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_I258.vcf.gz --id UKB-b:16606 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_I258.txt.gz --cohort_cases 5738 --cohort_controls 457272 --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": "2019-09-13T08:35:57.598409",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16606/ukb-b-16606.vcf.gz; Date=Sun May 10 07:04:45 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-16606/UKB-b-16606_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16606/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16606/UKB-b-16606_data.vcf.gz ...
Read summary statistics for 5124039 SNPs.
Dropped 1552 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, 1131326 SNPs remain.
After merging with regression SNP LD, 1131326 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0123 (0.0013)
Lambda GC: 1.1135
Mean Chi^2: 1.1233
Intercept: 1.0065 (0.0076)
Ratio: 0.0523 (0.0619)
Analysis finished at Thu Oct 17 14:46:10 2019
Total time elapsed: 59.57sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9092,
"inflation_factor": 1.0966,
"mean_EFFECT": 2.0449e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 6,
"n_p_sig": 325,
"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": 43989,
"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": 1131326,
"ldsc_nsnp_merge_regression_ld": 1131326,
"ldsc_observed_scale_h2_beta": 0.0123,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 1.0065,
"ldsc_intercept_se": 0.0076,
"ldsc_lambda_gc": 1.1135,
"ldsc_mean_chisq": 1.1233,
"ldsc_ratio": 0.0527
}
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 | 5122498 | 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 | 5124039 | 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 | 8.672538e+00 | 5.765089e+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 | 7.853849e+07 | 5.660779e+07 | 828.0000000 | 3.188923e+07 | 6.893482e+07 | 1.145108e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.000000e-06 | 3.249000e-04 | -0.0023032 | -2.038000e-04 | 1.300000e-06 | 2.077000e-04 | 4.714600e-03 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.003000e-04 | 7.080000e-05 | 0.0002212 | 2.405000e-04 | 2.747000e-04 | 3.450000e-04 | 8.656000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.831325e-01 | 2.933610e-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 | 4.831322e-01 | 2.933395e-01 | 0.0000000 | 2.252456e-01 | 4.771882e-01 | 7.370236e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.465531e-01 | 2.406940e-01 | 0.0609970 | 1.394220e-01 | 2.788970e-01 | 5.133150e-01 | 9.390030e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 43989 | 0.9914152 | NA | NA | NA | NA | NA | NA | NA | 3.403711e-01 | 2.368555e-01 | 0.0000000 | 1.449680e-01 | 2.819490e-01 | 5.027960e-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 | 0.0000221 | 0.0004072 | 0.9599999 | 0.9567884 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0009054 | 0.0004034 | 0.0250000 | 0.0247939 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0007156 | 0.0006449 | 0.2700001 | 0.2671406 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003569 | 0.0003972 | 0.3700002 | 0.3688299 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0016467 | 0.0007831 | 0.0350002 | 0.0354851 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0002400 | 0.0004537 | 0.5999997 | 0.5968635 | 0.240959 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0000792 | 0.0005660 | 0.8900000 | 0.8886870 | 0.913475 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0005637 | 0.0003802 | 0.1400000 | 0.1381728 | 0.116329 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0000747 | 0.0005571 | 0.8900000 | 0.8933944 | 0.067288 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001764 | 0.0002816 | 0.5300002 | 0.5310736 | 0.515645 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51213613 | rs34726907 | C | T | 0.0000116 | 0.0003486 | 0.9699999 | 0.9735131 | 0.127814 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000765 | 0.0003471 | 0.8300000 | 0.8256567 | 0.128328 | 0.1563500 | NA |
| 22 | 51217954 | rs9616974 | G | A | -0.0002897 | 0.0004404 | 0.5099998 | 0.5106652 | 0.073312 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0002917 | 0.0004406 | 0.5099998 | 0.5080163 | 0.073333 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0002996 | 0.0004401 | 0.5000000 | 0.4959793 | 0.073622 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0000625 | 0.0003397 | 0.8499999 | 0.8540719 | 0.137950 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0002685 | 0.0004410 | 0.5400003 | 0.5426525 | 0.073744 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0002935 | 0.0004413 | 0.5099998 | 0.5059938 | 0.073235 | 0.0625000 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0002497 | 0.0004429 | 0.5700002 | 0.5728054 | 0.073071 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0005802 | 0.0002709 | 0.0320000 | 0.0321958 