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_1757.vcf.gz --id UKB-b:2148 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1757.txt.gz --cohort_controls 423999 --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": "2019-09-13T07:57:51.800329",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-2148/ukb-b-2148.vcf.gz; Date=Sat May 9 20:54:29 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-2148/UKB-b-2148_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2148/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:20 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2148/UKB-b-2148_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: 0.0682 (0.0029)
Lambda GC: 1.4709
Mean Chi^2: 1.624
Intercept: 1.0551 (0.0097)
Ratio: 0.0884 (0.0156)
Analysis finished at Thu Oct 17 14:44:00 2019
Total time elapsed: 1.0m:40.32sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.3107,
"mean_EFFECT": -0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 79,
"n_p_sig": 5440,
"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": 0.0682,
"ldsc_observed_scale_h2_se": 0.0029,
"ldsc_intercept_beta": 1.0551,
"ldsc_intercept_se": 0.0097,
"ldsc_lambda_gc": 1.4709,
"ldsc_mean_chisq": 1.624,
"ldsc_ratio": 0.0883
}
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 | -8.440000e-05 | 5.245100e-03 | -0.0785092 | -1.797400e-03 | -2.400000e-05 | 1.722900e-03 | 8.847650e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.653200e-03 | 3.455600e-03 | 0.0010229 | 1.251800e-03 | 2.099200e-03 | 4.843200e-03 | 5.369720e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.566819e-01 | 3.003683e-01 | 0.0000000 | 1.900002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.566837e-01 | 3.003428e-01 | 0.0000000 | 1.862112e-01 | 4.405433e-01 | 7.168658e-01 | 9.999998e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035079e-01 | 2.568582e-01 | 0.0009740 | 1.317000e-02 | 7.792100e-02 | 3.164540e-01 | 9.990190e-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 | -0.0021385 | 0.0018808 | 0.2599998 | 0.2555480 | 0.623669 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0008256 | 0.0018641 | 0.6600001 | 0.6578266 | 0.400388 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0033662 | 0.0029794 | 0.2599998 | 0.2585529 | 0.103550 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0011566 | 0.0018347 | 0.5300002 | 0.5284521 | 0.456806 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0034104 | 0.0036148 | 0.3500000 | 0.3454389 | 0.074547 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0039518 | 0.0020968 | 0.0589997 | 0.0594759 | 0.240967 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0030514 | 0.0026124 | 0.2399999 | 0.2428001 | 0.913343 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0009394 | 0.0017551 | 0.5900000 | 0.5925052 | 0.116520 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0007452 | 0.0025722 | 0.7700005 | 0.7720331 | 0.067330 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0005856 | 0.0013010 | 0.6499995 | 0.6526179 | 0.515630 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0016544 | 0.0027334 | 0.5500004 | 0.5450184 | 0.041918 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0046189 | 0.0126840 | 0.7199992 | 0.7157447 | 0.001950 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0036055 | 0.0125722 | 0.7700005 | 0.7742762 | 0.001997 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0010529 | 0.0027300 | 0.6999999 | 0.6997348 | 0.049664 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0002701 | 0.0020413 | 0.8900000 | 0.8947289 | 0.073153 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0001051 | 0.0024041 | 0.9699999 | 0.9651330 | 0.054369 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0002982 | 0.0024159 | 0.9000000 | 0.9017529 | 0.053993 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0001512 | 0.0020486 | 0.9400001 | 0.9411693 | 0.072991 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0063430 | 0.0048269 | 0.1900002 | 0.1888121 | 0.020025 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0027153 | 0.0012525 | 0.0299999 | 0.0301622 | 0.298082 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623669 ES:SE:LP:AF:ID -0.00213846:0.00188083:0.585027:0.623669:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400388 ES:SE:LP:AF:ID 0.000825635:0.00186409:0.180456:0.400388:rs2462492
1 86028 rs114608975 T C . PASS AF=0.10355 ES:SE:LP:AF:ID 0.00336618:0.0029794:0.585027:0.10355:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456806 ES:SE:LP:AF:ID 0.00115656:0.00183473:0.275724:0.456806:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074547 ES:SE:LP:AF:ID 0.00341042:0.00361475:0.455932:0.074547:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240967 ES:SE:LP:AF:ID -0.00395179:0.00209682:1.22915:0.240967:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913343 ES:SE:LP:AF:ID 0.00305135:0.00261242:0.619789:0.913343:rs12025928
