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_6141_7.vcf.gz --id UKB-b:527 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6141_7.txt.gz --cohort_cases 2675 --cohort_controls 456925 --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": "2019-09-13T01:52:28.550414",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-527/ukb-b-527.vcf.gz; Date=Sun May 10 09:01:17 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-527/UKB-b-527_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-527/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-527/UKB-b-527_data.vcf.gz ...
Read summary statistics for 3797172 SNPs.
Dropped 665 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, 914226 SNPs remain.
After merging with regression SNP LD, 914226 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0007 (0.0012)
Lambda GC: 1.0146
Mean Chi^2: 1.0269
Intercept: 1.0196 (0.0092)
Ratio: 0.7303 (0.3415)
Analysis finished at Thu Oct 17 14:45:56 2019
Total time elapsed: 47.07sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8665,
"inflation_factor": 1,
"mean_EFFECT": -2.2677e-06,
"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": 30783,
"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": 914226,
"ldsc_nsnp_merge_regression_ld": 914226,
"ldsc_observed_scale_h2_beta": 0.0007,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0196,
"ldsc_intercept_se": 0.0092,
"ldsc_lambda_gc": 1.0146,
"ldsc_mean_chisq": 1.0269,
"ldsc_ratio": 0.7286
}
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 | TRUE |
| 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 | 3796510 | 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 | 3797172 | 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 | 8.662482e+00 | 5.769559e+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 | 7.855840e+07 | 5.676163e+07 | 828.0000000 | 3.160394e+07 | 6.888798e+07 | 1.147118e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.300000e-06 | 1.858000e-04 | -0.0009769 | -1.245000e-04 | -2.500000e-06 | 1.198000e-04 | 9.774000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.827000e-04 | 2.250000e-05 | 0.0001531 | 1.632000e-04 | 1.755000e-04 | 1.983000e-04 | 5.599000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.986722e-01 | 2.899273e-01 | 0.0000003 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.986746e-01 | 2.899017e-01 | 0.0000003 | 2.468762e-01 | 4.987681e-01 | 7.499442e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.026549e-01 | 2.061169e-01 | 0.1308420 | 2.227170e-01 | 3.584570e-01 | 5.570583e-01 | 8.691580e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 30783 | 0.9918932 | NA | NA | NA | NA | NA | NA | NA | 3.906392e-01 | 2.122330e-01 | 0.0000000 | 2.164540e-01 | 3.536340e-01 | 5.447280e-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 | 0.0001292 | 0.0002818 | 0.6499995 | 0.6465419 | 0.623772 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001781 | 0.0002791 | 0.5199996 | 0.5233593 | 0.400415 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000514 | 0.0002748 | 0.8499999 | 0.8516542 | 0.456897 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002977 | 0.0003140 | 0.3400001 | 0.3430441 | 0.240946 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000629 | 0.0001949 | 0.7499995 | 0.7469540 | 0.515651 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0005092 | 0.0002280 | 0.0259998 | 0.0255505 | 0.843240 | 0.639976 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0007368 | 0.0002461 | 0.0028000 | 0.0027532 | 0.132323 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0004715 | 0.0002208 | 0.0329997 | 0.0327519 | 0.838960 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | -0.0004712 | 0.0002206 | 0.0329997 | 0.0326869 | 0.838590 | 0.653355 | NA |
| 1 | 754182 | rs3131969 | A | G | -0.0004083 | 0.0002363 | 0.0840001 | 0.0839506 | 0.869151 | 0.678514 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51186228 | rs3865766 | C | T | -0.0000499 | 0.0001663 | 0.7600007 | 0.7641538 | 0.451096 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0000248 | 0.0002240 | 0.9100000 | 0.9119433 | 0.167637 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0003698 | 0.0002290 | 0.1100001 | 0.1064328 | 0.168491 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000035 | 0.0001717 | 0.9800000 | 0.9838290 | 0.386375 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0003324 | 0.0001944 | 0.0870001 | 0.0872439 | 0.254580 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0002245 | 0.0001927 | 0.2399999 | 0.2439276 | 0.271571 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0002389 | 0.0002564 | 0.3500000 | 0.3515335 | 0.132602 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001506 | 0.0001831 | 0.4100001 | 0.4108783 | 0.331473 | 0.3724040 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0000162 | 0.0002351 | 0.9500000 | 0.9451339 | 0.137942 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000504 | 0.0001874 | 0.7899998 | 0.7880067 | 0.298011 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623772 ES:SE:LP:AF:ID 0.000129203:0.000281751:0.187087:0.623772:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400415 ES:SE:LP:AF:ID 0.000178134:0.00027913:0.283997:0.400415:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456897 ES:SE:LP:AF:ID -5.13992e-05:0.00027485:0.0705811:0.456897:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240946 ES:SE:LP:AF:ID -0.000297718:0.000313993:0.468521:0.240946:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515651 ES:SE:LP:AF:ID -6.28858e-05:0.000194899:0.124939:0.515651:rs12029736
