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_20003_1140884516.vcf.gz --id UKB-b:9971 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140884516.txt.gz --cohort_cases 5469 --cohort_controls 457464 --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": "2019-09-13T04:22:06.726755",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-9971/ukb-b-9971.vcf.gz; Date=Sat May 9 20:49:34 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-9971/UKB-b-9971_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9971/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9971/UKB-b-9971_data.vcf.gz ...
Read summary statistics for 5046777 SNPs.
Dropped 1479 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, 1121831 SNPs remain.
After merging with regression SNP LD, 1121831 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0075 (0.002)
Lambda GC: 1.0544
Mean Chi^2: 1.097
Intercept: 1.0229 (0.0101)
Ratio: 0.2362 (0.1039)
Analysis finished at Thu Oct 17 14:44:21 2019
Total time elapsed: 1.0m:9.57sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9075,
"inflation_factor": 1.0475,
"mean_EFFECT": 2.2218e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 17,
"n_p_sig": 4402,
"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": 43199,
"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": 1121831,
"ldsc_nsnp_merge_regression_ld": 1121831,
"ldsc_observed_scale_h2_beta": 0.0075,
"ldsc_observed_scale_h2_se": 0.002,
"ldsc_intercept_beta": 1.0229,
"ldsc_intercept_se": 0.0101,
"ldsc_lambda_gc": 1.0544,
"ldsc_mean_chisq": 1.097,
"ldsc_ratio": 0.2361
}
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 | TRUE |
| 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 | 5045309 | 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 | 5046777 | 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 | 8.671007e+00 | 5.766506e+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 | 7.856052e+07 | 5.663156e+07 | 828.0000000 | 3.188284e+07 | 6.895942e+07 | 1.145765e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.000000e-07 | 3.176000e-04 | -0.0054263 | -1.960000e-04 | -1.200000e-06 | 1.937000e-04 | 5.411700e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.907000e-04 | 6.630000e-05 | 0.0002161 | 2.346000e-04 | 2.669000e-04 | 3.330000e-04 | 8.300000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.896143e-01 | 2.924910e-01 | 0.0000000 | 2.300001e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.896156e-01 | 2.924647e-01 | 0.0000000 | 2.343575e-01 | 4.869637e-01 | 7.434919e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.498481e-01 | 2.392739e-01 | 0.0639980 | 1.437800e-01 | 2.835790e-01 | 5.164210e-01 | 9.360020e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 43199 | 0.9914403 | NA | NA | NA | NA | NA | NA | NA | 3.433869e-01 | 2.357865e-01 | 0.0000000 | 1.487620e-01 | 2.859420e-01 | 5.057910e-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 | 0.0007403 | 0.0003977 | 0.0629999 | 0.0626714 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001618 | 0.0003940 | 0.6800001 | 0.6813717 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0014410 | 0.0006299 | 0.0219999 | 0.0221460 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003607 | 0.0003879 | 0.3500000 | 0.3524693 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0003363 | 0.0007648 | 0.6600001 | 0.6601601 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002590 | 0.0004431 | 0.5600000 | 0.5588433 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0001658 | 0.0005528 | 0.7600007 | 0.7642584 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0001425 | 0.0003713 | 0.6999999 | 0.7011760 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0000503 | 0.0005441 | 0.9299999 | 0.9262933 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000106 | 0.0002751 | 0.9699999 | 0.9691299 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | -0.0004983 | 0.0003405 | 0.1400000 | 0.1433317 | 0.127816 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0005496 | 0.0003390 | 0.1100001 | 0.1050014 | 0.128330 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | -0.0003584 | 0.0004302 | 0.4000000 | 0.4047909 | 0.073314 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0003555 | 0.0004304 | 0.4100001 | 0.4087415 | 0.073336 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0003744 | 0.0004299 | 0.3800004 | 0.3838312 | 0.073625 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0005708 | 0.0003318 | 0.0850002 | 0.0854239 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0003595 | 0.0004307 | 0.4000000 | 0.4039893 | 0.073747 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0003871 | 0.0004310 | 0.3700002 | 0.3690957 | 0.073238 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0003684 | 0.0004326 | 0.3900004 | 0.3944580 | 0.073073 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000323 | 0.0002646 | 0.9000000 | 0.9028668 