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_6153_1.vcf.gz --id UKB-b:17805 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6153_1.txt.gz --cohort_cases 31553 --cohort_controls 218157 --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": "2019-09-13T01:54:32.199573",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-17805/ukb-b-17805.vcf.gz; Date=Sun May 10 13:02:19 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-17805/UKB-b-17805_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17805/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17805/UKB-b-17805_data.vcf.gz ...
Read summary statistics for 7582681 SNPs.
Dropped 5317 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, 1277398 SNPs remain.
After merging with regression SNP LD, 1277398 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0494 (0.0035)
Lambda GC: 1.2029
Mean Chi^2: 1.2729
Intercept: 1.0322 (0.0082)
Ratio: 0.1179 (0.03)
Analysis finished at Thu Oct 17 14:41:55 2019
Total time elapsed: 1.0m:27.23sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9404,
"inflation_factor": 1.1999,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 41,
"n_p_sig": 2578,
"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": 70411,
"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": 1277398,
"ldsc_nsnp_merge_regression_ld": 1277398,
"ldsc_observed_scale_h2_beta": 0.0494,
"ldsc_observed_scale_h2_se": 0.0035,
"ldsc_intercept_beta": 1.0322,
"ldsc_intercept_se": 0.0082,
"ldsc_lambda_gc": 1.2029,
"ldsc_mean_chisq": 1.2729,
"ldsc_ratio": 0.118
}
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 | 1.0000000 | 3 | 58 | 0 | 7577387 | 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 | 7582681 | 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 | 8.663286e+00 | 5.764516e+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 | 7.866213e+07 | 5.643833e+07 | 828.0000000 | 3.219216e+07 | 6.909904e+07 | 1.145542e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.050000e-05 | 2.217600e-03 | -0.0398236 | -1.110900e-03 | -8.000000e-07 | 1.117000e-03 | 3.639490e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.827300e-03 | 9.975000e-04 | 0.0009041 | 1.037000e-03 | 1.395900e-03 | 2.360800e-03 | 1.087450e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.735251e-01 | 2.961560e-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 | 4.735261e-01 | 2.961299e-01 | 0.0000000 | 2.107076e-01 | 4.644711e-01 | 7.306102e-01 | 9.999997e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.575683e-01 | 2.608459e-01 | 0.0110930 | 4.425200e-02 | 1.536770e-01 | 4.067520e-01 | 9.889070e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 70411 | 0.9907142 | NA | NA | NA | NA | NA | NA | NA | 2.564859e-01 | 2.526908e-01 | 0.0000000 | 4.892170e-02 | 1.677320e-01 | 4.009580e-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 | 0.0018930 | 0.0016662 | 0.2599998 | 0.2559025 | 0.623403 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0009522 | 0.0016473 | 0.5600000 | 0.5632456 | 0.400976 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0009745 | 0.0026437 | 0.7099994 | 0.7124064 | 0.103437 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0005588 | 0.0016230 | 0.7300002 | 0.7306140 | 0.457088 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0012556 | 0.0032053 | 0.6999999 | 0.6952683 | 0.074451 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0027538 | 0.0018551 | 0.1400000 | 0.1376893 | 0.241186 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0001900 | 0.0023145 | 0.9299999 | 0.9345767 | 0.913577 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0004944 | 0.0015523 | 0.7499995 | 0.7501023 | 0.116703 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0013058 | 0.0022793 | 0.5700002 | 0.5667136 | 0.067069 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0008098 | 0.0011512 | 0.4799997 | 0.4817621 | 0.515134 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0002828 | 0.0013922 | 0.8400000 | 0.8390102 | 0.137913 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0001055 | 0.0018066 | 0.9500000 | 0.9534482 | 0.073612 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0008859 | 0.0024210 | 0.7099994 | 0.7144094 | 0.041843 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0009002 | 0.0024190 | 0.7099994 | 0.7097993 | 0.049574 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0001884 | 0.0018073 | 0.9199999 | 0.9169625 | 0.073145 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0008720 | 0.0021236 | 0.6800001 | 0.6813425 | 0.054643 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0008517 | 0.0021337 | 0.6899999 | 0.6897638 | 0.054278 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0003129 | 0.0018137 | 0.8600001 | 0.8630286 | 0.073012 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0056324 | 0.0042676 | 0.1900002 | 0.1868981 | 0.020061 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0013279 | 0.0011078 | 0.2300001 | 0.2306348 | 0.297860 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623403 ES:SE:LP:AF:ID 0.00189303:0.00166621:0.585027:0.623403:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400976 ES:SE:LP:AF:ID 0.000952173:0.00164728:0.251812:0.400976:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103437 ES:SE:LP:AF:ID -0.000974548:0.00264373:0.148742:0.103437:rs114608975
