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_41202_K409.vcf.gz --id UKB-b:5369 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41202_K409.txt.gz --cohort_cases 11789 --cohort_controls 451221 --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": "2019-09-12T20:24:29.439338",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-5369/ukb-b-5369.vcf.gz; Date=Sat May 9 17:47:51 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-5369/UKB-b-5369_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5369/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5369/UKB-b-5369_data.vcf.gz ...
Read summary statistics for 6189100 SNPs.
Dropped 3002 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, 1226615 SNPs remain.
After merging with regression SNP LD, 1226615 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.016 (0.0017)
Lambda GC: 1.1089
Mean Chi^2: 1.1478
Intercept: 0.9989 (0.0079)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:46:20 2019
Total time elapsed: 1.0m:3.38sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9271,
"inflation_factor": 1.0966,
"mean_EFFECT": -3.4499e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 14,
"n_p_sig": 962,
"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": 56503,
"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": 1226615,
"ldsc_nsnp_merge_regression_ld": 1226615,
"ldsc_observed_scale_h2_beta": 0.016,
"ldsc_observed_scale_h2_se": 0.0017,
"ldsc_intercept_beta": 0.9989,
"ldsc_intercept_se": 0.0079,
"ldsc_lambda_gc": 1.1089,
"ldsc_mean_chisq": 1.1478,
"ldsc_ratio": -0.0074
}
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 | 6186117 | 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 | 6189100 | 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 | 8.668864e+00 | 5.762814e+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 | 7.859410e+07 | 5.651835e+07 | 828.0000000 | 3.200389e+07 | 6.902564e+07 | 1.145317e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.400000e-06 | 5.502000e-04 | -0.0052351 | -3.229000e-04 | -3.300000e-06 | 3.173000e-04 | 4.354000e-03 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.904000e-04 | 1.761000e-04 | 0.0003120 | 3.461000e-04 | 4.207000e-04 | 5.920000e-04 | 1.736600e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.834198e-01 | 2.940520e-01 | 0.0000000 | 2.300001e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.834207e-01 | 2.940261e-01 | 0.0000000 | 2.253816e-01 | 4.788414e-01 | 7.383871e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.045134e-01 | 2.550807e-01 | 0.0296890 | 8.870400e-02 | 2.199130e-01 | 4.687912e-01 | 9.703110e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 56503 | 0.9908706 | NA | NA | NA | NA | NA | NA | NA | 3.014559e-01 | 2.478981e-01 | 0.0000000 | 9.804310e-02 | 2.280350e-01 | 4.596650e-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 | -0.0012836 | 0.0005743 | 0.0250000 | 0.0254081 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0003095 | 0.0005689 | 0.5900000 | 0.5863982 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0007418 | 0.0009096 | 0.4100001 | 0.4147506 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0003359 | 0.0005602 | 0.5500004 | 0.5487042 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0007810 | 0.0011045 | 0.4799997 | 0.4794869 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0001139 | 0.0006399 | 0.8600001 | 0.8586829 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0010263 | 0.0007983 | 0.2000000 | 0.1985369 | 0.913475 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0000568 | 0.0005362 | 0.9199999 | 0.9156550 | 0.116329 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0010718 | 0.0007858 | 0.1700000 | 0.1725660 | 0.067288 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000429 | 0.0003972 | 0.9100000 | 0.9140028 | 0.515645 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | -0.0002073 | 0.0006207 | 0.7400005 | 0.7384341 | 0.073622 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0001492 | 0.0004792 | 0.7600007 | 0.7555584 | 0.137950 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0001839 | 0.0006220 | 0.7700005 | 0.7674380 | 0.073744 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0000759 | 0.0008335 | 0.9299999 | 0.9274813 | 0.041954 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0005289 | 0.0008321 | 0.5300002 | 0.5250008 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0003080 | 0.0006224 | 0.6200004 | 0.6206904 | 0.073235 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0008173 | 0.0007327 | 0.2599998 | 0.2646775 | 0.054460 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0007645 | 0.0007363 | 0.2999998 | 0.2991136 | 0.054089 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0003099 | 0.0006246 | 0.6200004 | 0.6198492 | 0.073071 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0005624 | 0.0003820 | 0.1400000 | 0.1410181 