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_41200_P232.vcf.gz --id UKB-b:18786 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41200_P232.txt.gz --cohort_cases 2863 --cohort_controls 460147 --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": "2019-09-13T14:14:50.326159",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-18786/ukb-b-18786.vcf.gz; Date=Sun May 10 06:36:25 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-18786/UKB-b-18786_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18786/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-18786/UKB-b-18786_data.vcf.gz ...
Read summary statistics for 3933700 SNPs.
Dropped 728 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, 940026 SNPs remain.
After merging with regression SNP LD, 940026 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0059 (0.0013)
Lambda GC: 1.0599
Mean Chi^2: 1.0714
Intercept: 1.012 (0.0089)
Ratio: 0.1682 (0.1253)
Analysis finished at Thu Oct 17 14:41:08 2019
Total time elapsed: 49.42sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8728,
"inflation_factor": 1.0475,
"mean_EFFECT": -8.5559e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 4,
"n_p_sig": 36,
"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": 32041,
"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": 940026,
"ldsc_nsnp_merge_regression_ld": 940026,
"ldsc_observed_scale_h2_beta": 0.0059,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 1.012,
"ldsc_intercept_se": 0.0089,
"ldsc_lambda_gc": 1.0599,
"ldsc_mean_chisq": 1.0714,
"ldsc_ratio": 0.1681
}
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 | 3932975 | 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 | 3933700 | 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 | 8.659796e+00 | 5.767055e+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 | 7.856557e+07 | 5.675456e+07 | 828.0000000 | 3.161624e+07 | 6.889583e+07 | 1.147087e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -9.000000e-07 | 1.974000e-04 | -0.0016018 | -1.310000e-04 | -1.500000e-06 | 1.289000e-04 | 1.185800e-03 | ▁▁▇▅▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.892000e-04 | 2.510000e-05 | 0.0001568 | 1.674000e-04 | 1.810000e-04 | 2.064000e-04 | 5.735000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.899553e-01 | 2.916993e-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 | 4.899577e-01 | 2.916750e-01 | 0.0000000 | 2.352186e-01 | 4.866238e-01 | 7.424037e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.970261e-01 | 2.106937e-01 | 0.1222500 | 2.132440e-01 | 3.504395e-01 | 5.537600e-01 | 8.777500e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 32041 | 0.9918547 | NA | NA | NA | NA | NA | NA | NA | 3.856307e-01 | 2.154619e-01 | 0.0000000 | 2.084660e-01 | 3.464460e-01 | 5.413340e-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 | -0.0001556 | 0.0002886 | 0.5900000 | 0.5897697 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0003800 | 0.0002859 | 0.1800002 | 0.1838251 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000441 | 0.0002815 | 0.8800001 | 0.8754884 | 0.456846 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0000288 | 0.0003215 | 0.9299999 | 0.9287452 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001754 | 0.0001996 | 0.3800004 | 0.3795226 | 0.515645 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0003483 | 0.0002335 | 0.1400000 | 0.1358848 | 0.843204 | 0.639976 | NA |
| 1 | 731718 | rs142557973 | T | C | -0.0002783 | 0.0002556 | 0.2800000 | 0.2762338 | 0.122312 | 0.154353 | NA |
| 1 | 736289 | rs79010578 | T | A | -0.0003678 | 0.0002520 | 0.1400000 | 0.1444888 | 0.132335 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0003255 | 0.0002262 | 0.1499999 | 0.1500382 | 0.838945 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | 0.0003530 | 0.0002259 | 0.1199999 | 0.1181726 | 0.838573 | 0.653355 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51197266 | rs61290853 | A | G | 0.0000160 | 0.0001758 | 0.9299999 | 0.9274515 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0002885 | 0.0001991 | 0.1499999 | 0.1472737 | 0.254562 | 0.0984425 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0003662 | 0.0002650 | 0.1700000 | 0.1669733 | 0.129958 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0002348 | 0.0001974 | 0.2300001 | 0.2341648 | 0.271547 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0003907 | 0.0002626 | 0.1400000 | 0.1368242 | 0.132638 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001613 | 0.0001875 | 0.3900004 | 0.3897343 | 0.331457 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0002613 | 0.0002471 | 0.2900000 | 0.2902369 | 0.127814 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0002678 | 0.0002460 | 0.2800000 | 0.2763521 | 0.128328 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0002366 | 0.0002408 | 0.3300000 | 0.3258439 | 0.137950 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001061 | 0.0001920 | 0.5800000 | 0.5804627 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.000155592:0.000288577:0.229148:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000379969:0.000285891:0.744727:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 4.41074e-05:0.000281493:0.0555173:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -2.87534e-05:0.000321541:0.0315171:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 0.000175409:0.000199606:0.420216:0.515645:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID 0.000348251:0.000233523:0.853872:0.843204:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID -0.000278313:0.00025561:0.552842:0.122312:rs58276399
