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_6149_1.vcf.gz --id UKB-b:6458 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6149_1.txt.gz --cohort_cases 47102 --cohort_controls 414011 --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": "2019-09-13T01:53:32.690217",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6458/ukb-b-6458.vcf.gz; Date=Sun May 10 00:46:53 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-6458/UKB-b-6458_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6458/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-6458/UKB-b-6458_data.vcf.gz ...
Read summary statistics for 8153645 SNPs.
Dropped 6407 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, 1283559 SNPs remain.
After merging with regression SNP LD, 1283559 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0295 (0.0032)
Lambda GC: 1.2149
Mean Chi^2: 1.3021
Intercept: 1.0346 (0.0096)
Ratio: 0.1147 (0.0319)
Analysis finished at Thu Oct 17 14:41:51 2019
Total time elapsed: 1.0m:33.21sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9438,
"inflation_factor": 1.1999,
"mean_EFFECT": 8.8361e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 41,
"n_p_sig": 8037,
"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": 76349,
"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": 1283559,
"ldsc_nsnp_merge_regression_ld": 1283559,
"ldsc_observed_scale_h2_beta": 0.0295,
"ldsc_observed_scale_h2_se": 0.0032,
"ldsc_intercept_beta": 1.0346,
"ldsc_intercept_se": 0.0096,
"ldsc_lambda_gc": 1.2149,
"ldsc_mean_chisq": 1.3021,
"ldsc_ratio": 0.1145
}
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 | TRUE |
| 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 | 8147267 | 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 | 8153645 | 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 | 8.658285e+00 | 5.763183e+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 | 7.871551e+07 | 5.639615e+07 | 828.0000000 | 3.229655e+07 | 6.919535e+07 | 1.145318e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.800000e-06 | 1.730800e-03 | -0.0175202 | -7.963000e-04 | -1.100000e-06 | 8.066000e-04 | 3.865670e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.380800e-03 | 8.590000e-04 | 0.0006076 | 7.066000e-04 | 9.976000e-04 | 1.829000e-03 | 7.480000e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.723103e-01 | 2.968635e-01 | 0.0000000 | 2.099999e-01 | 4.600002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.723111e-01 | 2.968398e-01 | 0.0000000 | 2.084355e-01 | 4.632842e-01 | 7.294671e-01 | 9.999999e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.413136e-01 | 2.605161e-01 | 0.0074310 | 3.261100e-02 | 1.306190e-01 | 3.824680e-01 | 9.925690e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 76349 | 0.9906362 | NA | NA | NA | NA | NA | NA | NA | 2.406973e-01 | 2.523802e-01 | 0.0000000 | 3.334660e-02 | 1.463660e-01 | 3.777960e-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 | -0.0002550 | 0.0011179 | 0.8200001 | 0.8195933 | 0.623778 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001111 | 0.0011074 | 0.9199999 | 0.9200834 | 0.400420 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0007955 | 0.0017707 | 0.6499995 | 0.6532629 | 0.103559 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001911 | 0.0010905 | 0.8600001 | 0.8608883 | 0.456853 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0013695 | 0.0021505 | 0.5199996 | 0.5242301 | 0.074493 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0011501 | 0.0012457 | 0.3599996 | 0.3558806 | 0.240946 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0012455 | 0.0015538 | 0.4199997 | 0.4227948 | 0.913466 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0005733 | 0.0010439 | 0.5800000 | 0.5828711 | 0.116320 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0034532 | 0.0015297 | 0.0239999 | 0.0239791 | 0.067275 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0008869 | 0.0007732 | 0.2500000 | 0.2513586 | 0.515632 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0004300 | 0.0009330 | 0.6400000 | 0.6448852 | 0.137978 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0001607 | 0.0012111 | 0.8900000 | 0.8944332 | 0.073754 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0003616 | 0.0016230 | 0.8200001 | 0.8236870 | 0.041956 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0013268 | 0.0016202 | 0.4100001 | 0.4128383 | 0.049743 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0002106 | 0.0012119 | 0.8600001 | 0.8620170 | 0.073246 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0014314 | 0.0014267 | 0.3200000 | 0.3157358 | 0.054465 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0013631 | 0.0014336 | 0.3400001 | 0.3417009 | 0.054094 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0001384 | 0.0012162 | 0.9100000 | 0.9094094 | 0.073081 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0035165 | 0.0028654 | 0.2200002 | 0.2197397 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0004278 | 0.0007440 | 0.5700002 | 0.5652623 | 0.297941 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623778 ES:SE:LP:AF:ID -0.000254955:0.00111789:0.0861861:0.623778:rs10399793
