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_6158_2.vcf.gz --id UKB-b:17579 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6158_2.txt.gz --cohort_cases 2122 --cohort_controls 12829 --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": "2019-09-13T01:56:03.407137",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-17579/ukb-b-17579.vcf.gz; Date=Sat May 9 12:57:16 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-17579/UKB-b-17579_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17579/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17579/UKB-b-17579_data.vcf.gz ...
Read summary statistics for 3320626 SNPs.
Dropped 484 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, 817024 SNPs remain.
After merging with regression SNP LD, 817024 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0478 (0.0463)
Lambda GC: 1.0165
Mean Chi^2: 1.0197
Intercept: 1.0032 (0.011)
Ratio: 0.1624 (0.5595)
Analysis finished at Thu Oct 17 14:41:01 2019
Total time elapsed: 41.96sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8406,
"inflation_factor": 1,
"mean_EFFECT": 0,
"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": 26614,
"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": 817024,
"ldsc_nsnp_merge_regression_ld": 817024,
"ldsc_observed_scale_h2_beta": 0.0478,
"ldsc_observed_scale_h2_se": 0.0463,
"ldsc_intercept_beta": 1.0032,
"ldsc_intercept_se": 0.011,
"ldsc_lambda_gc": 1.0165,
"ldsc_mean_chisq": 1.0197,
"ldsc_ratio": 0.1624
}
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 | 3320145 | 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 | 3320626 | 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 | 8.661347e+00 | 5.771895e+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 | 7.855313e+07 | 5.673020e+07 | 828.0000000 | 3.158226e+07 | 6.892664e+07 | 1.147890e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.050000e-05 | 4.543400e-03 | -0.0268033 | -3.019600e-03 | -3.100000e-06 | 3.034600e-03 | 2.908600e-02 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.487100e-03 | 4.190000e-04 | 0.0038642 | 4.123000e-03 | 4.357200e-03 | 4.782800e-03 | 9.639800e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.983723e-01 | 2.896086e-01 | 0.0000002 | 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 | 4.983746e-01 | 2.895838e-01 | 0.0000002 | 2.472935e-01 | 4.975260e-01 | 7.495135e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.224745e-01 | 1.885457e-01 | 0.1649390 | 2.571472e-01 | 3.867520e-01 | 5.676230e-01 | 8.350610e-01 | ▇▆▅▃▂ |
| numeric | AF_reference | 26614 | 0.9919852 | NA | NA | NA | NA | NA | NA | NA | 4.080320e-01 | 2.003268e-01 | 0.0000000 | 2.454070e-01 | 3.793930e-01 | 5.547120e-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 | 0.0080313 | 0.0071857 | 0.2599998 | 0.2637065 | 0.621207 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0044315 | 0.0071504 | 0.5400003 | 0.5354226 | 0.399983 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0140308 | 0.0070433 | 0.0460002 | 0.0463625 | 0.455967 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0076050 | 0.0079221 | 0.3400001 | 0.3370671 | 0.242887 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0009946 | 0.0049850 | 0.8400000 | 0.8418582 | 0.514509 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0047210 | 0.0058011 | 0.4199997 | 0.4157553 | 0.707462 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0039750 | 0.0047470 | 0.4000000 | 0.4023870 | 0.757696 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0054980 | 0.0053368 | 0.2999998 | 0.3029108 | 0.263472 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | -0.0015121 | 0.0050615 | 0.7700005 | 0.7651362 | 0.209055 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | -0.0015821 | 0.0050638 | 0.7499995 | 0.7547149 | 0.208909 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51182485 | rs6009961 | A | G | 0.0004439 | 0.0046924 | 0.9199999 | 0.9246337 | 0.716355 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0057048 | 0.0043331 | 0.1900002 | 0.1879876 | 0.384677 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0035114 | 0.0042302 | 0.4100001 | 0.4064888 | 0.453659 | 0.4532750 | NA |
| 22 | 51192586 | rs5771006 | G | A | -0.0090504 | 0.0056992 | 0.1100001 | 0.1122830 | 0.168849 | 0.0848642 | NA |
