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_2976.vcf.gz --id UKB-b:7435 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2976.txt.gz --cohort_controls 20911 --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": "2019-09-12T19:38:11.692243",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7435/ukb-b-7435.vcf.gz; Date=Sun May 10 06:54:08 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-7435/UKB-b-7435_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7435/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7435/UKB-b-7435_data.vcf.gz ...
Read summary statistics for 6996342 SNPs.
Dropped 4293 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, 1263675 SNPs remain.
After merging with regression SNP LD, 1263675 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0413 (0.0241)
Lambda GC: 1.0269
Mean Chi^2: 1.0314
Intercept: 1.014 (0.0068)
Ratio: 0.4466 (0.2173)
Analysis finished at Thu Oct 17 14:42:19 2019
Total time elapsed: 2.0m:0.95sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9359,
"inflation_factor": 1.0475,
"mean_EFFECT": -0.0002,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 6,
"n_p_sig": 5462,
"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": 64373,
"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": 1263675,
"ldsc_nsnp_merge_regression_ld": 1263675,
"ldsc_observed_scale_h2_beta": 0.0413,
"ldsc_observed_scale_h2_se": 0.0241,
"ldsc_intercept_beta": 1.014,
"ldsc_intercept_se": 0.0068,
"ldsc_lambda_gc": 1.0269,
"ldsc_mean_chisq": 1.0314,
"ldsc_ratio": 0.4459
}
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 | 1.000000 | 3 | 58 | 0 | 6992071 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 6996342 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.663263e+00 | 5.763963e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.863054e+07 | 5.645810e+07 | 828.0000000 | 3.213385e+07 | 6.906864e+07 | 1.145196e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | -2.329000e-04 | 1.941890e-02 | -0.3815150 | -1.028900e-02 | -5.720000e-05 | 1.003990e-02 | 2.211930e-01 | ▁▁▁▇▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.699060e-02 | 7.908000e-03 | 0.0094183 | 1.064270e-02 | 1.368260e-02 | 2.134030e-02 | 9.739590e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.933462e-01 | 2.905752e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.933468e-01 | 2.905493e-01 | 0.0000000 | 2.403775e-01 | 4.911998e-01 | 7.448761e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 2.760606e-01 | 2.597576e-01 | 0.0167380 | 6.004000e-02 | 1.796725e-01 | 4.329830e-01 | 9.832620e-01 | ▇▃▂▁▁ |
| numeric | AF_reference | 64373 | 0.990799 | NA | NA | NA | NA | NA | NA | NA | 2.743348e-01 | 2.517882e-01 | 0.0000000 | 6.789140e-02 | 1.916930e-01 | 4.255190e-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 | -0.0423671 | 0.0173709 | 0.0150000 | 0.0147292 | 0.623469 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0319885 | 0.0170983 | 0.0610000 | 0.0613647 | 0.402075 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0127479 | 0.0275743 | 0.6400000 | 0.6438584 | 0.103303 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0014741 | 0.0169023 | 0.9299999 | 0.9305022 | 0.459026 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0007598 | 0.0339330 | 0.9800000 | 0.9821354 | 0.073605 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0198618 | 0.0192647 | 0.2999998 | 0.3025434 | 0.241104 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0139122 | 0.0245936 | 0.5700002 | 0.5716088 | 0.914885 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0589360 | 0.0162341 | 0.0002800 | 0.0002830 | 0.115349 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0323684 | 0.0240685 | 0.1800002 | 0.1786753 | 0.066360 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0204992 | 0.0120082 | 0.0879995 | 0.0878034 | 0.513741 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0045638 | 0.0144318 | 0.7499995 | 0.7518247 | 0.138818 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0137182 | 0.0186616 | 0.4600002 | 0.4622767 | 0.074716 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0004558 | 0.0250142 | 0.9900000 | 0.9854620 | 0.042881 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0112800 | 0.0249500 | 0.6499995 | 0.6511939 | 0.050810 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0149706 | 0.0186750 | 0.4199997 | 0.4227621 | 0.074198 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0253466 | 0.0220955 | 0.2500000 | 0.2513244 | 0.054774 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0230281 | 0.0222008 | 0.2999998 | 0.2996126 | 0.054386 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0137442 | 0.0187748 | 0.4600002 | 0.4641346 | 0.073806 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0435951 | 0.0445976 | 0.3300000 | 0.3283112 | 0.020115 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0250823 | 0.0115792 | 0.0299999 | 0.0302996 | 0.297859 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623469 ES:SE:LP:AF:ID -0.0423671:0.0173709:1.82391:0.623469:rs10399793
1 54676 rs2462492 C T . PASS AF=0.402075 ES:SE:LP:AF:ID 0.0319885:0.0170983:1.21467:0.402075:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103303 ES:SE:LP:AF:ID -0.0127479:0.0275743:0.19382:0.103303:rs114608975
