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_20004_1478.vcf.gz --id UKB-b:11497 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20004_1478.txt.gz --cohort_cases 72111 --cohort_controls 390822 --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": "2019-09-13T01:16:59.087855",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11497/ukb-b-11497.vcf.gz; Date=Sun May 10 03:19:45 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-11497/UKB-b-11497_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11497/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:52 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11497/UKB-b-11497_data.vcf.gz ...
Read summary statistics for 8745295 SNPs.
Dropped 7710 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, 1286143 SNPs remain.
After merging with regression SNP LD, 1286143 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0371 (0.0026)
Lambda GC: 1.3196
Mean Chi^2: 1.43
Intercept: 1.0917 (0.0109)
Ratio: 0.2132 (0.0252)
Analysis finished at Thu Oct 17 14:42:25 2019
Total time elapsed: 1.0m:33.46sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9467,
"inflation_factor": 1.2544,
"mean_EFFECT": -8.536e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 79,
"n_p_sig": 6502,
"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": 85523,
"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": 1286143,
"ldsc_nsnp_merge_regression_ld": 1286143,
"ldsc_observed_scale_h2_beta": 0.0371,
"ldsc_observed_scale_h2_se": 0.0026,
"ldsc_intercept_beta": 1.0917,
"ldsc_intercept_se": 0.0109,
"ldsc_lambda_gc": 1.3196,
"ldsc_mean_chisq": 1.43,
"ldsc_ratio": 0.2133
}
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 | 8737621 | 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 | 8745295 | 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 | 8.649866e+00 | 5.760879e+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 | 7.877110e+07 | 5.635673e+07 | 828.0000000 | 3.239708e+07 | 6.931142e+07 | 1.145597e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -8.500000e-06 | 2.455500e-03 | -0.0290833 | -1.069900e-03 | -1.100000e-05 | 1.047300e-03 | 7.211710e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.877300e-03 | 1.308900e-03 | 0.0007253 | 8.576000e-04 | 1.279500e-03 | 2.551700e-03 | 1.288090e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.632153e-01 | 2.983455e-01 | 0.0000000 | 2.000000e-01 | 4.500005e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.632162e-01 | 2.983205e-01 | 0.0000000 | 1.957451e-01 | 4.505549e-01 | 7.215683e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.262006e-01 | 2.592012e-01 | 0.0048540 | 2.363800e-02 | 1.098300e-01 | 3.575660e-01 | 9.951460e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 85523 | 0.9902207 | NA | NA | NA | NA | NA | NA | NA | 2.260630e-01 | 2.511671e-01 | 0.0000000 | 2.116610e-02 | 1.265970e-01 | 3.548320e-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 | 0.0009523 | 0.0013341 | 0.4799997 | 0.4753608 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0002690 | 0.0013217 | 0.8400000 | 0.8386980 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0006009 | 0.0021131 | 0.7800007 | 0.7761147 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0015387 | 0.0013014 | 0.2399999 | 0.2370525 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0011980 | 0.0025659 | 0.6400000 | 0.6405807 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0015607 | 0.0014865 | 0.2900000 | 0.2937378 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0030694 | 0.0018545 | 0.0980009 | 0.0978896 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0018452 | 0.0012457 | 0.1400000 | 0.1385448 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0020814 | 0.0018255 | 0.2500000 | 0.2542015 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0013074 | 0.0009228 | 0.1600000 | 0.1565451 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0006606 | 0.0011138 | 0.5500004 | 0.5530997 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0006115 | 0.0014457 | 0.6700003 | 0.6723072 | 0.073747 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0009278 | 0.0019374 | 0.6300007 | 0.6320028 | 0.041955 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0012867 | 0.0019342 | 0.5099998 | 0.5059093 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0006222 | 0.0014466 | 0.6700003 | 0.6671212 | 0.073238 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0024627 | 0.0017032 | 0.1499999 | 0.1481894 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0024839 | 0.0017114 | 0.1499999 | 0.1466816 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0005583 | 0.0014519 | 0.6999999 | 0.7005777 | 0.073073 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0054059 | 0.0034203 | 0.1100001 | 0.1139816 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0012268 | 0.0008880 | 0.1700000 | 0.1671138 