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.00128355:0.000574259:1.60206:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000309525:0.000568914:0.229148:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID 0.000741815:0.000909575:0.387216:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -0.000335932:0.000560164:0.259637:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID -0.00078103:0.00110451:0.318759:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -0.000113927:0.000639856:0.0655015:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913475 ES:SE:LP:AF:ID 0.00102634:0.000798254:0.69897:0.913475:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID -5.67901e-05:0.000536219:0.0362122:0.116329:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067288 ES:SE:LP:AF:ID -0.00107179:0.000785767:0.769551:0.067288:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -4.28951e-05:0.00039721:0.0409586:0.515645:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033009 ES:SE:LP:AF:ID 0.000185107:0.00100133:0.0705811:0.033009:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036624 ES:SE:LP:AF:ID 0.000403111:0.000909556:0.180456:0.036624:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036741 ES:SE:LP:AF:ID 0.000357072:0.000906115:0.161151:0.036741:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036439 ES:SE:LP:AF:ID 0.00043508:0.000912653:0.200659:0.036439:rs12184279
1 720381 rs116801199 G T . PASS AF=0.03698 ES:SE:LP:AF:ID 0.000256317:0.000902521:0.107905:0.03698:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037077 ES:SE:LP:AF:ID 0.000259568:0.00089943:0.113509:0.037077:rs12565286
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID 0.000552171:0.000655372:0.39794:0.1012:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959091 ES:SE:LP:AF:ID 0.000311613:0.00086748:0.142668:0.959091:rs2977670
1 724849 rs12126395 C A . PASS AF=0.03145 ES:SE:LP:AF:ID -0.00114524:0.00157491:0.327902:0.03145:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053255 ES:SE:LP:AF:ID -0.000350885:0.00125281:0.107905:0.053255:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036594 ES:SE:LP:AF:ID 0.000310241:0.000905265:0.136677:0.036594:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03691 ES:SE:LP:AF:ID 0.000281756:0.000897025:0.124939:0.03691:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID 0.000167036:0.000464703:0.142668:0.843204:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055912 ES:SE:LP:AF:ID -0.000295005:0.000752429:0.154902:0.055912:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID 0.000118598:0.000508657:0.0861861:0.122312:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID 0.000117629:0.000508871:0.0861861:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID -0.000284172:0.000501543:0.244125:0.132335:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036825 ES:SE:LP:AF:ID 0.000238266:0.00088795:0.102373:0.036825:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -4.37035e-05:0.000450033:0.0362122:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID -4.07314e-05:0.000449549:0.0315171:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID -0.000122459:0.000482382:0.09691:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID 0.00016586:0.000483369:0.136677:0.129876:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037336 ES:SE:LP:AF:ID 0.000151754:0.000872894:0.0655015:0.037336:rs12184312
1 754105 rs12184325 C T . PASS AF=0.03758 ES:SE:LP:AF:ID 0.000228581:0.000867379:0.102373:0.03758:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID -9.42882e-05:0.000481437:0.0757207:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID -0.000100958:0.000481628:0.0809219:0.869215:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037538 ES:SE:LP:AF:ID 0.00022005:0.000871131:0.09691:0.037538:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID -9.53059e-05:0.000481428:0.0757207:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID 3.4747e-05:0.000448301:0.0268721:0.838026:rs3115859
1 754629 rs10454459 A G . PASS AF=0.03755 ES:SE:LP:AF:ID 0.000247879:0.000872362:0.107905:0.03755:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID 2.85809e-05:0.000449561:0.0222764:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID 6.88342e-06:0.000455641:0.00436481:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID -9.32162e-05:0.000480871:0.0705811:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID -5.08564e-05:0.000479661:0.0362122:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID -9.2307e-05:0.000478741:0.0705811:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID -8.31654e-05:0.000480054:0.0655015:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID -8.28996e-05:0.00048009:0.0655015:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID -8.296e-05:0.000480101:0.0655015:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID -7.72829e-05:0.00048142:0.0604807:0.869584:rs3131954
1 759036 rs114525117 G A . PASS AF=0.037599 ES:SE:LP:AF:ID 5.89734e-06:0.000867254:0.00436481:0.037599:rs114525117