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_5265.vcf.gz --id UKB-b:4717 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5265.txt.gz --cohort_controls 97465 --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": "2019-09-12T23:17:01.939288",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4717/ukb-b-4717.vcf.gz; Date=Sat May 9 20:54:24 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-4717/UKB-b-4717_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4717/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:39 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4717/UKB-b-4717_data.vcf.gz ...
Read summary statistics for 9006125 SNPs.
Dropped 8731 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, 1287218 SNPs remain.
After merging with regression SNP LD, 1287218 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2472 (0.0151)
Lambda GC: 1.2895
Mean Chi^2: 1.5143
Intercept: 1.0582 (0.0101)
Ratio: 0.1132 (0.0196)
Analysis finished at Thu Oct 17 14:46:14 2019
Total time elapsed: 1.0m:35.5sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9479,
"inflation_factor": 1.1999,
"mean_EFFECT": -0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 131,
"n_p_sig": 10351,
"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": 93411,
"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": 1287218,
"ldsc_nsnp_merge_regression_ld": 1287218,
"ldsc_observed_scale_h2_beta": 0.2472,
"ldsc_observed_scale_h2_se": 0.0151,
"ldsc_intercept_beta": 1.0582,
"ldsc_intercept_se": 0.0101,
"ldsc_lambda_gc": 1.2895,
"ldsc_mean_chisq": 1.5143,
"ldsc_ratio": 0.1132
}
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 | 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 | 8997433 | 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 | 9006125 | 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 | 8.643115e+00 | 5.758251e+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 | 7.878828e+07 | 5.634116e+07 | 828.0000000 | 3.242940e+07 | 6.934977e+07 | 1.145449e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -8.500000e-05 | 1.536240e-02 | -0.3659250 | -6.339900e-03 | -5.330000e-05 | 6.255600e-03 | 2.287590e-01 | ▁▁▂▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.170060e-02 | 8.570500e-03 | 0.0042463 | 5.071200e-03 | 7.763400e-03 | 1.604950e-02 | 9.908410e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.679262e-01 | 2.982712e-01 | 0.0000000 | 2.000000e-01 | 4.600002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.679286e-01 | 2.982471e-01 | 0.0000000 | 2.019786e-01 | 4.574278e-01 | 7.264095e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.202507e-01 | 2.585404e-01 | 0.0035920 | 2.053500e-02 | 1.015670e-01 | 3.473310e-01 | 9.964080e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 93411 | 0.9896281 | NA | NA | NA | NA | NA | NA | NA | 2.205580e-01 | 2.504474e-01 | 0.0000000 | 1.797120e-02 | 1.190100e-01 | 3.456470e-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 | 0.0051784 | 0.0078539 | 0.5099998 | 0.5096805 | 0.623865 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0029167 | 0.0077999 | 0.7099994 | 0.7084449 | 0.398679 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0212907 | 0.0123748 | 0.0850002 | 0.0853437 | 0.103994 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0024458 | 0.0076654 | 0.7499995 | 0.7496724 | 0.455564 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0190297 | 0.0150072 | 0.2000000 | 0.2047844 | 0.074844 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0076311 | 0.0087754 | 0.3800004 | 0.3845166 | 0.240283 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0010346 | 0.0108685 | 0.9199999 | 0.9241626 | 0.912849 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0060190 | 0.0072905 | 0.4100001 | 0.4090294 | 0.117836 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0053507 | 0.0106672 | 0.6200004 | 0.6159468 | 0.067682 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0038667 | 0.0054077 | 0.4700002 | 0.4745818 | 0.514115 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0021530 | 0.0065641 | 0.7400005 | 0.7429191 | 0.137113 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0016168 | 0.0085587 | 0.8499999 | 0.8501667 | 0.072486 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0018729 | 0.0113914 | 0.8700001 | 0.8694084 | 0.041684 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0051109 | 0.0114508 | 0.6600001 | 0.6553573 | 0.049030 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0018722 | 0.0085612 | 0.8300000 | 0.8268966 | 0.072021 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0037732 | 0.0100104 | 0.7099994 | 0.7062234 | 0.054470 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0029969 | 0.0100641 | 0.7700005 | 0.7658732 | 0.054067 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0009146 | 0.0085917 | 0.9199999 | 0.9152277 | 0.071886 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0119917 | 0.0202326 | 0.5500004 | 0.5533873 | 0.020049 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0007619 | 0.0052212 | 0.8800001 | 0.8839749 | 0.298167 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623865 ES:SE:LP:AF:ID 0.00517838:0.00785394:0.29243:0.623865:rs10399793
1 54676 rs2462492 C T . PASS AF=0.398679 ES:SE:LP:AF:ID -0.00291673:0.00779988:0.148742:0.398679:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103994 ES:SE:LP:AF:ID 0.0212907:0.0123748:1.07058:0.103994:rs114608975
