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_2267.vcf.gz --id UKB-b:7422 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2267.txt.gz --cohort_controls 459416 --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": "2019-09-13T04:00:21.845655",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7422/ukb-b-7422.vcf.gz; Date=Sun May 10 14:39:41 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-7422/UKB-b-7422_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7422/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7422/UKB-b-7422_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: 0.0587 (0.0049)
Lambda GC: 1.4238
Mean Chi^2: 1.5997
Intercept: 1.0648 (0.0108)
Ratio: 0.108 (0.0179)
Analysis finished at Thu Oct 17 14:42:59 2019
Total time elapsed: 2.0m:41.94sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.3107,
"mean_EFFECT": -0.0002,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 51,
"n_p_sig": 5401,
"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": 0.0587,
"ldsc_observed_scale_h2_se": 0.0049,
"ldsc_intercept_beta": 1.0648,
"ldsc_intercept_se": 0.0108,
"ldsc_lambda_gc": 1.4238,
"ldsc_mean_chisq": 1.5997,
"ldsc_ratio": 0.1081
}
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 | -2.052000e-04 | 9.077200e-03 | -0.1684140 | -3.044900e-03 | -5.830000e-05 | 2.871800e-03 | 3.673680e-01 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.199600e-03 | 5.869200e-03 | 0.0017349 | 2.123500e-03 | 3.560800e-03 | 8.216500e-03 | 9.109080e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.581774e-01 | 3.002704e-01 | 0.0000000 | 1.900002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.581787e-01 | 3.002458e-01 | 0.0000000 | 1.870311e-01 | 4.433748e-01 | 7.184307e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035075e-01 | 2.568628e-01 | 0.0009870 | 1.316900e-02 | 7.791300e-02 | 3.164560e-01 | 9.990110e-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 | 0.0006112 | 0.0031906 | 0.8499999 | 0.8480732 | 0.623814 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0022994 | 0.0031612 | 0.4700002 | 0.4669814 | 0.400374 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0063525 | 0.0050546 | 0.2099999 | 0.2088353 | 0.103542 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0044470 | 0.0031123 | 0.1499999 | 0.1530472 | 0.456853 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0006677 | 0.0061358 | 0.9100000 | 0.9133424 | 0.074522 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0015286 | 0.0035562 | 0.6700003 | 0.6673170 | 0.240930 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0005171 | 0.0044347 | 0.9100000 | 0.9071827 | 0.913448 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0019719 | 0.0029790 | 0.5099998 | 0.5080024 | 0.116339 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0079174 | 0.0043659 | 0.0700003 | 0.0697587 | 0.067302 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002085 | 0.0022071 | 0.9199999 | 0.9247203 | 0.515660 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0064859 | 0.0046351 | 0.1600000 | 0.1617213 | 0.041949 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0125379 | 0.0216024 | 0.5600000 | 0.5616491 | 0.001935 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0087562 | 0.0214000 | 0.6800001 | 0.6824179 | 0.001984 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0094766 | 0.0046279 | 0.0409996 | 0.0405897 | 0.049709 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0066604 | 0.0034612 | 0.0539995 | 0.0543151 | 0.073219 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0058662 | 0.0040741 | 0.1499999 | 0.1498998 | 0.054477 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0060406 | 0.0040938 | 0.1400000 | 0.1400691 | 0.054105 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0070322 | 0.0034736 | 0.0430002 | 0.0429230 | 0.073057 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0124052 | 0.0081780 | 0.1299999 | 0.1292931 | 0.020060 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0012860 | 0.0021243 | 0.5400003 | 0.5449237 | 0.297922 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623814 ES:SE:LP:AF:ID 0.000611247:0.0031906:0.0705811:0.623814:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400374 ES:SE:LP:AF:ID 0.00229944:0.00316118:0.327902:0.400374:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103542 ES:SE:LP:AF:ID -0.00635253:0.00505463:0.677781:0.103542:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456853 ES:SE:LP:AF:ID 0.00444704:0.00311232:0.823909:0.456853:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074522 ES:SE:LP:AF:ID -0.00066772:0.0061358:0.0409586:0.074522:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24093 ES:SE:LP:AF:ID -0.00152856:0.00355617:0.173925:0.24093:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913448 ES:SE:LP:AF:ID 0.000517051:0.00443467:0.0409586:0.913448:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116339 