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_6153_5.vcf.gz --id UKB-b:9547 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6153_5.txt.gz --cohort_cases 6501 --cohort_controls 243209 --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": "2019-09-13T01:54:39.600519",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-9547/ukb-b-9547.vcf.gz; Date=Sun May 10 13:43:13 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-9547/UKB-b-9547_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9547/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9547/UKB-b-9547_data.vcf.gz ...
Read summary statistics for 5315551 SNPs.
Dropped 1782 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, 1153821 SNPs remain.
After merging with regression SNP LD, 1153821 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0049 (0.0018)
Lambda GC: 1.0277
Mean Chi^2: 1.0247
Intercept: 0.9992 (0.0067)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:43:45 2019
Total time elapsed: 58.95sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9131,
"inflation_factor": 1.0475,
"mean_EFFECT": -2.7915e-07,
"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": 46100,
"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": 1153821,
"ldsc_nsnp_merge_regression_ld": 1153821,
"ldsc_observed_scale_h2_beta": 0.0049,
"ldsc_observed_scale_h2_se": 0.0018,
"ldsc_intercept_beta": 0.9992,
"ldsc_intercept_se": 0.0067,
"ldsc_lambda_gc": 1.0277,
"ldsc_mean_chisq": 1.0247,
"ldsc_ratio": -0.0324
}
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 | 5313782 | 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 | 5315551 | 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 | 8.672706e+00 | 5.763101e+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 | 7.853217e+07 | 5.657134e+07 | 828.0000000 | 3.191464e+07 | 6.893964e+07 | 1.144906e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.000000e-07 | 6.315000e-04 | -0.0044750 | -3.965000e-04 | -9.000000e-07 | 3.928000e-04 | 4.705200e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.043000e-04 | 1.547000e-04 | 0.0004343 | 4.743000e-04 | 5.474000e-04 | 7.001000e-04 | 2.045000e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.958425e-01 | 2.898951e-01 | 0.0000004 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.958447e-01 | 2.898685e-01 | 0.0000004 | 2.439235e-01 | 4.942878e-01 | 7.471857e-01 | 9.999993e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.387144e-01 | 2.441042e-01 | 0.0538380 | 1.292980e-01 | 2.678700e-01 | 5.055340e-01 | 9.461620e-01 | ▇▃▂▂▂ |
| numeric | AF_reference | 46100 | 0.9913273 | NA | NA | NA | NA | NA | NA | NA | 3.332310e-01 | 2.393880e-01 | 0.0000000 | 1.359820e-01 | 2.717650e-01 | 4.952080e-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 | -0.0003468 | 0.0008007 | 0.6600001 | 0.6649268 | 0.623403 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0010301 | 0.0007916 | 0.1900002 | 0.1931803 | 0.400976 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0015557 | 0.0012705 | 0.2200002 | 0.2207711 | 0.103437 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000961 | 0.0007799 | 0.9000000 | 0.9019396 | 0.457088 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0007015 | 0.0015404 | 0.6499995 | 0.6487897 | 0.074451 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0012668 | 0.0008915 | 0.1600000 | 0.1553079 | 0.241186 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0018038 | 0.0011123 | 0.1000000 | 0.1048564 | 0.913577 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0000098 | 0.0007460 | 0.9900000 | 0.9895673 | 0.116703 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0006158 | 0.0010954 | 0.5700002 | 0.5740183 | 0.067069 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0006548 | 0.0005532 | 0.2399999 | 0.2365719 | 0.515134 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51217954 | rs9616974 | G | A | -0.0001981 | 0.0008663 | 0.8200001 | 0.8190994 | 0.073210 | 0.0621006 | NA |
| 22 | 51218224 | rs9616975 | C | A | -0.0002108 | 0.0008667 | 0.8100000 | 0.8078693 | 0.073231 | 0.0619010 | NA |
| 22 | 51218377 | rs2519461 | G | C | -0.0000229 | 0.0008656 | 0.9800000 | 0.9788538 | 0.073533 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0002573 | 0.0006686 | 0.6999999 | 0.7003938 | 0.137913 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0002345 | 0.0008676 | 0.7899998 | 0.7869222 | 0.073612 | 0.0654952 | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0002361 | 0.0008679 | 0.7899998 | 0.7856009 | 0.073145 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0006477 | 0.0010199 | 0.5300002 | 0.5253671 | 0.054643 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0007042 | 0.0010248 | 0.4899999 | 0.4919574 | 0.054278 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0002156 | 0.0008711 | 0.8000000 | 0.8045245 | 0.073012 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0011087 | 0.0005320 | 0.0369999 | 0.0371685 | 0.297860 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623403 ES:SE:LP:AF:ID -0.00034681:0.000800723:0.180456:0.623403:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400976 ES:SE:LP:AF:ID 0.00103009:0.000791629:0.721246:0.400976:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103437 ES:SE:LP:AF:ID -0.00155569:0.00127049:0.657577:0.103437:rs114608975
