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_41204_E039.vcf.gz --id UKB-b:4226 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_E039.txt.gz --cohort_cases 9674 --cohort_controls 453336 --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": "2019-09-13T11:14:38.233152",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4226/ukb-b-4226.vcf.gz; Date=Sun May 10 12:56:17 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-4226/UKB-b-4226_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4226/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4226/UKB-b-4226_data.vcf.gz ...
Read summary statistics for 5902792 SNPs.
Dropped 2609 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, 1207885 SNPs remain.
After merging with regression SNP LD, 1207885 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0187 (0.0019)
Lambda GC: 1.1457
Mean Chi^2: 1.2123
Intercept: 1.0341 (0.0081)
Ratio: 0.1607 (0.038)
Analysis finished at Thu Oct 17 14:45:16 2019
Total time elapsed: 1.0m:11.37sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9231,
"inflation_factor": 1.1474,
"mean_EFFECT": -9.5676e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 36,
"n_p_sig": 8931,
"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": 53092,
"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": 1207885,
"ldsc_nsnp_merge_regression_ld": 1207885,
"ldsc_observed_scale_h2_beta": 0.0187,
"ldsc_observed_scale_h2_se": 0.0019,
"ldsc_intercept_beta": 1.0341,
"ldsc_intercept_se": 0.0081,
"ldsc_lambda_gc": 1.1457,
"ldsc_mean_chisq": 1.2123,
"ldsc_ratio": 0.1606
}
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 | 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 | 5900200 | 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 | 5902792 | 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 | 8.672337e+00 | 5.763114e+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 | 7.859148e+07 | 5.652932e+07 | 828.0000000 | 3.199664e+07 | 6.904926e+07 | 1.145198e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-06 | 5.014000e-04 | -0.0084244 | -2.915000e-04 | -2.100000e-06 | 2.877000e-04 | 8.475900e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.299000e-04 | 1.391000e-04 | 0.0002857 | 3.151000e-04 | 3.761000e-04 | 5.117000e-04 | 1.465700e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.773430e-01 | 2.954011e-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 | 4.773440e-01 | 2.953740e-01 | 0.0000000 | 2.161964e-01 | 4.711731e-01 | 7.332714e-01 | 1.000000e+00 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.154514e-01 | 2.522980e-01 | 0.0361800 | 1.011788e-01 | 2.349590e-01 | 4.810782e-01 | 9.638190e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 53092 | 0.9910056 | NA | NA | NA | NA | NA | NA | NA | 3.117169e-01 | 2.456626e-01 | 0.0000000 | 1.098240e-01 | 2.420130e-01 | 4.714460e-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 | 0.0009669 | 0.0005257 | 0.0659994 | 0.0658699 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0007674 | 0.0005208 | 0.1400000 | 0.1406017 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0000676 | 0.0008326 | 0.9400001 | 0.9352630 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002269 | 0.0005128 | 0.6600001 | 0.6581943 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0002294 | 0.0010111 | 0.8200001 | 0.8205196 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0003747 | 0.0005857 | 0.5199996 | 0.5223157 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0002670 | 0.0007307 | 0.7099994 | 0.7147803 | 0.913475 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0002651 | 0.0004908 | 0.5900000 | 0.5890916 | 0.116329 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0008122 | 0.0007193 | 0.2599998 | 0.2588330 | 0.067288 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0004946 | 0.0003636 | 0.1700000 | 0.1737571 | 0.515645 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | -0.0006419 | 0.0005683 | 0.2599998 | 0.2586596 | 0.073622 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0001593 | 0.0004387 | 0.7199992 | 0.7164951 | 0.137950 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0006879 | 0.0005695 | 0.2300001 | 0.2270789 | 0.073744 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0008843 | 0.0007631 | 0.2500000 | 0.2465146 | 0.041954 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0010757 | 0.0007618 | 0.1600000 | 0.1579669 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0006587 | 0.0005698 | 0.2500000 | 0.2476764 | 0.073235 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0004004 | 0.0006708 | 0.5500004 | 0.5506454 | 0.054460 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0003580 | 0.0006741 | 0.5999997 | 0.5953320 | 0.054089 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0006684 | 0.0005719 | 0.2399999 | 0.2424690 | 0.073071 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0003886 | 0.0003498 | 0.2700001 | 0.2665790 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 