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_6142_100.vcf.gz --id UKB-b:6748 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6142_100.txt.gz --cohort_cases 2324 --cohort_controls 458918 --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": "2019-09-13T01:51:36.837693",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6748/ukb-b-6748.vcf.gz; Date=Sun May 10 14:38:38 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-6748/UKB-b-6748_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6748/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6748/UKB-b-6748_data.vcf.gz ...
Read summary statistics for 3512932 SNPs.
Dropped 541 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, 857619 SNPs remain.
After merging with regression SNP LD, 857619 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0018 (0.0012)
Lambda GC: 1.0447
Mean Chi^2: 1.0401
Intercept: 1.0218 (0.0085)
Ratio: 0.5435 (0.213)
Analysis finished at Thu Oct 17 14:40:56 2019
Total time elapsed: 38.48sQC metrics
Metrics
Metrics
{
"af_correlation": 0.852,
"inflation_factor": 1.0475,
"mean_EFFECT": -1.0842e-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": 28261,
"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": 857619,
"ldsc_nsnp_merge_regression_ld": 857619,
"ldsc_observed_scale_h2_beta": 0.0018,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0218,
"ldsc_intercept_se": 0.0085,
"ldsc_lambda_gc": 1.0447,
"ldsc_mean_chisq": 1.0401,
"ldsc_ratio": 0.5436
}
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 | 3512394 | 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 | 3512932 | 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 | 8.663837e+00 | 5.773412e+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 | 7.853202e+07 | 5.672280e+07 | 828.0000000 | 3.159183e+07 | 6.890565e+07 | 1.146915e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.100000e-06 | 1.705000e-04 | -0.0010121 | -1.153000e-04 | -1.600000e-06 | 1.122000e-04 | 1.025600e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.663000e-04 | 1.740000e-05 | 0.0001423 | 1.511000e-04 | 1.608000e-04 | 1.785000e-04 | 5.204000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.927543e-01 | 2.903841e-01 | 0.0000001 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.927601e-01 | 2.903576e-01 | 0.0000001 | 2.388062e-01 | 4.903534e-01 | 7.441048e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.145633e-01 | 1.959920e-01 | 0.1506030 | 2.428560e-01 | 3.752350e-01 | 5.636932e-01 | 8.493970e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 28261 | 0.9919552 | NA | NA | NA | NA | NA | NA | NA | 4.010268e-01 | 2.053785e-01 | 0.0000000 | 2.334270e-01 | 3.692090e-01 | 5.511180e-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 | -0.0000115 | 0.0002618 | 0.9699999 | 0.9650394 | 0.623778 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000367 | 0.0002594 | 0.8900000 | 0.8873660 | 0.400378 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001867 | 0.0002554 | 0.4600002 | 0.4648287 | 0.456860 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0001249 | 0.0002917 | 0.6700003 | 0.6686340 | 0.240951 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0004309 | 0.0001811 | 0.0170000 | 0.0173419 | 0.515631 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0000691 | 0.0002119 | 0.7400005 | 0.7443586 | 0.843206 | 0.639976 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0001275 | 0.0002052 | 0.5300002 | 0.5343913 | 0.838943 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | -0.0001071 | 0.0002050 | 0.5999997 | 0.6013482 | 0.838571 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | -0.0001306 | 0.0002044 | 0.5199996 | 0.5229561 | 0.838023 | 0.663938 | NA |
| 1 | 754964 | rs3131966 | C | T | -0.0001406 | 0.0002050 | 0.4899999 | 0.4927961 | 0.838653 | 0.663339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51182485 | rs6009961 | A | G | 0.0000859 | 0.0001710 | 0.6200004 | 0.6152158 | 0.715499 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0001547 | 0.0001585 | 0.3300000 | 0.3291829 | 0.381774 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0002222 | 0.0001545 | 0.1499999 | 0.1505364 | 0.451023 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0002504 | 0.0002082 | 0.2300001 | 0.2291507 | 0.167632 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0002491 | 0.0002128 | 0.2399999 | 0.2418619 | 0.168492 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0003582 | 0.0001595 | 0.0250000 | 0.0247429 | 0.386323 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0003068 | 0.0001806 | 0.0890000 | 0.0893783 | 0.254601 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0003218 | 0.0001790 | 0.0719996 | 0.0722515 | 0.271580 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0002041 | 0.0001701 | 0.2300001 | 0.2303371 | 0.331418 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0003248 | 0.0001742 | 0.0619998 | 0.0622500 | 0.297948 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623778 ES:SE:LP:AF:ID -1.14765e-05:0.000261837:0.0132283:0.623778:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400378 ES:SE:LP:AF:ID -3.67348e-05:0.000259357:0.05061:0.400378:rs2462492
