.3127746 | 0.297999 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623754 ES:SE:LP:AF:ID -0.00236725:0.00537111:0.180456:0.623754:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400415 ES:SE:LP:AF:ID -0.000479439:0.00532081:0.0315171:0.400415:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.000413998:0.00850714:0.0177288:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456858 ES:SE:LP:AF:ID 0.00329081:0.00523938:0.275724:0.456858:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074509 ES:SE:LP:AF:ID 0.0119822:0.0103301:0.60206:0.074509:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240954 ES:SE:LP:AF:ID -0.00868047:0.00598501:0.823909:0.240954:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913468 ES:SE:LP:AF:ID -0.00336507:0.00746524:0.187087:0.913468:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116339 ES:SE:LP:AF:ID -0.00180209:0.00501504:0.142668:0.116339:rs12238997
1 705882 rs72631875 G A . PASS AF=0.06729 ES:SE:LP:AF:ID -0.0128892:0.00734919:1.10237:0.06729:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515633 ES:SE:LP:AF:ID -0.00549309:0.00371524:0.853872:0.515633:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033005 ES:SE:LP:AF:ID -0.00244126:0.00936607:0.102373:0.033005:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036622 ES:SE:LP:AF:ID -0.0028642:0.00850744:0.130768:0.036622:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID -0.00264034:0.00847539:0.119186:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036436 ES:SE:LP:AF:ID -0.00281746:0.00853641:0.130768:0.036436:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016404 ES:SE:LP:AF:ID 0.00295263:0.0131453:0.0861861:0.016404:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036976 ES:SE:LP:AF:ID -0.00259146:0.00844183:0.119186:0.036976:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037072 ES:SE:LP:AF:ID -0.00147459:0.00841293:0.0655015:0.037072:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101211 ES:SE:LP:AF:ID 0.00328581:0.00612936:0.229148:0.101211:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959098 ES:SE:LP:AF:ID 0.000400057:0.00811427:0.0177288:0.959098:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031445 ES:SE:LP:AF:ID -0.00826824:0.0147328:0.244125:0.031445:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053259 ES:SE:LP:AF:ID 0.00946566:0.0117154:0.376751:0.053259:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036591 ES:SE:LP:AF:ID -0.00260704:0.00846733:0.119186:0.036591:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID -0.0027379:0.00839024:0.130768:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID 0.00218927:0.00434651:0.21467:0.843204:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055928 ES:SE:LP:AF:ID -0.011623:0.00703659:1.00436:0.055928:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122318 ES:SE:LP:AF:ID -0.00318578:0.00475741:0.30103:0.122318:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025709 ES:SE:LP:AF:ID -0.0251235:0.0117029:1.49485:0.025709:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121561 ES:SE:LP:AF:ID -0.00366454:0.00475942:0.356547:0.121561:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132344 ES:SE:LP:AF:ID -0.00467945:0.00469089:0.49485:0.132344:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011133 ES:SE:LP:AF:ID 0.0030051:0.0170565:0.0655015:0.011133:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005701 ES:SE:LP:AF:ID -0.00289994:0.022015:0.0457575:0.005701:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002266 ES:SE:LP:AF:ID 0.0493381:0.0370556:0.744727:0.002266:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001028 ES:SE:LP:AF:ID -0.0711508:0.060697:0.619789:0.001028:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036821 ES:SE:LP:AF:ID -0.00300426:0.00830548:0.142668:0.036821:rs146277091
1 752566 rs3094315 G A . PASS AF=0.83894 ES:SE:LP:AF:ID 0.00229758:0.00420932:0.229148:0.83894:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838569 ES:SE:LP:AF:ID 0.00227021:0.00420479:0.229148:0.838569:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869769 ES:SE:LP:AF:ID 0.00281984:0.00451182:0.275724:0.869769:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129883 ES:SE:LP:AF:ID -0.0031049:0.00452102:0.309804:0.129883:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037332 ES:SE:LP:AF:ID -0.00252085:0.00816467:0.119186:0.037332:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037575 ES:SE:LP:AF:ID -0.0025279:0.00811314:0.119186:0.037575:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869112 ES:SE:LP:AF:ID 0.00288945:0.00450299:0.283997:0.869112:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86921 ES:SE:LP:AF:ID 0.00303211:0.00450478:0.30103:0.86921:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037534 ES:SE:LP:AF:ID -0.00182431:0.00814817:0.0861861:0.037534:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869115 ES:SE:LP:AF:ID 0.00283511:0.0045029:0.275724:0.869115:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005123 ES:SE:LP:AF:ID -0.0173654:0.0231163:0.346787:0.005123:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005089 ES:SE:LP:AF:ID -0.0171251:0.0231769:0.337242:0.005089:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838021 ES:SE:LP:AF:ID 0.00222416:0.00419312:0.221849:0.838021:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037547 ES:SE:LP:AF:ID -0.00179981:0.00815965:0.0809219:0.037547:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838653 ES:SE:LP:AF:ID 0.00196405:0.00420491:0.19382:0.838653:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013775 ES:SE:LP:AF:ID -0.0146002:0.0146759:0.49485:0.013775:rs181660517