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.000952252:0.00133409:0.318759:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000269036:0.00132166:0.0757207:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.00060093:0.00211308:0.107905:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.00153871:0.00130136:0.619789:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID -0.00119799:0.00256591:0.19382:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID 0.00156073:0.00148647:0.537602:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID 0.00306945:0.00185446:1.00877:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 0.00184523:0.00124574:0.853872:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID -0.00208143:0.00182549:0.60206:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -0.00130739:0.000922785:0.79588:0.51565:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID -0.00125595:0.00232641:0.229148:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036621 ES:SE:LP:AF:ID -0.00151189:0.00211312:0.327902:0.036621:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID -0.00163053:0.00210513:0.356547:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036437 ES:SE:LP:AF:ID -0.00165167:0.0021203:0.356547:0.036437:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016407 ES:SE:LP:AF:ID -0.0039531:0.00326467:0.638272:0.016407:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036976 ES:SE:LP:AF:ID -0.00159197:0.00209681:0.346787:0.036976:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037072 ES:SE:LP:AF:ID -0.0014605:0.00208963:0.318759:0.037072:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.00231987:0.00152254:0.886057:0.101199:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959096 ES:SE:LP:AF:ID 0.00101568:0.00201541:0.21467:0.959096:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031451 ES:SE:LP:AF:ID -0.00700389:0.00365855:1.25181:0.031451:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053254 ES:SE:LP:AF:ID 0.000527708:0.00291054:0.0655015:0.053254:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03659 ES:SE:LP:AF:ID -0.00137:0.00210316:0.29243:0.03659:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID -0.00135273:0.00208401:0.283997:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -0.00139074:0.00107959:0.69897:0.843212:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID 0.00366588:0.00174803:1.4437:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 0.00185859:0.00118171:0.920819:0.122307:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025713 ES:SE:LP:AF:ID -2.40491e-05:0.00290675:0.00436481:0.025713:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 0.00190896:0.0011822:0.958607:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 0.00196901:0.00116518:1.04096:0.13233:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011134 ES:SE:LP:AF:ID -0.00324694:0.00423652:0.356547:0.011134:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005697 ES:SE:LP:AF:ID 0.0073358:0.00546984:0.744727:0.005697:rs61770167
1 752478 rs146277091 G A . PASS AF=0.036821 ES:SE:LP:AF:ID -0.00152143:0.00206293:0.337242:0.036821:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -0.00161693:0.00104551:0.920819:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -0.00167112:0.00104438:0.958607:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -0.00213865:0.00112067:1.25181:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 0.00205113:0.00112296:1.16749:0.129871:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037332 ES:SE:LP:AF:ID -0.00153492:0.00202795:0.346787:0.037332:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037576 ES:SE:LP:AF:ID -0.00143796:0.00201514:0.318759:0.037576:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -0.00211679:0.00111847:1.23657:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -0.00203933:0.00111891:1.16749:0.869221:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037534 ES:SE:LP:AF:ID -0.00167981:0.00202386:0.387216:0.037534:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -0.00212383:0.00111845:1.23657:0.869126:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005122 ES:SE:LP:AF:ID 0.00487869:0.00574263:0.39794:0.005122:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005088 ES:SE:LP:AF:ID 0.00500259:0.00575769:0.420216:0.005088:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -0.00161454:0.00104149:0.920819:0.838033:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037547 ES:SE:LP:AF:ID -0.00156865:0.00202672:0.356547:0.037547:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -0.00155706:0.00104441:0.853872:0.838664:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013773 ES:SE:LP:AF:ID 0.00195463:0.00364566:0.229148:0.013773:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005546 ES:SE:LP:AF:ID 0.00091307:0.00562549:0.0604807:0.005546:rs184270342
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -0.00142801:0.00105854:0.744727:0.839777:rs3131965