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_41245_1460.vcf.gz --id UKB-b:19304 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1460.txt.gz --cohort_cases 2768 --cohort_controls 458333 --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": "2019-09-13T13:13:00.538637",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19304/ukb-b-19304.vcf.gz; Date=Sun May 10 05:26:27 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-19304/UKB-b-19304_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19304/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19304/UKB-b-19304_data.vcf.gz ...
Read summary statistics for 3867413 SNPs.
Dropped 690 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, 927465 SNPs remain.
After merging with regression SNP LD, 927465 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0025 (0.0011)
Lambda GC: 1.0146
Mean Chi^2: 1.0218
Intercept: 0.9966 (0.008)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:59 2019
Total time elapsed: 49.73sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8698,
"inflation_factor": 1,
"mean_EFFECT": 5.3832e-07,
"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": 31439,
"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": 927465,
"ldsc_nsnp_merge_regression_ld": 927465,
"ldsc_observed_scale_h2_beta": 0.0025,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 0.9966,
"ldsc_intercept_se": 0.008,
"ldsc_lambda_gc": 1.0146,
"ldsc_mean_chisq": 1.0218,
"ldsc_ratio": -0.156
}
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 | 3866726 | 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 | 3867413 | 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 | 8.661614e+00 | 5.767897e+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 | 7.857037e+07 | 5.675525e+07 | 828.0000000 | 3.161784e+07 | 6.891505e+07 | 1.147207e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.000000e-07 | 1.903000e-04 | -0.0012644 | -1.247000e-04 | 6.000000e-07 | 1.261000e-04 | 1.267500e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.863000e-04 | 2.380000e-05 | 0.0001552 | 1.656000e-04 | 1.785000e-04 | 2.027000e-04 | 5.678000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.965523e-01 | 2.898510e-01 | 0.0000002 | 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 | 4.965544e-01 | 2.898259e-01 | 0.0000002 | 2.447991e-01 | 4.954417e-01 | 7.472541e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.996920e-01 | 2.084671e-01 | 0.1264460 | 2.178650e-01 | 3.543260e-01 | 5.552420e-01 | 8.735540e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 31439 | 0.9918708 | NA | NA | NA | NA | NA | NA | NA | 3.879948e-01 | 2.138974e-01 | 0.0000000 | 2.120610e-01 | 3.500400e-01 | 5.429310e-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 | 0.0000482 | 0.0002856 | 0.8700001 | 0.8660724 | 0.623778 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001140 | 0.0002830 | 0.6899999 | 0.6870092 | 0.400402 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001803 | 0.0002786 | 0.5199996 | 0.5176577 | 0.456859 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0001292 | 0.0003183 | 0.6800001 | 0.6847432 | 0.240945 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0004776 | 0.0001976 | 0.0160000 | 0.0156418 | 0.515672 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0001590 | 0.0002312 | 0.4899999 | 0.4914172 | 0.843193 | 0.639976 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0001903 | 0.0002495 | 0.4500005 | 0.4456674 | 0.132339 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0001921 | 0.0002239 | 0.3900004 | 0.3909130 | 0.838935 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | -0.0002216 | 0.0002236 | 0.3200000 | 0.3216997 | 0.838562 | 0.653355 | NA |
| 1 | 753405 | rs3115860 | C | A | -0.0002869 | 0.0002400 | 0.2300001 | 0.2318488 | 0.869773 | 0.751797 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51197266 | rs61290853 | A | G | -0.0001186 | 0.0001740 | 0.5000000 | 0.4954231 | 0.386348 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0000061 | 0.0001970 | 0.9800000 | 0.9752553 | 0.254573 | 0.0984425 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0000873 | 0.0002623 | 0.7400005 | 0.7393165 | 0.129933 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0000944 | 0.0001953 | 0.6300007 | 0.6287780 | 0.271568 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0000582 | 0.0002600 | 0.8200001 | 0.8229327 | 0.132615 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001449 | 0.0001856 | 0.4400003 | 0.4350327 | 0.331445 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0001082 | 0.0002445 | 0.6600001 | 0.6580310 | 0.127813 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0001167 | 0.0002435 | 0.6300007 | 0.6318683 | 0.128324 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0000995 | 0.0002383 | 0.6800001 | 0.6763632 | 0.137947 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000964 | 0.0001900 | 0.6100002 | 0.6120797 | 0.297975 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623778 ES:SE:LP:AF:ID 4.81678e-05:0.000285609:0.0604807:0.623778:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400402 ES:SE:LP:AF:ID -0.000114034:0.000283021:0.161151:0.400402:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456859 ES:SE:LP:AF:ID -0.000180258:0.000278623:0.283997:0.456859:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240945 ES:SE:LP:AF:ID -0.000129219:0.000318274:0.167491:0.240945:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515672 ES:SE:LP:AF:ID -0.000477613:0.000197592:1.79588:0.515672:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843193 ES:SE:LP:AF:ID -0.000159047:0.000231154:0.309804:0.843193:rs4951859
