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_20002_1265.vcf.gz --id UKB-b:16868 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1265.txt.gz --cohort_cases 13597 --cohort_controls 449336 --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": "2019-09-12T22:31:45.942445",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16868/ukb-b-16868.vcf.gz; Date=Sun May 10 05:48:54 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-16868/UKB-b-16868_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16868/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16868/UKB-b-16868_data.vcf.gz ...
Read summary statistics for 6386693 SNPs.
Dropped 3343 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, 1237765 SNPs remain.
After merging with regression SNP LD, 1237765 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0175 (0.0015)
Lambda GC: 1.1484
Mean Chi^2: 1.1863
Intercept: 1.0237 (0.0082)
Ratio: 0.1272 (0.0442)
Analysis finished at Thu Oct 17 14:46:29 2019
Total time elapsed: 1.0m:9.98sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9295,
"inflation_factor": 1.1474,
"mean_EFFECT": -2.8978e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 16,
"n_p_sig": 804,
"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": 58533,
"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": 1237765,
"ldsc_nsnp_merge_regression_ld": 1237765,
"ldsc_observed_scale_h2_beta": 0.0175,
"ldsc_observed_scale_h2_se": 0.0015,
"ldsc_intercept_beta": 1.0237,
"ldsc_intercept_se": 0.0082,
"ldsc_lambda_gc": 1.1484,
"ldsc_mean_chisq": 1.1863,
"ldsc_ratio": 0.1272
}
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 | 6383371 | 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 | 6386693 | 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 | 8.666984e+00 | 5.763469e+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 | 7.857469e+07 | 5.649219e+07 | 828.0000000 | 3.201966e+07 | 6.901473e+07 | 1.144853e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.900000e-06 | 6.201000e-04 | -0.0052863 | -3.622000e-04 | -3.700000e-06 | 3.557000e-04 | 5.691300e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.473000e-04 | 2.104000e-04 | 0.0003375 | 3.759000e-04 | 4.628000e-04 | 6.675000e-04 | 2.925400e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.794392e-01 | 2.948129e-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 | 4.794368e-01 | 2.947875e-01 | 0.0000000 | 2.187902e-01 | 4.726705e-01 | 7.346643e-01 | 9.999995e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.972158e-01 | 2.566320e-01 | 0.0257410 | 8.093000e-02 | 2.095190e-01 | 4.597730e-01 | 9.742580e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 58533 | 0.9908352 | NA | NA | NA | NA | NA | NA | NA | 2.945249e-01 | 2.491751e-01 | 0.0000000 | 9.005590e-02 | 2.186500e-01 | 4.514780e-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 | 0.0004739 | 0.0006210 | 0.4500005 | 0.4454400 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001269 | 0.0006152 | 0.8400000 | 0.8365546 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0014394 | 0.0009837 | 0.1400000 | 0.1433816 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0003155 | 0.0006058 | 0.5999997 | 0.6025567 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0013059 | 0.0011944 | 0.2700001 | 0.2742519 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0003311 | 0.0006920 | 0.6300007 | 0.6323183 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0002080 | 0.0008633 | 0.8100000 | 0.8095770 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000916 | 0.0005799 | 0.8700001 | 0.8745207 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0007800 | 0.0008498 | 0.3599996 | 0.3586529 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001347 | 0.0004296 | 0.7499995 | 0.7539268 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | 0.0003252 | 0.0006713 | 0.6300007 | 0.6280759 | 0.073625 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0000370 | 0.0005182 | 0.9400001 | 0.9430838 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0003851 | 0.0006727 | 0.5700002 | 0.5669424 | 0.073747 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0001162 | 0.0009014 | 0.9000000 | 0.8974324 | 0.041955 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0000186 | 0.0009000 | 0.9800000 | 0.9834843 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0003576 | 0.0006731 | 0.5999997 | 0.5952315 | 0.073238 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0005962 | 0.0007925 | 0.4500005 | 0.4518306 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0006286 | 0.0007963 | 0.4299995 | 0.4298759 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0003106 | 0.0006755 | 0.6499995 | 0.6456214 | 0.073073 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000167 | 0.0004132 | 0.9699999 | 0.9677266 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.000473868:0.000621027:0.346787:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000126926:0.000615242:0.0757207:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.00143939:0.000983651:0.853872:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -0.000315452:0.000605791:0.221849:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID 0.00130592:0.00119445:0.568636:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.000331081:0.000691963:0.200659:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID -0.000208022:0.000863263:0.091515:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 9.15773e-05:0.000579901:0.0604807:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID -0.000780041:0.000849778:0.443698:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 0.000134653:0.000429562:0.124939:0.51565:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID -0.00169842:0.00108296:0.920819:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036621 ES:SE:LP:AF:ID -0.00157192:0.000983673:0.958607:0.036621:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID -0.00159266:0.000979951:1:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036437 ES:SE:LP:AF:ID -0.00148335:0.000987013:0.886057:0.036437:rs12184279
1 720381 rs116801199 G T . PASS AF=0.036976 ES:SE:LP:AF:ID -0.00154841:0.00097608:0.958607:0.036976:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037072 ES:SE:LP:AF:ID -0.00157054:0.000972736:0.958607:0.037072:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.000629746:0.00070875:0.431798:0.101199:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959096 ES:SE:LP:AF:ID 0.00146117:0.000938185:0.920819:0.959096:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031451 ES:SE:LP:AF:ID 0.00383266:0.00170308:1.61979:0.031451:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053254 ES:SE:LP:AF:ID 0.00104011:0.00135487:0.356547:0.053254:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03659 ES:SE:LP:AF:ID -0.00162267:0.000979033:1.01323:0.03659:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID -0.00156405:0.000970121:0.958607:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID 0.000457642:0.000502558:0.443698:0.843212:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID 0.000725912:0.00081372:0.431798:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 6.23028e-05:0.000550093:0.0409586:0.122307:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 4.62769e-05:0.000550324:0.0315171:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID -0.000417955:0.000542399:0.356547:0.13233:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036821 ES:SE:LP:AF:ID -0.00133727:0.000960309:0.79588:0.036821:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID 0.000381938:0.000486692:0.366532:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID 0.000385644:0.000486168:0.366532:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -5.16161e-05:0.000521677:0.0362122:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID -4.14627e-05:0.000522745:0.0268721:0.129871:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037332 ES:SE:LP:AF:ID -0.00136016:0.000944026:0.823909:0.037332:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037576 ES:SE:LP:AF:ID -0.001358:0.000938061:0.823909:0.037576:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -3.22947e-05:0.000520656:0.0222764:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -4.34464e-05:0.000520862:0.0315171:0.869221:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037534 ES:SE:LP:AF:ID -0.00142817:0.000942119:0.886057:0.037534:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -3.41114e-05:0.000520645:0.0222764:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID 0.000417184:0.000484818:0.408935:0.838033:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037547 ES:SE:LP:AF:ID -0.00142454:0.00094345:0.886057:0.037547:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID 0.000395237:0.000486181:0.376751:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID 0.000352984:0.000492756:0.327902:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID -5.53658e-05:0.000520043:0.0362122:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -5.08236e-05:0.000518735:0.0362122:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID -5.95009e-05:0.000517741:0.0409586:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID -6.46931e-05:0.00051916:0.0457575:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID -6.53049e-05:0.0005192:0.0457575:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID -6.23192e-05:0.000519211:0.0457575:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID -5.33205e-05:0.000520637:0.0362122:0.869589:rs3131954
1 759036 rs114525117 G A . PASS AF=0.037596 ES:SE:LP:AF:ID -0.000901054:0.000937925:0.468521:0.037596:rs114525117