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_6152_7.vcf.gz --id UKB-b:13704 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6152_7.txt.gz --cohort_cases 3819 --cohort_controls 458194 --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": "2019-09-13T01:54:23.465613",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13704/ukb-b-13704.vcf.gz; Date=Sat May 9 15:36:20 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-13704/UKB-b-13704_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13704/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:52 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13704/UKB-b-13704_data.vcf.gz ...
Read summary statistics for 4459431 SNPs.
Dropped 1007 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, 1034012 SNPs remain.
After merging with regression SNP LD, 1034012 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.006 (0.0012)
Lambda GC: 1.0662
Mean Chi^2: 1.0633
Intercept: 1.0039 (0.0079)
Ratio: 0.0622 (0.1248)
Analysis finished at Thu Oct 17 14:43:46 2019
Total time elapsed: 53.99sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8917,
"inflation_factor": 1.0475,
"mean_EFFECT": -4.8471e-08,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 6,
"n_p_sig": 379,
"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": 37061,
"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": 1034012,
"ldsc_nsnp_merge_regression_ld": 1034012,
"ldsc_observed_scale_h2_beta": 0.006,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0039,
"ldsc_intercept_se": 0.0079,
"ldsc_lambda_gc": 1.0662,
"ldsc_mean_chisq": 1.0633,
"ldsc_ratio": 0.0616
}
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 | 4458430 | 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 | 4459431 | 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 | 8.656083e+00 | 5.765309e+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 | 7.860821e+07 | 5.673129e+07 | 828.0000000 | 3.173377e+07 | 6.896812e+07 | 1.147412e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 0.000000e+00 | 2.406000e-04 | -0.0025201 | -1.569000e-04 | -1.000000e-06 | 1.560000e-04 | 3.566200e-03 | ▁▅▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.298000e-04 | 3.980000e-05 | 0.0001818 | 1.955000e-04 | 2.162000e-04 | 2.563000e-04 | 6.650000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.902864e-01 | 2.912490e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.902881e-01 | 2.912235e-01 | 0.0000000 | 2.354799e-01 | 4.864600e-01 | 7.426005e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.745821e-01 | 2.259147e-01 | 0.0916480 | 1.787030e-01 | 3.185020e-01 | 5.377890e-01 | 9.083510e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 37061 | 0.9916893 | NA | NA | NA | NA | NA | NA | NA | 3.657341e-01 | 2.260483e-01 | 0.0000000 | 1.791130e-01 | 3.176920e-01 | 5.259580e-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 | 0.0000690 | 0.0003346 | 0.8400000 | 0.8365343 | 0.623775 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000224 | 0.0003314 | 0.9500000 | 0.9461080 | 0.400400 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0000374 | 0.0005299 | 0.9400001 | 0.9437188 | 0.103554 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001351 | 0.0003263 | 0.6800001 | 0.6789968 | 0.456851 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0000707 | 0.0003728 | 0.8499999 | 0.8495673 | 0.240953 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0003033 | 0.0003124 | 0.3300000 | 0.3316280 | 0.116345 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0003863 | 0.0002314 | 0.0949992 | 0.0951020 | 0.515623 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | -0.0007328 | 0.0003818 | 0.0549997 | 0.0549253 | 0.101232 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0002658 | 0.0002708 | 0.3300000 | 0.3262185 | 0.843205 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0003792 | 0.0002964 | 0.2000000 | 0.2006719 | 0.122325 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0002484 | 0.0002308 | 0.2800000 | 0.2818717 | 0.254576 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0005646 | 0.0003085 | 0.0669993 | 0.0672198 | 0.120740 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0001048 | 0.0003073 | 0.7300002 | 0.7331356 | 0.129960 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0001552 | 0.0002288 | 0.5000000 | 0.4977160 | 0.271563 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0000583 | 0.0003045 | 0.8499999 | 0.8481935 | 0.132634 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0002219 | 0.0002174 | 0.3100002 | 0.3074705 | 0.331448 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0000155 | 0.0002865 | 0.9599999 | 0.9569657 | 0.127792 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000422 | 0.0002853 | 0.8800001 | 0.8823487 | 0.128308 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0000588 | 0.0002792 | 0.8300000 | 0.8333100 | 0.137929 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002350 | 0.0002226 | 0.2900000 | 0.2911180 | 0.297980 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623775 ES:SE:LP:AF:ID 6.90371e-05:0.000334598:0.0757207:0.623775:rs10399793
