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_1140874744.vcf.gz --id UKB-b:8602 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140874744.txt.gz --cohort_cases 3840 --cohort_controls 459093 --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": "2019-09-13T12:45:25.637575",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-8602/ukb-b-8602.vcf.gz; Date=Sun May 10 12:29:13 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-8602/UKB-b-8602_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8602/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8602/UKB-b-8602_data.vcf.gz ...
Read summary statistics for 4468384 SNPs.
Dropped 1013 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, 1035487 SNPs remain.
After merging with regression SNP LD, 1035487 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.01 (0.0014)
Lambda GC: 1.0989
Mean Chi^2: 1.1246
Intercept: 1.0277 (0.0088)
Ratio: 0.2225 (0.071)
Analysis finished at Thu Oct 17 14:42:50 2019
Total time elapsed: 56.23sQC metrics
Metrics
Metrics
{
"af_correlation": 0.892,
"inflation_factor": 1.0966,
"mean_EFFECT": -2.4079e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 10,
"n_p_sig": 318,
"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": 37147,
"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": 1035487,
"ldsc_nsnp_merge_regression_ld": 1035487,
"ldsc_observed_scale_h2_beta": 0.01,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 1.0277,
"ldsc_intercept_se": 0.0088,
"ldsc_lambda_gc": 1.0989,
"ldsc_mean_chisq": 1.1246,
"ldsc_ratio": 0.2223
}
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 | 4467377 | 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 | 4468384 | 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 | 8.656813e+00 | 5.765506e+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 | 7.860068e+07 | 5.672806e+07 | 828.0000000 | 3.172821e+07 | 6.895979e+07 | 1.147353e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.000000e-07 | 2.456000e-04 | -0.0019993 | -1.600000e-04 | -5.000000e-07 | 1.588000e-04 | 3.611500e-03 | ▁▇▂▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.299000e-04 | 4.000000e-05 | 0.0001817 | 1.955000e-04 | 2.162000e-04 | 2.565000e-04 | 6.647000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.840315e-01 | 2.931863e-01 | 0.0000000 | 2.300001e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.840311e-01 | 2.931597e-01 | 0.0000000 | 2.261691e-01 | 4.787256e-01 | 7.379059e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.742306e-01 | 2.261617e-01 | 0.0911460 | 1.781440e-01 | 3.180300e-01 | 5.375102e-01 | 9.088530e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 37147 | 0.9916867 | NA | NA | NA | NA | NA | NA | NA | 3.654276e-01 | 2.262198e-01 | 0.0000000 | 1.787140e-01 | 3.172920e-01 | 5.257590e-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 | -0.0001402 | 0.0003344 | 0.6800001 | 0.6751392 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000128 | 0.0003313 | 0.9699999 | 0.9691323 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0000649 | 0.0005297 | 0.9000000 | 0.9024744 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001876 | 0.0003262 | 0.5700002 | 0.5653570 | 0.456851 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0004460 | 0.0003726 | 0.2300001 | 0.2313531 | 0.240960 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0003369 | 0.0003123 | 0.2800000 | 0.2806509 | 0.116325 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001902 | 0.0002313 | 0.4100001 | 0.4109045 | 0.515650 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | -0.0002375 | 0.0003817 | 0.5300002 | 0.5337417 | 0.101199 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0002440 | 0.0002706 | 0.3700002 | 0.3672867 | 0.843212 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | -0.0003545 | 0.0002962 | 0.2300001 | 0.2314901 | 0.122307 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0002575 | 0.0002307 | 0.2599998 | 0.2644566 | 0.254557 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0002184 | 0.0003084 | 0.4799997 | 0.4788640 | 0.120739 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0002269 | 0.0003071 | 0.4600002 | 0.4600934 | 0.129955 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0001789 | 0.0002287 | 0.4299995 | 0.4342211 | 0.271547 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0001692 | 0.0003044 | 0.5800000 | 0.5781808 | 0.132635 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000214 | 0.0002173 | 0.9199999 | 0.9217398 | 0.331455 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0001287 | 0.0002863 | 0.6499995 | 0.6531416 | 0.127816 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000901 | 0.0002851 | 0.7499995 | 0.7521225 | 0.128330 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0000514 | 0.0002791 | 0.8499999 | 0.8538640 | 0.137953 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000092 | 0.0002225 | 0.9699999 | 0.9668907 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.000140166:0.000334441:0.167491:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -1.28212e-05:0.000331326:0.0132283:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID 6.49105e-05:0.000529725:0.0457575:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000187554:0.000326236:0.244125:0.456851:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.000446008:0.000372642:0.638272:0.24096:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID -0.000336921:0.000312294:0.552842:0.116325:rs12238997