1 693731 rs12238997 A G . PASS AF=0.11652 ES:SE:LP:AF:ID -0.000939351:0.00175511:0.229148:0.11652:rs12238997
1 705882 rs72631875 G A . PASS AF=0.06733 ES:SE:LP:AF:ID -0.000745203:0.00257218:0.113509:0.06733:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51563 ES:SE:LP:AF:ID 0.000585617:0.001301:0.187087:0.51563:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033018 ES:SE:LP:AF:ID -0.00225065:0.00327927:0.309804:0.033018:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036639 ES:SE:LP:AF:ID -0.00204467:0.00297865:0.309804:0.036639:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036759 ES:SE:LP:AF:ID -0.00209281:0.00296712:0.318759:0.036759:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036453 ES:SE:LP:AF:ID -0.00194888:0.00298879:0.29243:0.036453:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016407 ES:SE:LP:AF:ID -0.00288822:0.00460323:0.275724:0.016407:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036994 ES:SE:LP:AF:ID -0.00168739:0.00295572:0.244125:0.036994:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037088 ES:SE:LP:AF:ID -0.00151924:0.00294571:0.21467:0.037088:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101237 ES:SE:LP:AF:ID -0.00200341:0.00214587:0.455932:0.101237:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959048 ES:SE:LP:AF:ID 0.00319456:0.00283982:0.585027:0.959048:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031452 ES:SE:LP:AF:ID -0.00249718:0.00515459:0.200659:0.031452:rs12126395
1 725060 rs865924913 A T . PASS AF=0.05325 ES:SE:LP:AF:ID 0.00167277:0.0041027:0.167491:0.05325:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03661 ES:SE:LP:AF:ID -0.00180054:0.0029645:0.267606:0.03661:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036917 ES:SE:LP:AF:ID -0.00147687:0.00293819:0.207608:0.036917:rs116720794
1 729679 rs4951859 C G . PASS AF=0.84302 ES:SE:LP:AF:ID 0.00215043:0.0015213:0.79588:0.84302:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055955 ES:SE:LP:AF:ID -0.00137496:0.00246395:0.236572:0.055955:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122523 ES:SE:LP:AF:ID -0.00138587:0.00166494:0.387216:0.122523:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025644 ES:SE:LP:AF:ID -0.000828685:0.00410642:0.0757207:0.025644:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121755 ES:SE:LP:AF:ID -0.00143912:0.0016657:0.408935:0.121755:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132484 ES:SE:LP:AF:ID -0.00184349:0.00164181:0.585027:0.132484:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011125 ES:SE:LP:AF:ID 0.0144542:0.00597548:1.79588:0.011125:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005744 ES:SE:LP:AF:ID 0.000313089:0.00767779:0.0132283:0.005744:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002283 ES:SE:LP:AF:ID 0.000970801:0.012919:0.0268721:0.002283:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001045 ES:SE:LP:AF:ID -0.00264991:0.0210287:0.0457575:0.001045:rs139221807
1 752478 rs146277091 G A . PASS AF=0.03684 ES:SE:LP:AF:ID -0.0017969:0.0029079:0.267606:0.03684:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838737 ES:SE:LP:AF:ID 0.00270995:0.00147307:1.18046:0.838737:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838367 ES:SE:LP:AF:ID 0.0027655:0.00147149:1.22185:0.838367:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869533 ES:SE:LP:AF:ID 0.00245115:0.00157868:0.920819:0.869533:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130111 ES:SE:LP:AF:ID -0.00245809:0.00158185:0.920819:0.130111:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037354 ES:SE:LP:AF:ID -0.0014351:0.00285847:0.207608:0.037354:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037599 ES:SE:LP:AF:ID -0.00151423:0.0028404:0.229148:0.037599:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868872 ES:SE:LP:AF:ID 0.0025621:0.00157554:1:0.868872:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868965 ES:SE:LP:AF:ID 0.00250567:0.00157612:0.958607:0.868965:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037557 ES:SE:LP:AF:ID -0.00147372:0.00285262:0.21467:0.037557:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868875 ES:SE:LP:AF:ID 0.00256009:0.00157551:1:0.868875:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005138 ES:SE:LP:AF:ID -0.0178206:0.00809007:1.55284:0.005138:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005104 ES:SE:LP:AF:ID -0.0178012:0.00811068:1.55284:0.005104:rs142682604
1 754503 rs3115859 G A . PASS AF=0.837827 ES:SE:LP:AF:ID 0.00279173:0.00146749:1.24413:0.837827:rs3115859
1 754629 rs10454459 A G . PASS AF=0.03757 ES:SE:LP:AF:ID -0.00140035:0.00285667:0.207608:0.03757:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838455 ES:SE:LP:AF:ID 0.00279282:0.00147157:1.23657:0.838455:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013746 ES:SE:LP:AF:ID -0.00503692:0.00514368:0.481486:0.013746:rs181660517