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_41244_400.vcf.gz --id UKB-b:16944 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41244_400.txt.gz --cohort_cases 2512 --cohort_controls 439982 --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": "2019-09-12T19:12:42.804492",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16944/ukb-b-16944.vcf.gz; Date=Sun May 10 09:40:14 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-16944/UKB-b-16944_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16944/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:25 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16944/UKB-b-16944_data.vcf.gz ...
Read summary statistics for 3671611 SNPs.
Dropped 618 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, 889626 SNPs remain.
After merging with regression SNP LD, 889626 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0001 (0.0012)
Lambda GC: 1.0182
Mean Chi^2: 1.0147
Intercept: 1.0157 (0.0093)
Ratio: 1.0691 (0.635)
Analysis finished at Thu Oct 17 14:46:07 2019
Total time elapsed: 42.15sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8607,
"inflation_factor": 1,
"mean_EFFECT": -1.6679e-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": 29616,
"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": 889626,
"ldsc_nsnp_merge_regression_ld": 889626,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0157,
"ldsc_intercept_se": 0.0093,
"ldsc_lambda_gc": 1.0182,
"ldsc_mean_chisq": 1.0147,
"ldsc_ratio": 1.068
}
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 | 3670996 | 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 | 3671611 | 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 | 8.661636e+00 | 5.771980e+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 | 7.855035e+07 | 5.676934e+07 | 828.0000000 | 3.157426e+07 | 6.888251e+07 | 1.147239e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.700000e-06 | 1.845000e-04 | -0.0009821 | -1.248000e-04 | -1.700000e-06 | 1.206000e-04 | 1.175000e-03 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.822000e-04 | 2.090000e-05 | 0.0001541 | 1.640000e-04 | 1.755000e-04 | 1.968000e-04 | 5.638000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.978520e-01 | 2.895088e-01 | 0.0000024 | 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 | 4.978525e-01 | 2.894840e-01 | 0.0000024 | 2.461861e-01 | 4.968053e-01 | 7.486300e-01 | 9.999990e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.080073e-01 | 2.017934e-01 | 0.1393320 | 2.312740e-01 | 3.660520e-01 | 5.602960e-01 | 8.606680e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 29616 | 0.9919338 | NA | NA | NA | NA | NA | NA | NA | 3.952536e-01 | 2.093745e-01 | 0.0000000 | 2.236420e-01 | 3.606230e-01 | 5.477240e-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 | -0.0004244 | 0.0002836 | 0.1299999 | 0.1344294 | 0.623774 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0003371 | 0.0002809 | 0.2300001 | 0.2300713 | 0.400491 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003110 | 0.0002767 | 0.2599998 | 0.2610099 | 0.456757 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0006098 | 0.0003160 | 0.0539995 | 0.0536133 | 0.240950 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000723 | 0.0001962 | 0.7099994 | 0.7126432 | 0.515671 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0000106 | 0.0002296 | 0.9599999 | 0.9630664 | 0.843320 | 0.639976 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0000287 | 0.0002223 | 0.9000000 | 0.8973226 | 0.839039 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | 0.0000372 | 0.0002221 | 0.8700001 | 0.8669441 | 0.838668 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | 0.0000480 | 0.0002215 | 0.8300000 | 0.8283572 | 0.838122 | 0.663938 | NA |
| 1 | 754964 | rs3131966 | C | T | 0.0000452 | 0.0002221 | 0.8400000 | 0.8387788 | 0.838752 | 0.663339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51182485 | rs6009961 | A | G | -0.0001103 | 0.0001853 | 0.5500004 | 0.5515935 | 0.715513 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000394 | 0.0001718 | 0.8200001 | 0.8187695 | 0.381785 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0000112 | 0.0001674 | 0.9500000 | 0.9466369 | 0.450991 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0000155 | 0.0002256 | 0.9500000 | 0.9450566 | 0.167674 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | -0.0001939 | 0.0002305 | 0.4000000 | 0.4003070 | 0.168595 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000197 | 0.0001729 | 0.9100000 | 0.9090640 | 0.386283 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0001738 | 0.0001957 | 0.3700002 | 0.3744396 | 0.254638 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0001736 | 0.0001940 | 0.3700002 | 0.3708653 | 0.271623 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000690 | 0.0001843 | 0.7099994 | 0.7081688 | 0.331491 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002026 | 0.0001887 | 0.2800000 | 0.2829039 | 0.297987 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623774 ES:SE:LP:AF:ID -0.000424444:0.000283556:0.886057:0.623774:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400491 ES:SE:LP:AF:ID 0.000337086:0.000280864:0.638272:0.400491:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456757 ES:SE:LP:AF:ID 0.000310996:0.000276685:0.585027:0.456757:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24095 ES:SE:LP:AF:ID -0.000609767:0.00031595:1.26761:0.24095:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515671 ES:SE:LP:AF:ID -7.22684e-05:0.000196217:0.148742:0.515671:rs12029736
