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_20095_2.vcf.gz --id UKB-b:3650 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20095_2.txt.gz --cohort_cases 4696 --cohort_controls 60253 --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": "2019-09-13T11:46:28.087065",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3650/ukb-b-3650.vcf.gz; Date=Sat May 9 15:58:50 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-3650/UKB-b-3650_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3650/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3650/UKB-b-3650_data.vcf.gz ...
Read summary statistics for 4804672 SNPs.
Dropped 1240 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, 1088110 SNPs remain.
After merging with regression SNP LD, 1088110 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0127 (0.0076)
Lambda GC: 1.0168
Mean Chi^2: 1.0195
Intercept: 1.0022 (0.0081)
Ratio: 0.111 (0.4132)
Analysis finished at Thu Oct 17 14:44:41 2019
Total time elapsed: 56.57sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9017,
"inflation_factor": 1,
"mean_EFFECT": -5.7869e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 0,
"n_p_sig": 0,
"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": 40598,
"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": 1088110,
"ldsc_nsnp_merge_regression_ld": 1088110,
"ldsc_observed_scale_h2_beta": 0.0127,
"ldsc_observed_scale_h2_se": 0.0076,
"ldsc_intercept_beta": 1.0022,
"ldsc_intercept_se": 0.0081,
"ldsc_lambda_gc": 1.0168,
"ldsc_mean_chisq": 1.0195,
"ldsc_ratio": 0.1128
}
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 | FALSE |
| 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 | 4803441 | 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 | 4804672 | 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 | 8.668454e+00 | 5.767475e+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 | 7.857240e+07 | 5.666611e+07 | 828.0000000 | 3.184158e+07 | 6.892509e+07 | 1.146437e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -5.800000e-06 | 1.873000e-03 | -0.0165496 | -1.205300e-03 | -3.100000e-06 | 1.194300e-03 | 1.263740e-02 | ▁▁▇▃▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.813700e-03 | 3.703000e-04 | 0.0013864 | 1.498000e-03 | 1.683400e-03 | 2.054300e-03 | 5.278800e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.965159e-01 | 2.893189e-01 | 0.0000003 | 2.399999e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.965197e-01 | 2.892939e-01 | 0.0000003 | 2.449831e-01 | 4.963357e-01 | 7.466511e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.600195e-01 | 2.342304e-01 | 0.0745320 | 1.578100e-01 | 2.979535e-01 | 5.256720e-01 | 9.254680e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 40598 | 0.9915503 | NA | NA | NA | NA | NA | NA | NA | 3.526315e-01 | 2.321015e-01 | 0.0000000 | 1.611420e-01 | 2.991210e-01 | 5.147760e-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 | 0.0005835 | 0.0025551 | 0.8200001 | 0.8193527 | 0.623812 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000097 | 0.0025477 | 1.0000000 | 0.9969495 | 0.399144 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0037367 | 0.0040555 | 0.3599996 | 0.3568491 | 0.103536 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0028067 | 0.0025058 | 0.2599998 | 0.2626846 | 0.455916 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0034984 | 0.0028540 | 0.2200002 | 0.2202823 | 0.242057 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0037709 | 0.0035412 | 0.2900000 | 0.2869318 | 0.912862 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0029673 | 0.0023798 | 0.2099999 | 0.2124419 | 0.117313 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0015194 | 0.0017664 | 0.3900004 | 0.3897114 | 0.513304 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | 0.0029816 | 0.0028826 | 0.2999998 | 0.3009768 | 0.102736 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0025875 | 0.0020580 | 0.2099999 | 0.2086549 | 0.841441 | 0.6399760 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | 0.0008815 | 0.0017607 | 0.6200004 | 0.6166264 | 0.254586 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0030449 | 0.0023528 | 0.2000000 | 0.1956158 | 0.120936 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0023506 | 0.0023466 | 0.3200000 | 0.3164780 | 0.129762 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0016653 | 0.0017466 | 0.3400001 | 0.3403500 | 0.271468 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0022864 | 0.0023256 | 0.3300000 | 0.3255491 | 0.132383 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0010373 | 0.0016602 | 0.5300002 | 0.5321124 | 0.331351 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0006845 | 0.0022032 | 0.7600007 | 0.7560406 | 0.126422 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0004196 | 0.0021946 | 0.8499999 | 0.8483714 | 0.126887 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0008343 | 0.0021485 | 0.6999999 | 0.6977648 | 0.136315 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0004387 | 0.0016961 | 0.8000000 | 0.7959043 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID 0.00058353:0.00255511:0.0861861:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID -9.74039e-06:0.00254768:-0:0.399144:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103536 ES:SE:LP:AF:ID -0.00373668:0.0040555:0.443698:0.103536:rs114608975
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID 0.00280669:0.00250582:0.585027:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID 0.00349837:0.002854:0.657577:0.242057:rs6680723
1 546697 rs12025928 A G . PASS AF=0.912862 ES:SE:LP:AF:ID 0.00377093:0.00354119:0.537602:0.912862:rs12025928
1 693731 rs12238997 A G . PASS AF=0.117313 ES:SE:LP:AF:ID -0.00296726:0.00237975:0.677781:0.117313:rs12238997
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.00151937:0.00176642:0.408935:0.513304:rs12029736
1 722670 rs116030099 T C . PASS AF=0.102736 ES:SE:LP:AF:ID 0.00298156:0.00288257:0.522879:0.102736:rs116030099
1 729679 rs4951859 C G . PASS AF=0.841441 ES:SE:LP:AF:ID 0.00258753:0.00205805:0.677781:0.841441:rs4951859
1 731718 rs58276399 T C . PASS AF=0.123078 ES:SE:LP:AF:ID -0.00290258:0.00226049:0.69897:0.123078:rs58276399
1 734349 rs141242758 T C . PASS AF=0.12233 ES:SE:LP:AF:ID -0.00290973:0.00226122:0.69897:0.12233:rs141242758
1 736289 rs79010578 T A . PASS AF=0.134139 ES:SE:LP:AF:ID -0.00202052:0.00221975:0.443698:0.134139:rs79010578
1 752566 rs3094315 G A . PASS AF=0.837029 ES:SE:LP:AF:ID 0.00269823:0.00199091:0.744727:0.837029:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836733 ES:SE:LP:AF:ID 0.00281032:0.00198945:0.79588:0.836733:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868562 ES:SE:LP:AF:ID 0.00286101:0.00213777:0.744727:0.868562:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131004 ES:SE:LP:AF:ID -0.00293875:0.00214326:0.769551:0.131004:rs2073813
