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_20110_5.vcf.gz --id UKB-b:13584 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20110_5.txt.gz --cohort_cases 35102 --cohort_controls 388356 --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": "2019-09-13T11:48:43.456695",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13584/ukb-b-13584.vcf.gz; Date=Sun May 10 13:50:51 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-13584/UKB-b-13584_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13584/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13584/UKB-b-13584_data.vcf.gz ...
Read summary statistics for 7737055 SNPs.
Dropped 5607 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, 1279581 SNPs remain.
After merging with regression SNP LD, 1279581 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0089 (0.0015)
Lambda GC: 1.1089
Mean Chi^2: 1.1384
Intercept: 1.0628 (0.0071)
Ratio: 0.4535 (0.0515)
Analysis finished at Thu Oct 17 14:44:18 2019
Total time elapsed: 1.0m:28.01sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9413,
"inflation_factor": 1.0966,
"mean_EFFECT": -5.3938e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 13,
"n_p_sig": 936,
"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": 72051,
"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": 1279581,
"ldsc_nsnp_merge_regression_ld": 1279581,
"ldsc_observed_scale_h2_beta": 0.0089,
"ldsc_observed_scale_h2_se": 0.0015,
"ldsc_intercept_beta": 1.0628,
"ldsc_intercept_se": 0.0071,
"ldsc_lambda_gc": 1.1089,
"ldsc_mean_chisq": 1.1384,
"ldsc_ratio": 0.4538
}
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 | 7731474 | 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 | 7737055 | 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 | 8.661984e+00 | 5.764342e+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 | 7.869260e+07 | 5.643525e+07 | 828.0000000 | 3.221559e+07 | 6.915123e+07 | 1.145726e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -5.400000e-06 | 1.444200e-03 | -0.0135405 | -7.058000e-04 | -7.400000e-06 | 6.844000e-04 | 1.484820e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.203900e-03 | 6.824000e-04 | 0.0005779 | 6.648000e-04 | 9.057000e-04 | 1.566600e-03 | 6.920800e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.844653e-01 | 2.929881e-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 | 4.844666e-01 | 2.929617e-01 | 0.0000000 | 2.276717e-01 | 4.790864e-01 | 7.386441e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.529671e-01 | 2.608374e-01 | 0.0099710 | 4.071200e-02 | 1.471720e-01 | 3.999990e-01 | 9.900290e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 72051 | 0.9906875 | NA | NA | NA | NA | NA | NA | NA | 2.520252e-01 | 2.526771e-01 | 0.0000000 | 4.432910e-02 | 1.617410e-01 | 3.947680e-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 | -0.0008371 | 0.0010640 | 0.4299995 | 0.4314421 | 0.623822 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0007545 | 0.0010532 | 0.4700002 | 0.4737479 | 0.400468 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0011822 | 0.0016837 | 0.4799997 | 0.4825860 | 0.103595 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0007526 | 0.0010372 | 0.4700002 | 0.4680984 | 0.456935 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0000039 | 0.0020465 | 1.0000000 | 0.9984857 | 0.074513 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0009722 | 0.0011855 | 0.4100001 | 0.4121438 | 0.240958 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0013017 | 0.0014776 | 0.3800004 | 0.3783520 | 0.913408 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0005494 | 0.0009933 | 0.5800000 | 0.5801952 | 0.116281 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0016316 | 0.0014551 | 0.2599998 | 0.2621511 | 0.067342 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000539 | 0.0007359 | 0.9400001 | 0.9416024 | 0.515816 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0005728 | 0.0008877 | 0.5199996 | 0.5187253 | 0.137919 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0003738 | 0.0011519 | 0.7499995 | 0.7455955 | 0.073766 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0002653 | 0.0015439 | 0.8600001 | 0.8635387 | 0.041968 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0016432 | 0.0015406 | 0.2900000 | 0.2861662 | 0.049756 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0004700 | 0.0011526 | 0.6800001 | 0.6834476 | 0.073263 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0017458 | 0.0013575 | 0.2000000 | 0.1984399 | 0.054410 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0017053 | 0.0013640 | 0.2099999 | 0.2112373 | 0.054041 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0005393 | 0.0011568 | 0.6400000 | 0.6410877 | 0.073095 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0049841 | 0.0027245 | 0.0669993 | 0.0673479 | 0.020027 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0011524 | 0.0007077 | 0.1000000 | 0.1034786 | 0.298128 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623822 ES:SE:LP:AF:ID -0.000837069:0.00106399:0.366532:0.623822:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400468 ES:SE:LP:AF:ID 0.000754528:0.00105323:0.327902:0.400468:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103595 ES:SE:LP:AF:ID 0.00118223:0.00168373:0.318759:0.103595:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456935 ES:SE:LP:AF:ID -0.000752568:0.0010372:0.327902:0.456935:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074513 ES:SE:LP:AF:ID -3.88398e-06:0.0020465:-0:0.074513:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240958 ES:SE:LP:AF:ID -0.000972244:0.00118548:0.387216:0.240958:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913408 ES:SE:LP:AF:ID -0.00130169:0.00147762:0.420216:0.913408:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116281 ES:SE:LP:AF:ID 0.000549406:0.000993322:0.236572:0.116281:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067342 ES:SE:LP:AF:ID 0.00163159:0.00145506:0.585027:0.067342:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515816 ES:SE:LP:AF:ID -5.3907e-05:0.000735872:0.0268721:0.515816:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032973 ES:SE:LP:AF:ID -0.00128906:0.00185553:0.309804:0.032973:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036602 ES:SE:LP:AF:ID -0.00162204:0.00168486:0.468521:0.036602:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036711 ES:SE:LP:AF:ID -0.00161779:0.00167869:0.468521:0.036711:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036414 ES:SE:LP:AF:ID -0.00153049:0.0016908:0.431798:0.036414:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016438 ES:SE:LP:AF:ID -0.00168519:0.00259976:0.283997:0.016438:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036951 ES:SE:LP:AF:ID -0.00170182:0.00167204:0.508638:0.036951:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037047 ES:SE:LP:AF:ID -0.00179015:0.00166634:0.552842:0.037047:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101279 ES:SE:LP:AF:ID 0.000285876:0.00121344:0.091515:0.101279:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959142 ES:SE:LP:AF:ID 0.00162933:0.00160761:0.508638:0.959142:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031481 ES:SE:LP:AF:ID -0.00392675:0.0029139:0.744727:0.031481:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053288 ES:SE:LP:AF:ID 0.00213825:0.00231813:0.443698:0.053288:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036561 ES:SE:LP:AF:ID -0.00136147:0.00167724:0.376751:0.036561:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036875 ES:SE:LP:AF:ID -0.00130595:0.00166206:0.366532:0.036875:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843263 ES:SE:LP:AF:ID -0.000345189:0.000860741:0.161151:0.843263:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055908 ES:SE:LP:AF:ID 0.00125501:0.00139362:0.431798:0.055908:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122272 ES:SE:LP:AF:ID 0.000716013:0.000942218:0.346787:0.122272:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025727 ES:SE:LP:AF:ID 0.00137443:0.0023151:0.259637:0.025727:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12152 ES:SE:LP:AF:ID 0.000681691:0.0009426:0.327902:0.12152:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132292 ES:SE:LP:AF:ID 0.000662161:0.000929018:0.318759:0.132292:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011112 ES:SE:LP:AF:ID 0.0027957:0.00338144:0.387216:0.011112:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036801 ES:SE:LP:AF:ID -0.0016849:0.00164491:0.508638:0.036801:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838969 ES:SE:LP:AF:ID -0.000264292:0.000833577:0.124939:0.838969:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838595 ES:SE:LP:AF:ID -0.000262251:0.000832641:0.124939:0.838595:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869819 ES:SE:LP:AF:ID -0.000466814:0.000893611:0.221849:0.869819:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129836 ES:SE:LP:AF:ID 0.000540141:0.000895419:0.259637:0.129836:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037302 ES:SE:LP:AF:ID -0.00161421:0.0016173:0.49485:0.037302:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037544 ES:SE:LP:AF:ID -0.00150272:0.00160713:0.455932:0.037544:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869161 ES:SE:LP:AF:ID -0.00048382:0.000891829:0.229148:0.869161:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869258 ES:SE:LP:AF:ID -0.000456706:0.000892185:0.21467:0.869258:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037504 ES:SE:LP:AF:ID -0.00167183:0.001614:0.522879:0.037504:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869162 ES:SE:LP:AF:ID -0.000478992:0.000891811:0.229148:0.869162:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838044 ES:SE:LP:AF:ID -0.000218031:0.000830316:0.102373:0.838044:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037516 ES:SE:LP:AF:ID -0.00168048:0.00161631:0.522879:0.037516:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838679 ES:SE:LP:AF:ID -0.000252847:0.000832667:0.119186:0.838679:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013752 ES:SE:LP:AF:ID 0.000659038:0.00290851:0.0861861:0.013752:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839788 ES:SE:LP:AF:ID -0.000146542:0.000843918:0.0655015:0.839788:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869446 ES:SE:LP:AF:ID -0.000427542:0.000890834:0.200659:0.869446:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868993 ES:SE:LP:AF:ID -0.000448765:0.000888594:0.21467:0.868993:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867942 ES:SE:LP:AF:ID -0.000529543:0.000886847:0.259637:0.867942:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869138 ES:SE:LP:AF:ID -0.000424772:0.000889323:0.200659:0.869138:rs4951929