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID -0.000367789:0.000252035:0.853872:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID 0.000325521:0.000226151:0.823909:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID 0.000352979:0.000225908:0.920819:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID 0.000251177:0.000242407:0.522879:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID -0.000268725:0.000242903:0.568636:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID 0.000276205:0.000241932:0.60206:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID 0.000272764:0.000242028:0.585027:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID 0.000276134:0.000241927:0.60206:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID 0.000342855:0.00022528:0.886057:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID 0.000343156:0.000225914:0.886057:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID 0.000316364:0.000228969:0.769551:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID 0.000259575:0.000241647:0.552842:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID 0.000256941:0.000241039:0.537602:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID 0.000285406:0.000240577:0.619789:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID 0.000259547:0.000241237:0.552842:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID 0.000259634:0.000241255:0.552842:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID 0.000259406:0.000241261:0.552842:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID 0.000256693:0.000241923:0.537602:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID 0.000328317:0.000224853:0.853872:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID 0.000326111:0.000225012:0.823909:0.838427:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID 0.000252568:0.000240388:0.537602:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.00027422:0.000234019:0.619789:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID 6.9936e-05:0.000191:0.148742:0.761297:rs2977608
1 769223 rs60320384 C G . PASS AF=0.129581 ES:SE:LP:AF:ID -0.000234203:0.000242758:0.481486:0.129581:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868906 ES:SE:LP:AF:ID 0.000248149:0.000241461:0.522879:0.868906:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129682 ES:SE:LP:AF:ID -0.000239101:0.000242601:0.49485:0.129682:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868916 ES:SE:LP:AF:ID 0.000244382:0.000241466:0.508638:0.868916:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 0.000330123:0.000213354:0.920819:0.265385:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870039 ES:SE:LP:AF:ID 0.000286311:0.000241958:0.619789:0.870039:rs2980319
1 778745 rs1055606 A G . PASS AF=0.12858 ES:SE:LP:AF:ID -0.000274222:0.000242914:0.585027:0.12858:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID -0.000270279:0.000242502:0.568636:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86878 ES:SE:LP:AF:ID 0.00028413:0.000241313:0.619789:0.86878:rs2977612
1 782981 rs6594026 C T . PASS AF=0.129518 ES:SE:LP:AF:ID -0.000267742:0.000242424:0.568636:0.129518:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868533 ES:SE:LP:AF:ID 0.00028191:0.000241256:0.619789:0.868533:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868474 ES:SE:LP:AF:ID 0.000281785:0.000241407:0.619789:0.868474:rs2980300
1 787399 rs2905055 G T . PASS AF=0.860776 ES:SE:LP:AF:ID 0.000259451:0.000241242:0.552842:0.860776:rs2905055
1 787606 rs3863622 G T . PASS AF=0.128466 ES:SE:LP:AF:ID -0.000283712:0.00024373:0.619789:0.128466:rs3863622
1 787685 rs2905054 G T . PASS AF=0.861487 ES:SE:LP:AF:ID 0.00020512:0.000241399:0.39794:0.861487:rs2905054
1 787844 rs2905053 C T . PASS AF=0.869224 ES:SE:LP:AF:ID 0.000296394:0.00024229:0.657577:0.869224:rs2905053
1 791191 rs111818025 G A . PASS AF=0.127884 ES:SE:LP:AF:ID -0.000285531:0.000245292:0.619789:0.127884:rs111818025
1 795988 rs59380221 C T . PASS AF=0.143053 ES:SE:LP:AF:ID -0.000149067:0.000250221:0.259637:0.143053:rs59380221
1 796375 rs12083781 T C . PASS AF=0.123822 ES:SE:LP:AF:ID -0.000314749:0.000252086:0.677781:0.123822:rs12083781
1 798400 rs10900604 A G . PASS AF=0.206591 ES:SE:LP:AF:ID -8.25152e-05:0.000203758:0.161151:0.206591:rs10900604