1 91536 rs6702460 G T . PASS AF=0.457088 ES:SE:LP:AF:ID 9.60981e-05:0.000779944:0.0457575:0.457088:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074451 ES:SE:LP:AF:ID 0.000701548:0.00154036:0.187087:0.074451:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241186 ES:SE:LP:AF:ID 0.00126684:0.000891494:0.79588:0.241186:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913577 ES:SE:LP:AF:ID 0.00180381:0.00111226:1:0.913577:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116703 ES:SE:LP:AF:ID -9.75449e-06:0.000745996:0.00436481:0.116703:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067069 ES:SE:LP:AF:ID -0.000615755:0.00109537:0.244125:0.067069:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515134 ES:SE:LP:AF:ID 0.000654774:0.000553206:0.619789:0.515134:rs12029736
1 722670 rs116030099 T C . PASS AF=0.101076 ES:SE:LP:AF:ID -0.000449026:0.000913315:0.207608:0.101076:rs116030099
1 729679 rs4951859 C G . PASS AF=0.843032 ES:SE:LP:AF:ID -0.000344036:0.000647417:0.221849:0.843032:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056041 ES:SE:LP:AF:ID -0.000960381:0.00104745:0.443698:0.056041:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122705 ES:SE:LP:AF:ID -8.10065e-05:0.000707546:0.0409586:0.122705:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121928 ES:SE:LP:AF:ID -4.01557e-05:0.000707862:0.0222764:0.121928:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132582 ES:SE:LP:AF:ID 0.000587544:0.000698184:0.39794:0.132582:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838725 ES:SE:LP:AF:ID -0.000645632:0.000626665:0.522879:0.838725:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838352 ES:SE:LP:AF:ID -0.000577215:0.000626046:0.443698:0.838352:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869399 ES:SE:LP:AF:ID -6.8623e-05:0.000671122:0.0362122:0.869399:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130253 ES:SE:LP:AF:ID -1.98009e-05:0.000672612:0.00877392:0.130253:rs2073813
1 754182 rs3131969 A G . PASS AF=0.868732 ES:SE:LP:AF:ID 6.97168e-06:0.000669886:0.00436481:0.868732:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868802 ES:SE:LP:AF:ID 8.02859e-06:0.000670118:0.00436481:0.868802:rs3131968
1 754334 rs3131967 T C . PASS AF=0.868732 ES:SE:LP:AF:ID 5.76875e-06:0.000669863:0.00436481:0.868732:rs3131967
1 754503 rs3115859 G A . PASS AF=0.8378 ES:SE:LP:AF:ID -0.00061563:0.000624285:0.49485:0.8378:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838436 ES:SE:LP:AF:ID -0.000565164:0.00062604:0.431798:0.838436:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839574 ES:SE:LP:AF:ID -0.000722632:0.000634432:0.60206:0.839574:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869019 ES:SE:LP:AF:ID -7.40123e-05:0.000668994:0.0409586:0.869019:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868559 ES:SE:LP:AF:ID -4.738e-05:0.000667311:0.0268721:0.868559:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867537 ES:SE:LP:AF:ID 7.50831e-05:0.00066622:0.0409586:0.867537:rs3115853
1 757734 rs4951929 C T . PASS AF=0.868706 ES:SE:LP:AF:ID -4.12009e-05:0.000667871:0.0222764:0.868706:rs4951929
1 757936 rs4951862 C A . PASS AF=0.868716 ES:SE:LP:AF:ID -4.437e-05:0.000667923:0.0222764:0.868716:rs4951862
1 758144 rs3131956 A G . PASS AF=0.868723 ES:SE:LP:AF:ID -4.18245e-05:0.000667936:0.0222764:0.868723:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869194 ES:SE:LP:AF:ID -5.56596e-05:0.000669742:0.0315171:0.869194:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838075 ES:SE:LP:AF:ID -0.000603488:0.000623031:0.481486:0.838075:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838193 ES:SE:LP:AF:ID -0.000613326:0.00062347:0.481486:0.838193:rs3115850
1 761732 rs2286139 C T . PASS AF=0.861947 ES:SE:LP:AF:ID 6.52691e-05:0.000665769:0.0362122:0.861947:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706352 ES:SE:LP:AF:ID -3.61248e-05:0.000648397:0.0177288:0.706352:rs3115847
1 766007 rs61768174 A C . PASS AF=0.105672 ES:SE:LP:AF:ID -0.000300189:0.000745686:0.161151:0.105672:rs61768174
1 768253 rs2977608 A C . PASS AF=0.76105 ES:SE:LP:AF:ID 2.1773e-05:0.000529393:0.0132283:0.76105:rs2977608
1 768448 rs12562034 G A . PASS AF=0.106371 ES:SE:LP:AF:ID -4.05308e-05:0.000730824:0.0177288:0.106371:rs12562034
1 769223 rs60320384 C G . PASS AF=0.129992 ES:SE:LP:AF:ID 3.60464e-05:0.000672055:0.0177288:0.129992:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868484 ES:SE:LP:AF:ID -4.21478e-05:0.000668412:0.0222764:0.868484:rs2977605
1 771967 rs59066358 G A . PASS AF=0.13011 ES:SE:LP:AF:ID 2.67206e-05:0.000671567:0.0132283:0.13011:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868491 ES:SE:LP:AF:ID -4.3096e-05:0.000668402:0.0222764:0.868491:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265521 ES:SE:LP:AF:ID -0.000751576:0.000591238:0.69897:0.265521:rs12124819
1 777122 rs2980319 A T . PASS AF=0.869619 ES:SE:LP:AF:ID -8.75132e-05:0.000669756:0.0457575:0.869619:rs2980319
1 777232 rs112618790 C T . PASS AF=0.095024 ES:SE:LP:AF:ID 1.09258e-05:0.000777654:0.00436481:0.095024:rs112618790
1 778745 rs1055606 A G . PASS AF=0.129019 ES:SE:LP:AF:ID 9.0236e-05:0.000672435:0.05061:0.129019:rs1055606
1 779322 rs4040617 A G . PASS AF=0.129312 ES:SE:LP:AF:ID 7.9779e-05:0.000671356:0.0409586:0.129312:rs4040617
1 780785 rs2977612 T A . PASS AF=0.868397 ES:SE:LP:AF:ID -2.33897e-05:0.000668215:0.0132283:0.868397:rs2977612