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_20111_5.vcf.gz --id UKB-b:12227 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20111_5.txt.gz --cohort_cases 16586 --cohort_controls 345223 --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": "2019-09-13T11:49:08.590560",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12227/ukb-b-12227.vcf.gz; Date=Sat May 9 19:01:40 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-12227/UKB-b-12227_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12227/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12227/UKB-b-12227_data.vcf.gz ...
Read summary statistics for 6666971 SNPs.
Dropped 3785 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, 1251331 SNPs remain.
After merging with regression SNP LD, 1251331 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0056 (0.0015)
Lambda GC: 1.0913
Mean Chi^2: 1.0928
Intercept: 1.052 (0.0075)
Ratio: 0.5604 (0.0809)
Analysis finished at Thu Oct 17 14:42:44 2019
Total time elapsed: 1.0m:17.66sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9326,
"inflation_factor": 1.0966,
"mean_EFFECT": -1.8612e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 6,
"n_p_sig": 89,
"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": 61205,
"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": 1251331,
"ldsc_nsnp_merge_regression_ld": 1251331,
"ldsc_observed_scale_h2_beta": 0.0056,
"ldsc_observed_scale_h2_se": 0.0015,
"ldsc_intercept_beta": 1.052,
"ldsc_intercept_se": 0.0075,
"ldsc_lambda_gc": 1.0913,
"ldsc_mean_chisq": 1.0928,
"ldsc_ratio": 0.5603
}
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 | TRUE |
| 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 | 6663208 | 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 | 6666971 | 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 | 8.664130e+00 | 5.763910e+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 | 7.860413e+07 | 5.648067e+07 | 828.0000000 | 3.206894e+07 | 6.905468e+07 | 1.145046e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.900000e-06 | 9.100000e-04 | -0.0071087 | -5.076000e-04 | -2.600000e-06 | 5.036000e-04 | 8.062800e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.066000e-04 | 3.397000e-04 | 0.0004743 | 5.317000e-04 | 6.672000e-04 | 9.979000e-04 | 4.150600e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.860796e-01 | 2.917589e-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 | 4.860806e-01 | 2.917339e-01 | 0.0000000 | 2.307342e-01 | 4.807487e-01 | 7.386986e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.871814e-01 | 2.583340e-01 | 0.0211030 | 7.059100e-02 | 1.954930e-01 | 4.474510e-01 | 9.788970e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 61205 | 0.9908197 | NA | NA | NA | NA | NA | NA | NA | 2.849861e-01 | 2.505676e-01 | 0.0000000 | 7.967250e-02 | 2.060700e-01 | 4.394970e-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 | 0.0009424 | 0.0008732 | 0.2800000 | 0.2804491 | 0.623820 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0007577 | 0.0008642 | 0.3800004 | 0.3806100 | 0.400328 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0009427 | 0.0013827 | 0.5000000 | 0.4953753 | 0.103529 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0014089 | 0.0008518 | 0.0980009 | 0.0981427 | 0.456756 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0000697 | 0.0016764 | 0.9699999 | 0.9668541 | 0.074541 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0020971 | 0.0009730 | 0.0309999 | 0.0311386 | 0.240913 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0009369 | 0.0012152 | 0.4400003 | 0.4406889 | 0.913578 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0002302 | 0.0008156 | 0.7800007 | 0.7778015 | 0.116108 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0000729 | 0.0011979 | 0.9500000 | 0.9514881 | 0.067039 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002886 | 0.0006040 | 0.6300007 | 0.6328173 | 0.515725 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | 0.0013197 | 0.0009425 | 0.1600000 | 0.1614690 | 0.073844 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0003903 | 0.0007292 | 0.5900000 | 0.5924825 | 0.137825 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0013864 | 0.0009444 | 0.1400000 | 0.1421154 | 0.073975 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0012491 | 0.0012659 | 0.3200000 | 0.3237526 | 0.042076 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0024989 | 0.0012637 | 0.0479999 | 0.0479893 | 0.049824 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0013715 | 0.0009450 | 0.1499999 | 0.1467062 | 0.073451 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0022898 | 0.0011180 | 0.0409996 | 0.0405525 | 0.054159 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0023059 | 0.0011234 | 0.0400000 | 0.0401027 | 0.053794 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0014384 | 0.0009483 | 0.1299999 | 0.1293216 | 0.073300 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0003306 | 0.0005808 | 0.5700002 | 0.5692443 | 0.298064 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.62382 ES:SE:LP:AF:ID 0.000942423:0.000873171:0.552842:0.62382:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400328 ES:SE:LP:AF:ID -0.000757729:0.000864225:0.420216:0.400328:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103529 ES:SE:LP:AF:ID 0.00094271:0.00138271:0.30103:0.103529:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456756 