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 0.000190225:0.000231332:0.387216:0.51565:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID -0.000237523:0.000381683:0.275724:0.101199:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID 0.000244002:0.000270642:0.431798:0.843212:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID -0.000354461:0.000296241:0.638272:0.122307:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID -0.000349939:0.000296366:0.619789:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID -0.000105284:0.000292098:0.142668:0.13233:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID 0.000167264:0.000262098:0.283997:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID 0.000184936:0.000261816:0.318759:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID 0.00027029:0.000280939:0.468521:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID -0.000313854:0.000281514:0.585027:0.129871:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID 0.00028319:0.000280389:0.508638:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID 0.000304239:0.0002805:0.552842:0.869221:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID 0.000287653:0.000280383:0.522879:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID 0.000176479:0.000261089:0.30103:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID 0.000181454:0.000261823:0.309804:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID 0.000167108:0.000265364:0.275724:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID 0.000266816:0.000280059:0.468521:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID 0.000244607:0.000279354:0.420216:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID 0.000268107:0.000278819:0.468521:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID 0.000248648:0.000279583:0.431798:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID 0.000248975:0.000279604:0.431798:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID 0.000248798:0.000279611:0.431798:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID 0.000268147:0.000280378:0.468521:0.869589:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID 0.000224954:0.000260594:0.408935:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID 0.000222611:0.000260778:0.408935:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID 0.000249881:0.000278599:0.431798:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.000392829:0.000271214:0.823909:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105142 ES:SE:LP:AF:ID -0.000147526:0.000312437:0.19382:0.105142:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID 5.95669e-05:0.000221359:0.102373:0.761304:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106488 ES:SE:LP:AF:ID 0.000188926:0.000305101:0.267606:0.106488:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129576 ES:SE:LP:AF:ID -0.000286619:0.000281346:0.508638:0.129576:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868911 ES:SE:LP:AF:ID 0.000261246:0.000279843:0.455932:0.868911:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129677 ES:SE:LP:AF:ID -0.000288577:0.000281164:0.522879:0.129677:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868921 ES:SE:LP:AF:ID 0.000260674:0.000279848:0.455932:0.868921:rs2905039
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID 7.00398e-05:0.000247262:0.107905:0.26539:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870044 ES:SE:LP:AF:ID 0.000274194:0.000280419:0.481486:0.870044:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095139 ES:SE:LP:AF:ID 0.000283013:0.000324995:0.420216:0.095139:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128576 ES:SE:LP:AF:ID -0.000305128:0.000281526:0.552842:0.128576:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128873 ES:SE:LP:AF:ID -0.000314454:0.000281049:0.585027:0.128873:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868786 ES:SE:LP:AF:ID 0.000277236:0.000279672:0.49485:0.868786:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10187 ES:SE:LP:AF:ID -0.000138883:0.000316895:0.180456:0.10187:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129513 ES:SE:LP:AF:ID -0.000311444:0.000280958:0.568636:0.129513:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868539 ES:SE:LP:AF:ID 0.000286184:0.000279606:0.508638:0.868539:rs2905062
1 785989 rs2980300 T C . PASS AF=0.86848 ES:SE:LP:AF:ID 0.000287388:0.00027978:0.522879:0.86848:rs2980300