1 91536 rs6702460 G T . PASS AF=0.455564 ES:SE:LP:AF:ID -0.0024458:0.00766537:0.124939:0.455564:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074844 ES:SE:LP:AF:ID -0.0190297:0.0150072:0.69897:0.074844:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240283 ES:SE:LP:AF:ID -0.00763111:0.00877538:0.420216:0.240283:rs6680723
1 546697 rs12025928 A G . PASS AF=0.912849 ES:SE:LP:AF:ID -0.00103459:0.0108685:0.0362122:0.912849:rs12025928
1 693731 rs12238997 A G . PASS AF=0.117836 ES:SE:LP:AF:ID -0.00601905:0.00729049:0.387216:0.117836:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067682 ES:SE:LP:AF:ID 0.0053507:0.0106672:0.207608:0.067682:rs72631875
1 706368 rs12029736 A G . PASS AF=0.514115 ES:SE:LP:AF:ID 0.00386673:0.00540768:0.327902:0.514115:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033658 ES:SE:LP:AF:ID 0.0345621:0.0134895:2:0.033658:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037308 ES:SE:LP:AF:ID 0.0334579:0.0122712:2.19382:0.037308:rs12184267
1 715367 rs12184277 A G . PASS AF=0.03739 ES:SE:LP:AF:ID 0.0329086:0.0122312:2.14874:0.03739:rs12184277
1 717485 rs12184279 C A . PASS AF=0.037088 ES:SE:LP:AF:ID 0.0337213:0.0123154:2.20761:0.037088:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016799 ES:SE:LP:AF:ID -0.032392:0.0189355:1.06048:0.016799:rs144155419
1 720381 rs116801199 G T . PASS AF=0.03765 ES:SE:LP:AF:ID 0.0336503:0.0121787:2.24413:0.03765:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037735 ES:SE:LP:AF:ID 0.0328214:0.0121414:2.16115:0.037735:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101347 ES:SE:LP:AF:ID -0.000950907:0.00894232:0.0362122:0.101347:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958076 ES:SE:LP:AF:ID -0.0226261:0.0116864:1.27572:0.958076:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031781 ES:SE:LP:AF:ID -0.0127847:0.0213931:0.259637:0.031781:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052542 ES:SE:LP:AF:ID -0.0066484:0.017277:0.154902:0.052542:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037194 ES:SE:LP:AF:ID 0.0322256:0.0122275:2.07572:0.037194:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037508 ES:SE:LP:AF:ID 0.0331829:0.0121233:2.20761:0.037508:rs116720794
1 729679 rs4951859 C G . PASS AF=0.840954 ES:SE:LP:AF:ID -0.00194018:0.00631657:0.119186:0.840954:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056072 ES:SE:LP:AF:ID -0.00456482:0.0102853:0.180456:0.056072:rs148120343
1 731718 rs58276399 T C . PASS AF=0.123718 ES:SE:LP:AF:ID -0.00207654:0.00692154:0.119186:0.123718:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025846 ES:SE:LP:AF:ID -0.0107688:0.0170042:0.275724:0.025846:rs369030935
1 734349 rs141242758 T C . PASS AF=0.122917 ES:SE:LP:AF:ID -0.00234743:0.00692512:0.136677:0.122917:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133582 ES:SE:LP:AF:ID 0.0109969:0.00683026:0.958607:0.133582:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011213 ES:SE:LP:AF:ID 0.026138:0.0247438:0.537602:0.011213:rs181876450
1 740284 rs61770167 C T . PASS AF=0.006042 ES:SE:LP:AF:ID 0.0538221:0.0310303:1.08092:0.006042:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037471 ES:SE:LP:AF:ID 0.0316288:0.0119907:2.08092:0.037471:rs146277091
1 752566 rs3094315 G A . PASS AF=0.836757 ES:SE:LP:AF:ID -0.00261808:0.00611183:0.173925:0.836757:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836316 ES:SE:LP:AF:ID -0.00244705:0.00610486:0.161151:0.836316:rs3131972
1 753405 rs3115860 C A . PASS AF=0.867994 ES:SE:LP:AF:ID 0.00319997:0.00654747:0.200659:0.867994:rs3115860
1 753541 rs2073813 G A . PASS AF=0.13167 ES:SE:LP:AF:ID -0.00284055:0.00656185:0.173925:0.13167:rs2073813
1 754063 rs12184312 G T . PASS AF=0.0379 ES:SE:LP:AF:ID 0.0296452:0.0118034:1.92082:0.0379:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038147 ES:SE:LP:AF:ID 0.0306745:0.0117304:2.05061:0.038147:rs12184325
1 754182 rs3131969 A G . PASS AF=0.867294 ES:SE:LP:AF:ID 0.00363914:0.00653438:0.236572:0.867294:rs3131969
1 754192 rs3131968 A G . PASS AF=0.867397 ES:SE:LP:AF:ID 0.00352803:0.00653723:0.229148:0.867397:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038097 ES:SE:LP:AF:ID 0.0295772:0.0117779:1.92082:0.038097:rs12184313
1 754334 rs3131967 T C . PASS AF=0.867292 ES:SE:LP:AF:ID 0.00366723:0.00653398:0.244125:0.867292:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005052 ES:SE:LP:AF:ID 0.0126874:0.0340139:0.148742:0.005052:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005019 ES:SE:LP:AF:ID 0.0105946:0.0341063:0.119186:0.005019:rs142682604
1 754503 rs3115859 G A . PASS AF=0.835859 ES:SE:LP:AF:ID -0.00208064:0.00609189:0.136677:0.835859:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038115 ES:SE:LP:AF:ID 0.0301055:0.0117936:1.95861:0.038115:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83646 ES:SE:LP:AF:ID -0.00169911:0.00610819:0.107905:0.83646:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013132 ES:SE:LP:AF:ID 0.00777803:0.0219487:0.142668:0.013132:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005506 ES:SE:LP:AF:ID 0.00647193:0.0330709:0.0757207:0.005506:rs184270342
1 755775 rs3131965 A G . PASS AF=0.83774 ES:SE:LP:AF:ID -0.00131575:0.00619152:0.0809219:0.83774:rs3131965