1 736289 rs79010578 T A . PASS AF=0.132339 ES:SE:LP:AF:ID 0.000190274:0.000249488:0.346787:0.132339:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838935 ES:SE:LP:AF:ID -0.000192061:0.000223857:0.408935:0.838935:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838562 ES:SE:LP:AF:ID -0.000221596:0.000223615:0.49485:0.838562:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869773 ES:SE:LP:AF:ID -0.000286889:0.000239952:0.638272:0.869773:rs3115860
1 753541 rs2073813 G A . PASS AF=0.12988 ES:SE:LP:AF:ID 0.00031703:0.000240438:0.721246:0.12988:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869113 ES:SE:LP:AF:ID -0.00032197:0.00023948:0.744727:0.869113:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869211 ES:SE:LP:AF:ID -0.000317065:0.000239574:0.721246:0.869211:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869117 ES:SE:LP:AF:ID -0.00032251:0.000239476:0.744727:0.869117:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838015 ES:SE:LP:AF:ID -0.000245571:0.000222994:0.568636:0.838015:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838646 ES:SE:LP:AF:ID -0.000242044:0.000223621:0.552842:0.838646:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839762 ES:SE:LP:AF:ID -0.000211541:0.000226647:0.455932:0.839762:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869398 ES:SE:LP:AF:ID -0.000286744:0.000239201:0.638272:0.869398:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868948 ES:SE:LP:AF:ID -0.000273434:0.000238601:0.60206:0.868948:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867897 ES:SE:LP:AF:ID -0.000321772:0.00023814:0.744727:0.867897:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID -0.000282544:0.000238795:0.619789:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID -0.000283175:0.000238814:0.619789:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID -0.000283674:0.000238819:0.638272:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869582 ES:SE:LP:AF:ID -0.000290789:0.000239473:0.657577:0.869582:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838295 ES:SE:LP:AF:ID -0.000218692:0.000222569:0.481486:0.838295:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838416 ES:SE:LP:AF:ID -0.000221017:0.000222727:0.49485:0.838416:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862252 ES:SE:LP:AF:ID -0.000346235:0.000237952:0.823909:0.862252:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706757 ES:SE:LP:AF:ID -0.000181215:0.000231648:0.366532:0.706757:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761336 ES:SE:LP:AF:ID -0.000214674:0.000189082:0.585027:0.761336:rs2977608
1 769223 rs60320384 C G . PASS AF=0.129577 ES:SE:LP:AF:ID 0.00032426:0.000240301:0.744727:0.129577:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868909 ES:SE:LP:AF:ID -0.000319683:0.000239019:0.744727:0.868909:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129678 ES:SE:LP:AF:ID 0.000329961:0.000240146:0.769551:0.129678:rs59066358
1 772755 rs2905039 A C . PASS AF=0.86892 ES:SE:LP:AF:ID -0.000321298:0.000239025:0.744727:0.86892:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265364 ES:SE:LP:AF:ID -6.00642e-05:0.000211188:0.107905:0.265364:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870038 ES:SE:LP:AF:ID -0.000320226:0.00023951:0.744727:0.870038:rs2980319
1 778745 rs1055606 A G . PASS AF=0.128579 ES:SE:LP:AF:ID 0.000334373:0.000240454:0.79588:0.128579:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID 0.000325259:0.000240045:0.744727:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868779 ES:SE:LP:AF:ID -0.00037194:0.00023887:0.920819:0.868779:rs2977612
1 782981 rs6594026 C T . PASS AF=0.129519 ES:SE:LP:AF:ID 0.000360436:0.000239967:0.886057:0.129519:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868533 ES:SE:LP:AF:ID -0.000360502:0.000238817:0.886057:0.868533:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868474 ES:SE:LP:AF:ID -0.000370656:0.000238966:0.920819:0.868474:rs2980300
1 787399 rs2905055 G T . PASS AF=0.860779 ES:SE:LP:AF:ID -0.000358954:0.000238805:0.886057:0.860779:rs2905055
1 787606 rs3863622 G T . PASS AF=0.128466 ES:SE:LP:AF:ID 0.000335655:0.000241266:0.79588:0.128466:rs3863622
1 787685 rs2905054 G T . PASS AF=0.861489 ES:SE:LP:AF:ID -0.000321216:0.000238958:0.744727:0.861489:rs2905054
1 787844 rs2905053 C T . PASS AF=0.869225 ES:SE:LP:AF:ID -0.000367199:0.000239842:0.886057:0.869225:rs2905053
1 791191 rs111818025 G A . PASS AF=0.127887 ES:SE:LP:AF:ID 0.000331349:0.000242807:0.769551:0.127887:rs111818025
1 795988 rs59380221 C T . PASS AF=0.14304 ES:SE:LP:AF:ID 0.000578391:0.000247686:1.69897:0.14304:rs59380221
1 798400 rs10900604 A G . PASS AF=0.206546 ES:SE:LP:AF:ID 0.000325473:0.000201697:0.958607:0.206546:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206376 ES:SE:LP:AF:ID 0.000330808:0.000201783:1:0.206376:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772667 ES:SE:LP:AF:ID -0.000228998:0.000191952:0.638272:0.772667:rs11240779