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_6177_3.vcf.gz --id UKB-b:7350 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6177_3.txt.gz --cohort_cases 2891 --cohort_controls 206747 --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": "2019-09-13T02:00:20.571346",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7350/ukb-b-7350.vcf.gz; Date=Sun May 10 00:53:54 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-7350/UKB-b-7350_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7350/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7350/UKB-b-7350_data.vcf.gz ...
Read summary statistics for 3952523 SNPs.
Dropped 741 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, 943572 SNPs remain.
After merging with regression SNP LD, 943572 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0118 (0.0028)
Lambda GC: 1.0473
Mean Chi^2: 1.0646
Intercept: 1.0109 (0.0088)
Ratio: 0.1685 (0.1366)
Analysis finished at Thu Oct 17 14:41:02 2019
Total time elapsed: 42.79sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8735,
"inflation_factor": 1.0475,
"mean_EFFECT": 3.524e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 7,
"n_p_sig": 5379,
"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": 32223,
"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": 943572,
"ldsc_nsnp_merge_regression_ld": 943572,
"ldsc_observed_scale_h2_beta": 0.0118,
"ldsc_observed_scale_h2_se": 0.0028,
"ldsc_intercept_beta": 1.0109,
"ldsc_intercept_se": 0.0088,
"ldsc_lambda_gc": 1.0473,
"ldsc_mean_chisq": 1.0646,
"ldsc_ratio": 0.1687
}
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 | 3951786 | 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 | 3952523 | 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 | 8.659105e+00 | 5.766311e+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 | 7.857136e+07 | 5.675289e+07 | 828.0000000 | 3.162691e+07 | 6.890476e+07 | 1.147031e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.500000e-06 | 4.634000e-04 | -0.0042725 | -2.890000e-04 | 5.000000e-07 | 2.877000e-04 | 1.284830e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.197000e-04 | 5.620000e-05 | 0.0003473 | 3.710000e-04 | 4.014000e-04 | 4.584000e-04 | 1.268600e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.895862e-01 | 2.923248e-01 | 0.0000000 | 2.300001e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.895859e-01 | 2.922996e-01 | 0.0000000 | 2.336546e-01 | 4.869480e-01 | 7.429063e-01 | 9.999995e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.962609e-01 | 2.113164e-01 | 0.1210660 | 2.119650e-01 | 3.494120e-01 | 5.532750e-01 | 8.789340e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 32223 | 0.9918475 | NA | NA | NA | NA | NA | NA | NA | 3.849525e-01 | 2.158738e-01 | 0.0000000 | 2.074680e-01 | 3.454470e-01 | 5.409350e-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 | 0.0008370 | 0.0006389 | 0.1900002 | 0.1901487 | 0.624267 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001784 | 0.0006344 | 0.7800007 | 0.7785493 | 0.399695 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0004666 | 0.0006242 | 0.4500005 | 0.4547075 | 0.456603 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0003418 | 0.0007126 | 0.6300007 | 0.6314580 | 0.240711 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0009107 | 0.0004425 | 0.0400000 | 0.0395733 | 0.516309 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0007101 | 0.0005174 | 0.1700000 | 0.1698992 | 0.843414 | 0.639976 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0013659 | 0.0005672 | 0.0160000 | 0.0160406 | 0.121895 | 0.154353 | NA |
| 1 | 734349 | rs141242758 | T | C | 0.0013721 | 0.0005675 | 0.0160000 | 0.0156037 | 0.121160 | 0.152556 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0006547 | 0.0005589 | 0.2399999 | 0.2414584 | 0.132047 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0006004 | 0.0005014 | 0.2300001 | 0.2311498 | 0.839209 | 0.718251 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51197266 | rs61290853 | A | G | -0.0002200 | 0.0003902 | 0.5700002 | 0.5728599 | 0.386265 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000468 | 0.0004413 | 0.9199999 | 0.9155712 | 0.254258 | 0.0984425 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0010365 | 0.0005867 | 0.0769999 | 0.0772996 | 0.130150 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0002418 | 0.0004380 | 0.5800000 | 0.5809089 | 0.271179 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0012613 | 0.0005818 | 0.0299999 | 0.0301772 | 0.132731 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000631 | 0.0004160 | 0.8800001 | 0.8794925 | 0.331534 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0004175 | 0.0005468 | 0.4500005 | 0.4451749 | 0.127711 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0003646 | 0.0005445 | 0.5000000 | 0.5031065 | 0.128204 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0002448 | 0.0005327 | 0.6499995 | 0.6457831 | 0.137965 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001433 | 0.0004257 | 0.7400005 | 0.7364911 | 0.298014 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.624267 ES:SE:LP:AF:ID 0.00083701:0.000638871:0.721246:0.624267:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399695 ES:SE:LP:AF:ID -0.000178396:0.000634387:0.107905:0.399695:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456603 ES:SE:LP:AF:ID 0.000466609:0.000624151:0.346787:0.456603:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240711 ES:SE:LP:AF:ID 0.000341803:0.000712571:0.200659:0.240711:rs6680723