1 91536 rs6702460 G T . PASS AF=0.45686 ES:SE:LP:AF:ID 0.000186684:0.00025541:0.337242:0.45686:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240951 ES:SE:LP:AF:ID 0.000124866:0.000291727:0.173925:0.240951:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515631 ES:SE:LP:AF:ID -0.00043091:0.000181102:1.76955:0.515631:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843206 ES:SE:LP:AF:ID -6.9089e-05:0.000211873:0.130768:0.843206:rs4951859
1 752566 rs3094315 G A . PASS AF=0.838943 ES:SE:LP:AF:ID -0.000127483:0.000205182:0.275724:0.838943:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838571 ES:SE:LP:AF:ID -0.000107085:0.000204962:0.221849:0.838571:rs3131972
1 754503 rs3115859 G A . PASS AF=0.838023 ES:SE:LP:AF:ID -0.000130564:0.000204391:0.283997:0.838023:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838653 ES:SE:LP:AF:ID -0.000140579:0.000204965:0.309804:0.838653:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839766 ES:SE:LP:AF:ID -0.000107552:0.000207736:0.221849:0.839766:rs3131965
1 760912 rs1048488 C T . PASS AF=0.838305 ES:SE:LP:AF:ID -0.000162278:0.000204004:0.366532:0.838305:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838424 ES:SE:LP:AF:ID -0.000160143:0.000204146:0.366532:0.838424:rs3115850
1 763394 rs3115847 G A . PASS AF=0.706739 ES:SE:LP:AF:ID -7.71872e-05:0.000212322:0.142668:0.706739:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761308 ES:SE:LP:AF:ID -0.000215245:0.000173295:0.677781:0.761308:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265404 ES:SE:LP:AF:ID 9.83152e-05:0.000193574:0.21467:0.265404:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206592 ES:SE:LP:AF:ID -0.000143571:0.000184877:0.356547:0.206592:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206421 ES:SE:LP:AF:ID -0.000161551:0.000184956:0.420216:0.206421:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772644 ES:SE:LP:AF:ID 8.33006e-06:0.000175951:0.0177288:0.772644:rs11240779
1 808928 rs11240780 C T . PASS AF=0.77287 ES:SE:LP:AF:ID 2.57419e-05:0.000176246:0.0555173:0.77287:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340398 ES:SE:LP:AF:ID 4.24381e-05:0.00024834:0.0655015:0.340398:rs74461805
1 830181 rs28444699 A G . PASS AF=0.69725 ES:SE:LP:AF:ID -0.000454834:0.00016614:2.20761:0.69725:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705391 ES:SE:LP:AF:ID -0.000413228:0.000163138:1.95861:0.705391:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705435 ES:SE:LP:AF:ID -0.000418686:0.000163132:2:0.705435:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705621 ES:SE:LP:AF:ID -0.000407748:0.00016314:1.92082:0.705621:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705649 ES:SE:LP:AF:ID -0.000414062:0.000163157:1.95861:0.705649:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730122 ES:SE:LP:AF:ID -0.000404161:0.000167606:1.79588:0.730122:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294383 ES:SE:LP:AF:ID 0.000416345:0.000163149:1.95861:0.294383:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236702 ES:SE:LP:AF:ID 0.000384337:0.000173698:1.56864:0.236702:rs13303211
1 833302 rs28752186 C T . PASS AF=0.23669 ES:SE:LP:AF:ID 0.00038459:0.0001737:1.56864:0.23669:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239748 ES:SE:LP:AF:ID 0.000391859:0.000173143:1.61979:0.239748:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236692 ES:SE:LP:AF:ID 0.000384618:0.000173698:1.56864:0.236692:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212421 ES:SE:LP:AF:ID 0.000385457:0.000180536:1.48149:0.212421:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212316 ES:SE:LP:AF:ID 0.000378346:0.000180568:1.4437:0.212316:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237177 ES:SE:LP:AF:ID 0.000383851:0.000173565:1.56864:0.237177:rs4411087
1 834928 rs4422949 A G . PASS AF=0.21296 ES:SE:LP:AF:ID 0.00039142:0.000180311:1.52288:0.21296:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212923 ES:SE:LP:AF:ID 0.000386323:0.000180347:1.49485:0.212923:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241159 ES:SE:LP:AF:ID 0.000339702:0.000172356:1.3098:0.241159:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213548 ES:SE:LP:AF:ID 0.000390411:0.000180081:1.52288:0.213548:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269502 ES:SE:LP:AF:ID 0.000472712:0.000166312:2.34679:0.269502:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213518 ES:SE:LP:AF:ID 0.000395504:0.000180103:1.55284:0.213518:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214589 ES:SE:LP:AF:ID 0.000395149:0.000179756:1.55284:0.214589:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246194 ES:SE:LP:AF:ID 0.000495059:0.000171173:2.42022:0.246194:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270011 ES:SE:LP:AF:ID 0.000466567:0.000166431:2.29243:0.270011:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400131 ES:SE:LP:AF:ID 0.00020188:0.000150474:0.744727:0.400131:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID 0.000456756:0.000174791:2.04576:0.237102:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215396 ES:SE:LP:AF:ID 0.000385456:0.000179875:1.49485:0.215396:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235321 ES:SE:LP:AF:ID 0.000221324:0.000177407:0.677781:0.235321:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362623 ES:SE:LP:AF:ID -0.000186312:0.000186796:0.49485:0.362623:rs11516185
1 844300 rs61769713 C G . PASS AF=0.818839 ES:SE:LP:AF:ID -6.07551e-05:0.000191987:0.124939:0.818839:rs61769713