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_6156_15.vcf.gz --id UKB-b:10002 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6156_15.txt.gz --cohort_cases 3177 --cohort_controls 28140 --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": "2019-09-13T01:55:45.485480",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10002/ukb-b-10002.vcf.gz; Date=Sun May 10 06:19:12 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-10002/UKB-b-10002_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10002/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-10002/UKB-b-10002_data.vcf.gz ...
Read summary statistics for 4132972 SNPs.
Dropped 811 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, 977448 SNPs remain.
After merging with regression SNP LD, 977448 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0009 (0.0155)
Lambda GC: 1.0068
Mean Chi^2: 1.0091
Intercept: 1.0085 (0.0079)
Ratio: 0.9335 (0.8668)
Analysis finished at Thu Oct 17 14:41:07 2019
Total time elapsed: 48.26sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8811,
"inflation_factor": 1,
"mean_EFFECT": -0,
"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": 33827,
"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": 977448,
"ldsc_nsnp_merge_regression_ld": 977448,
"ldsc_observed_scale_h2_beta": 0.0009,
"ldsc_observed_scale_h2_se": 0.0155,
"ldsc_intercept_beta": 1.0085,
"ldsc_intercept_se": 0.0079,
"ldsc_lambda_gc": 1.0068,
"ldsc_mean_chisq": 1.0091,
"ldsc_ratio": 0.9341
}
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 | TRUE |
| 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 | 4132166 | 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 | 4132972 | 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 | 8.655970e+00 | 5.766180e+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 | 7.858048e+07 | 5.674378e+07 | 828.0000000 | 3.164569e+07 | 6.892858e+07 | 1.146837e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.760000e-05 | 2.890900e-03 | -0.0159193 | -1.916700e-03 | -1.960000e-05 | 1.885700e-03 | 2.206480e-02 | ▁▆▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.852100e-03 | 4.191000e-04 | 0.0023165 | 2.489300e-03 | 2.712900e-03 | 3.137600e-03 | 8.469200e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.991288e-01 | 2.891204e-01 | 0.0000014 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.991294e-01 | 2.890943e-01 | 0.0000014 | 2.479286e-01 | 4.982745e-01 | 7.498273e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.886992e-01 | 2.168455e-01 | 0.1101670 | 1.999918e-01 | 3.385860e-01 | 5.481980e-01 | 8.898330e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 33827 | 0.9918153 | NA | NA | NA | NA | NA | NA | NA | 3.783942e-01 | 2.196564e-01 | 0.0000000 | 1.974840e-01 | 3.356630e-01 | 5.363420e-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 | -0.0057740 | 0.0042741 | 0.1800002 | 0.1767254 | 0.623710 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0003307 | 0.0042536 | 0.9400001 | 0.9380323 | 0.399162 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0095982 | 0.0041825 | 0.0219999 | 0.0217401 | 0.455304 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0044296 | 0.0047962 | 0.3599996 | 0.3557171 | 0.240720 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0027663 | 0.0039846 | 0.4899999 | 0.4875260 | 0.118189 | 0.141773 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0009275 | 0.0029590 | 0.7499995 | 0.7539417 | 0.513640 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0024227 | 0.0034441 | 0.4799997 | 0.4817864 | 0.840353 | 0.639976 | NA |
