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_41246_1170.vcf.gz --id UKB-b:6951 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1170.txt.gz --cohort_cases 4010 --cohort_controls 440225 --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": "2019-09-13T13:15:27.286233",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6951/ukb-b-6951.vcf.gz; Date=Sun May 10 03:27:17 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-6951/UKB-b-6951_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6951/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6951/UKB-b-6951_data.vcf.gz ...
Read summary statistics for 4541568 SNPs.
Dropped 1055 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, 1047413 SNPs remain.
After merging with regression SNP LD, 1047413 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0054 (0.0012)
Lambda GC: 1.0574
Mean Chi^2: 1.0643
Intercept: 1.0135 (0.0073)
Ratio: 0.2105 (0.1138)
Analysis finished at Thu Oct 17 14:41:08 2019
Total time elapsed: 50.15sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8942,
"inflation_factor": 1.0475,
"mean_EFFECT": 1.5946e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 2,
"n_p_sig": 148,
"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": 37865,
"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": 1047413,
"ldsc_nsnp_merge_regression_ld": 1047413,
"ldsc_observed_scale_h2_beta": 0.0054,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0135,
"ldsc_intercept_se": 0.0073,
"ldsc_lambda_gc": 1.0574,
"ldsc_mean_chisq": 1.0643,
"ldsc_ratio": 0.21
}
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 | 4540521 | 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 | 4541568 | 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 | 8.658010e+00 | 5.765403e+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 | 7.859123e+07 | 5.672268e+07 | 828.0000000 | 3.175273e+07 | 6.893183e+07 | 1.147116e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.000000e-07 | 2.584000e-04 | -0.0019571 | -1.678000e-04 | -5.000000e-07 | 1.673000e-04 | 2.179600e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.463000e-04 | 4.440000e-05 | 0.0001933 | 2.082000e-04 | 2.311000e-04 | 2.757000e-04 | 7.070000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.897937e-01 | 2.911698e-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 | 4.897955e-01 | 2.911431e-01 | 0.0000000 | 2.357091e-01 | 4.858845e-01 | 7.419374e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.711650e-01 | 2.280525e-01 | 0.0872820 | 1.736138e-01 | 3.136130e-01 | 5.350810e-01 | 9.127180e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 37865 | 0.9916626 | NA | NA | NA | NA | NA | NA | NA | 3.626787e-01 | 2.275868e-01 | 0.0000000 | 1.749200e-01 | 3.134980e-01 | 5.235620e-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 | 0.0003367 | 0.0003557 | 0.3400001 | 0.3439489 | 0.623753 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0005779 | 0.0003525 | 0.1000000 | 0.1010716 | 0.400404 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0005118 | 0.0005635 | 0.3599996 | 0.3637116 | 0.103535 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001115 | 0.0003470 | 0.7499995 | 0.7479000 | 0.456862 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0000558 | 0.0003964 | 0.8900000 | 0.8880211 | 0.240917 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0003666 | 0.0003323 | 0.2700001 | 0.2699067 | 0.116362 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000433 | 0.0002461 | 0.8600001 | 0.8604853 | 0.515729 | 0.2751600 | NA |
| 1 | 722670 | rs116030099 | T | C | 0.0001193 | 0.0004060 | 0.7700005 | 0.7689348 | 0.101237 | 0.0413339 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0000735 | 0.0002879 | 0.8000000 | 0.7984070 | 0.843124 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0002668 | 0.0003151 | 0.4000000 | 0.3971853 | 0.122352 | 0.1543530 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0004328 | 0.0002454 | 0.0779992 | 0.0778182 | 0.254396 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0003327 | 0.0003280 | 0.3100002 | 0.3104457 | 0.120826 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0005382 | 0.0003268 | 0.1000000 | 0.0996122 | 0.129942 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0003124 | 0.0002433 | 0.2000000 | 0.1992210 | 0.271383 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0005468 | 0.0003239 | 0.0909997 | 0.0913415 | 0.132630 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0008281 | 0.0002312 | 0.0003400 | 0.0003413 | 0.331624 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0001056 | 0.0003045 | 0.7300002 | 0.7287330 | 0.127877 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0001323 | 0.0003032 | 0.6600001 | 0.6625426 | 0.128398 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0002637 | 0.0002968 | 0.3700002 | 0.3743011 | 0.138005 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0005485 | 0.0002366 | 0.0200000 | 0.0204474 | 0.298160 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623753 ES:SE:LP:AF:ID 0.000336658:0.000355728:0.468521:0.623753:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400404 ES:SE:LP:AF:ID -0.000577912:0.000352454:1:0.400404:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103535 ES:SE:LP:AF:ID 0.000511803:0.000563464:0.443698:0.103535:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456862 ES:SE:LP:AF:ID -0.000111515:0.000346956:0.124939:0.456862:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240917 ES:SE:LP:AF:ID 5.58185e-05:0.000396414:0.05061:0.240917:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116362 ES:SE:LP:AF:ID 0.000366565:0.000332251:0.568636:0.116362:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515729 ES:SE:LP:AF:ID -4.3254e-05:0.000246102:0.0655015:0.515729:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101237 ES:SE:LP:AF:ID 0.00011928:0.000406034:0.113509:0.101237:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843124 ES:SE:LP:AF:ID -7.35349e-05:0.00028791:0.09691:0.843124:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122352 ES:SE:LP:AF:ID 0.000266811:0.000315134:0.39794:0.122352:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121595 ES:SE:LP:AF:ID 0.000291268:0.000315264:0.443698:0.121595:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132416 ES:SE:LP:AF:ID -0.000107115:0.000310682:0.136677:0.132416:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838817 ES:SE:LP:AF:ID -4.40948e-05:0.000278754:0.0604807:0.838817:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838438 ES:SE:LP:AF:ID -2.63208e-05:0.000278443:0.0362122:0.838438:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869697 ES:SE:LP:AF:ID -0.000249047:0.000298805:0.39794:0.869697:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129969 ES:SE:LP:AF:ID 0.000216536:0.000299399:0.327902:0.129969:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869036 ES:SE:LP:AF:ID -0.000232467:0.000298211:0.356547:0.869036:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869136 ES:SE:LP:AF:ID -0.000239839:0.000298333:0.376751:0.869136:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869039 ES:SE:LP:AF:ID -0.000232862:0.000298206:0.366532:0.869039:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837887 ES:SE:LP:AF:ID -2.18509e-05:0.000277667:0.0268721:0.837887:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838518 ES:SE:LP:AF:ID -3.91794e-05:0.000278447:0.05061:0.838518:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839637 ES:SE:LP:AF:ID -0.000104967:0.000282229:0.148742:0.839637:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869326 ES:SE:LP:AF:ID -0.000241721:0.000297877:0.376751:0.869326:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868878 ES:SE:LP:AF:ID -0.000220105:0.000297142:0.337242:0.868878:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867821 ES:SE:LP:AF:ID -0.000193326:0.000296554:0.29243:0.867821:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869017 ES:SE:LP:AF:ID -0.000231258:0.000297379:0.356547:0.869017:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869025 ES:SE:LP:AF:ID -0.000231533:0.000297402:0.356547:0.869025:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869033 ES:SE:LP:AF:ID -0.00023197:0.000297409:0.356547:0.869033:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869508 ES:SE:LP:AF:ID -0.000242:0.000298215:0.376751:0.869508:rs3131954
1 760912 rs1048488 C T . PASS AF=0.83817 ES:SE:LP:AF:ID -8.37342e-05:0.000277152:0.119186:0.83817:rs1048488
1 761147 rs3115850 T C . PASS AF=0.83829 ES:SE:LP:AF:ID -8.49342e-05:0.000277348:0.119186:0.83829:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862178 ES:SE:LP:AF:ID -0.000235679:0.000296343:0.366532:0.862178:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706664 ES:SE:LP:AF:ID -0.000287412:0.000288508:0.49485:0.706664:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105174 ES:SE:LP:AF:ID 0.000176668:0.000332369:0.221849:0.105174:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761327 ES:SE:LP:AF:ID 0.000113836:0.000235566:0.200659:0.761327:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106397 ES:SE:LP:AF:ID -0.000492365:0.000324752:0.886057:0.106397:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129657 ES:SE:LP:AF:ID 0.000248903:0.000299255:0.387216:0.129657:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868839 ES:SE:LP:AF:ID -0.000256573:0.00029766:0.408935:0.868839:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129757 ES:SE:LP:AF:ID 0.000255449:0.000299061:0.408935:0.129757:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868849 ES:SE:LP:AF:ID -0.000239145:0.000297666:0.376751:0.868849:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265286 ES:SE:LP:AF:ID 0.000750714:0.000263099:2.36653:0.265286:rs12124819
1 777122 rs2980319 A T . PASS AF=0.869997 ES:SE:LP:AF:ID -0.000254954:0.000298295:0.408935:0.869997:rs2980319
1 777232 rs112618790 C T . PASS AF=0.09508 ES:SE:LP:AF:ID -0.000483414:0.000345862:0.79588:0.09508:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128624 ES:SE:LP:AF:ID 0.000252318:0.000299472:0.39794:0.128624:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128919 ES:SE:LP:AF:ID 0.000237461:0.000298969:0.366532:0.128919:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868735 ES:SE:LP:AF:ID -0.000253217:0.000297501:0.408935:0.868735:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101876 ES:SE:LP:AF:ID 0.00020171:0.000337144:0.259637:0.101876:rs61768199
1 782981 rs6594026 C T . PASS AF=0.129568 ES:SE:LP:AF:ID 0.000213449:0.00029887:0.318759:0.129568:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868491 ES:SE:LP:AF:ID -0.000221176:0.000297433:0.337242:0.868491:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868433 ES:SE:LP:AF:ID -0.000215161:0.000297619:0.327902:0.868433:rs2980300