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.000740254:0.000397662:1.20066:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000161756:0.000393958:0.167491:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.00144103:0.000629861:1.65758:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000360679:0.000387906:0.455932:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID 0.000336295:0.000764842:0.180456:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.00025901:0.000443085:0.251812:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID 0.000165773:0.000552773:0.119186:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 0.000142491:0.000371328:0.154902:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID 5.03379e-05:0.000544138:0.0315171:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -1.06448e-05:0.000275062:0.0132283:0.51565:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.000146163:0.000453834:0.124939:0.101199:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -0.000161734:0.000321803:0.207608:0.843212:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 0.000157682:0.000352241:0.187087:0.122307:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 0.000149459:0.000352389:0.173925:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 0.000125186:0.000347314:0.142668:0.13233:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -0.00015517:0.000311643:0.207608:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -0.000170936:0.000311308:0.236572:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -0.000138814:0.000334046:0.167491:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 0.000161626:0.000334729:0.200659:0.129871:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -0.000144722:0.000333392:0.180456:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -0.000124299:0.000333524:0.148742:0.869221:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -0.00014602:0.000333385:0.180456:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -0.000149101:0.000310444:0.200659:0.838033:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -0.000155821:0.000311316:0.207608:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -0.000116234:0.000315527:0.148742:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID -0.00014121:0.000332999:0.173925:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -0.000129924:0.000332162:0.154902:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID -0.000153912:0.000331525:0.19382:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID -0.000153029:0.000332433:0.187087:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID -0.00015353:0.000332459:0.19382:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID -0.00015407:0.000332467:0.19382:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID -0.000143866:0.000333379:0.173925:0.869589:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838313 ES:SE:LP:AF:ID -0.000143122:0.000309855:0.19382:0.838313:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838434 ES:SE:LP:AF:ID -0.000143923:0.000310074:0.19382:0.838434:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862261 ES:SE:LP:AF:ID -0.000113052:0.000331264:0.136677:0.862261:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000194191:0.000322483:0.259637:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105142 ES:SE:LP:AF:ID 7.13742e-05:0.000371498:0.0705811:0.105142:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761304 ES:SE:LP:AF:ID 7.97629e-06:0.000263204:0.00877392:0.761304:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106488 ES:SE:LP:AF:ID -0.000211626:0.000362775:0.251812:0.106488:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129576 ES:SE:LP:AF:ID 0.000167068:0.00033453:0.207608:0.129576:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868911 ES:SE:LP:AF:ID -0.000167727:0.000332743:0.21467:0.868911:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129677 ES:SE:LP:AF:ID 0.000174177:0.000334314:0.221849:0.129677:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868921 ES:SE:LP:AF:ID -0.000160257:0.000332749:0.200659:0.868921:rs2905039
1 776546 rs12124819 A G . PASS AF=0.26539 ES:SE:LP:AF:ID -4.51026e-05:0.000294003:0.0555173:0.26539:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870044 ES:SE:LP:AF:ID -0.00013223:0.000333428:0.161151:0.870044:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095139 ES:SE:LP:AF:ID -0.00023673:0.00038643:0.267606:0.095139:rs112618790
1 778745 rs1055606 A G . PASS AF=0.128576 ES:SE:LP:AF:ID 0.000139329:0.000334744:0.167491:0.128576:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128873 ES:SE:LP:AF:ID 0.00014178:0.000334176:0.173925:0.128873:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868786 ES:SE:LP:AF:ID -0.000128751:0.000332539:0.154902:0.868786:rs2977612
1 781845 rs61768199 A G . PASS AF=0.10187 ES:SE:LP:AF:ID 0.000107504:0.000376799:0.107905:0.10187:rs61768199