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_6152_8.vcf.gz --id UKB-b:20296 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6152_8.txt.gz --cohort_cases 53257 --cohort_controls 408756 --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": "2019-09-13T01:54:29.512130",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-20296/ukb-b-20296.vcf.gz; Date=Sat May 9 15:51:46 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-20296/UKB-b-20296_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20296/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20296/UKB-b-20296_data.vcf.gz ...
Read summary statistics for 8331512 SNPs.
Dropped 6782 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, 1284550 SNPs remain.
After merging with regression SNP LD, 1284550 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0513 (0.0039)
Lambda GC: 1.3241
Mean Chi^2: 1.5507
Intercept: 1.0695 (0.0118)
Ratio: 0.1263 (0.0215)
Analysis finished at Thu Oct 17 14:43:41 2019
Total time elapsed: 1.0m:33.75sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9448,
"inflation_factor": 1.2544,
"mean_EFFECT": 5.2502e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 104,
"n_p_sig": 18297,
"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": 78482,
"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": 1284550,
"ldsc_nsnp_merge_regression_ld": 1284550,
"ldsc_observed_scale_h2_beta": 0.0513,
"ldsc_observed_scale_h2_se": 0.0039,
"ldsc_intercept_beta": 1.0695,
"ldsc_intercept_se": 0.0118,
"ldsc_lambda_gc": 1.3241,
"ldsc_mean_chisq": 1.5507,
"ldsc_ratio": 0.1262
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| 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 | 8324760 | 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 | 8331512 | 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 | 8.654711e+00 | 5.762216e+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 | 7.874106e+07 | 5.638482e+07 | 828.0000000 | 3.233543e+07 | 6.923611e+07 | 1.145488e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.300000e-06 | 1.972500e-03 | -0.0363162 | -8.923000e-04 | -3.000000e-07 | 8.930000e-04 | 2.772670e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.506600e-03 | 9.723000e-04 | 0.0006380 | 7.456000e-04 | 1.069500e-03 | 2.011600e-03 | 9.978400e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.617621e-01 | 2.998671e-01 | 0.0000000 | 1.900002e-01 | 4.500005e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.617635e-01 | 2.998441e-01 | 0.0000000 | 1.928518e-01 | 4.488769e-01 | 7.214226e-01 | 9.999999e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.365693e-01 | 2.601860e-01 | 0.0065720 | 2.964200e-02 | 1.242520e-01 | 3.746470e-01 | 9.934280e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 78482 | 0.9905801 | NA | NA | NA | NA | NA | NA | NA | 2.360839e-01 | 2.520828e-01 | 0.0000000 | 2.915340e-02 | 1.401760e-01 | 3.710060e-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 | 0.0006907 | 0.0011735 | 0.5600000 | 0.5561258 | 0.623775 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0014150 | 0.0011624 | 0.2200002 | 0.2235070 | 0.400400 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0006581 | 0.0018586 | 0.7199992 | 0.7232610 | 0.103554 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0010454 | 0.0011446 | 0.3599996 | 0.3610699 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0007927 | 0.0022568 | 0.7300002 | 0.7253920 | 0.074510 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0014319 | 0.0013075 | 0.2700001 | 0.2734645 | 0.240953 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0007666 | 0.0016308 | 0.6400000 | 0.6382989 | 0.913457 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000699 | 0.0010957 | 0.9500000 | 0.9491213 | 0.116345 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0000770 | 0.0016056 | 0.9599999 | 0.9617694 | 0.067291 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001629 | 0.0008117 | 0.8400000 | 0.8408998 | 0.515623 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0019176 | 0.0009800 | 0.0500000 | 0.0503890 | 0.137929 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0021168 | 0.0012722 | 0.0959997 | 0.0961241 | 0.073728 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0026365 | 0.0017046 | 0.1199999 | 0.1219458 | 0.041951 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0016990 | 0.0017020 | 0.3200000 | 0.3181415 | 0.049721 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0021664 | 0.0012730 | 0.0890000 | 0.0887975 | 0.073218 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0009632 | 0.0014985 | 0.5199996 | 0.5203580 | 0.054458 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0011520 | 0.0015057 | 0.4400003 | 0.4442373 | 0.054087 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0021824 | 0.0012776 | 0.0879995 | 0.0876002 | 0.073054 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0054069 | 0.0030093 | 0.0719996 | 0.0723793 | 0.020042 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0005353 | 0.0007813 | 0.4899999 | 0.4932494 | 0.297980 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623775 ES:SE:LP:AF:ID 0.000690723:0.00117349:0.251812:0.623775:rs10399793
