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_20003_1140870788.vcf.gz --id UKB-b:11886 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140870788.txt.gz --cohort_cases 2831 --cohort_controls 460102 --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": "2019-09-13T12:41:00.288073",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11886/ukb-b-11886.vcf.gz; Date=Sat May 9 23:01:21 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-11886/UKB-b-11886_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11886/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11886/UKB-b-11886_data.vcf.gz ...
Read summary statistics for 3911890 SNPs.
Dropped 715 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, 935953 SNPs remain.
After merging with regression SNP LD, 935953 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0031 (0.001)
Lambda GC: 1.0117
Mean Chi^2: 1.0192
Intercept: 0.9872 (0.0074)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:01 2019
Total time elapsed: 50.82sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8718,
"inflation_factor": 1,
"mean_EFFECT": -4.1808e-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": 31851,
"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": 935953,
"ldsc_nsnp_merge_regression_ld": 935953,
"ldsc_observed_scale_h2_beta": 0.0031,
"ldsc_observed_scale_h2_se": 0.001,
"ldsc_intercept_beta": 0.9872,
"ldsc_intercept_se": 0.0074,
"ldsc_lambda_gc": 1.0117,
"ldsc_mean_chisq": 1.0192,
"ldsc_ratio": -0.6667
}
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 | 3911178 | 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 | 3911890 | 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 | 8.660592e+00 | 5.767776e+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 | 7.856403e+07 | 5.675322e+07 | 828.0000000 | 3.161513e+07 | 6.890062e+07 | 1.147054e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -4.000000e-07 | 1.916000e-04 | -0.0012536 | -1.264000e-04 | -3.000000e-07 | 1.259000e-04 | 1.215200e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.881000e-04 | 2.460000e-05 | 0.0001562 | 1.667000e-04 | 1.801000e-04 | 2.051000e-04 | 5.712000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.972345e-01 | 2.898986e-01 | 0.0000039 | 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 | 4.972344e-01 | 2.898726e-01 | 0.0000039 | 2.446874e-01 | 4.969012e-01 | 7.488857e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.979359e-01 | 2.099822e-01 | 0.1236320 | 2.147580e-01 | 3.517050e-01 | 5.542858e-01 | 8.763680e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 31851 | 0.9918579 | NA | NA | NA | NA | NA | NA | NA | 3.864483e-01 | 2.149495e-01 | 0.0000000 | 2.096650e-01 | 3.476440e-01 | 5.419330e-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 | -0.0001537 | 0.0002874 | 0.5900000 | 0.5928281 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002131 | 0.0002847 | 0.4500005 | 0.4541494 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003078 | 0.0002804 | 0.2700001 | 0.2722472 | 0.456851 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0005251 | 0.0003202 | 0.1000000 | 0.1010469 | 0.240960 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001678 | 0.0001988 | 0.4000000 | 0.3987537 | 0.515650 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0000565 | 0.0002326 | 0.8100000 | 0.8080486 | 0.843212 | 0.639976 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0001717 | 0.0002510 | 0.4899999 | 0.4940294 | 0.132330 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0002244 | 0.0002252 | 0.3200000 | 0.3190411 | 0.838951 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | -0.0002171 | 0.0002250 | 0.3300000 | 0.3346746 | 0.838580 | 0.653355 | NA |
| 1 | 753405 | rs3115860 | C | A | -0.0002660 | 0.0002414 | 0.2700001 | 0.2705505 | 0.869781 | 0.751797 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51197266 | rs61290853 | A | G | 0.0002652 | 0.0001751 | 0.1299999 | 0.1299119 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0004001 | 0.0001983 | 0.0439997 | 0.0435627 | 0.254557 | 0.0984425 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0000834 | 0.0002639 | 0.7499995 | 0.7521335 | 0.129955 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0003709 | 0.0001966 | 0.0589997 | 0.0591394 | 0.271547 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0001145 | 0.0002616 | 0.6600001 | 0.6614977 | 0.132635 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0003985 | 0.0001868 | 0.0329997 | 0.0328618 | 0.331455 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0001294 | 0.0002461 | 0.5999997 | 0.5989809 | 0.127816 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0001090 | 0.0002450 | 0.6600001 | 0.6564813 | 0.128330 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0000706 | 0.0002398 | 0.7700005 | 0.7685982 | 0.137953 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0003942 | 0.0001912 | 0.0389996 | 0.0392011 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.000153686:0.000287402:0.229148:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000213121:0.000284725:0.346787:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000307798:0.000280351:0.568636:0.456851:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID 0.000525113:0.00032023:1:0.24096:rs6680723