1 91536 rs6702460 G T . PASS AF=0.457088 ES:SE:LP:AF:ID 0.000558803:0.00162297:0.136677:0.457088:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074451 ES:SE:LP:AF:ID 0.00125557:0.00320531:0.154902:0.074451:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241186 ES:SE:LP:AF:ID -0.00275379:0.00185509:0.853872:0.241186:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913577 ES:SE:LP:AF:ID -0.000189991:0.00231448:0.0315171:0.913577:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116703 ES:SE:LP:AF:ID -0.000494424:0.00155233:0.124939:0.116703:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067069 ES:SE:LP:AF:ID 0.00130583:0.00227934:0.244125:0.067069:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515134 ES:SE:LP:AF:ID -0.000809801:0.00115115:0.318759:0.515134:rs12029736
1 714596 rs149887893 T C . PASS AF=0.03287 ES:SE:LP:AF:ID -0.00439891:0.0029085:0.886057:0.03287:rs149887893
1 715265 rs12184267 C T . PASS AF=0.03645 ES:SE:LP:AF:ID -0.00411926:0.00264371:0.920819:0.03645:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036557 ES:SE:LP:AF:ID -0.00416023:0.00263428:0.958607:0.036557:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036249 ES:SE:LP:AF:ID -0.00448271:0.00265371:1.04096:0.036249:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016355 ES:SE:LP:AF:ID -0.000470914:0.00407901:0.0409586:0.016355:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036782 ES:SE:LP:AF:ID -0.00398288:0.00262442:0.886057:0.036782:rs116801199
1 721290 rs12565286 G C . PASS AF=0.036873 ES:SE:LP:AF:ID -0.00402129:0.00261568:0.920819:0.036873:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101076 ES:SE:LP:AF:ID 0.000705066:0.0019005:0.148742:0.101076:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959309 ES:SE:LP:AF:ID 0.00459282:0.00252233:1.16115:0.959309:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031432 ES:SE:LP:AF:ID 0.00604154:0.00454978:0.744727:0.031432:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053418 ES:SE:LP:AF:ID 0.00122281:0.00362095:0.130768:0.053418:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036416 ES:SE:LP:AF:ID -0.00395406:0.00263098:0.886057:0.036416:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036745 ES:SE:LP:AF:ID -0.00375245:0.00260706:0.823909:0.036745:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843032 ES:SE:LP:AF:ID 0.00153477:0.0013472:0.60206:0.843032:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056041 ES:SE:LP:AF:ID 0.000854422:0.00217961:0.154902:0.056041:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122705 ES:SE:LP:AF:ID 9.34879e-05:0.00147232:0.0222764:0.122705:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025586 ES:SE:LP:AF:ID -0.00452624:0.00363497:0.677781:0.025586:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121928 ES:SE:LP:AF:ID 0.000156381:0.00147298:0.0362122:0.121928:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132582 ES:SE:LP:AF:ID -0.00111971:0.00145283:0.356547:0.132582:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011132 ES:SE:LP:AF:ID 0.00496741:0.00528808:0.455932:0.011132:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036653 ES:SE:LP:AF:ID -0.0034167:0.00258107:0.721246:0.036653:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838725 ES:SE:LP:AF:ID 0.000874353:0.00130401:0.30103:0.838725:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838352 ES:SE:LP:AF:ID 0.00103622:0.00130272:0.366532:0.838352:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869399 ES:SE:LP:AF:ID -0.000278561:0.00139652:0.0757207:0.869399:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130253 ES:SE:LP:AF:ID -1.4455e-05:0.00139962:0.00436481:0.130253:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037121 ES:SE:LP:AF:ID -0.00323471:0.00253843:0.69897:0.037121:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037384 ES:SE:LP:AF:ID -0.00314205:0.00252148:0.677781:0.037384:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868732 ES:SE:LP:AF:ID -9.01406e-05:0.00139395:0.0222764:0.868732:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868802 ES:SE:LP:AF:ID -0.000115684:0.00139443:0.0315171:0.868802:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037334 ES:SE:LP:AF:ID -0.00327105:0.00253268:0.69897:0.037334:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868732 ES:SE:LP:AF:ID -8.45117e-05:0.0013939:0.0222764:0.868732:rs3131967
1 754503 rs3115859 G A . PASS AF=0.8378 ES:SE:LP:AF:ID 0.00104527:0.00129906:0.376751:0.8378:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037347 ES:SE:LP:AF:ID -0.00333627:0.00253648:0.721246:0.037347:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838436 ES:SE:LP:AF:ID 0.000985918:0.00130271:0.346787:0.838436:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013889 ES:SE:LP:AF:ID -0.00291826:0.00452828:0.283997:0.013889:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839574 ES:SE:LP:AF:ID 0.000703464:0.00132018:0.229148:0.839574:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869019 ES:SE:LP:AF:ID -0.000341075:0.00139209:0.091515:0.869019:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868559 ES:SE:LP:AF:ID -0.000444844:0.00138859:0.124939:0.868559:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867537 ES:SE:LP:AF:ID -0.000238894:0.00138632:0.0655015:0.867537:rs3115853
1 757734 rs4951929 C T . PASS AF=0.868706 ES:SE:LP:AF:ID -0.000381759:0.00138976:0.107905:0.868706:rs4951929