1 54676 rs2462492 C T . PASS AF=0.40042 ES:SE:LP:AF:ID -0.000111109:0.00110745:0.0362122:0.40042:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103559 ES:SE:LP:AF:ID -0.000795454:0.00177069:0.187087:0.103559:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456853 ES:SE:LP:AF:ID -0.000191103:0.0010905:0.0655015:0.456853:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074493 ES:SE:LP:AF:ID 0.00136954:0.00215053:0.283997:0.074493:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240946 ES:SE:LP:AF:ID 0.00115013:0.00124575:0.443698:0.240946:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913466 ES:SE:LP:AF:ID -0.00124549:0.00155379:0.376751:0.913466:rs12025928
1 693731 rs12238997 A G . PASS AF=0.11632 ES:SE:LP:AF:ID -0.000573289:0.00104387:0.236572:0.11632:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067275 ES:SE:LP:AF:ID 0.00345322:0.00152969:1.61979:0.067275:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515632 ES:SE:LP:AF:ID 0.000886947:0.000773238:0.60206:0.515632:rs12029736
1 714596 rs149887893 T C . PASS AF=0.03298 ES:SE:LP:AF:ID -0.00106002:0.00195004:0.229148:0.03298:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036592 ES:SE:LP:AF:ID -0.00184909:0.00177135:0.522879:0.036592:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036708 ES:SE:LP:AF:ID -0.00172769:0.00176467:0.481486:0.036708:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036409 ES:SE:LP:AF:ID -0.00172974:0.00177733:0.481486:0.036409:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016393 ES:SE:LP:AF:ID -0.00314002:0.00273696:0.60206:0.016393:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036947 ES:SE:LP:AF:ID -0.0017136:0.00175767:0.481486:0.036947:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037044 ES:SE:LP:AF:ID -0.00143348:0.00175163:0.387216:0.037044:rs12565286
1 722670 rs116030099 T C . PASS AF=0.10124 ES:SE:LP:AF:ID 8.60947e-05:0.00127545:0.0222764:0.10124:rs116030099
1 723891 rs2977670 G C . PASS AF=0.95913 ES:SE:LP:AF:ID 0.00137636:0.00168948:0.376751:0.95913:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031445 ES:SE:LP:AF:ID 0.00189234:0.00306621:0.267606:0.031445:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053268 ES:SE:LP:AF:ID -0.00340231:0.00243806:0.79588:0.053268:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036562 ES:SE:LP:AF:ID -0.00187994:0.00176297:0.537602:0.036562:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036879 ES:SE:LP:AF:ID -0.00191548:0.00174692:0.568636:0.036879:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843257 ES:SE:LP:AF:ID 0.00102802:0.000904746:0.585027:0.843257:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055921 ES:SE:LP:AF:ID -0.000634843:0.00146459:0.180456:0.055921:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122298 ES:SE:LP:AF:ID -0.000762533:0.000990238:0.356547:0.122298:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025706 ES:SE:LP:AF:ID -0.00145831:0.00243586:0.259637:0.025706:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121541 ES:SE:LP:AF:ID -0.000843465:0.000990658:0.408935:0.121541:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132304 ES:SE:LP:AF:ID -0.000843054:0.000976457:0.408935:0.132304:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011129 ES:SE:LP:AF:ID -0.00242228:0.00355048:0.30103:0.011129:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036791 ES:SE:LP:AF:ID -0.00177357:0.00172932:0.508638:0.036791:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838991 ES:SE:LP:AF:ID 0.00108225:0.000876175:0.657577:0.838991:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838618 ES:SE:LP:AF:ID 0.0010135:0.000875231:0.60206:0.838618:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869789 ES:SE:LP:AF:ID 0.00092604:0.000939148:0.49485:0.869789:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129861 ES:SE:LP:AF:ID -0.000699882:0.000941059:0.337242:0.129861:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037302 ES:SE:LP:AF:ID -0.00162866:0.00169997:0.468521:0.037302:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037546 ES:SE:LP:AF:ID -0.00161989:0.00168922:0.468521:0.037546:rs12184325
1 754182 rs3131969 A G . PASS AF=0.86913 ES:SE:LP:AF:ID 0.000831574:0.000937308:0.431798:0.86913:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869227 ES:SE:LP:AF:ID 0.000880598:0.000937679:0.455932:0.869227:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037504 ES:SE:LP:AF:ID -0.00166183:0.00169654:0.481486:0.037504:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869133 ES:SE:LP:AF:ID 0.000844065:0.000937289:0.431798:0.869133:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838069 ES:SE:LP:AF:ID 0.00100044:0.000872789:0.60206:0.838069:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037517 ES:SE:LP:AF:ID -0.00164705:0.00169894:0.481486:0.037517:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838699 ES:SE:LP:AF:ID 0.00103883:0.00087524:0.619789:0.838699:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013777 ES:SE:LP:AF:ID -0.000976707:0.00305428:0.124939:0.013777:rs181660517
1 755775 rs3131965 A G . PASS AF=0.83981 ES:SE:LP:AF:ID 0.00092658:0.000887073:0.522879:0.83981:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869411 ES:SE:LP:AF:ID 0.000837712:0.000936187:0.431798:0.869411:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868957 ES:SE:LP:AF:ID 0.000805037:0.000933825:0.408935:0.868957:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867909 ES:SE:LP:AF:ID 0.000726709:0.000932033:0.356547:0.867909:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869101 ES:SE:LP:AF:ID 0.000803803:0.000934592:0.408935:0.869101:rs4951929