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_6147_6.vcf.gz --id UKB-b:9139 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6147_6.txt.gz --cohort_cases 3745 --cohort_controls 456791 --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": "2019-09-13T01:53:05.081525",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-9139/ukb-b-9139.vcf.gz; Date=Sat May 9 15:02:47 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-9139/UKB-b-9139_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9139/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9139/UKB-b-9139_data.vcf.gz ...
Read summary statistics for 4425623 SNPs.
Dropped 985 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, 1028470 SNPs remain.
After merging with regression SNP LD, 1028470 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0046 (0.0012)
Lambda GC: 1.066
Mean Chi^2: 1.0738
Intercept: 1.029 (0.0084)
Ratio: 0.3921 (0.1142)
Analysis finished at Thu Oct 17 14:43:12 2019
Total time elapsed: 53.54sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8906,
"inflation_factor": 1.0475,
"mean_EFFECT": 1.4544e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 75,
"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": 36700,
"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": 1028470,
"ldsc_nsnp_merge_regression_ld": 1028470,
"ldsc_observed_scale_h2_beta": 0.0046,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.029,
"ldsc_intercept_se": 0.0084,
"ldsc_lambda_gc": 1.066,
"ldsc_mean_chisq": 1.0738,
"ldsc_ratio": 0.393
}
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 | 4424644 | 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 | 4425623 | 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 | 8.655790e+00 | 5.764992e+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 | 7.860999e+07 | 5.672508e+07 | 828.0000000 | 3.175023e+07 | 6.897617e+07 | 1.147316e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.500000e-06 | 2.388000e-04 | -0.0015554 | -1.544000e-04 | 8.000000e-07 | 1.556000e-04 | 1.629000e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.276000e-04 | 3.880000e-05 | 0.0001806 | 1.942000e-04 | 2.143000e-04 | 2.534000e-04 | 6.605000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.899783e-01 | 2.918492e-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 | 4.899792e-01 | 2.918257e-01 | 0.0000000 | 2.346455e-01 | 4.869342e-01 | 7.427021e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.760423e-01 | 2.250419e-01 | 0.0934580 | 1.807570e-01 | 3.206400e-01 | 5.389440e-01 | 9.065420e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 36700 | 0.9917074 | NA | NA | NA | NA | NA | NA | NA | 3.670570e-01 | 2.254323e-01 | 0.0000000 | 1.811100e-01 | 3.196880e-01 | 5.271570e-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 | 0.0000054 | 0.0003324 | 0.9900000 | 0.9871068 | 0.623741 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001074 | 0.0003293 | 0.7400005 | 0.7443918 | 0.400426 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0006389 | 0.0005265 | 0.2200002 | 0.2249449 | 0.103554 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0002052 | 0.0003242 | 0.5300002 | 0.5268855 | 0.456883 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0004952 | 0.0003704 | 0.1800002 | 0.1812689 | 0.240976 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0000442 | 0.0003104 | 0.8900000 | 0.8867463 | 0.116324 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000854 | 0.0002299 | 0.7099994 | 0.7102389 | 0.515659 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | 0.0001235 | 0.0003794 | 0.7400005 | 0.7447261 | 0.101191 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0002718 | 0.0002690 | 0.3100002 | 0.3122512 | 0.843219 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0001543 | 0.0002944 | 0.5999997 | 0.6001869 | 0.122302 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0002825 | 0.0002293 | 0.2200002 | 0.2178848 | 0.254597 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0001757 | 0.0003065 | 0.5700002 | 0.5664709 | 0.120739 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0003973 | 0.0003053 | 0.1900002 | 0.1932277 | 0.129917 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0003858 | 0.0002273 | 0.0899995 | 0.0896320 | 0.271583 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0003488 | 0.0003026 | 0.2500000 | 0.2490515 | 0.132592 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0002662 | 0.0002160 | 0.2200002 | 0.2178270 | 0.331417 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0002629 | 0.0002846 | 0.3599996 | 0.3555937 | 0.127819 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0002721 | 0.0002834 | 0.3400001 | 0.3368789 | 0.128335 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0002718 | 0.0002774 | 0.3300000 | 0.3270570 | 0.137953 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001508 | 0.0002211 | 0.5000000 | 0.4951610 | 0.297968 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623741 ES:SE:LP:AF:ID 5.37153e-06:0.000332398:0.00436481:0.623741:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400426 ES:SE:LP:AF:ID 0.000107365:0.000329297:0.130768:0.400426:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103554 ES:SE:LP:AF:ID 0.000638892:0.000526494:0.657577:0.103554:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456883 ES:SE:LP:AF:ID 0.000205166:0.000324236:0.275724:0.456883:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240976 