1 754182 rs3131969 A G . PASS AF=0.867976 ES:SE:LP:AF:ID 0.00296916:0.00213433:0.79588:0.867976:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86805 ES:SE:LP:AF:ID 0.00292184:0.0021352:0.769551:0.86805:rs3131968
1 754334 rs3131967 T C . PASS AF=0.867987 ES:SE:LP:AF:ID 0.0029597:0.00213428:0.769551:0.867987:rs3131967
1 754503 rs3115859 G A . PASS AF=0.836159 ES:SE:LP:AF:ID 0.0028553:0.00198363:0.823909:0.836159:rs3115859
1 754964 rs3131966 C T . PASS AF=0.836793 ES:SE:LP:AF:ID 0.00283276:0.00198906:0.823909:0.836793:rs3131966
1 755775 rs3131965 A G . PASS AF=0.838109 ES:SE:LP:AF:ID 0.00292106:0.00201706:0.823909:0.838109:rs3131965
1 755890 rs3115858 A T . PASS AF=0.868228 ES:SE:LP:AF:ID 0.00300854:0.00213153:0.79588:0.868228:rs3115858
1 756604 rs3131962 A G . PASS AF=0.867742 ES:SE:LP:AF:ID 0.00291466:0.00212588:0.769551:0.867742:rs3131962
1 757640 rs3115853 G A . PASS AF=0.866644 ES:SE:LP:AF:ID 0.00338051:0.00212199:0.958607:0.866644:rs3115853
1 757734 rs4951929 C T . PASS AF=0.867911 ES:SE:LP:AF:ID 0.00299548:0.00212804:0.79588:0.867911:rs4951929
1 757936 rs4951862 C A . PASS AF=0.867923 ES:SE:LP:AF:ID 0.00299541:0.00212818:0.79588:0.867923:rs4951862
1 758144 rs3131956 A G . PASS AF=0.867932 ES:SE:LP:AF:ID 0.00299404:0.00212826:0.79588:0.867932:rs3131956
1 758626 rs3131954 C T . PASS AF=0.868398 ES:SE:LP:AF:ID 0.00303638:0.00213386:0.823909:0.868398:rs3131954
1 760912 rs1048488 C T . PASS AF=0.836369 ES:SE:LP:AF:ID 0.00284314:0.00197868:0.823909:0.836369:rs1048488
1 761147 rs3115850 T C . PASS AF=0.836495 ES:SE:LP:AF:ID 0.00281729:0.00198003:0.823909:0.836495:rs3115850
1 761732 rs2286139 C T . PASS AF=0.860851 ES:SE:LP:AF:ID 0.00278678:0.00212072:0.721246:0.860851:rs2286139
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID -0.00019679:0.00206945:0.0362122:0.705804:rs3115847
1 766007 rs61768174 A C . PASS AF=0.106094 ES:SE:LP:AF:ID -0.00242862:0.00238129:0.508638:0.106094:rs61768174
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID 0.0015304:0.0016773:0.443698:0.758252:rs2977608
1 768448 rs12562034 G A . PASS AF=0.108262 ES:SE:LP:AF:ID 0.000675701:0.00230858:0.113509:0.108262:rs12562034
1 769223 rs60320384 C G . PASS AF=0.13066 ES:SE:LP:AF:ID -0.00298501:0.00214244:0.79588:0.13066:rs60320384
1 771823 rs2977605 T C . PASS AF=0.867852 ES:SE:LP:AF:ID 0.00286293:0.00213074:0.744727:0.867852:rs2977605
1 771967 rs59066358 G A . PASS AF=0.130756 ES:SE:LP:AF:ID -0.0029555:0.002141:0.769551:0.130756:rs59066358
1 772755 rs2905039 A C . PASS AF=0.867878 ES:SE:LP:AF:ID 0.00284339:0.00213086:0.744727:0.867878:rs2905039
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID -0.00226033:0.00189529:0.638272:0.263729:rs12124819
1 777122 rs2980319 A T . PASS AF=0.869067 ES:SE:LP:AF:ID 0.00288814:0.00213647:0.744727:0.869067:rs2980319
1 777232 rs112618790 C T . PASS AF=0.096639 ES:SE:LP:AF:ID 0.00111669:0.00246088:0.187087:0.096639:rs112618790
1 778745 rs1055606 A G . PASS AF=0.129594 ES:SE:LP:AF:ID -0.00305253:0.00214462:0.823909:0.129594:rs1055606
1 779322 rs4040617 A G . PASS AF=0.129863 ES:SE:LP:AF:ID -0.00305696:0.00214096:0.823909:0.129863:rs4040617
1 780785 rs2977612 T A . PASS AF=0.867843 ES:SE:LP:AF:ID 0.0028783:0.00213116:0.744727:0.867843:rs2977612
1 781845 rs61768199 A G . PASS AF=0.102807 ES:SE:LP:AF:ID -0.00282732:0.00241698:0.619789:0.102807:rs61768199
1 782981 rs6594026 C T . PASS AF=0.130424 ES:SE:LP:AF:ID -0.00307133:0.00214137:0.823909:0.130424:rs6594026
1 785050 rs2905062 G A . PASS AF=0.867493 ES:SE:LP:AF:ID 0.00321618:0.00213053:0.886057:0.867493:rs2905062