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_1130.vcf.gz --id UKB-b:73 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1130.txt.gz --cohort_cases 3432 --cohort_controls 457680 --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": "2019-09-12T19:14:09.508155",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-73/ukb-b-73.vcf.gz; Date=Sun May 10 12:02:25 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-73/UKB-b-73_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-73/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-73/UKB-b-73_data.vcf.gz ...
Read summary statistics for 4272453 SNPs.
Dropped 892 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, 1001830 SNPs remain.
After merging with regression SNP LD, 1001830 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.003 (0.0012)
Lambda GC: 1.0217
Mean Chi^2: 1.0215
Intercept: 0.9918 (0.0083)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:06 2019
Total time elapsed: 47.44sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8856,
"inflation_factor": 1.0475,
"mean_EFFECT": -6.8776e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 51,
"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": 35203,
"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": 1001830,
"ldsc_nsnp_merge_regression_ld": 1001830,
"ldsc_observed_scale_h2_beta": 0.003,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 0.9918,
"ldsc_intercept_se": 0.0083,
"ldsc_lambda_gc": 1.0217,
"ldsc_mean_chisq": 1.0215,
"ldsc_ratio": -0.3814
}
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 | 4271566 | 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 | 4272453 | 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 | 8.654986e+00 | 5.765755e+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 | 7.859181e+07 | 5.672532e+07 | 828.0000000 | 3.169573e+07 | 6.896081e+07 | 1.146868e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -7.000000e-07 | 2.210000e-04 | -0.0012686 | -1.457000e-04 | -1.400000e-06 | 1.429000e-04 | 2.711300e-03 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.145000e-04 | 3.390000e-05 | 0.0001727 | 1.853000e-04 | 2.031000e-04 | 2.374000e-04 | 6.318000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.951674e-01 | 2.904582e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.951644e-01 | 2.904325e-01 | 0.0000000 | 2.421970e-01 | 4.933216e-01 | 7.476701e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.826291e-01 | 2.209042e-01 | 0.1019820 | 1.906250e-01 | 3.297680e-01 | 5.439190e-01 | 8.980180e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 35203 | 0.9917605 | NA | NA | NA | NA | NA | NA | NA | 3.729294e-01 | 2.224983e-01 | 0.0000000 | 1.896960e-01 | 3.280750e-01 | 5.319490e-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 | -0.0004742 | 0.0003178 | 0.1400000 | 0.1357258 | 0.623777 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000250 | 0.0003150 | 0.9400001 | 0.9367620 | 0.400402 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0012813 | 0.0005035 | 0.0109999 | 0.0109332 | 0.103555 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0003091 | 0.0003101 | 0.3200000 | 0.3187517 | 0.456860 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0003420 | 0.0003542 | 0.3300000 | 0.3342671 | 0.240946 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0002927 | 0.0002968 | 0.3200000 | 0.3240459 | 0.116322 | 0.1417730 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000152 | 0.0002199 | 0.9400001 | 0.9448742 | 0.515665 | 0.2751600 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0003163 | 0.0002572 | 0.2200002 | 0.2187957 | 0.843196 | 0.6399760 | NA |
| 1 | 731718 | rs142557973 | T | C | -0.0002285 | 0.0002816 | 0.4199997 | 0.4170417 | 0.122313 | 0.1543530 | NA |
| 1 | 734349 | rs141242758 | T | C | -0.0002314 | 0.0002817 | 0.4100001 | 0.4113992 | 0.121556 | 0.1525560 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | 0.0000264 | 0.0002193 | 0.9000000 | 0.9042646 | 0.254573 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0002749 | 0.0002931 | 0.3500000 | 0.3481811 | 0.120744 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0001058 | 0.0002919 | 0.7199992 | 0.7169900 | 0.129931 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000327 | 0.0002174 | 0.8800001 | 0.8802576 | 0.271569 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0000838 | 0.0002893 | 0.7700005 | 0.7720999 | 0.132614 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000735 | 0.0002066 | 0.7199992 | 0.7219320 | 0.331444 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0000468 | 0.0002721 | 0.8600001 | 0.8633259 | 0.127817 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000424 | 0.0002710 | 0.8800001 | 0.8755936 | 0.128328 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0000002 | 0.0002652 | 1.0000000 | 0.9994861 | 0.137951 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000414 | 0.0002115 | 0.8400000 | 0.8448465 | 0.297972 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623777 ES:SE:LP:AF:ID -0.000474182:0.000317838:0.853872:0.623777:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400402 ES:SE:LP:AF:ID -2.49886e-05:0.000314955:0.0268721:0.400402:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103555 ES:SE:LP:AF:ID 0.00128132:0.0005035:1.95861:0.103555:rs114608975