1 729679 rs4951859 C G . PASS AF=0.84332 ES:SE:LP:AF:ID -1.06326e-05:0.000229616:0.0177288:0.84332:rs4951859
1 752566 rs3094315 G A . PASS AF=0.839039 ES:SE:LP:AF:ID 2.86899e-05:0.000222326:0.0457575:0.839039:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838668 ES:SE:LP:AF:ID 3.72081e-05:0.000222083:0.0604807:0.838668:rs3131972
1 754503 rs3115859 G A . PASS AF=0.838122 ES:SE:LP:AF:ID 4.80169e-05:0.000221471:0.0809219:0.838122:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838752 ES:SE:LP:AF:ID 4.51861e-05:0.000222093:0.0757207:0.838752:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839873 ES:SE:LP:AF:ID 3.71629e-05:0.000225114:0.0604807:0.839873:rs3131965
1 760912 rs1048488 C T . PASS AF=0.838393 ES:SE:LP:AF:ID 2.84277e-05:0.000221052:0.0457575:0.838393:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838513 ES:SE:LP:AF:ID 2.62549e-05:0.000221208:0.0409586:0.838513:rs3115850
1 763394 rs3115847 G A . PASS AF=0.70673 ES:SE:LP:AF:ID 6.96322e-05:0.000229979:0.119186:0.70673:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761385 ES:SE:LP:AF:ID 0.000137468:0.000187756:0.337242:0.761385:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265308 ES:SE:LP:AF:ID 0.000299933:0.000209679:0.823909:0.265308:rs12124819
1 795988 rs59380221 C T . PASS AF=0.142963 ES:SE:LP:AF:ID -7.10934e-05:0.000245989:0.113509:0.142963:rs59380221
1 798400 rs10900604 A G . PASS AF=0.206464 ES:SE:LP:AF:ID -2.62063e-06:0.000200306:0.00436481:0.206464:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206294 ES:SE:LP:AF:ID -1.31314e-06:0.000200392:0.00436481:0.206294:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772707 ES:SE:LP:AF:ID 5.57077e-05:0.000190624:0.113509:0.772707:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772932 ES:SE:LP:AF:ID 5.55479e-05:0.000190942:0.113509:0.772932:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340448 ES:SE:LP:AF:ID -0.000214314:0.000268925:0.366532:0.340448:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697259 ES:SE:LP:AF:ID 5.03171e-05:0.000179982:0.107905:0.697259:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705452 ES:SE:LP:AF:ID 7.96771e-05:0.000176692:0.187087:0.705452:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705496 ES:SE:LP:AF:ID 7.10423e-05:0.000176685:0.161151:0.705496:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705679 ES:SE:LP:AF:ID 7.78712e-05:0.000176694:0.180456:0.705679:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705706 ES:SE:LP:AF:ID 8.04902e-05:0.000176712:0.187087:0.705706:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730137 ES:SE:LP:AF:ID 7.06712e-05:0.000181555:0.154902:0.730137:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294326 ES:SE:LP:AF:ID -8.07296e-05:0.000176704:0.187087:0.294326:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236652 ES:SE:LP:AF:ID -7.5805e-05:0.000188139:0.161151:0.236652:rs13303211
1 833302 rs28752186 C T . PASS AF=0.23664 ES:SE:LP:AF:ID -7.5235e-05:0.000188139:0.161151:0.23664:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239711 ES:SE:LP:AF:ID -5.51622e-05:0.000187534:0.113509:0.239711:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236644 ES:SE:LP:AF:ID -7.53515e-05:0.000188138:0.161151:0.236644:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212413 ES:SE:LP:AF:ID -7.47075e-05:0.000195559:0.154902:0.212413:rs28593608
1 834198 rs28385272 T C . PASS AF=0.21231 ES:SE:LP:AF:ID -6.20749e-05:0.000195594:0.124939:0.21231:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237133 ES:SE:LP:AF:ID -8.24481e-05:0.000187992:0.180456:0.237133:rs4411087
1 834928 rs4422949 A G . PASS AF=0.21296 ES:SE:LP:AF:ID -8.73825e-05:0.000195311:0.187087:0.21296:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212919 ES:SE:LP:AF:ID -8.28201e-05:0.000195352:0.173925:0.212919:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241114 ES:SE:LP:AF:ID -5.32189e-05:0.000186678:0.107905:0.241114:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213548 ES:SE:LP:AF:ID -6.90225e-05:0.000195062:0.142668:0.213548:rs28731045
1 836896 rs28705752 T C . PASS AF=0.26956 ES:SE:LP:AF:ID -8.56374e-05:0.000180096:0.200659:0.26956:rs28705752
1 836924 rs72890788 G A . PASS AF=0.21352 ES:SE:LP:AF:ID -7.42347e-05:0.000195084:0.154902:0.21352:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214584 ES:SE:LP:AF:ID -6.29466e-05:0.000194714:0.124939:0.214584:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246278 ES:SE:LP:AF:ID -6.84653e-05:0.00018538:0.148742:0.246278:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270071 ES:SE:LP:AF:ID -4.80201e-05:0.000180222:0.102373:0.270071:rs28562941
1 840753 rs4970382 T C . PASS AF=0.39997 ES:SE:LP:AF:ID -0.000178051:0.000163029:0.568636:0.39997:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237228 ES:SE:LP:AF:ID -0.000106907:0.00018928:0.244125:0.237228:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215395 ES:SE:LP:AF:ID -5.15119e-05:0.000194831:0.102373:0.215395:rs7419119
1 842362 rs28540380 C T . PASS AF=0.23534 ES:SE:LP:AF:ID -1.64133e-05:0.000192173:0.0315171:0.23534:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362508 ES:SE:LP:AF:ID -2.86304e-05:0.00020232:0.05061:0.362508:rs11516185