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_41204_I251.vcf.gz --id UKB-b:7436 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_I251.txt.gz --cohort_cases 5771 --cohort_controls 457239 --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": "2019-09-13T08:36:06.773597",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7436/ukb-b-7436.vcf.gz; Date=Sun May 10 03:21:41 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-7436/UKB-b-7436_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7436/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7436/UKB-b-7436_data.vcf.gz ...
Read summary statistics for 5132947 SNPs.
Dropped 1563 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, 1132422 SNPs remain.
After merging with regression SNP LD, 1132422 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0104 (0.0014)
Lambda GC: 1.0934
Mean Chi^2: 1.1176
Intercept: 1.018 (0.0079)
Ratio: 0.1534 (0.067)
Analysis finished at Thu Oct 17 14:41:48 2019
Total time elapsed: 1.0m:29.82sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9094,
"inflation_factor": 1.0966,
"mean_EFFECT": 1.0096e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 7,
"n_p_sig": 286,
"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": 44100,
"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": 1132422,
"ldsc_nsnp_merge_regression_ld": 1132422,
"ldsc_observed_scale_h2_beta": 0.0104,
"ldsc_observed_scale_h2_se": 0.0014,
"ldsc_intercept_beta": 1.018,
"ldsc_intercept_se": 0.0079,
"ldsc_lambda_gc": 1.0934,
"ldsc_mean_chisq": 1.1176,
"ldsc_ratio": 0.1531
}
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 | 5131395 | 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 | 5132947 | 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 | 8.672272e+00 | 5.764779e+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 | 7.853651e+07 | 5.660793e+07 | 828.0000000 | 3.188982e+07 | 6.893059e+07 | 1.145060e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.000000e-06 | 3.277000e-04 | -0.0024921 | -2.048000e-04 | 1.000000e-06 | 2.059000e-04 | 5.421900e-03 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.016000e-04 | 7.140000e-05 | 0.0002220 | 2.413000e-04 | 2.758000e-04 | 3.466000e-04 | 8.684000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.839174e-01 | 2.933419e-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 | 4.839190e-01 | 2.933153e-01 | 0.0000000 | 2.250763e-01 | 4.789101e-01 | 7.381217e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.461806e-01 | 2.408576e-01 | 0.0606490 | 1.389330e-01 | 2.783550e-01 | 5.129720e-01 | 9.393510e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 44100 | 0.9914084 | NA | NA | NA | NA | NA | NA | NA | 3.400352e-01 | 2.369710e-01 | 0.0000000 | 1.445690e-01 | 2.815500e-01 | 5.023960e-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 | -0.0001098 | 0.0004085 | 0.7899998 | 0.7881275 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000619 | 0.0004047 | 0.8800001 | 0.8785160 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0003120 | 0.0006470 | 0.6300007 | 0.6296065 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000774 | 0.0003985 | 0.8499999 | 0.8459362 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0007965 | 0.0007857 | 0.3100002 | 0.3106808 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0000666 | 0.0004551 | 0.8800001 | 0.8835900 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0000475 | 0.0005678 | 0.9299999 | 0.9333520 | 0.913475 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0004852 | 0.0003814 | 0.2000000 | 0.2033884 | 0.116329 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0008013 | 0.0005589 | 0.1499999 | 0.1516619 | 0.067288 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0003311 | 0.0002825 | 0.2399999 | 0.2413166 | 0.515645 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | 0.0001578 | 0.0003497 | 0.6499995 | 0.6519282 | 0.127814 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0001573 | 0.0003482 | 0.6499995 | 0.6515730 | 0.128328 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | 0.0005277 | 0.0004419 | 0.2300001 | 0.2323940 | 0.073312 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | 0.0005307 | 0.0004421 | 0.2300001 | 0.2299177 | 0.073333 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | 0.0005357 | 0.0004415 | 0.2300001 | 0.2250065 | 0.073622 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0001974 | 0.0003408 | 0.5600000 | 0.5624925 | 0.137950 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0005316 | 0.0004424 | 0.2300001 | 0.2295227 | 0.073744 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0005853 | 0.0004427 | 0.1900002 | 0.1861285 | 0.073235 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0005972 | 0.0004443 | 0.1800002 | 0.1789431 | 0.073071 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0004000 | 0.0002717 | 0.1400000 | 0.1410324 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.000109778:0.000408487:0.102373:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 6.18564e-05:0.000404685:0.0555173:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.000312039:0.000647007:0.200659:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -7.74234e-05:0.000398461:0.0705811:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID 0.00079651:0.000785672:0.508638:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 6.66427e-05:0.000455148:0.0555173:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913475 ES:SE:LP:AF:ID 4.74859e-05:0.000567821:0.0315171:0.913475:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID -0.00048516:0.000381428:0.69897:0.116329:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067288 ES:SE:LP:AF:ID -0.000801343:0.000558939:0.823909:0.067288:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000331061:0.000282547:0.619789:0.515645:rs12029736
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID 0.000651728:0.000466185:0.79588:0.1012:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID 0.000156772:0.000330557:0.19382:0.843204:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID -0.000416716:0.000361822:0.60206:0.122312:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID -0.00039731:0.000361974:0.568636:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID -0.000373827:0.000356762:0.537602:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID 0.000223932:0.000320122:0.318759:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID 0.000181984:0.000319777:0.244125:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID 0.000409942:0.000343133:0.638272:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID -0.000374365:0.000343835:0.552842:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID 0.000373846:0.00034246:0.568636:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID 0.000360747:0.000342596:0.537602:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID 0.000371995:0.000342454:0.552842:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID 0.000196842:0.00031889:0.267606:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID 0.000186246:0.000319786:0.251812:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID 0.000235229:0.000324111:0.327902:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID 0.00043148:0.000342057:0.677781:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID 0.000446462:0.000341197:0.721246:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID 0.000404399:0.000340542:0.619789:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID 0.00044652:0.000341476:0.721246:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID 0.000446776:0.000341502:0.721246:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID 0.000445975:0.00034151:0.721246:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID 0.000437975:0.000342448:0.69897:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID 0.000248717:0.000318285:0.366532:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID 0.000253063:0.00031851:0.366532:0.838427:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID 0.000362587:0.000340275:0.537602:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.000535612:0.000331259:0.958607:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105145 ES:SE:LP:AF:ID -0.000524178:0.000381604:0.769551:0.105145:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID 1.9496e-05:0.000270365:0.0268721:0.761297:rs2977608
1 768448 rs12562034 G A . PASS AF=0.10649 ES:SE:LP:AF:ID 0.000470376:0.000372647:0.677781:0.10649:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129581 ES:SE:LP:AF:ID -0.000426609:0.00034363:0.677781:0.129581:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868906 ES:SE:LP:AF:ID 0.00045565:0.000341794:0.744727:0.868906:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129682 ES:SE:LP:AF:ID -0.000453901:0.000343408:0.721246:0.129682:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868916 ES:SE:LP:AF:ID 0.000447992:0.0003418:0.721246:0.868916:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 0.000232912:0.000302008:0.356547:0.265385:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870039 ES:SE:LP:AF:ID 0.000384213:0.000342498:0.585027:0.870039:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095141 ES:SE:LP:AF:ID 0.00056741:0.000396949:0.823909:0.095141:rs112618790
1 778745 rs1055606 A G . PASS AF=0.12858 ES:SE:LP:AF:ID -0.000371383:0.00034385:0.552842:0.12858:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID -0.000368756:0.000343267:0.552842:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86878 ES:SE:LP:AF:ID 0.000309481:0.000341584:0.443698:0.86878:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101873 ES:SE:LP:AF:ID -0.000526055:0.000387048:0.769551:0.101873:rs61768199