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_41246_1130.vcf.gz --id UKB-b:4947 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1130.txt.gz --cohort_cases 2465 --cohort_controls 441657 --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": "2019-09-13T13:16:37.629158",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4947/ukb-b-4947.vcf.gz; Date=Sun May 10 14:51:08 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-4947/UKB-b-4947_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4947/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:49 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4947/UKB-b-4947_data.vcf.gz ...
Read summary statistics for 3633777 SNPs.
Dropped 602 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, 882029 SNPs remain.
After merging with regression SNP LD, 882029 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0018 (0.0012)
Lambda GC: 1.0339
Mean Chi^2: 1.0323
Intercept: 1.0146 (0.0088)
Ratio: 0.451 (0.2733)
Analysis finished at Thu Oct 17 14:45:36 2019
Total time elapsed: 47.24sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8588,
"inflation_factor": 1.0475,
"mean_EFFECT": 6.3538e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 23,
"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": 29304,
"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": 882029,
"ldsc_nsnp_merge_regression_ld": 882029,
"ldsc_observed_scale_h2_beta": 0.0018,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0146,
"ldsc_intercept_se": 0.0088,
"ldsc_lambda_gc": 1.0339,
"ldsc_mean_chisq": 1.0323,
"ldsc_ratio": 0.452
}
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 | 3633178 | 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 | 3633777 | 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 | 5.771785e+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 | 7.854265e+07 | 5.675568e+07 | 828.0000000 | 3.157979e+07 | 6.887974e+07 | 1.147135e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.000000e-07 | 1.835000e-04 | -0.0011914 | -1.213000e-04 | 2.000000e-07 | 1.226000e-04 | 1.063000e-03 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.790000e-04 | 2.010000e-05 | 0.0001518 | 1.615000e-04 | 1.726000e-04 | 1.930000e-04 | 5.553000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.938097e-01 | 2.902439e-01 | 0.0000000 | 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 | 4.938075e-01 | 2.902187e-01 | 0.0000000 | 2.408533e-01 | 4.917865e-01 | 7.456406e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.096165e-01 | 2.004532e-01 | 0.1419880 | 2.341080e-01 | 3.683020e-01 | 5.611310e-01 | 8.580120e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 29304 | 0.9919357 | NA | NA | NA | NA | NA | NA | NA | 3.966702e-01 | 2.084714e-01 | 0.0000000 | 2.258390e-01 | 3.628190e-01 | 5.485220e-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 | -0.0003512 | 0.0002794 | 0.2099999 | 0.2087297 | 0.623740 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000063 | 0.0002768 | 0.9800000 | 0.9817054 | 0.400419 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001966 | 0.0002725 | 0.4700002 | 0.4706342 | 0.456849 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0000303 | 0.0003114 | 0.9199999 | 0.9224111 | 0.240910 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000775 | 0.0001933 | 0.6899999 | 0.6886571 | 0.515724 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0001838 | 0.0002261 | 0.4199997 | 0.4162796 | 0.843128 | 0.639976 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0002070 | 0.0002190 | 0.3400001 | 0.3443690 | 0.838821 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | 0.0002244 | 0.0002187 | 0.2999998 | 0.3049678 | 0.838442 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | 0.0002271 | 0.0002181 | 0.2999998 | 0.2978033 | 0.837891 | 0.663938 | NA |
| 1 | 754964 | rs3131966 | C | T | 0.0002182 | 0.0002187 | 0.3200000 | 0.3184625 | 0.838523 | 0.663339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51182485 | rs6009961 | A | G | -0.0000602 | 0.0001825 | 0.7400005 | 0.7416068 | 0.715537 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000982 | 0.0001692 | 0.5600000 | 0.5616355 | 0.382029 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0002269 | 0.0001649 | 0.1700000 | 0.1689210 | 0.451299 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | 0.0000543 | 0.0002221 | 0.8100000 | 0.8068325 | 0.167729 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0002178 | 0.0002271 | 0.3400001 | 0.3375870 | 0.168342 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0002962 | 0.0001703 | 0.0819993 | 0.0819486 | 0.386525 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0001978 | 0.0001928 | 0.2999998 | 0.3049067 | 0.254382 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0001997 | 0.0001911 | 0.2999998 | 0.2960391 | 0.271368 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0001476 | 0.0001816 | 0.4199997 | 0.4162811 | 0.331611 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000713 | 0.0001859 | 0.6999999 | 0.7011763 | 0.298156 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.62374 ES:SE:LP:AF:ID -0.000351223:0.000279399:0.677781:0.62374:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400419 ES:SE:LP:AF:ID -6.34783e-06:0.000276824:0.00877392:0.400419:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456849 ES:SE:LP:AF:ID -0.000196606:0.000272516:0.327902:0.456849:rs6702460
