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_6148_1.vcf.gz --id UKB-b:10181 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6148_1.txt.gz --cohort_cases 2940 --cohort_controls 147702 --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": "2019-09-13T01:53:05.688209",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10181/ukb-b-10181.vcf.gz; Date=Sun May 10 12:40:01 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-10181/UKB-b-10181_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10181/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-10181/UKB-b-10181_data.vcf.gz ...
Read summary statistics for 3985423 SNPs.
Dropped 751 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, 949947 SNPs remain.
After merging with regression SNP LD, 949947 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0213 (0.004)
Lambda GC: 1.0863
Mean Chi^2: 1.09
Intercept: 1.0208 (0.0092)
Ratio: 0.2307 (0.1024)
Analysis finished at Thu Oct 17 14:41:09 2019
Total time elapsed: 50.48sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8753,
"inflation_factor": 1.0966,
"mean_EFFECT": -4.1091e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 4,
"n_p_sig": 1889,
"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": 32536,
"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": 949947,
"ldsc_nsnp_merge_regression_ld": 949947,
"ldsc_observed_scale_h2_beta": 0.0213,
"ldsc_observed_scale_h2_se": 0.004,
"ldsc_intercept_beta": 1.0208,
"ldsc_intercept_se": 0.0092,
"ldsc_lambda_gc": 1.0863,
"ldsc_mean_chisq": 1.09,
"ldsc_ratio": 0.2311
}
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 | 3984676 | 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 | 3985423 | 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 | 8.658805e+00 | 5.766558e+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 | 7.858232e+07 | 5.675869e+07 | 828.0000000 | 3.163101e+07 | 6.891396e+07 | 1.147139e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -4.100000e-06 | 6.337000e-04 | -0.0042058 | -4.165000e-04 | -8.200000e-06 | 4.045000e-04 | 1.118950e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.883000e-04 | 8.010000e-05 | 0.0004853 | 5.188000e-04 | 5.621000e-04 | 6.433000e-04 | 1.781900e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.847873e-01 | 2.930280e-01 | 0.0000000 | 2.300001e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.847878e-01 | 2.930026e-01 | 0.0000000 | 2.276336e-01 | 4.791430e-01 | 7.383521e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.949319e-01 | 2.123542e-01 | 0.1190480 | 2.097660e-01 | 3.474580e-01 | 5.524185e-01 | 8.809520e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 32536 | 0.9918362 | NA | NA | NA | NA | NA | NA | NA | 3.838255e-01 | 2.165937e-01 | 0.0000000 | 2.056710e-01 | 3.438500e-01 | 5.403350e-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 | 0.0013573 | 0.0008943 | 0.1299999 | 0.1291007 | 0.623755 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0003507 | 0.0008870 | 0.6899999 | 0.6925661 | 0.399226 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0007778 | 0.0008737 | 0.3700002 | 0.3733284 | 0.456028 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0015919 | 0.0009969 | 0.1100001 | 0.1102816 | 0.241037 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002545 | 0.0006175 | 0.6800001 | 0.6802007 | 0.515025 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0008719 | 0.0007215 | 0.2300001 | 0.2268420 | 0.841466 | 0.639976 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0008905 | 0.0007903 | 0.2599998 | 0.2598383 | 0.123488 | 0.154353 | NA |
