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_48.vcf.gz --id UKB-b:9405 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_48.txt.gz --cohort_controls 462166 --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",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-9405/ukb-b-9405.vcf.gz; Date=Sun May 10 11:26:57 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-9405/UKB-b-9405_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9405/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:37 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9405/UKB-b-9405_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1865 (0.006)
Lambda GC: 2.2194
Mean Chi^2: 2.8448
Intercept: 1.1498 (0.0156)
Ratio: 0.0812 (0.0084)
Analysis finished at Thu Oct 17 14:44:07 2019
Total time elapsed: 1.0m:29.75sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.7665,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 374,
"n_p_sig": 41008,
"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": 184849,
"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": 1289166,
"ldsc_nsnp_merge_regression_ld": 1289166,
"ldsc_observed_scale_h2_beta": 0.1865,
"ldsc_observed_scale_h2_se": 0.006,
"ldsc_intercept_beta": 1.1498,
"ldsc_intercept_se": 0.0156,
"ldsc_lambda_gc": 2.2194,
"ldsc_mean_chisq": 2.8448,
"ldsc_ratio": 0.0812
}
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 | 9837196 | 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 | 9851866 | 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 | 8.622825e+00 | 5.748290e+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 | 7.886027e+07 | 5.628334e+07 | 828.0000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.440000e-05 | 9.527200e-03 | -0.1767200 | -3.565000e-03 | 1.440000e-05 | 3.621100e-03 | 1.593450e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.164500e-03 | 5.834600e-03 | 0.0017236 | 2.111800e-03 | 3.541300e-03 | 8.170700e-03 | 9.063050e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.076434e-01 | 3.099674e-01 | 0.0000000 | 1.199999e-01 | 3.700002e-01 | 6.700003e-01 | 1.000000e+00 | ▇▅▅▃▃ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.076447e-01 | 3.099450e-01 | 0.0000000 | 1.159796e-01 | 3.693320e-01 | 6.737559e-01 | 9.999999e-01 | ▇▅▃▃▃ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035077e-01 | 2.568614e-01 | 0.0009930 | 1.316920e-02 | 7.791400e-02 | 3.164530e-01 | 9.990050e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.0000000 | 1.198080e-02 | 9.984030e-02 | 3.202880e-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 | 0.0000576 | 0.0031678 | 0.9900000 | 0.9854809 | 0.623766 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0032106 | 0.0031383 | 0.3100002 | 0.3062913 | 0.400412 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0063297 | 0.0050177 | 0.2099999 | 0.2071392 | 0.103548 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0039547 | 0.0030900 | 0.2000000 | 0.2006017 | 0.456853 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0094046 | 0.0060937 | 0.1199999 | 0.1227473 | 0.074494 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0000245 | 0.0035298 | 0.9900000 | 0.9944653 | 0.240946 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0028583 | 0.0044027 | 0.5199996 | 0.5161935 | 0.913454 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0003753 | 0.0029585 | 0.9000000 | 0.8990617 | 0.116280 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0021086 | 0.0043341 | 0.6300007 | 0.6265979 | 0.067302 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0005050 | 0.0021911 | 0.8200001 | 0.8177066 | 0.515671 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0013783 | 0.0046194 | 0.7700005 | 0.7654233 | 0.041957 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0009193 | 0.0215196 | 0.9699999 | 0.9659238 | 0.001938 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0040953 | 0.0213164 | 0.8499999 | 0.8476481 | 0.001987 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0020726 | 0.0046123 | 0.6499995 | 0.6531754 | 0.049730 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0029762 | 0.0034496 | 0.3900004 | 0.3882737 | 0.073237 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0006215 | 0.0040611 | 0.8800001 | 0.8783645 | 0.054468 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0008003 | 0.0040808 | 0.8400000 | 0.8445150 | 0.054097 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0027396 | 0.0034621 | 0.4299995 | 0.4287581 | 0.073071 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0063696 | 0.0081547 | 0.4299995 | 0.4347458 | 0.020047 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0016204 | 0.0021175 | 0.4400003 | 0.4441367 | 0.297933 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623766 ES:SE:LP:AF:ID 5.76487e-05:0.00316785:0.00436481:0.623766:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400412 ES:SE:LP:AF:ID 0.00321057:0.00313828:0.508638:0.400412:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103548 ES:SE:LP:AF:ID 0.00632967:0.00501769:0.677781:0.103548:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456853 ES:SE:LP:AF:ID 0.00395469:0.00308999:0.69897:0.456853:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074494 ES:SE:LP:AF:ID -0.00940463:0.00609367:0.920819:0.074494:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240946 ES:SE:LP:AF:ID 2.44857e-05:0.00352981:0.00436481:0.240946:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913454 ES:SE:LP:AF:ID -0.00285832:0.00440267:0.283997:0.913454:rs12025928