1 54676 rs2462492 C T . PASS AF=0.4004 ES:SE:LP:AF:ID -0.00141495:0.00116241:0.657577:0.4004:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103554 ES:SE:LP:AF:ID 0.000658141:0.00185861:0.142668:0.103554:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.00104535:0.00114455:0.443698:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.07451 ES:SE:LP:AF:ID 0.000792717:0.00225677:0.136677:0.07451:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240953 ES:SE:LP:AF:ID 0.00143189:0.00130752:0.568636:0.240953:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913457 ES:SE:LP:AF:ID -0.000766604:0.0016308:0.19382:0.913457:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116345 ES:SE:LP:AF:ID 6.99156e-05:0.00109568:0.0222764:0.116345:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067291 ES:SE:LP:AF:ID -7.69597e-05:0.00160556:0.0177288:0.067291:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515623 ES:SE:LP:AF:ID 0.000162941:0.000811691:0.0757207:0.515623:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032997 ES:SE:LP:AF:ID -0.00128801:0.00204651:0.275724:0.032997:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036613 ES:SE:LP:AF:ID -0.00205667:0.00185886:0.568636:0.036613:rs12184267
1 715367 rs12184277 A G . PASS AF=0.03673 ES:SE:LP:AF:ID -0.00176766:0.00185183:0.468521:0.03673:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036429 ES:SE:LP:AF:ID -0.00201205:0.00186519:0.552842:0.036429:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016412 ES:SE:LP:AF:ID 0.000951631:0.0028711:0.130768:0.016412:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036969 ES:SE:LP:AF:ID -0.00200959:0.00184448:0.552842:0.036969:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037066 ES:SE:LP:AF:ID -0.00178848:0.00183816:0.481486:0.037066:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101232 ES:SE:LP:AF:ID -0.00332986:0.00133895:1.88606:0.101232:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959107 ES:SE:LP:AF:ID 0.00233578:0.00177301:0.721246:0.959107:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031449 ES:SE:LP:AF:ID 0.00158716:0.00321792:0.207608:0.031449:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053254 ES:SE:LP:AF:ID 0.000272659:0.00255965:0.0362122:0.053254:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036585 ES:SE:LP:AF:ID -0.00188686:0.00185003:0.508638:0.036585:rs28454925
1 729632 rs116720794 C T . PASS AF=0.0369 ES:SE:LP:AF:ID -0.00175622:0.00183322:0.468521:0.0369:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843205 ES:SE:LP:AF:ID 0.00105366:0.000949622:0.568636:0.843205:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055924 ES:SE:LP:AF:ID -0.000453881:0.00153741:0.113509:0.055924:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122325 ES:SE:LP:AF:ID -0.000444849:0.00103938:0.173925:0.122325:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025712 ES:SE:LP:AF:ID -0.00206252:0.00255685:0.376751:0.025712:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121569 ES:SE:LP:AF:ID -0.000358107:0.00103981:0.136677:0.121569:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132336 ES:SE:LP:AF:ID -0.00113278:0.0010249:0.568636:0.132336:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011128 ES:SE:LP:AF:ID 0.000761422:0.00372721:0.0757207:0.011128:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036813 ES:SE:LP:AF:ID -0.00208313:0.00181472:0.60206:0.036813:rs146277091
1 752566 rs3094315 G A . PASS AF=0.83894 ES:SE:LP:AF:ID 0.000790602:0.000919644:0.408935:0.83894:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838567 ES:SE:LP:AF:ID 0.000756582:0.000918649:0.387216:0.838567:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869759 ES:SE:LP:AF:ID 0.000280457:0.000985734:0.107905:0.869759:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129892 ES:SE:LP:AF:ID -0.000113462:0.000987741:0.0409586:0.129892:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037324 ES:SE:LP:AF:ID -0.0020584:0.00178396:0.60206:0.037324:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037568 ES:SE:LP:AF:ID -0.00201386:0.0017727:0.585027:0.037568:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869101 ES:SE:LP:AF:ID 0.000263009:0.000983802:0.102373:0.869101:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869199 ES:SE:LP:AF:ID 0.000227648:0.000984193:0.0861861:0.869199:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037526 ES:SE:LP:AF:ID -0.00200612:0.00178036:0.585027:0.037526:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869104 ES:SE:LP:AF:ID 0.000274265:0.000983782:0.107905:0.869104:rs3131967
1 754503 rs3115859 G A . PASS AF=0.83802 ES:SE:LP:AF:ID 0.00080679:0.000916102:0.420216:0.83802:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037539 ES:SE:LP:AF:ID -0.00193819:0.00178287:0.552842:0.037539:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83865 ES:SE:LP:AF:ID 0.000804922:0.000918676:0.420216:0.83865:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013773 ES:SE:LP:AF:ID -0.00124118:0.00320655:0.154902:0.013773:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839764 ES:SE:LP:AF:ID 0.000844876:0.000931096:0.443698:0.839764:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869384 ES:SE:LP:AF:ID 0.000335681:0.000982647:0.136677:0.869384:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868931 ES:SE:LP:AF:ID 0.000351257:0.000980174:0.142668:0.868931:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867883 ES:SE:LP:AF:ID 0.000380724:0.000978292:0.154902:0.867883:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869075 ES:SE:LP:AF:ID 0.000332405:0.000980977:0.136677:0.869075:rs4951929