ES:SE:LP:AF:ID 0.00197194:0.00297898:0.29243:0.116339:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067302 ES:SE:LP:AF:ID 0.00791741:0.00436588:1.1549:0.067302:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51566 ES:SE:LP:AF:ID -0.000208544:0.00220706:0.0362122:0.51566:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032991 ES:SE:LP:AF:ID 0.0013331:0.0055653:0.091515:0.032991:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036606 ES:SE:LP:AF:ID 0.00156722:0.00505504:0.119186:0.036606:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036722 ES:SE:LP:AF:ID 0.00227526:0.00503591:0.187087:0.036722:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036425 ES:SE:LP:AF:ID 0.00205677:0.00507187:0.161151:0.036425:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016407 ES:SE:LP:AF:ID -0.0108669:0.00780821:0.79588:0.016407:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036961 ES:SE:LP:AF:ID 0.00222681:0.00501597:0.180456:0.036961:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037058 ES:SE:LP:AF:ID 0.00194306:0.00499875:0.154902:0.037058:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101236 ES:SE:LP:AF:ID -0.00188548:0.00364061:0.221849:0.101236:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959109 ES:SE:LP:AF:ID -0.00311858:0.0048209:0.283997:0.959109:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031456 ES:SE:LP:AF:ID 0.0103085:0.00874874:0.619789:0.031456:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053262 ES:SE:LP:AF:ID 0.00184582:0.00695987:0.102373:0.053262:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036576 ES:SE:LP:AF:ID 0.00228491:0.00503106:0.187087:0.036576:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036895 ES:SE:LP:AF:ID 0.00208918:0.00498511:0.167491:0.036895:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843226 ES:SE:LP:AF:ID -0.00409693:0.00258212:0.958607:0.843226:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055906 ES:SE:LP:AF:ID 0.00760719:0.00418104:1.16115:0.055906:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122315 ES:SE:LP:AF:ID 0.00277463:0.00282604:0.481486:0.122315:rs58276399
1 732989 rs369030935 C T . PASS AF=0.0257 ES:SE:LP:AF:ID -0.00283828:0.00695364:0.167491:0.0257:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121557 ES:SE:LP:AF:ID 0.00276921:0.00282723:0.481486:0.121557:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132317 ES:SE:LP:AF:ID 0.00518317:0.00278693:1.20066:0.132317:rs79010578
1 736689 rs181876450 T C . PASS AF=0.01113 ES:SE:LP:AF:ID 0.00373649:0.0101327:0.148742:0.01113:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005702 ES:SE:LP:AF:ID -0.0324768:0.0130757:1.88606:0.005702:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002273 ES:SE:LP:AF:ID 0.0300739:0.0219625:0.769551:0.002273:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001031 ES:SE:LP:AF:ID 0.00709404:0.0359778:0.0757207:0.001031:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036807 ES:SE:LP:AF:ID 0.00142362:0.00493492:0.113509:0.036807:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838965 ES:SE:LP:AF:ID -0.00481921:0.00250067:1.26761:0.838965:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838593 ES:SE:LP:AF:ID -0.00485873:0.00249795:1.284:0.838593:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869771 ES:SE:LP:AF:ID -0.00384147:0.0026802:0.823909:0.869771:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129878 ES:SE:LP:AF:ID 0.00344707:0.0026856:0.69897:0.129878:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037317 ES:SE:LP:AF:ID 0.00128652:0.00485126:0.102373:0.037317:rs12184312
1 754105 rs12184325 C T . PASS AF=0.03756 ES:SE:LP:AF:ID 0.000956693:0.00482067:0.0757207:0.03756:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869114 ES:SE:LP:AF:ID -0.00397961:0.00267494:0.853872:0.869114:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869209 ES:SE:LP:AF:ID -0.00392904:0.00267597:0.853872:0.869209:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037518 ES:SE:LP:AF:ID 0.00113798:0.00484149:0.091515:0.037518:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869116 ES:SE:LP:AF:ID -0.0039657:0.00267489:0.853872:0.869116:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005129 ES:SE:LP:AF:ID 0.0171121:0.0137237:0.677781:0.005129:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005094 ES:SE:LP:AF:ID 0.0173665:0.0137597:0.677781:0.005094:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838049 ES:SE:LP:AF:ID -0.00475194:0.00249105:1.25181:0.838049:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037531 ES:SE:LP:AF:ID 0.00106149:0.00484829:0.0809219:0.037531:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838681 ES:SE:LP:AF:ID -0.00487407:0.00249806:1.29243:0.838681:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013792 ES:SE:LP:AF:ID -0.000576574:0.0087132:0.0222764:0.013792:rs181660517