1 91536 rs6702460 G T . PASS AF=0.45686 ES:SE:LP:AF:ID -0.000309136:0.000310058:0.49485:0.45686:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240946 ES:SE:LP:AF:ID -0.000341984:0.000354184:0.481486:0.240946:rs6680723
1 693731 rs12238997 A G . PASS AF=0.116322 ES:SE:LP:AF:ID -0.000292737:0.00029684:0.49485:0.116322:rs12238997
1 706368 rs12029736 A G . PASS AF=0.515665 ES:SE:LP:AF:ID 1.52041e-05:0.000219887:0.0268721:0.515665:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843196 ES:SE:LP:AF:ID 0.000316335:0.000257238:0.657577:0.843196:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122313 ES:SE:LP:AF:ID -0.000228517:0.000281576:0.376751:0.122313:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121556 ES:SE:LP:AF:ID -0.000231393:0.000281694:0.387216:0.121556:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID -0.00051277:0.000277642:1.18709:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838939 ES:SE:LP:AF:ID 0.000515612:0.000249118:1.42022:0.838939:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838566 ES:SE:LP:AF:ID 0.00050965:0.000248848:1.38722:0.838566:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869775 ES:SE:LP:AF:ID 0.000467112:0.000267029:1.09691:0.869775:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129878 ES:SE:LP:AF:ID -0.000469512:0.00026757:1.10237:0.129878:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869115 ES:SE:LP:AF:ID 0.000463746:0.000266504:1.08619:0.869115:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869213 ES:SE:LP:AF:ID 0.000486415:0.000266608:1.16749:0.869213:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869119 ES:SE:LP:AF:ID 0.000462802:0.000266499:1.08619:0.869119:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838019 ES:SE:LP:AF:ID 0.000507615:0.000248158:1.38722:0.838019:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838649 ES:SE:LP:AF:ID 0.000501447:0.000248855:1.35655:0.838649:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839765 ES:SE:LP:AF:ID 0.000509961:0.000252222:1.36653:0.839765:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID 0.000461765:0.000266193:1.08092:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.86895 ES:SE:LP:AF:ID 0.000451635:0.000265525:1.05061:0.86895:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867899 ES:SE:LP:AF:ID 0.000434551:0.000265013:1:0.867899:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869092 ES:SE:LP:AF:ID 0.000450354:0.000265741:1.04576:0.869092:rs4951929
1 757936 rs4951862 C A . PASS AF=0.8691 ES:SE:LP:AF:ID 0.000450018:0.000265762:1.04576:0.8691:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869108 ES:SE:LP:AF:ID 0.000449502:0.000265768:1.04096:0.869108:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID 0.000463965:0.000266496:1.08619:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838299 ES:SE:LP:AF:ID 0.000511785:0.000247685:1.40894:0.838299:rs1048488
1 761147 rs3115850 T C . PASS AF=0.83842 ES:SE:LP:AF:ID 0.000510127:0.00024786:1.39794:0.83842:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID 0.000498509:0.000264803:1.22185:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.70676 ES:SE:LP:AF:ID 0.000485199:0.00025779:1.22185:0.70676:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105128 ES:SE:LP:AF:ID -0.000404809:0.00029699:0.769551:0.105128:rs61768174
1 768253 rs2977608 A C . PASS AF=0.76133 ES:SE:LP:AF:ID 0.000166007:0.000210416:0.366532:0.76133:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106463 ES:SE:LP:AF:ID 0.0002033:0.000290027:0.318759:0.106463:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129575 ES:SE:LP:AF:ID -0.000412249:0.000267418:0.920819:0.129575:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868911 ES:SE:LP:AF:ID 0.000428511:0.000265991:0.958607:0.868911:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129676 ES:SE:LP:AF:ID -0.000411328:0.000267244:0.920819:0.129676:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868922 ES:SE:LP:AF:ID 0.00042616:0.000265997:0.958607:0.868922:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265359 ES:SE:LP:AF:ID 0.000123638:0.00023502:0.221849:0.265359:rs12124819
1 777122 rs2980319 A T . PASS AF=0.87004 ES:SE:LP:AF:ID 0.000438934:0.000266537:1:0.87004:rs2980319
1 778745 rs1055606 A G . PASS AF=0.128577 ES:SE:LP:AF:ID -0.00041971:0.000267587:0.920819:0.128577:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128875 ES:SE:LP:AF:ID -0.000418181:0.000267132:0.920819:0.128875:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868781 ES:SE:LP:AF:ID 0.000426383:0.000265825:0.958607:0.868781:rs2977612
1 782981 rs6594026 C T . PASS AF=0.129517 ES:SE:LP:AF:ID -0.000414527:0.000267045:0.920819:0.129517:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868534 ES:SE:LP:AF:ID 0.00042711:0.000265766:0.958607:0.868534:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868476 ES:SE:LP:AF:ID 0.000422163:0.000265932:0.958607:0.868476:rs2980300
1 787399 rs2905055 G T . PASS AF=0.860781 ES:SE:LP:AF:ID 0.000426263:0.000265752:0.958607:0.860781:rs2905055
1 787606 rs3863622 G T . PASS AF=0.128464 ES:SE:LP:AF:ID -0.000421389:0.000268491:0.920819:0.128464:rs3863622
1 787685 rs2905054 G T . PASS AF=0.861491 ES:SE:LP:AF:ID 0.000322382:0.000265923:0.638272:0.861491:rs2905054