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_41200_K636.vcf.gz --id UKB-b:13566 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41200_K636.txt.gz --cohort_cases 5339 --cohort_controls 457671 --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": "2019-09-13T04:45:51.767968",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13566/ukb-b-13566.vcf.gz; Date=Sat May 9 18:06:32 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-13566/UKB-b-13566_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13566/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13566/UKB-b-13566_data.vcf.gz ...
Read summary statistics for 5008208 SNPs.
Dropped 1436 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, 1116637 SNPs remain.
After merging with regression SNP LD, 1116637 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0034 (0.0011)
Lambda GC: 1.0725
Mean Chi^2: 1.0658
Intercept: 1.032 (0.0076)
Ratio: 0.4866 (0.1152)
Analysis finished at Thu Oct 17 14:43:51 2019
Total time elapsed: 1.0m:2.55sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9066,
"inflation_factor": 1.0966,
"mean_EFFECT": -2.4622e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 2,
"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": 42773,
"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": 1116637,
"ldsc_nsnp_merge_regression_ld": 1116637,
"ldsc_observed_scale_h2_beta": 0.0034,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.032,
"ldsc_intercept_se": 0.0076,
"ldsc_lambda_gc": 1.0725,
"ldsc_mean_chisq": 1.0658,
"ldsc_ratio": 0.4863
}
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 | 5006783 | 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 | 5008208 | 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 | 8.667935e+00 | 5.765660e+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 | 7.857371e+07 | 5.664091e+07 | 828.0000000 | 3.188596e+07 | 6.896648e+07 | 1.146041e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.500000e-06 | 3.028000e-04 | -0.0021642 | -1.971000e-04 | -4.400000e-06 | 1.904000e-04 | 3.152700e-03 | ▁▆▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.865000e-04 | 6.420000e-05 | 0.0002139 | 2.321000e-04 | 2.636000e-04 | 3.276000e-04 | 8.124000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.886417e-01 | 2.913191e-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 | 4.886421e-01 | 2.912925e-01 | 0.0000000 | 2.326787e-01 | 4.845902e-01 | 7.405565e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.514166e-01 | 2.384895e-01 | 0.0655560 | 1.460070e-01 | 2.857790e-01 | 5.179102e-01 | 9.344440e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 42773 | 0.9914594 | NA | NA | NA | NA | NA | NA | NA | 3.448159e-01 | 2.352200e-01 | 0.0000000 | 1.505590e-01 | 2.881390e-01 | 5.071880e-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 | -0.0001991 | 0.0003937 | 0.6100002 | 0.6131523 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001147 | 0.0003900 | 0.7700005 | 0.7687229 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0004117 | 0.0006236 | 0.5099998 | 0.5091674 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001573 | 0.0003840 | 0.6800001 | 0.6821672 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0003773 | 0.0007573 | 0.6200004 | 0.6182686 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002359 | 0.0004387 | 0.5900000 | 0.5907621 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0000786 | 0.0005473 | 0.8900000 | 0.8857481 | 0.913475 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0000758 | 0.0003676 | 0.8400000 | 0.8366203 | 0.116329 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0011695 | 0.0005387 | 0.0299999 | 0.0299424 | 0.067288 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002304 | 0.0002723 | 0.4000000 | 0.3975537 | 0.515645 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | -0.0002563 | 0.0003371 | 0.4500005 | 0.4470383 | 0.127814 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0001886 | 0.0003356 | 0.5700002 | 0.5741246 | 0.128328 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | -0.0001613 | 0.0004259 | 0.6999999 | 0.7049369 | 0.073312 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0001665 | 0.0004261 | 0.6999999 | 0.6960288 | 0.073333 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0001884 | 0.0004256 | 0.6600001 | 0.6579458 | 0.073622 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0001406 | 0.0003285 | 0.6700003 | 0.6686495 | 0.137950 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0001445 | 0.0004264 | 0.7300002 | 0.7347848 | 0.073744 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0001424 | 0.0004267 | 0.7400005 | 0.7385401 | 0.073235 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0001350 | 0.0004282 | 0.7499995 | 0.7525069 | 0.073071 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0007233 | 0.0002619 | 0.0056999 | 0.0057493 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.000199053:0.000393713:0.21467:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000114692:0.000390048:0.113509:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID 0.000411664:0.000623606:0.29243:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000157271:0.000384049:0.167491:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID 0.000377345:0.000757256:0.207608:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -0.000235896:0.000438687:0.229148:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913475 ES:SE:LP:AF:ID 7.86373e-05:0.000547284:0.05061:0.913475:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID -7.58125e-05:0.000367632:0.0757207:0.116329:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067288 ES:SE:LP:AF:ID -0.00116949:0.000538724:1.52288:0.067288:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000230389:0.000272328:0.39794:0.515645:rs12029736
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID 2.00827e-05:0.000449324:0.0177288:0.1012:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID -0.00016316:0.000318601:0.21467:0.843204:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID -5.62935e-05:0.000348736:0.0604807:0.122312:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID -5.15475e-05:0.000348883:0.0555173:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID -1.78932e-05:0.000343859:0.0177288:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -7.21157e-05:0.000308544:0.0861861:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID -7.56733e-05:0.000308212:0.091515:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID 0.000100461:0.000330722:0.119186:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID -0.000131938:0.000331399:0.161151:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID 0.00011487:0.000330074:0.136677:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID 0.00011534:0.000330205:0.136677:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID 0.000104133:0.000330068:0.124939:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID -6.10062e-05:0.000307356:0.0757207:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID -7.83107e-05:0.00030822:0.09691:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID -6.47091e-05:0.000312389:0.0757207:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID 0.000107856:0.000329686:0.130768:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID 7.94548e-05:0.000328856:0.091515:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID 0.000112365:0.000328226:0.136677:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID 9.6384e-05:0.000329126:0.113509:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID 9.49802e-05:0.000329151:0.113509:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID 0.000101097:0.000329158:0.119186:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID 0.000113162:0.000330062:0.136677:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID -5.67597e-05:0.000306773:0.0705811:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID -4.85955e-05:0.00030699:0.0604807:0.838427:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID 9.51805e-05:0.000327968:0.113509:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000125518:0.000319278:0.161151:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105145 ES:SE:LP:AF:ID -0.000263407:0.000367802:0.327902:0.105145:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID 0.000552239:0.000260587:1.46852:0.761297:rs2977608
1 768448 rs12562034 G A . PASS AF=0.10649 ES:SE:LP:AF:ID -0.000964747:0.000359169:2.14267:0.10649:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129581 ES:SE:LP:AF:ID -0.000117314:0.000331201:0.142668:0.129581:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868906 ES:SE:LP:AF:ID 0.000114227:0.000329432:0.136677:0.868906:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129682 ES:SE:LP:AF:ID -0.000124153:0.000330988:0.148742:0.129682:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868916 ES:SE:LP:AF:ID 0.00011574:0.000329438:0.136677:0.868916:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 0.000533855:0.000291085:1.17393:0.265385:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870039 ES:SE:LP:AF:ID 6.59176e-05:0.00033011:0.0757207:0.870039:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095141 ES:SE:LP:AF:ID -0.0010024:0.000382592:2.05552:0.095141:rs112618790
1 778745 rs1055606 A G . PASS AF=0.12858 ES:SE:LP:AF:ID -8.17615e-05:0.000331414:0.091515:0.12858:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID -9.53027e-05:0.000330852:0.113509:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86878 ES:SE:LP:AF:ID 4.35747e-05:0.00032923:0.05061:0.86878:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101873 ES:SE:LP:AF:ID -0.000191347:0.00037305:0.21467:0.101873:rs61768199