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_20087_188.vcf.gz --id UKB-b:11943 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20087_188.txt.gz --cohort_cases 2603 --cohort_controls 62346 --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": "2019-09-13T13:38:44.304702",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11943/ukb-b-11943.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-11943/UKB-b-11943_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11943/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:11 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11943/UKB-b-11943_data.vcf.gz ...
Read summary statistics for 3743311 SNPs.
Dropped 649 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, 903789 SNPs remain.
After merging with regression SNP LD, 903789 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0028 (0.0075)
Lambda GC: 1.0096
Mean Chi^2: 1.0115
Intercept: 1.0075 (0.0088)
Ratio: 0.6519 (0.7624)
Analysis finished at Thu Oct 17 14:42:01 2019
Total time elapsed: 49.61sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8643,
"inflation_factor": 1,
"mean_EFFECT": 8.0127e-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": 30308,
"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": 903789,
"ldsc_nsnp_merge_regression_ld": 903789,
"ldsc_observed_scale_h2_beta": 0.0028,
"ldsc_observed_scale_h2_se": 0.0075,
"ldsc_intercept_beta": 1.0075,
"ldsc_intercept_se": 0.0088,
"ldsc_lambda_gc": 1.0096,
"ldsc_mean_chisq": 1.0115,
"ldsc_ratio": 0.6522
}
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 | 3742665 | 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 | 3743311 | 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 | 8.662858e+00 | 5.770426e+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 | 7.855864e+07 | 5.675590e+07 | 828.0000000 | 3.159543e+07 | 6.890648e+07 | 1.147255e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.000000e-06 | 1.262600e-03 | -0.0076134 | -8.286000e-04 | 4.400000e-06 | 8.392000e-04 | 7.272100e-03 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.247300e-03 | 1.489000e-04 | 0.0010503 | 1.117800e-03 | 1.199500e-03 | 1.350800e-03 | 3.860100e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.989672e-01 | 2.891244e-01 | 0.0000023 | 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 | 4.989661e-01 | 2.890995e-01 | 0.0000023 | 2.485046e-01 | 4.991459e-01 | 7.491010e-01 | 9.999992e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.050154e-01 | 2.042948e-01 | 0.1344610 | 2.263300e-01 | 3.618840e-01 | 5.585770e-01 | 8.655390e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 30308 | 0.9919034 | NA | NA | NA | NA | NA | NA | NA | 3.926932e-01 | 2.110101e-01 | 0.0000000 | 2.194490e-01 | 3.566290e-01 | 5.461260e-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 | -0.0008089 | 0.0019357 | 0.6800001 | 0.6760482 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0028357 | 0.0019301 | 0.1400000 | 0.1417784 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0016655 | 0.0018984 | 0.3800004 | 0.3803040 | 0.455916 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0028969 | 0.0021621 | 0.1800002 | 0.1803031 | 0.242057 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0022926 | 0.0013382 | 0.0870001 | 0.0866805 | 0.513304 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0033485 | 0.0015591 | 0.0320000 | 0.0317419 | 0.841441 | 0.639976 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0029120 | 0.0015083 | 0.0539995 | 0.0535257 | 0.837029 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | 0.0029703 | 0.0015072 | 0.0490004 | 0.0487453 | 0.836733 | 0.653355 | NA |
| 1 | 754503 | rs3115859 | G | A | 0.0031309 | 0.0015028 | 0.0369999 | 0.0372105 | 0.836159 | 0.663938 | NA |
| 1 | 754964 | rs3131966 | C | T | 0.0031463 | 0.0015069 | 0.0369999 | 0.0368011 | 0.836793 | 0.663339 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51186143 | rs2879914 | T | C | 0.0010810 | 0.0011717 | 0.3599996 | 0.3562166 | 0.380077 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0009719 | 0.0011413 | 0.3900004 | 0.3944361 | 0.449547 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | 0.0011386 | 0.0015386 | 0.4600002 | 0.4592789 | 0.167396 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | -0.0013084 | 0.0015691 | 0.4000000 | 0.4043538 | 0.168560 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0006797 | 0.0011761 | 0.5600000 | 0.5633112 | 0.386693 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000858 | 0.0013339 | 0.9500000 | 0.9486985 | 0.254586 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0001550 | 0.0013232 | 0.9100000 | 0.9067358 | 0.271468 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0006867 | 0.0012577 | 0.5900000 | 0.5850714 | 0.331351 | 0.3724040 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0011040 | 0.0016276 | 0.5000000 | 0.4975950 | 0.136315 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0011701 | 0.0012849 | 0.3599996 | 0.3625050 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID -0.000808855:0.0019357:0.167491:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID 0.00283566:0.00193007:0.853872:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID -0.0016655:0.00189836:0.420216:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID -0.00289687:0.00216213:0.744727:0.242057:rs6680723