| 1 | 731718 | rs142557973 | T | C | 0.0011246 | 0.0037866 | 0.7700005 | 0.7664633 | 0.124107 | 0.154353 | NA |
| 1 | 734349 | rs141242758 | T | C | 0.0011313 | 0.0037882 | 0.7700005 | 0.7652234 | 0.123373 | 0.152556 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0015516 | 0.0037362 | 0.6800001 | 0.6779239 | 0.134053 | 0.139577 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | -0.0012977 | 0.0029402 | 0.6600001 | 0.6589559 | 0.257301 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | 0.0065720 | 0.0039622 | 0.0969996 | 0.0971801 | 0.120551 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0101218 | 0.0039977 | 0.0109999 | 0.0113453 | 0.126382 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0022520 | 0.0029190 | 0.4400003 | 0.4404074 | 0.274349 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0094087 | 0.0039613 | 0.0179999 | 0.0175415 | 0.128916 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0019829 | 0.0028045 | 0.4799997 | 0.4795355 | 0.328738 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0053531 | 0.0036685 | 0.1400000 | 0.1445041 | 0.128115 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0054337 | 0.0036546 | 0.1400000 | 0.1370658 | 0.128577 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0046447 | 0.0035740 | 0.1900002 | 0.1937399 | 0.138957 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0040375 | 0.0028668 | 0.1600000 | 0.1590281 | 0.295451 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.62371 ES:SE:LP:AF:ID -0.00577395:0.00427413:0.744727:0.62371:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399162 ES:SE:LP:AF:ID 0.000330692:0.00425364:0.0268721:0.399162:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455304 ES:SE:LP:AF:ID -0.00959824:0.00418246:1.65758:0.455304:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24072 ES:SE:LP:AF:ID 0.0044296:0.00479624:0.443698:0.24072:rs6680723
1 693731 rs12238997 A G . PASS AF=0.118189 ES:SE:LP:AF:ID 0.00276627:0.00398455:0.309804:0.118189:rs12238997
1 706368 rs12029736 A G . PASS AF=0.51364 ES:SE:LP:AF:ID -0.000927503:0.00295905:0.124939:0.51364:rs12029736
1 729679 rs4951859 C G . PASS AF=0.840353 ES:SE:LP:AF:ID -0.00242267:0.00344407:0.318759:0.840353:rs4951859
1 731718 rs58276399 T C . PASS AF=0.124107 ES:SE:LP:AF:ID 0.00112463:0.00378658:0.113509:0.124107:rs58276399
1 734349 rs141242758 T C . PASS AF=0.123373 ES:SE:LP:AF:ID 0.00113125:0.00378815:0.113509:0.123373:rs141242758
1 736289 rs79010578 T A . PASS AF=0.134053 ES:SE:LP:AF:ID 0.00155164:0.00373621:0.167491:0.134053:rs79010578
1 752566 rs3094315 G A . PASS AF=0.835828 ES:SE:LP:AF:ID -0.00244755:0.00334413:0.337242:0.835828:rs3094315
1 752721 rs3131972 A G . PASS AF=0.835345 ES:SE:LP:AF:ID -0.00252606:0.00333817:0.346787:0.835345:rs3131972
1 753405 rs3115860 C A . PASS AF=0.866915 ES:SE:LP:AF:ID -0.00123388:0.00357822:0.136677:0.866915:rs3115860
1 753541 rs2073813 G A . PASS AF=0.132666 ES:SE:LP:AF:ID 0.00149996:0.00358736:0.167491:0.132666:rs2073813
1 754182 rs3131969 A G . PASS AF=0.866153 ES:SE:LP:AF:ID -0.00126439:0.00356906:0.142668:0.866153:rs3131969
1 754192 rs3131968 A G . PASS AF=0.866318 ES:SE:LP:AF:ID -0.00126992:0.003571:0.142668:0.866318:rs3131968
1 754334 rs3131967 T C . PASS AF=0.86615 ES:SE:LP:AF:ID -0.00115837:0.00356873:0.124939:0.86615:rs3131967
1 754503 rs3115859 G A . PASS AF=0.834923 ES:SE:LP:AF:ID -0.0019607:0.00333278:0.251812:0.834923:rs3115859