1 693731 rs12238997 A G . PASS AF=0.11628 ES:SE:LP:AF:ID -0.000375276:0.0029585:0.0457575:0.11628:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067302 ES:SE:LP:AF:ID 0.00210863:0.0043341:0.200659:0.067302:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515671 ES:SE:LP:AF:ID -0.000505039:0.0021911:0.0861861:0.515671:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033001 ES:SE:LP:AF:ID -0.00073977:0.00552421:0.05061:0.033001:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036617 ES:SE:LP:AF:ID -0.00105779:0.00501779:0.0809219:0.036617:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036734 ES:SE:LP:AF:ID -0.00114126:0.00499873:0.0861861:0.036734:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036432 ES:SE:LP:AF:ID -0.00253273:0.00503487:0.21467:0.036432:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016395 ES:SE:LP:AF:ID -0.00616461:0.00775527:0.366532:0.016395:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036971 ES:SE:LP:AF:ID -0.00171913:0.0049791:0.136677:0.036971:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037067 ES:SE:LP:AF:ID -0.00205695:0.00496203:0.167491:0.037067:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101204 ES:SE:LP:AF:ID -0.00033224:0.00361508:0.0315171:0.101204:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959102 ES:SE:LP:AF:ID 0.00330554:0.00478593:0.309804:0.959102:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031453 ES:SE:LP:AF:ID 0.000774878:0.0086868:0.0315171:0.031453:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053258 ES:SE:LP:AF:ID 0.00164318:0.00691078:0.091515:0.053258:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036588 ES:SE:LP:AF:ID -0.00256383:0.004994:0.21467:0.036588:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036903 ES:SE:LP:AF:ID -0.00323007:0.00494859:0.29243:0.036903:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843261 ES:SE:LP:AF:ID 0.0016529:0.00256379:0.283997:0.843261:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055887 ES:SE:LP:AF:ID -0.00570297:0.00415145:0.769551:0.055887:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122258 ES:SE:LP:AF:ID -0.000567956:0.00280644:0.0757207:0.122258:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025711 ES:SE:LP:AF:ID -0.0103993:0.00690242:0.886057:0.025711:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121501 ES:SE:LP:AF:ID -0.000290195:0.00280762:0.0362122:0.121501:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132297 ES:SE:LP:AF:ID -0.0007595:0.00276701:0.107905:0.132297:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011137 ES:SE:LP:AF:ID 0.0081947:0.010059:0.376751:0.011137:rs181876450
1 740284 rs61770167 C T . PASS AF=0.00569 ES:SE:LP:AF:ID 0.0232307:0.0129968:1.13077:0.00569:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002272 ES:SE:LP:AF:ID 0.00189601:0.021821:0.0315171:0.002272:rs112573343
1 746189 rs139221807 A G . PASS AF=0.00103 ES:SE:LP:AF:ID -0.00524914:0.0357399:0.0555173:0.00103:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036819 ES:SE:LP:AF:ID -0.00140657:0.00489852:0.113509:0.036819:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838999 ES:SE:LP:AF:ID 0.00145822:0.00248284:0.251812:0.838999:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83863 ES:SE:LP:AF:ID 0.00189267:0.00248016:0.346787:0.83863:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869831 ES:SE:LP:AF:ID 0.00067606:0.00266145:0.09691:0.869831:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129815 ES:SE:LP:AF:ID -0.00140429:0.00266694:0.221849:0.129815:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037331 ES:SE:LP:AF:ID -0.00315678:0.00481549:0.29243:0.037331:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037574 ES:SE:LP:AF:ID -0.0031126:0.00478515:0.283997:0.037574:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869177 ES:SE:LP:AF:ID 0.00109501:0.00265626:0.167491:0.869177:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869275 ES:SE:LP:AF:ID 0.0011041:0.00265731:0.167491:0.869275:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037531 ES:SE:LP:AF:ID -0.00319483:0.0048059:0.29243:0.037531:rs12184313
1 754334 rs3131967 T C . PASS AF=0.86918 ES:SE:LP:AF:ID 0.00110922:0.0026562:0.167491:0.86918:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005122 ES:SE:LP:AF:ID -0.0100641:0.0136364:0.337242:0.005122:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005087 ES:SE:LP:AF:ID -0.0104673:0.0136722:0.356547:0.005087:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838085 ES:SE:LP:AF:ID 0.00199688:0.00247331:0.376751:0.838085:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037544 ES:SE:LP:AF:ID -0.00344827:0.00481268:0.327902:0.037544:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838716 ES:SE:LP:AF:ID 0.00199889:0.00248027:0.376751:0.838716:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013771 ES:SE:LP:AF:ID 0.00642992:0.00865709:0.337242:0.013771:rs181660517