1 91536 rs6702460 G T . PASS AF=0.459026 ES:SE:LP:AF:ID 0.0014741:0.0169023:0.0315171:0.459026:rs6702460
1 234313 rs8179466 C T . PASS AF=0.073605 ES:SE:LP:AF:ID 0.000759822:0.033933:0.00877392:0.073605:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241104 ES:SE:LP:AF:ID -0.0198618:0.0192647:0.522879:0.241104:rs6680723
1 546697 rs12025928 A G . PASS AF=0.914885 ES:SE:LP:AF:ID 0.0139122:0.0245936:0.244125:0.914885:rs12025928
1 693731 rs12238997 A G . PASS AF=0.115349 ES:SE:LP:AF:ID -0.058936:0.0162341:3.55284:0.115349:rs12238997
1 705882 rs72631875 G A . PASS AF=0.06636 ES:SE:LP:AF:ID 0.0323684:0.0240685:0.744727:0.06636:rs72631875
1 706368 rs12029736 A G . PASS AF=0.513741 ES:SE:LP:AF:ID 0.0204992:0.0120082:1.05552:0.513741:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032611 ES:SE:LP:AF:ID 0.0421457:0.0304125:0.769551:0.032611:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036024 ES:SE:LP:AF:ID 0.0372045:0.0276734:0.744727:0.036024:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036074 ES:SE:LP:AF:ID 0.0359707:0.0275977:0.721246:0.036074:rs12184277
1 717485 rs12184279 C A . PASS AF=0.035711 ES:SE:LP:AF:ID 0.036801:0.0278445:0.721246:0.035711:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016954 ES:SE:LP:AF:ID -0.0519142:0.0416233:0.677781:0.016954:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036308 ES:SE:LP:AF:ID 0.037245:0.027497:0.744727:0.036308:rs116801199
1 721290 rs12565286 G C . PASS AF=0.036426 ES:SE:LP:AF:ID 0.0369101:0.0273909:0.744727:0.036426:rs12565286
1 722670 rs116030099 T C . PASS AF=0.102581 ES:SE:LP:AF:ID -0.0104075:0.0196834:0.221849:0.102581:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959502 ES:SE:LP:AF:ID -0.0261879:0.0263486:0.49485:0.959502:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031683 ES:SE:LP:AF:ID -0.0619747:0.0476394:0.721246:0.031683:rs12126395
1 725060 rs865924913 A T . PASS AF=0.054262 ES:SE:LP:AF:ID 0.0485922:0.0370947:0.721246:0.054262:rs865924913
1 726794 rs28454925 C G . PASS AF=0.035858 ES:SE:LP:AF:ID 0.035977:0.0276344:0.721246:0.035858:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036184 ES:SE:LP:AF:ID 0.0331128:0.0273785:0.638272:0.036184:rs116720794
1 729679 rs4951859 C G . PASS AF=0.844169 ES:SE:LP:AF:ID 0.0400744:0.0140425:2.36653:0.844169:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055767 ES:SE:LP:AF:ID -0.0729174:0.0226903:2.88606:0.055767:rs148120343
1 731718 rs58276399 T C . PASS AF=0.12154 ES:SE:LP:AF:ID -0.0595152:0.0153983:3.95861:0.12154:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025221 ES:SE:LP:AF:ID -0.0391222:0.0380985:0.522879:0.025221:rs369030935
1 734349 rs141242758 T C . PASS AF=0.120914 ES:SE:LP:AF:ID -0.0591074:0.015405:3.92082:0.120914:rs141242758
1 736289 rs79010578 T A . PASS AF=0.130717 ES:SE:LP:AF:ID -0.0372352:0.0152468:1.82391:0.130717:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036266 ES:SE:LP:AF:ID 0.0354978:0.0270069:0.721246:0.036266:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839899 ES:SE:LP:AF:ID 0.0416823:0.0135979:2.65758:0.839899:rs3094315
1 752721 rs3131972 A G . PASS AF=0.839391 ES:SE:LP:AF:ID 0.0427237:0.013567:2.79588:0.839391:rs3131972
1 753405 rs3115860 C A . PASS AF=0.870533 ES:SE:LP:AF:ID 0.0607186:0.0146166:4.48149:0.870533:rs3115860
1 753541 rs2073813 G A . PASS AF=0.128952 ES:SE:LP:AF:ID -0.0645471:0.0146353:5:0.128952:rs2073813
1 754063 rs12184312 G T . PASS AF=0.036718 ES:SE:LP:AF:ID 0.0339549:0.0265741:0.69897:0.036718:rs12184312
1 754105 rs12184325 C T . PASS AF=0.036942 ES:SE:LP:AF:ID 0.0325451:0.0264141:0.657577:0.036942:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869807 ES:SE:LP:AF:ID 0.0624861:0.0145776:4.74473:0.869807:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869991 ES:SE:LP:AF:ID 0.062307:0.0145899:4.72125:0.869991:rs3131968
1 754211 rs12184313 G A . PASS AF=0.036841 ES:SE:LP:AF:ID 0.0355597:0.0265394:0.744727:0.036841:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869827 ES:SE:LP:AF:ID 0.0626899:0.0145764:4.76955:0.869827:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838932 ES:SE:LP:AF:ID 0.0434834:0.0135412:2.88606:0.838932:rs3115859
1 754629 rs10454459 A G . PASS AF=0.036838 ES:SE:LP:AF:ID 0.0365841:0.0265767:0.769551:0.036838:rs10454459
1 754964 rs3131966 C T . PASS AF=0.839563 ES:SE:LP:AF:ID 0.043882:0.0135803:2.92082:0.839563:rs3131966
1 755775 rs3131965 A G . PASS AF=0.840597 ES:SE:LP:AF:ID 0.0426258:0.0137707:2.69897:0.840597:rs3131965
1 755890 rs3115858 A T . PASS AF=0.870155 ES:SE:LP:AF:ID 0.0601691:0.0145722:4.4437:0.870155:rs3115858
1 756604 rs3131962 A G . PASS AF=0.869662 ES:SE:LP:AF:ID 0.0586763:0.0145365:4.26761:0.869662:rs3131962
1 757640 rs3115853 G A . PASS AF=0.868358 ES:SE:LP:AF:ID 0.0615727:0.0144885:4.67778:0.868358:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869841 ES:SE:LP:AF:ID 0.0593435:0.0145477:4.34679:0.869841:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869846 ES:SE:LP:AF:ID 0.0593608:0.0145487:4.34679:0.869846:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869857 ES:SE:LP:AF:ID 0.0593505:0.014549:4.34679:0.869857:rs3131956