1 706368 rs12029736 A G . PASS AF=0.516309 ES:SE:LP:AF:ID -0.000910728:0.000442493:1.39794:0.516309:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843414 ES:SE:LP:AF:ID -0.000710117:0.000517379:0.769551:0.843414:rs4951859
1 731718 rs58276399 T C . PASS AF=0.121895 ES:SE:LP:AF:ID 0.00136588:0.000567228:1.79588:0.121895:rs58276399
1 734349 rs141242758 T C . PASS AF=0.12116 ES:SE:LP:AF:ID 0.00137214:0.000567456:1.79588:0.12116:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132047 ES:SE:LP:AF:ID 0.000654707:0.000558934:0.619789:0.132047:rs79010578
1 752566 rs3094315 G A . PASS AF=0.839209 ES:SE:LP:AF:ID -0.00060035:0.000501377:0.638272:0.839209:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838837 ES:SE:LP:AF:ID -0.000571858:0.000500775:0.60206:0.838837:rs3131972
1 753405 rs3115860 C A . PASS AF=0.870174 ES:SE:LP:AF:ID -0.00101702:0.000537929:1.22915:0.870174:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129469 ES:SE:LP:AF:ID 0.000986108:0.000538907:1.17393:0.129469:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869527 ES:SE:LP:AF:ID -0.000988175:0.000536793:1.18046:0.869527:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869655 ES:SE:LP:AF:ID -0.000959662:0.000537036:1.13077:0.869655:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869532 ES:SE:LP:AF:ID -0.000991986:0.000536791:1.18709:0.869532:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838292 ES:SE:LP:AF:ID -0.000574462:0.000499399:0.60206:0.838292:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838917 ES:SE:LP:AF:ID -0.000578625:0.0005008:0.60206:0.838917:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839989 ES:SE:LP:AF:ID -0.000608344:0.000507648:0.638272:0.839989:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869803 ES:SE:LP:AF:ID -0.000989975:0.00053626:1.18709:0.869803:rs3115858
1 756604 rs3131962 A G . PASS AF=0.869356 ES:SE:LP:AF:ID -0.000968263:0.000534905:1.1549:0.869356:rs3131962
1 757640 rs3115853 G A . PASS AF=0.868288 ES:SE:LP:AF:ID -0.000988699:0.000533708:1.19382:0.868288:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869497 ES:SE:LP:AF:ID -0.000968066:0.000535332:1.14874:0.869497:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869505 ES:SE:LP:AF:ID -0.000968482:0.000535373:1.1549:0.869505:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869513 ES:SE:LP:AF:ID -0.000966783:0.000535387:1.14874:0.869513:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869995 ES:SE:LP:AF:ID -0.000994423:0.000536884:1.19382:0.869995:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838576 ES:SE:LP:AF:ID -0.000533295:0.000498514:0.552842:0.838576:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838699 ES:SE:LP:AF:ID -0.000559415:0.000498865:0.585027:0.838699:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862592 ES:SE:LP:AF:ID -0.000875425:0.000533227:1:0.862592:rs2286139
1 763394 rs3115847 G A . PASS AF=0.707131 ES:SE:LP:AF:ID -0.000288979:0.000518811:0.236572:0.707131:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761579 ES:SE:LP:AF:ID -0.000711447:0.000423315:1.03152:0.761579:rs2977608
1 769223 rs60320384 C G . PASS AF=0.129129 ES:SE:LP:AF:ID 0.000984205:0.000538729:1.16749:0.129129:rs60320384
1 771823 rs2977605 T C . PASS AF=0.869365 ES:SE:LP:AF:ID -0.000966685:0.000535908:1.14874:0.869365:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129208 ES:SE:LP:AF:ID 0.000968921:0.000538436:1.14267:0.129208:rs59066358
1 772755 rs2905039 A C . PASS AF=0.86938 ES:SE:LP:AF:ID -0.000963726:0.000535941:1.14267:0.86938:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265334 ES:SE:LP:AF:ID -0.000613597:0.000472972:0.721246:0.265334:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870493 ES:SE:LP:AF:ID -0.000989428:0.000537037:1.18709:0.870493:rs2980319
1 778745 rs1055606 A G . PASS AF=0.128094 ES:SE:LP:AF:ID 0.00099927:0.000539122:1.19382:0.128094:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128397 ES:SE:LP:AF:ID 0.000978526:0.000538145:1.16115:0.128397:rs4040617
1 780785 rs2977612 T A . PASS AF=0.869202 ES:SE:LP:AF:ID -0.000946604:0.000535379:1.11351:0.869202:rs2977612
1 782981 rs6594026 C T . PASS AF=0.129066 ES:SE:LP:AF:ID 0.000896438:0.000537839:1.01773:0.129066:rs6594026
1 785050 rs2905062 G A . PASS AF=0.869002 ES:SE:LP:AF:ID -0.000937095:0.000535305:1.09691:0.869002:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868937 ES:SE:LP:AF:ID -0.000940631:0.000535651:1.10237:0.868937:rs2980300
1 787399 rs2905055 G T . PASS AF=0.861146 ES:SE:LP:AF:ID -0.000844269:0.000535206:0.958607:0.861146:rs2905055
1 787606 rs3863622 G T . PASS AF=0.127981 ES:SE:LP:AF:ID 0.000971541:0.000540944:1.14267:0.127981:rs3863622
1 787685 rs2905054 G T . PASS AF=0.861947 ES:SE:LP:AF:ID -0.00105094:0.00053563:1.30103:0.861947:rs2905054
1 787844 rs2905053 C T . PASS AF=0.869697 ES:SE:LP:AF:ID -0.00100606:0.000537708:1.21467:0.869697:rs2905053
1 791191 rs111818025 G A . PASS AF=0.127414 ES:SE:LP:AF:ID 0.000950602:0.000544417:1.09151:0.127414:rs111818025
1 795988 rs59380221 C T . PASS AF=0.142796 ES:SE:LP:AF:ID 0.0011987:0.000554996:1.50864:0.142796:rs59380221
1 796375 rs12083781 T C . PASS AF=0.123418 ES:SE:LP:AF:ID 0.0010119:0.00055958:1.14874:0.123418:rs12083781