1 54676 rs2462492 C T . PASS AF=0.4004 ES:SE:LP:AF:ID 2.24037e-05:0.00033144:0.0222764:0.4004:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103554 ES:SE:LP:AF:ID -3.74126e-05:0.000529949:0.0268721:0.103554:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -0.000135053:0.000326347:0.167491:0.456851:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240953 ES:SE:LP:AF:ID 7.07122e-05:0.000372816:0.0705811:0.240953:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116345 ES:SE:LP:AF:ID 0.000303303:0.000312413:0.481486:0.116345:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515623 ES:SE:LP:AF:ID 0.000386288:0.000231438:1.02228:0.515623:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101232 ES:SE:LP:AF:ID -0.00073281:0.000381778:1.25964:0.101232:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843205 ES:SE:LP:AF:ID -0.000265828:0.000270767:0.481486:0.843205:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122325 ES:SE:LP:AF:ID 0.000379234:0.00029636:0.69897:0.122325:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121569 ES:SE:LP:AF:ID 0.00038957:0.000296483:0.721246:0.121569:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132336 ES:SE:LP:AF:ID 0.000283449:0.00029223:0.481486:0.132336:rs79010578
1 752566 rs3094315 G A . PASS AF=0.83894 ES:SE:LP:AF:ID -0.000249659:0.000262219:0.468521:0.83894:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838567 ES:SE:LP:AF:ID -0.000234732:0.000261935:0.431798:0.838567:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869759 ES:SE:LP:AF:ID -0.000338164:0.000281063:0.638272:0.869759:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129892 ES:SE:LP:AF:ID 0.000305777:0.000281636:0.552842:0.129892:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869101 ES:SE:LP:AF:ID -0.000299469:0.000280512:0.537602:0.869101:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869199 ES:SE:LP:AF:ID -0.000297434:0.000280624:0.537602:0.869199:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869104 ES:SE:LP:AF:ID -0.000299685:0.000280507:0.537602:0.869104:rs3131967
1 754503 rs3115859 G A . PASS AF=0.83802 ES:SE:LP:AF:ID -0.000215522:0.000261209:0.387216:0.83802:rs3115859
1 754964 rs3131966 C T . PASS AF=0.83865 ES:SE:LP:AF:ID -0.000218771:0.000261943:0.39794:0.83865:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839764 ES:SE:LP:AF:ID -0.000217382:0.000265484:0.387216:0.839764:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869384 ES:SE:LP:AF:ID -0.000325253:0.000280183:0.60206:0.869384:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868931 ES:SE:LP:AF:ID -0.000317494:0.000279478:0.585027:0.868931:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867883 ES:SE:LP:AF:ID -0.000311395:0.000278941:0.585027:0.867883:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869075 ES:SE:LP:AF:ID -0.000327277:0.000279707:0.619789:0.869075:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869083 ES:SE:LP:AF:ID -0.000326867:0.000279728:0.619789:0.869083:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869091 ES:SE:LP:AF:ID -0.000326048:0.000279735:0.619789:0.869091:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869568 ES:SE:LP:AF:ID -0.000326474:0.000280503:0.619789:0.869568:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838301 ES:SE:LP:AF:ID -0.000235676:0.000260714:0.431798:0.838301:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838422 ES:SE:LP:AF:ID -0.000239669:0.000260898:0.443698:0.838422:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862245 ES:SE:LP:AF:ID -0.000306923:0.00027873:0.568636:0.862245:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706732 ES:SE:LP:AF:ID -9.81767e-05:0.000271334:0.142668:0.706732:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105154 ES:SE:LP:AF:ID 0.000376682:0.000312577:0.638272:0.105154:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761315 ES:SE:LP:AF:ID 0.00015134:0.000221473:0.309804:0.761315:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106459 ES:SE:LP:AF:ID -0.000740259:0.000305282:1.82391:0.106459:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129596 ES:SE:LP:AF:ID 0.000369938:0.000281467:0.721246:0.129596:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868891 ES:SE:LP:AF:ID -0.000378059:0.000279966:0.744727:0.868891:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129697 ES:SE:LP:AF:ID 0.000360041:0.000281286:0.69897:0.129697:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868901 ES:SE:LP:AF:ID -0.000388631:0.000279971:0.769551:0.868901:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265394 ES:SE:LP:AF:ID -1.02403e-05:0.000247362:0.0132283:0.265394:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870021 ES:SE:LP:AF:ID -0.000372138:0.000280541:0.744727:0.870021:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095119 ES:SE:LP:AF:ID -0.000775069:0.000325181:1.76955:0.095119:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128598 ES:SE:LP:AF:ID 0.000376372:0.000281648:0.744727:0.128598:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128894 ES:SE:LP:AF:ID 0.000375377:0.00028117:0.744727:0.128894:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868766 ES:SE:LP:AF:ID -0.000353894:0.000279799:0.677781:0.868766:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101887 ES:SE:LP:AF:ID 0.000366199:0.000317029:0.60206:0.101887:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129533 ES:SE:LP:AF:ID 0.000343065:0.000281085:0.657577:0.129533:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868519 ES:SE:LP:AF:ID -0.00036598:0.000279733:0.721246:0.868519:rs2905062
1 785989 rs2980300 T C . PASS AF=0.86846 ES:SE:LP:AF:ID -0.000362406:0.000279908:0.69897:0.86846:rs2980300