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID 0.00229257:0.0013382:1.06048:0.513304:rs12029736
1 729679 rs4951859 C G . PASS AF=0.841441 ES:SE:LP:AF:ID 0.00334846:0.00155913:1.49485:0.841441:rs4951859
1 752566 rs3094315 G A . PASS AF=0.837029 ES:SE:LP:AF:ID 0.00291195:0.00150827:1.26761:0.837029:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836733 ES:SE:LP:AF:ID 0.00297033:0.00150716:1.3098:0.836733:rs3131972
1 754503 rs3115859 G A . PASS AF=0.836159 ES:SE:LP:AF:ID 0.0031309:0.00150275:1.4318:0.836159:rs3115859
1 754964 rs3131966 C T . PASS AF=0.836793 ES:SE:LP:AF:ID 0.00314629:0.00150687:1.4318:0.836793:rs3131966
1 755775 rs3131965 A G . PASS AF=0.838109 ES:SE:LP:AF:ID 0.00332728:0.00152808:1.5376:0.838109:rs3131965
1 760912 rs1048488 C T . PASS AF=0.836369 ES:SE:LP:AF:ID 0.00312209:0.00149901:1.4318:0.836369:rs1048488
1 761147 rs3115850 T C . PASS AF=0.836495 ES:SE:LP:AF:ID 0.00310877:0.00150003:1.42022:0.836495:rs3115850
1 761732 rs2286139 C T . PASS AF=0.860851 ES:SE:LP:AF:ID 0.00360985:0.00160661:1.60206:0.860851:rs2286139
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID 0.00297662:0.00156777:1.23657:0.705804:rs3115847
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID 0.00194309:0.00127069:0.886057:0.758252:rs2977608
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID 0.00252736:0.00143583:1.10791:0.263729:rs12124819
1 787399 rs2905055 G T . PASS AF=0.859749 ES:SE:LP:AF:ID 0.00305792:0.00161466:1.23657:0.859749:rs2905055
1 787685 rs2905054 G T . PASS AF=0.860334 ES:SE:LP:AF:ID 0.00325819:0.00161557:1.35655:0.860334:rs2905054
1 795988 rs59380221 C T . PASS AF=0.144405 ES:SE:LP:AF:ID -0.0028073:0.00167007:1.03152:0.144405:rs59380221
1 798400 rs10900604 A G . PASS AF=0.209824 ES:SE:LP:AF:ID -0.00186337:0.00135471:0.769551:0.209824:rs10900604
1 798959 rs11240777 G A . PASS AF=0.209642 ES:SE:LP:AF:ID -0.00186894:0.00135543:0.769551:0.209642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.76828 ES:SE:LP:AF:ID 0.00131918:0.00128822:0.508638:0.76828:rs11240779
1 808928 rs11240780 C T . PASS AF=0.768417 ES:SE:LP:AF:ID 0.00124644:0.00128986:0.481486:0.768417:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID -0.00101852:0.00183252:0.236572:0.340108:rs74461805
1 824398 rs7538305 A C . PASS AF=0.14012 ES:SE:LP:AF:ID -0.00304524:0.00161743:1.22185:0.14012:rs7538305
1 830181 rs28444699 A G . PASS AF=0.696612 ES:SE:LP:AF:ID 0.00288959:0.001226:1.74473:0.696612:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705031 ES:SE:LP:AF:ID 0.00328794:0.0012038:2.20066:0.705031:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705083 ES:SE:LP:AF:ID 0.00329461:0.00120379:2.20761:0.705083:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705261 ES:SE:LP:AF:ID 0.00327921:0.00120374:2.19382:0.705261:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705286 ES:SE:LP:AF:ID 0.00327566:0.00120391:2.18709:0.705286:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730154 ES:SE:LP:AF:ID 0.00373818:0.00123712:2.60206:0.730154:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294729 ES:SE:LP:AF:ID -0.00327843:0.00120387:2.18709:0.294729:rs28765502
1 833223 rs13303211 C T . PASS AF=0.237157 ES:SE:LP:AF:ID -0.00263449:0.00128166:1.39794:0.237157:rs13303211
1 833302 rs28752186 C T . PASS AF=0.237168 ES:SE:LP:AF:ID -0.00268241:0.00128168:1.4437:0.237168:rs28752186
1 833641 rs28594623 T C . PASS AF=0.240204 ES:SE:LP:AF:ID -0.00243501:0.00127787:1.24413:0.240204:rs28594623
1 833824 rs28484835 T C . PASS AF=0.23716 ES:SE:LP:AF:ID -0.0026816:0.00128174:1.4437:0.23716:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212467 ES:SE:LP:AF:ID -0.00319903:0.00133236:1.79588:0.212467:rs28593608
1 834198 rs28385272 T C . PASS AF=0.21236 ES:SE:LP:AF:ID -0.00321691:0.00133269:1.79588:0.21236:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237633 ES:SE:LP:AF:ID -0.00274051:0.00128077:1.49485:0.237633:rs4411087
1 834928 rs4422949 A G . PASS AF=0.213048 ES:SE:LP:AF:ID -0.00327474:0.00133037:1.85387:0.213048:rs4422949
1 834999 rs28570054 G A . PASS AF=0.213007 ES:SE:LP:AF:ID -0.00329531:0.00133062:1.88606:0.213007:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241744 ES:SE:LP:AF:ID -0.00321447:0.0012712:1.95861:0.241744:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213504 ES:SE:LP:AF:ID -0.00323689:0.00132902:1.82391:0.213504:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269687 ES:SE:LP:AF:ID -0.00226751:0.00122831:1.18709:0.269687:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213501 ES:SE:LP:AF:ID -0.00330189:0.00132919:1.88606:0.213501:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214599 ES:SE:LP:AF:ID -0.00313625:0.00132663:1.74473:0.214599:rs4970384
1 838555 rs4970383 C A . PASS AF=0.245985 ES:SE:LP:AF:ID -0.00252403:0.00126456:1.33724:0.245985:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270067 ES:SE:LP:AF:ID -0.00227446:0.00122932:1.19382:0.270067:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID -0.00261241:0.00111129:1.72125:0.400406:rs4970382