ES:SE:LP:AF:ID 0.00140886:0.000851828:1.00877:0.456756:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074541 ES:SE:LP:AF:ID 6.96615e-05:0.0016764:0.0132283:0.074541:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240913 ES:SE:LP:AF:ID -0.00209711:0.000973003:1.50864:0.240913:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913578 ES:SE:LP:AF:ID 0.000936933:0.00121517:0.356547:0.913578:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116108 ES:SE:LP:AF:ID 0.000230152:0.000815606:0.107905:0.116108:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067039 ES:SE:LP:AF:ID -7.28786e-05:0.00119791:0.0222764:0.067039:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515725 ES:SE:LP:AF:ID -0.000288563:0.000603985:0.200659:0.515725:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID 0.000966246:0.00152263:0.275724:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036652 ES:SE:LP:AF:ID 0.000809304:0.00138222:0.251812:0.036652:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036768 ES:SE:LP:AF:ID 0.000723918:0.00137702:0.221849:0.036768:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036477 ES:SE:LP:AF:ID 0.000846936:0.00138678:0.267606:0.036477:rs12184279
1 720381 rs116801199 G T . PASS AF=0.037007 ES:SE:LP:AF:ID 0.000746039:0.00137162:0.229148:0.037007:rs116801199
1 721290 rs12565286 G C . PASS AF=0.03711 ES:SE:LP:AF:ID 0.000731593:0.00136681:0.229148:0.03711:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101198 ES:SE:LP:AF:ID 0.000842621:0.000996716:0.39794:0.101198:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959069 ES:SE:LP:AF:ID -0.000475287:0.00131816:0.142668:0.959069:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031466 ES:SE:LP:AF:ID -0.0043908:0.00238964:1.18046:0.031466:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053298 ES:SE:LP:AF:ID -0.00170996:0.00190252:0.431798:0.053298:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03663 ES:SE:LP:AF:ID 0.000746115:0.0013755:0.229148:0.03663:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036957 ES:SE:LP:AF:ID 0.000918039:0.0013628:0.30103:0.036957:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843355 ES:SE:LP:AF:ID -0.000206457:0.000706695:0.113509:0.843355:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055663 ES:SE:LP:AF:ID -0.00103529:0.00114615:0.431798:0.055663:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122081 ES:SE:LP:AF:ID -0.000142293:0.000773726:0.0705811:0.122081:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025757 ES:SE:LP:AF:ID 0.00305097:0.00190054:0.958607:0.025757:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12132 ES:SE:LP:AF:ID -5.82702e-05:0.000774046:0.0268721:0.12132:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132136 ES:SE:LP:AF:ID -0.000166906:0.000762886:0.0809219:0.132136:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036861 ES:SE:LP:AF:ID 0.000759262:0.00134925:0.244125:0.036861:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838993 ES:SE:LP:AF:ID 3.08179e-06:0.000684169:-0:0.838993:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838631 ES:SE:LP:AF:ID 8.26901e-06:0.000683454:0.00436481:0.838631:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869899 ES:SE:LP:AF:ID 0.000235294:0.000733488:0.124939:0.869899:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129748 ES:SE:LP:AF:ID -0.000153656:0.000734951:0.0809219:0.129748:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037372 ES:SE:LP:AF:ID 0.000671447:0.00132641:0.21467:0.037372:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037603 ES:SE:LP:AF:ID 0.0006622:0.00131829:0.207608:0.037603:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869249 ES:SE:LP:AF:ID 0.00021792:0.000732083:0.113509:0.869249:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86935 ES:SE:LP:AF:ID 0.000272946:0.000732388:0.148742:0.86935:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037584 ES:SE:LP:AF:ID 0.000668607:0.00132361:0.21467:0.037584:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869253 ES:SE:LP:AF:ID 0.000234742:0.000732077:0.124939:0.869253:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838079 ES:SE:LP:AF:ID 2.44798e-05:0.000681565:0.0132283:0.838079:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037592 ES:SE:LP:AF:ID 0.000642353:0.00132562:0.200659:0.037592:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838704 ES:SE:LP:AF:ID -1.38784e-07:0.000683495:-0:0.838704:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839796 ES:SE:LP:AF:ID -7.90543e-07:0.000692652:-0:0.839796:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869529 ES:SE:LP:AF:ID 0.000232869:0.000731205:0.124939:0.869529:rs3115858
1 756604 rs3131962 A G . PASS AF=0.869056 ES:SE:LP:AF:ID 0.000272124:0.000729311:0.148742:0.869056:rs3131962
1 757640 rs3115853 G A . PASS AF=0.868029 ES:SE:LP:AF:ID 0.000268112:0.000727967:0.148742:0.868029:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869214 ES:SE:LP:AF:ID 0.000269053:0.00072994:0.148742:0.869214:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869223 ES:SE:LP:AF:ID 0.000269379:0.000729997:0.148742:0.869223:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869231 ES:SE:LP:AF:ID 0.000272251:0.000730012:0.148742:0.869231:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869714 ES:SE:LP:AF:ID 0.000223697:0.00073204:0.119186:0.869714:rs3131954