0.000966869:0.00052567:1.18046:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000767393:0.000520777:0.853872:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -6.76291e-05:0.000832614:0.0268721:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000226852:0.000512767:0.180456:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID 0.000229386:0.00101106:0.0861861:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -0.00037473:0.000585717:0.283997:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913475 ES:SE:LP:AF:ID -0.000267034:0.000730712:0.148742:0.913475:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID -0.000265133:0.000490848:0.229148:0.116329:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067288 ES:SE:LP:AF:ID -0.00081218:0.000719282:0.585027:0.067288:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 0.000494581:0.000363601:0.769551:0.515645:rs12029736
1 715265 rs12184267 C T . PASS AF=0.036624 ES:SE:LP:AF:ID 0.000770509:0.000832597:0.455932:0.036624:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036741 ES:SE:LP:AF:ID 0.000783382:0.000829447:0.468521:0.036741:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036439 ES:SE:LP:AF:ID 0.000722199:0.000835432:0.408935:0.036439:rs12184279
1 720381 rs116801199 G T . PASS AF=0.03698 ES:SE:LP:AF:ID 0.000785236:0.000826157:0.468521:0.03698:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037077 ES:SE:LP:AF:ID 0.00069929:0.000823328:0.39794:0.037077:rs12565286
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID 0.000714828:0.00059992:0.638272:0.1012:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959091 ES:SE:LP:AF:ID -0.000706818:0.000794081:0.431798:0.959091:rs2977670
1 725060 rs865924913 A T . PASS AF=0.053255 ES:SE:LP:AF:ID -0.000672959:0.00114681:0.251812:0.053255:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036594 ES:SE:LP:AF:ID 0.000629019:0.000828669:0.346787:0.036594:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03691 ES:SE:LP:AF:ID 0.000666104:0.000821126:0.376751:0.03691:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID -3.0121e-05:0.000425384:0.0268721:0.843204:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055912 ES:SE:LP:AF:ID 9.54351e-05:0.000688765:0.05061:0.055912:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID -0.00010669:0.000465618:0.0861861:0.122312:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID -0.000119606:0.000465814:0.09691:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID 0.000153956:0.000459107:0.130768:0.132335:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036825 ES:SE:LP:AF:ID 0.000600088:0.00081282:0.337242:0.036825:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -0.000217887:0.000411955:0.221849:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID -0.000205194:0.000411512:0.207608:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID -9.04871e-05:0.000441567:0.0757207:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID 9.18144e-05:0.000442471:0.0757207:0.129876:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037336 ES:SE:LP:AF:ID 0.000547199:0.000799037:0.309804:0.037336:rs12184312
1 754105 rs12184325 C T . PASS AF=0.03758 ES:SE:LP:AF:ID 0.000604751:0.000793989:0.346787:0.03758:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID -7.74405e-05:0.000440702:0.0655015:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID -5.99717e-05:0.000440877:0.05061:0.869215:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037538 ES:SE:LP:AF:ID 0.000564289:0.000797423:0.318759:0.037538:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID -7.73695e-05:0.000440694:0.0655015:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID -0.000198034:0.00041037:0.200659:0.838026:rs3115859
1 754629 rs10454459 A G . PASS AF=0.03755 ES:SE:LP:AF:ID 0.000585267:0.00079855:0.337242:0.03755:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID -0.00019457:0.000411523:0.19382:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID -0.000189975:0.000417089:0.187087:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID -0.00014371:0.000440184:0.130768:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID -0.000155123:0.000439076:0.142668:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID -0.000111019:0.000438234:0.09691:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID -0.000142524:0.000439435:0.124939:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID -0.000142715:0.000439469:0.124939:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID -0.000143993:0.000439479:0.130768:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID -0.000147883:0.000440686:0.130768:0.869584:rs3131954
1 759036 rs114525117 G A . PASS AF=0.037599 ES:SE:LP:AF:ID 0.000554694:0.000793874:0.318759:0.037599:rs114525117
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID -0.000256972:0.000409592:0.275724:0.838306:rs1048488
1 760998 rs148828841 C A . PASS AF=0.037036 ES:SE:LP:AF:ID 0.00056772:0.00080873:0.318759:0.037036:rs148828841