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 2.20617e-05:0.000407161:0.0177288:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000905408:0.000403372:1.60206:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.000715635:0.000644907:0.568636:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000356921:0.000397167:0.431798:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID 0.00164673:0.000783121:1.45593:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.000239954:0.000453671:0.221849:0.240959:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913475 ES:SE:LP:AF:ID 7.92176e-05:0.000565978:0.05061:0.913475:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116329 ES:SE:LP:AF:ID -0.000563681:0.00038019:0.853872:0.116329:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067288 ES:SE:LP:AF:ID -7.46605e-05:0.000557125:0.05061:0.067288:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000176404:0.00028163:0.275724:0.515645:rs12029736
1 722670 rs116030099 T C . PASS AF=0.1012 ES:SE:LP:AF:ID 0.000254779:0.000464672:0.236572:0.1012:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID 0.00016107:0.000329484:0.207608:0.843204:rs4951859
1 731718 rs58276399 T C . PASS AF=0.122312 ES:SE:LP:AF:ID -0.000494382:0.000360648:0.769551:0.122312:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121554 ES:SE:LP:AF:ID -0.000463909:0.0003608:0.69897:0.121554:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID 2.21308e-05:0.000355604:0.0222764:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID 2.92724e-05:0.000319083:0.0315171:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID 2.43828e-05:0.00031874:0.0268721:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID 0.000375639:0.000342019:0.568636:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID -0.000382012:0.000342719:0.585027:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID 0.000389514:0.000341348:0.60206:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID 0.000377277:0.000341484:0.568636:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID 0.000387963:0.000341342:0.585027:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID 3.65003e-05:0.000317855:0.0409586:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID 3.32414e-05:0.000318748:0.0362122:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID 0.000112217:0.000323059:0.136677:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID 0.000384714:0.000340947:0.585027:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID 0.000381767:0.000340089:0.585027:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID 0.000395106:0.000339437:0.619789:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID 0.000399058:0.000340368:0.619789:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID 0.000399165:0.000340394:0.619789:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID 0.000398233:0.000340402:0.619789:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID 0.000390393:0.000341336:0.60206:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID 4.11217e-05:0.000317252:0.0457575:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID 4.42707e-05:0.000317476:0.05061:0.838427:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID 0.000310751:0.000339171:0.443698:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 0.000361378:0.000330184:0.568636:0.706753:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105145 ES:SE:LP:AF:ID -0.000388364:0.000380366:0.508638:0.105145:rs61768174
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -5.41093e-05:0.000269488:0.0757207:0.761297:rs2977608
1 768448 rs12562034 G A . PASS AF=0.10649 ES:SE:LP:AF:ID 0.000584183:0.000371438:0.920819:0.10649:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129581 ES:SE:LP:AF:ID -0.000375379:0.000342514:0.568636:0.129581:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868906 ES:SE:LP:AF:ID 0.000395278:0.000340685:0.60206:0.868906:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129682 ES:SE:LP:AF:ID -0.000404213:0.000342293:0.619789:0.129682:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868916 ES:SE:LP:AF:ID 0.000400817:0.000340691:0.619789:0.868916:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -0.000301073:0.000301028:0.49485:0.265385:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870039 ES:SE:LP:AF:ID 0.000386747:0.000341386:0.585027:0.870039:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095141 ES:SE:LP:AF:ID 0.000700453:0.00039566:1.11351:0.095141:rs112618790
1 778745 rs1055606 A G . PASS AF=0.12858 ES:SE:LP:AF:ID -0.000393596:0.000342734:0.60206:0.12858:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID -0.00040414:0.000342153:0.619789:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86878 ES:SE:LP:AF:ID 0.000407656:0.000340475:0.638272:0.86878:rs2977612
1 781845 rs61768199 A G . PASS AF=0.101873 ES:SE:LP:AF:ID -0.000397943:0.000385792:0.522879:0.101873:rs61768199