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -0.000167753:0.000198795:0.39794:0.51565:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -5.6503e-05:0.000232576:0.091515:0.843212:rs4951859
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 0.000171672:0.000251014:0.309804:0.13233:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -0.00022443:0.000225234:0.49485:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -0.000217058:0.000224991:0.481486:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -0.000266001:0.000241425:0.568636:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 0.000289245:0.000241919:0.638272:0.129871:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -0.000271062:0.000240952:0.585027:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -0.000273362:0.000241047:0.585027:0.869221:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -0.000272213:0.000240947:0.585027:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -0.000233653:0.000224367:0.522879:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -0.000241683:0.000224997:0.552842:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -0.000242305:0.00022804:0.537602:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID -0.000293788:0.000240668:0.657577:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -0.000284256:0.000240063:0.619789:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID -0.000262363:0.000239603:0.568636:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID -0.00029166:0.000240259:0.657577:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID -0.000291902:0.000240278:0.657577:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID -0.000292117:0.000240283:0.657577:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID -0.000292457:0.000240943:0.657577:0.869589:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID -0.000252265:0.000223941:0.585027:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID -0.000255707:0.000224099:0.60206:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID -0.000291789:0.000239414:0.657577:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000260624:0.000233068:0.585027:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID -0.000315425:0.000190225:1.01323:0.761304:rs2977608
1 769223 rs60320384 C G . PASS AF=0.129576 ES:SE:LP:AF:ID 0.000328426:0.000241774:0.769551:0.129576:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868911 ES:SE:LP:AF:ID -0.000311941:0.000240483:0.721246:0.868911:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129677 ES:SE:LP:AF:ID 0.00032417:0.000241618:0.744727:0.129677:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868921 ES:SE:LP:AF:ID -0.000313588:0.000240488:0.721246:0.868921:rs2905039
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID -1.93249e-05:0.000212485:0.0315171:0.26539:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870044 ES:SE:LP:AF:ID -0.000317324:0.000240978:0.721246:0.870044:rs2980319
1 778745 rs1055606 A G . PASS AF=0.128576 ES:SE:LP:AF:ID 0.000342992:0.00024193:0.79588:0.128576:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128873 ES:SE:LP:AF:ID 0.00033668:0.000241519:0.79588:0.128873:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868786 ES:SE:LP:AF:ID -0.000331891:0.000240336:0.769551:0.868786:rs2977612
1 782981 rs6594026 C T . PASS AF=0.129513 ES:SE:LP:AF:ID 0.000327437:0.000241441:0.744727:0.129513:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868539 ES:SE:LP:AF:ID -0.000294799:0.00024028:0.657577:0.868539:rs2905062
1 785989 rs2980300 T C . PASS AF=0.86848 ES:SE:LP:AF:ID -0.000291862:0.000240429:0.657577:0.86848:rs2980300
1 787399 rs2905055 G T . PASS AF=0.86078 ES:SE:LP:AF:ID -0.000261822:0.000240264:0.552842:0.86078:rs2905055
1 787606 rs3863622 G T . PASS AF=0.128461 ES:SE:LP:AF:ID 0.000316877:0.000242743:0.721246:0.128461:rs3863622
1 787685 rs2905054 G T . PASS AF=0.861493 ES:SE:LP:AF:ID -0.000274728:0.000240423:0.60206:0.861493:rs2905054
1 787844 rs2905053 C T . PASS AF=0.869229 ES:SE:LP:AF:ID -0.000292171:0.000241308:0.638272:0.869229:rs2905053
1 791191 rs111818025 G A . PASS AF=0.127879 ES:SE:LP:AF:ID 0.000335521:0.000244298:0.769551:0.127879:rs111818025
1 795988 rs59380221 C T . PASS AF=0.143051 ES:SE:LP:AF:ID 0.000359669:0.000249207:0.823909:0.143051:rs59380221
1 796375 rs12083781 T C . PASS AF=0.123818 ES:SE:LP:AF:ID 0.000281599:0.000251065:0.585027:0.123818:rs12083781
1 798400 rs10900604 A G . PASS AF=0.20658 ES:SE:LP:AF:ID 0.000332331:0.000202935:1:0.20658:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206409 ES:SE:LP:AF:ID 0.000335003:0.000203022:1.00436:0.206409:rs11240777