| 1 | 734349 | rs141242758 | T | C | 0.0008926 | 0.0007907 | 0.2599998 | 0.2589418 | 0.122705 | 0.152556 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0013234 | 0.0007798 | 0.0899995 | 0.0896866 | 0.133330 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0009735 | 0.0006981 | 0.1600000 | 0.1631619 | 0.837165 | 0.718251 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | 0.0001351 | 0.0006161 | 0.8300000 | 0.8264283 | 0.254430 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | -0.0000557 | 0.0008196 | 0.9500000 | 0.9458065 | 0.121632 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0020537 | 0.0008211 | 0.0120000 | 0.0123754 | 0.129945 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0000471 | 0.0006114 | 0.9400001 | 0.9386299 | 0.271050 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0020337 | 0.0008136 | 0.0120000 | 0.0124377 | 0.132649 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001675 | 0.0005803 | 0.7700005 | 0.7727947 | 0.331587 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0015935 | 0.0007662 | 0.0379997 | 0.0375584 | 0.127190 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0015908 | 0.0007630 | 0.0369999 | 0.0370902 | 0.127722 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0017057 | 0.0007462 | 0.0219999 | 0.0222706 | 0.137511 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000436 | 0.0005932 | 0.9400001 | 0.9413824 | 0.298254 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623755 ES:SE:LP:AF:ID 0.00135729:0.000894332:0.886057:0.623755:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399226 ES:SE:LP:AF:ID -0.000350693:0.000886988:0.161151:0.399226:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456028 ES:SE:LP:AF:ID 0.000777806:0.000873688:0.431798:0.456028:rs6702460
1 534192 rs6680723 C T . PASS AF=0.241037 ES:SE:LP:AF:ID -0.0015919:0.000996851:0.958607:0.241037:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515025 ES:SE:LP:AF:ID -0.000254524:0.000617493:0.167491:0.515025:rs12029736
1 729679 rs4951859 C G . PASS AF=0.841466 ES:SE:LP:AF:ID -0.000871898:0.000721451:0.638272:0.841466:rs4951859
1 731718 rs58276399 T C . PASS AF=0.123488 ES:SE:LP:AF:ID 0.000890525:0.000790332:0.585027:0.123488:rs58276399
1 734349 rs141242758 T C . PASS AF=0.122705 ES:SE:LP:AF:ID 0.000892619:0.000790701:0.585027:0.122705:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13333 ES:SE:LP:AF:ID 0.00132344:0.000779846:1.04576:0.13333:rs79010578
1 752566 rs3094315 G A . PASS AF=0.837165 ES:SE:LP:AF:ID -0.000973478:0.000698076:0.79588:0.837165:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836738 ES:SE:LP:AF:ID -0.00104297:0.000697296:0.886057:0.836738:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868304 ES:SE:LP:AF:ID -0.000932983:0.000748147:0.677781:0.868304:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131427 ES:SE:LP:AF:ID 0.00105235:0.00074954:0.79588:0.131427:rs2073813
1 754182 rs3131969 A G . PASS AF=0.867591 ES:SE:LP:AF:ID -0.00102338:0.000746635:0.769551:0.867591:rs3131969
1 754192 rs3131968 A G . PASS AF=0.867698 ES:SE:LP:AF:ID -0.00104502:0.000746968:0.79588:0.867698:rs3131968
1 754334 rs3131967 T C . PASS AF=0.867589 ES:SE:LP:AF:ID -0.00101952:0.000746588:0.769551:0.867589:rs3131967
1 754503 rs3115859 G A . PASS AF=0.836223 ES:SE:LP:AF:ID -0.00102389:0.000695498:0.853872:0.836223:rs3115859
1 754964 rs3131966 C T . PASS AF=0.836834 ES:SE:LP:AF:ID -0.000993663:0.000697362:0.823909:0.836834:rs3131966