1 729679 rs4951859 C G . PASS AF=0.84324 ES:SE:LP:AF:ID -0.000509208:0.00022804:1.58503:0.84324:rs4951859
1 736289 rs79010578 T A . PASS AF=0.132323 ES:SE:LP:AF:ID 0.000736777:0.000246082:2.55284:0.132323:rs79010578
1 752566 rs3094315 G A . PASS AF=0.83896 ES:SE:LP:AF:ID -0.000471503:0.000220833:1.48149:0.83896:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83859 ES:SE:LP:AF:ID -0.000471175:0.000220597:1.48149:0.83859:rs3131972
1 754182 rs3131969 A G . PASS AF=0.869151 ES:SE:LP:AF:ID -0.0004083:0.000236256:1.07572:0.869151:rs3131969
1 754334 rs3131967 T C . PASS AF=0.869154 ES:SE:LP:AF:ID -0.000408684:0.000236251:1.07572:0.869154:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838041 ES:SE:LP:AF:ID -0.000446756:0.000219982:1.37675:0.838041:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838673 ES:SE:LP:AF:ID -0.000458876:0.000220601:1.42022:0.838673:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83978 ES:SE:LP:AF:ID -0.000457776:0.000223579:1.38722:0.83978:rs3131965
1 756604 rs3131962 A G . PASS AF=0.868979 ES:SE:LP:AF:ID -0.000402769:0.000235383:1.06048:0.868979:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867935 ES:SE:LP:AF:ID -0.000420143:0.000234933:1.13077:0.867935:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869124 ES:SE:LP:AF:ID -0.000410297:0.000235578:1.08619:0.869124:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869132 ES:SE:LP:AF:ID -0.000410311:0.000235596:1.08619:0.869132:rs4951862
1 758144 rs3131956 A G . PASS AF=0.86914 ES:SE:LP:AF:ID -0.00041003:0.000235601:1.08619:0.86914:rs3131956
1 760912 rs1048488 C T . PASS AF=0.83832 ES:SE:LP:AF:ID -0.000451012:0.000219561:1.39794:0.83832:rs1048488
1 761147 rs3115850 T C . PASS AF=0.83844 ES:SE:LP:AF:ID -0.000455763:0.000219716:1.42022:0.83844:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862291 ES:SE:LP:AF:ID -0.000396622:0.000234753:1.04096:0.862291:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706756 ES:SE:LP:AF:ID -0.000364436:0.000228525:0.958607:0.706756:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761378 ES:SE:LP:AF:ID -0.000161756:0.000186514:0.408935:0.761378:rs2977608
1 771823 rs2977605 T C . PASS AF=0.868945 ES:SE:LP:AF:ID -0.000402281:0.000235801:1.05552:0.868945:rs2977605
1 772755 rs2905039 A C . PASS AF=0.868955 ES:SE:LP:AF:ID -0.00040291:0.000235806:1.05552:0.868955:rs2905039
1 776546 rs12124819 A G . PASS AF=0.26542 ES:SE:LP:AF:ID -6.23557e-05:0.000208299:0.119186:0.26542:rs12124819
1 780785 rs2977612 T A . PASS AF=0.868822 ES:SE:LP:AF:ID -0.00038447:0.00023566:1:0.868822:rs2977612
1 785050 rs2905062 G A . PASS AF=0.868577 ES:SE:LP:AF:ID -0.000387506:0.000235604:1:0.868577:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868518 ES:SE:LP:AF:ID -0.000403745:0.000235751:1.06048:0.868518:rs2980300
1 787399 rs2905055 G T . PASS AF=0.860818 ES:SE:LP:AF:ID -0.000438253:0.000235585:1.20066:0.860818:rs2905055
1 787685 rs2905054 G T . PASS AF=0.861534 ES:SE:LP:AF:ID -0.00043143:0.000235739:1.17393:0.861534:rs2905054
1 795988 rs59380221 C T . PASS AF=0.143033 ES:SE:LP:AF:ID 0.000443099:0.000244318:1.1549:0.143033:rs59380221
1 798400 rs10900604 A G . PASS AF=0.206547 ES:SE:LP:AF:ID 0.000205136:0.000198963:0.522879:0.206547:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206375 ES:SE:LP:AF:ID 0.000190793:0.000199049:0.468521:0.206375:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772679 ES:SE:LP:AF:ID -0.00037479:0.00018935:1.31876:0.772679:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772905 ES:SE:LP:AF:ID -0.000372258:0.000189669:1.30103:0.772905:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340433 ES:SE:LP:AF:ID -0.000314319:0.000267237:0.619789:0.340433:rs74461805
1 824398 rs7538305 A C . PASS AF=0.138668 ES:SE:LP:AF:ID 0.000210589:0.000236001:0.431798:0.138668:rs7538305
1 830181 rs28444699 A G . PASS AF=0.697236 ES:SE:LP:AF:ID 2.78151e-05:0.00017878:0.0555173:0.697236:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705385 ES:SE:LP:AF:ID -3.53175e-05:0.000175558:0.0757207:0.705385:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705429 ES:SE:LP:AF:ID -3.42955e-05:0.000175551:0.0705811:0.705429:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705614 ES:SE:LP:AF:ID -3.90423e-05:0.00017556:0.0861861:0.705614:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705643 ES:SE:LP:AF:ID -4.01173e-05:0.000175578:0.0861861:0.705643:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730111 ES:SE:LP:AF:ID 9.56792e-05:0.000180364:0.221849:0.730111:rs4553118
1 832918 rs28765502 T C . PASS AF=0.29439 ES:SE:LP:AF:ID 3.84925e-05:0.00017557:0.0809219:0.29439:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236743 ES:SE:LP:AF:ID -8.0572e-05:0.000186914:0.173925:0.236743:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236731 ES:SE:LP:AF:ID -8.39633e-05:0.000186916:0.187087:0.236731:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23979 ES:SE:LP:AF:ID -6.76404e-05:0.000186315:0.142668:0.23979:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236733 ES:SE:LP:AF:ID -8.39462e-05:0.000186915:0.187087:0.236733:rs28484835