1 534192 rs6680723 C T . PASS AF=0.24091 ES:SE:LP:AF:ID -3.03275e-05:0.00031138:0.0362122:0.24091:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515724 ES:SE:LP:AF:ID -7.74535e-05:0.000193306:0.161151:0.515724:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843128 ES:SE:LP:AF:ID 0.00018383:0.000226143:0.376751:0.843128:rs4951859
1 752566 rs3094315 G A . PASS AF=0.838821 ES:SE:LP:AF:ID 0.000207034:0.000218952:0.468521:0.838821:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838442 ES:SE:LP:AF:ID 0.000224358:0.000218707:0.522879:0.838442:rs3131972
1 754503 rs3115859 G A . PASS AF=0.837891 ES:SE:LP:AF:ID 0.000227075:0.000218099:0.522879:0.837891:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838523 ES:SE:LP:AF:ID 0.000218192:0.000218712:0.49485:0.838523:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839643 ES:SE:LP:AF:ID 0.000229:0.000221682:0.522879:0.839643:rs3131965
1 760912 rs1048488 C T . PASS AF=0.838177 ES:SE:LP:AF:ID 0.000196297:0.000217695:0.431798:0.838177:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838297 ES:SE:LP:AF:ID 0.000207909:0.00021785:0.468521:0.838297:rs3115850
1 763394 rs3115847 G A . PASS AF=0.706654 ES:SE:LP:AF:ID 0.000164468:0.000226601:0.327902:0.706654:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761329 ES:SE:LP:AF:ID 0.000204175:0.000185036:0.568636:0.761329:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265284 ES:SE:LP:AF:ID -7.3464e-05:0.000206648:0.142668:0.265284:rs12124819
1 795988 rs59380221 C T . PASS AF=0.143055 ES:SE:LP:AF:ID -8.89815e-05:0.000242321:0.148742:0.143055:rs59380221
1 798400 rs10900604 A G . PASS AF=0.20657 ES:SE:LP:AF:ID -0.000157637:0.000197365:0.376751:0.20657:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206399 ES:SE:LP:AF:ID -0.000157989:0.000197449:0.376751:0.206399:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772649 ES:SE:LP:AF:ID 0.000145417:0.000187849:0.356547:0.772649:rs11240779
1 808928 rs11240780 C T . PASS AF=0.77288 ES:SE:LP:AF:ID 0.000143858:0.000188165:0.356547:0.77288:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340407 ES:SE:LP:AF:ID 6.12688e-05:0.000265086:0.0861861:0.340407:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697235 ES:SE:LP:AF:ID -7.25518e-05:0.000177291:0.167491:0.697235:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705404 ES:SE:LP:AF:ID 4.24879e-05:0.000174078:0.091515:0.705404:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705448 ES:SE:LP:AF:ID 4.08384e-05:0.000174072:0.091515:0.705448:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705634 ES:SE:LP:AF:ID 3.98228e-05:0.000174078:0.0861861:0.705634:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705661 ES:SE:LP:AF:ID 3.94279e-05:0.000174097:0.0861861:0.705661:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730097 ES:SE:LP:AF:ID -1.87194e-05:0.000178848:0.0362122:0.730097:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294372 ES:SE:LP:AF:ID -4.06122e-05:0.000174088:0.0861861:0.294372:rs28765502
1 833223 rs13303211 C T . PASS AF=0.2367 ES:SE:LP:AF:ID -2.70761e-05:0.00018539:0.0555173:0.2367:rs13303211
1 833302 rs28752186 C T . PASS AF=0.23669 ES:SE:LP:AF:ID -2.67731e-05:0.000185389:0.05061:0.23669:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239759 ES:SE:LP:AF:ID -2.09304e-05:0.000184794:0.0409586:0.239759:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236692 ES:SE:LP:AF:ID -2.70425e-05:0.000185387:0.0555173:0.236692:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212459 ES:SE:LP:AF:ID 3.30102e-05:0.000192692:0.0655015:0.212459:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212354 ES:SE:LP:AF:ID 3.70903e-05:0.000192727:0.0705811:0.212354:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID -3.70747e-05:0.000185247:0.0757207:0.237178:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212999 ES:SE:LP:AF:ID 2.17071e-05:0.000192452:0.0409586:0.212999:rs4422949
1 834999 rs28570054 G A . PASS AF=0.21296 ES:SE:LP:AF:ID 2.95588e-05:0.000192491:0.0555173:0.21296:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241147 ES:SE:LP:AF:ID -8.60914e-05:0.000183948:0.19382:0.241147:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213578 ES:SE:LP:AF:ID 1.99351e-05:0.00019221:0.0362122:0.213578:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269514 ES:SE:LP:AF:ID -4.63329e-06:0.000177494:0.00877392:0.269514:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213546 ES:SE:LP:AF:ID 2.09369e-05:0.000192233:0.0409586:0.213546:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214612 ES:SE:LP:AF:ID 1.04707e-05:0.000191874:0.0177288:0.214612:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246256 ES:SE:LP:AF:ID 4.1583e-05:0.000182687:0.0861861:0.246256:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID -5.22371e-06:0.000177614:0.00877392:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.40028 ES:SE:LP:AF:ID -0.00012929:0.000160593:0.376751:0.40028:rs4970382
1 841085 rs1574243 C G . PASS AF=0.23719 ES:SE:LP:AF:ID 3.99444e-05:0.00018652:0.0809219:0.23719:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215423 ES:SE:LP:AF:ID 3.36634e-05:0.000191994:0.0655015:0.215423:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235354 ES:SE:LP:AF:ID 2.10647e-06:0.000189376:0.00436481:0.235354:rs28540380
1 843405 rs11516185 A G . PASS AF=0.36261 ES:SE:LP:AF:ID 0.000315212:0.000199363:0.958607:0.36261:rs11516185