1 755775 rs3131965 A G . PASS AF=0.838125 ES:SE:LP:AF:ID -0.000868568:0.000707066:0.657577:0.838125:rs3131965
1 755890 rs3115858 A T . PASS AF=0.867966 ES:SE:LP:AF:ID -0.000893716:0.000745909:0.638272:0.867966:rs3115858
1 756604 rs3131962 A G . PASS AF=0.867516 ES:SE:LP:AF:ID -0.000821613:0.000744041:0.568636:0.867516:rs3131962
1 757640 rs3115853 G A . PASS AF=0.866267 ES:SE:LP:AF:ID -0.00102987:0.000742237:0.769551:0.866267:rs3115853
1 757734 rs4951929 C T . PASS AF=0.867655 ES:SE:LP:AF:ID -0.00086211:0.000744612:0.60206:0.867655:rs4951929
1 757936 rs4951862 C A . PASS AF=0.867667 ES:SE:LP:AF:ID -0.000862819:0.000744674:0.60206:0.867667:rs4951862
1 758144 rs3131956 A G . PASS AF=0.867675 ES:SE:LP:AF:ID -0.000861945:0.000744694:0.60206:0.867675:rs3131956
1 758626 rs3131954 C T . PASS AF=0.868154 ES:SE:LP:AF:ID -0.00090517:0.000746778:0.638272:0.868154:rs3131954
1 760912 rs1048488 C T . PASS AF=0.836569 ES:SE:LP:AF:ID -0.00081398:0.000694198:0.619789:0.836569:rs1048488
1 761147 rs3115850 T C . PASS AF=0.836695 ES:SE:LP:AF:ID -0.00084536:0.000694697:0.657577:0.836695:rs3115850
1 761732 rs2286139 C T . PASS AF=0.860527 ES:SE:LP:AF:ID -0.00100345:0.000741723:0.744727:0.860527:rs2286139
1 763394 rs3115847 G A . PASS AF=0.705447 ES:SE:LP:AF:ID -0.000900379:0.000724147:0.677781:0.705447:rs3115847
1 768253 rs2977608 A C . PASS AF=0.759101 ES:SE:LP:AF:ID -0.000548392:0.000589132:0.455932:0.759101:rs2977608
1 769223 rs60320384 C G . PASS AF=0.130972 ES:SE:LP:AF:ID 0.00092881:0.000749339:0.657577:0.130972:rs60320384
1 771823 rs2977605 T C . PASS AF=0.867541 ES:SE:LP:AF:ID -0.00091251:0.000745515:0.657577:0.867541:rs2977605
1 771967 rs59066358 G A . PASS AF=0.131045 ES:SE:LP:AF:ID 0.000901261:0.000748924:0.638272:0.131045:rs59066358
1 772755 rs2905039 A C . PASS AF=0.867564 ES:SE:LP:AF:ID -0.000912009:0.000745567:0.657577:0.867564:rs2905039
1 776546 rs12124819 A G . PASS AF=0.264636 ES:SE:LP:AF:ID -1.02044e-05:0.000662467:0.00436481:0.264636:rs12124819
1 777122 rs2980319 A T . PASS AF=0.86872 ES:SE:LP:AF:ID -0.000746314:0.00074737:0.49485:0.86872:rs2980319
1 778745 rs1055606 A G . PASS AF=0.12994 ES:SE:LP:AF:ID 0.000723983:0.000750039:0.481486:0.12994:rs1055606
1 779322 rs4040617 A G . PASS AF=0.130247 ES:SE:LP:AF:ID 0.000689639:0.000748723:0.443698:0.130247:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86741 ES:SE:LP:AF:ID -0.000817734:0.000745404:0.568636:0.86741:rs2977612
1 782981 rs6594026 C T . PASS AF=0.130893 ES:SE:LP:AF:ID 0.0007924:0.000748715:0.537602:0.130893:rs6594026
1 785050 rs2905062 G A . PASS AF=0.867042 ES:SE:LP:AF:ID -0.000887699:0.000744977:0.638272:0.867042:rs2905062
1 785989 rs2980300 T C . PASS AF=0.866961 ES:SE:LP:AF:ID -0.000910031:0.000745411:0.657577:0.866961:rs2980300
1 787399 rs2905055 G T . PASS AF=0.859238 ES:SE:LP:AF:ID -0.000863055:0.000744976:0.60206:0.859238:rs2905055
1 787606 rs3863622 G T . PASS AF=0.129806 ES:SE:LP:AF:ID 0.0007411:0.0007528:0.49485:0.129806:rs3863622
1 787685 rs2905054 G T . PASS AF=0.859929 ES:SE:LP:AF:ID -0.000935002:0.000745521:0.677781:0.859929:rs2905054
1 787844 rs2905053 C T . PASS AF=0.867709 ES:SE:LP:AF:ID -0.000956384:0.000748062:0.69897:0.867709:rs2905053
1 791191 rs111818025 G A . PASS AF=0.129192 ES:SE:LP:AF:ID 0.000637416:0.000757828:0.39794:0.129192:rs111818025
1 795988 rs59380221 C T . PASS AF=0.144258 ES:SE:LP:AF:ID 0.00107192:0.000773953:0.769551:0.144258:rs59380221
1 796375 rs12083781 T C . PASS AF=0.124928 ES:SE:LP:AF:ID 0.000854543:0.000779846:0.568636:0.124928:rs12083781