ES:SE:LP:AF:ID 0.000495167:0.000370396:0.744727:0.240976:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116324 ES:SE:LP:AF:ID 4.42038e-05:0.000310371:0.05061:0.116324:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515659 ES:SE:LP:AF:ID 8.54167e-05:0.000229902:0.148742:0.515659:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101191 ES:SE:LP:AF:ID 0.000123523:0.000379369:0.130768:0.101191:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843219 ES:SE:LP:AF:ID -0.000271819:0.000268992:0.508638:0.843219:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122302 ES:SE:LP:AF:ID 0.000154318:0.000294426:0.221849:0.122302:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121545 ES:SE:LP:AF:ID 0.000166771:0.000294546:0.244125:0.121545:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132334 ES:SE:LP:AF:ID 0.000221697:0.000290313:0.346787:0.132334:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838957 ES:SE:LP:AF:ID -0.000144912:0.0002605:0.236572:0.838957:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838587 ES:SE:LP:AF:ID -0.000166312:0.000260222:0.283997:0.838587:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869793 ES:SE:LP:AF:ID -0.000107494:0.000279229:0.154902:0.869793:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129854 ES:SE:LP:AF:ID 7.28486e-05:0.000279804:0.102373:0.129854:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869136 ES:SE:LP:AF:ID -0.000121267:0.000278684:0.180456:0.869136:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869235 ES:SE:LP:AF:ID -0.000128391:0.000278795:0.187087:0.869235:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869139 ES:SE:LP:AF:ID -0.000120321:0.000278678:0.173925:0.869139:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838038 ES:SE:LP:AF:ID -0.0001507:0.000259498:0.251812:0.838038:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838669 ES:SE:LP:AF:ID -0.000148758:0.000260227:0.244125:0.838669:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839784 ES:SE:LP:AF:ID -0.000169979:0.000263746:0.283997:0.839784:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869415 ES:SE:LP:AF:ID -9.85087e-05:0.000278354:0.142668:0.869415:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868963 ES:SE:LP:AF:ID -0.000108632:0.000277653:0.154902:0.868963:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867922 ES:SE:LP:AF:ID -0.000123286:0.000277128:0.180456:0.867922:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869106 ES:SE:LP:AF:ID -9.19899e-05:0.000277881:0.130768:0.869106:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869115 ES:SE:LP:AF:ID -9.18354e-05:0.000277902:0.130768:0.869115:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869122 ES:SE:LP:AF:ID -9.2645e-05:0.000277909:0.130768:0.869122:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869599 ES:SE:LP:AF:ID -9.41183e-05:0.000278671:0.130768:0.869599:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838314 ES:SE:LP:AF:ID -0.00010072:0.000258999:0.154902:0.838314:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838435 ES:SE:LP:AF:ID -0.000103668:0.000259182:0.161151:0.838435:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862275 ES:SE:LP:AF:ID -0.000129501:0.000276911:0.19382:0.862275:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706791 ES:SE:LP:AF:ID -0.000206459:0.000269596:0.356547:0.706791:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105132 ES:SE:LP:AF:ID 7.57252e-05:0.000310539:0.091515:0.105132:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761325 ES:SE:LP:AF:ID -9.43135e-05:0.000219999:0.173925:0.761325:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106482 ES:SE:LP:AF:ID 6.26037e-05:0.000303249:0.0757207:0.106482:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129566 ES:SE:LP:AF:ID 3.71083e-05:0.000279632:0.05061:0.129566:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868921 ES:SE:LP:AF:ID -4.9456e-05:0.000278139:0.0655015:0.868921:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129666 ES:SE:LP:AF:ID 1.786e-05:0.000279452:0.0222764:0.129666:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868931 ES:SE:LP:AF:ID -4.84562e-05:0.000278144:0.0655015:0.868931:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265403 ES:SE:LP:AF:ID 0.000510595:0.000245752:1.42022:0.265403:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870052 ES:SE:LP:AF:ID -7.052e-05:0.000278714:0.09691:0.870052:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095139 ES:SE:LP:AF:ID 4.43198e-05:0.000323009:0.05061:0.095139:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128566 ES:SE:LP:AF:ID 6.68244e-05:0.000279814:0.091515:0.128566:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128862 ES:SE:LP:AF:ID 5.50896e-05:0.00027934:0.0757207:0.128862:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868798 ES:SE:LP:AF:ID -8.60624e-05:0.000277974:0.119186:0.868798:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101863 ES:SE:LP:AF:ID 0.000166167:0.000314967:0.221849:0.101863:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129501 ES:SE:LP:AF:ID 3.15132e-05:0.000279253:0.0409586:0.129501:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868553 ES:SE:LP:AF:ID -0.000113166:0.000277913:0.167491:0.868553:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868493 ES:SE:LP:AF:ID -0.000128068:0.000278087:0.187087:0.868493:rs2980300