1 754964 rs3131966 C T . PASS AF=0.835586 ES:SE:LP:AF:ID -0.00186402:0.00334287:0.236572:0.835586:rs3131966
1 755775 rs3131965 A G . PASS AF=0.836772 ES:SE:LP:AF:ID -0.00245928:0.00338626:0.327902:0.836772:rs3131965
1 755890 rs3115858 A T . PASS AF=0.866643 ES:SE:LP:AF:ID -0.000934697:0.00356752:0.102373:0.866643:rs3115858
1 756604 rs3131962 A G . PASS AF=0.866271 ES:SE:LP:AF:ID -0.000930951:0.0035589:0.102373:0.866271:rs3131962
1 757640 rs3115853 G A . PASS AF=0.864793 ES:SE:LP:AF:ID -0.00140994:0.00354698:0.161151:0.864793:rs3115853
1 757734 rs4951929 C T . PASS AF=0.866378 ES:SE:LP:AF:ID -0.000963335:0.00356209:0.102373:0.866378:rs4951929
1 757936 rs4951862 C A . PASS AF=0.866402 ES:SE:LP:AF:ID -0.000915148:0.00356231:0.09691:0.866402:rs4951862
1 758144 rs3131956 A G . PASS AF=0.866407 ES:SE:LP:AF:ID -0.000924959:0.0035624:0.09691:0.866407:rs3131956
1 758626 rs3131954 C T . PASS AF=0.86679 ES:SE:LP:AF:ID -0.000901882:0.00357165:0.09691:0.86679:rs3131954
1 760912 rs1048488 C T . PASS AF=0.835422 ES:SE:LP:AF:ID -0.0017661:0.00332826:0.221849:0.835422:rs1048488
1 761147 rs3115850 T C . PASS AF=0.835566 ES:SE:LP:AF:ID -0.00181489:0.0033307:0.229148:0.835566:rs3115850
1 761732 rs2286139 C T . PASS AF=0.859182 ES:SE:LP:AF:ID -0.00122407:0.00354603:0.136677:0.859182:rs2286139
1 763394 rs3115847 G A . PASS AF=0.705081 ES:SE:LP:AF:ID -0.00179336:0.00345833:0.221849:0.705081:rs3115847
1 768253 rs2977608 A C . PASS AF=0.756826 ES:SE:LP:AF:ID -0.00019773:0.00281657:0.0268721:0.756826:rs2977608
1 769223 rs60320384 C G . PASS AF=0.131879 ES:SE:LP:AF:ID 0.000717987:0.00359232:0.0757207:0.131879:rs60320384
1 771823 rs2977605 T C . PASS AF=0.866392 ES:SE:LP:AF:ID -0.000756795:0.00356707:0.0809219:0.866392:rs2977605
1 771967 rs59066358 G A . PASS AF=0.131995 ES:SE:LP:AF:ID 0.000777331:0.00358898:0.0809219:0.131995:rs59066358
1 772755 rs2905039 A C . PASS AF=0.866394 ES:SE:LP:AF:ID -0.000805099:0.00356696:0.0861861:0.866394:rs2905039
1 776546 rs12124819 A G . PASS AF=0.264497 ES:SE:LP:AF:ID 0.00020599:0.00318185:0.0222764:0.264497:rs12124819
1 777122 rs2980319 A T . PASS AF=0.868007 ES:SE:LP:AF:ID -0.000904491:0.00357921:0.09691:0.868007:rs2980319
1 778745 rs1055606 A G . PASS AF=0.130462 ES:SE:LP:AF:ID 0.000818746:0.00359712:0.0861861:0.130462:rs1055606
1 779322 rs4040617 A G . PASS AF=0.130808 ES:SE:LP:AF:ID 0.000808837:0.00359009:0.0861861:0.130808:rs4040617
1 780785 rs2977612 T A . PASS AF=0.866654 ES:SE:LP:AF:ID -0.00126394:0.00356663:0.142668:0.866654:rs2977612
1 782981 rs6594026 C T . PASS AF=0.131418 ES:SE:LP:AF:ID 0.00140209:0.00358739:0.154902:0.131418:rs6594026
1 785050 rs2905062 G A . PASS AF=0.866227 ES:SE:LP:AF:ID -0.00140881:0.00356258:0.161151:0.866227:rs2905062
1 785989 rs2980300 T C . PASS AF=0.866195 ES:SE:LP:AF:ID -0.00150721:0.00356553:0.173925:0.866195:rs2980300
1 787399 rs2905055 G T . PASS AF=0.858325 ES:SE:LP:AF:ID -0.00182418:0.00355753:0.21467:0.858325:rs2905055
1 787606 rs3863622 G T . PASS AF=0.130284 ES:SE:LP:AF:ID 0.000993012:0.00361055:0.107905:0.130284:rs3863622
1 787685 rs2905054 G T . PASS AF=0.85926 ES:SE:LP:AF:ID -0.00266609:0.00356639:0.346787:0.85926:rs2905054
1 787844 rs2905053 C T . PASS AF=0.866941 ES:SE:LP:AF:ID -0.00125111:0.00357983:0.136677:0.866941:rs2905053
1 791191 rs111818025 G A . PASS AF=0.129746 ES:SE:LP:AF:ID 0.00118338:0.00363374:0.130768:0.129746:rs111818025
1 795988 rs59380221 C T . PASS AF=0.145021 ES:SE:LP:AF:ID -0.00187662:0.00370171:0.21467:0.145021:rs59380221