| 22 | 51193227 | rs34608236 | T | G | 0.0020739 | 0.0058354 | 0.7199992 | 0.7222896 | 0.167900 | 0.0692891 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0014155 | 0.0043668 | 0.7499995 | 0.7458162 | 0.388724 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0013785 | 0.0049709 | 0.7800007 | 0.7815435 | 0.252300 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0030030 | 0.0048974 | 0.5400003 | 0.5397464 | 0.270853 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0018045 | 0.0046493 | 0.6999999 | 0.6979232 | 0.333067 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0002742 | 0.0047436 | 0.9500000 | 0.9539004 | 0.300574 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.621207 ES:SE:LP:AF:ID 0.0080313:0.00718573:0.585027:0.621207:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399983 ES:SE:LP:AF:ID 0.00443147:0.00715042:0.267606:0.399983:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455967 ES:SE:LP:AF:ID 0.0140308:0.0070433:1.33724:0.455967:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242887 ES:SE:LP:AF:ID 0.007605:0.00792207:0.468521:0.242887:rs6680723
1 706368 rs12029736 A G . PASS AF=0.514509 ES:SE:LP:AF:ID -0.000994591:0.00498499:0.0757207:0.514509:rs12029736
1 763394 rs3115847 G A . PASS AF=0.707462 ES:SE:LP:AF:ID -0.00472097:0.00580109:0.376751:0.707462:rs3115847
1 768253 rs2977608 A C . PASS AF=0.757696 ES:SE:LP:AF:ID 0.00397495:0.00474697:0.39794:0.757696:rs2977608
1 776546 rs12124819 A G . PASS AF=0.263472 ES:SE:LP:AF:ID 0.00549798:0.00533675:0.522879:0.263472:rs12124819
1 798400 rs10900604 A G . PASS AF=0.209055 ES:SE:LP:AF:ID -0.00151209:0.00506151:0.113509:0.209055:rs10900604
1 798959 rs11240777 G A . PASS AF=0.208909 ES:SE:LP:AF:ID -0.00158206:0.00506375:0.124939:0.208909:rs11240777
1 808631 rs11240779 G A . PASS AF=0.769231 ES:SE:LP:AF:ID 0.00275934:0.00480564:0.244125:0.769231:rs11240779
1 808928 rs11240780 C T . PASS AF=0.769373 ES:SE:LP:AF:ID 0.0030677:0.00481105:0.283997:0.769373:rs11240780
1 814495 rs74461805 C A . PASS AF=0.338958 ES:SE:LP:AF:ID -0.00644191:0.00685566:0.455932:0.338958:rs74461805
1 830181 rs28444699 A G . PASS AF=0.693535 ES:SE:LP:AF:ID -0.00339146:0.00458297:0.337242:0.693535:rs28444699
1 831489 rs4970385 C T . PASS AF=0.701616 ES:SE:LP:AF:ID -0.000588344:0.00449306:0.0457575:0.701616:rs4970385
1 831909 rs9697642 C T . PASS AF=0.70166 ES:SE:LP:AF:ID -0.000642272:0.0044927:0.05061:0.70166:rs9697642
1 832066 rs9697380 G C . PASS AF=0.70186 ES:SE:LP:AF:ID -0.000580816:0.00449204:0.0457575:0.70186:rs9697380
1 832318 rs4500250 C A . PASS AF=0.701869 ES:SE:LP:AF:ID -0.000634292:0.00449243:0.05061:0.701869:rs4500250
1 832398 rs4553118 T C . PASS AF=0.728283 ES:SE:LP:AF:ID -0.00256861:0.00461368:0.236572:0.728283:rs4553118
1 832918 rs28765502 T C . PASS AF=0.298258 ES:SE:LP:AF:ID 0.000670006:0.00449285:0.0555173:0.298258:rs28765502
1 833223 rs13303211 C T . PASS AF=0.237765 ES:SE:LP:AF:ID 0.00161246:0.00480053:0.130768:0.237765:rs13303211
1 833302 rs28752186 C T . PASS AF=0.237782 ES:SE:LP:AF:ID 0.00160301:0.00480012:0.130768:0.237782:rs28752186
1 833641 rs28594623 T C . PASS AF=0.240995 ES:SE:LP:AF:ID 0.00166244:0.00477553:0.136677:0.240995:rs28594623
1 833824 rs28484835 T C . PASS AF=0.237791 ES:SE:LP:AF:ID 0.0016068:0.00480012:0.130768:0.237791:rs28484835
1 833927 rs28593608 T C . PASS AF=0.211653 ES:SE:LP:AF:ID 0.00376001:0.00498369:0.346787:0.211653:rs28593608
1 834198 rs28385272 T C . PASS AF=0.211642 ES:SE:LP:AF:ID 0.00373228:0.00498308:0.346787:0.211642:rs28385272
1 834832 rs4411087 G C . PASS AF=0.238134 ES:SE:LP:AF:ID 0.00150328:0.00479784:0.124939:0.238134:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212231 ES:SE:LP:AF:ID 0.00355918:0.00497605:0.327902:0.212231:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212213 ES:SE:LP:AF:ID 0.00381264:0.00497698:0.356547:0.212213:rs28570054
1 835499 rs4422948 A G . PASS AF=0.242207 ES:SE:LP:AF:ID 0.00141543:0.00476415:0.113509:0.242207:rs4422948
1 836529 rs28731045 C G . PASS AF=0.212687 ES:SE:LP:AF:ID 0.00346596:0.00497098:0.309804:0.212687:rs28731045
1 836896 rs28705752 T C . PASS AF=0.27104 ES:SE:LP:AF:ID 0.000766727:0.0045739:0.0604807:0.27104:rs28705752
1 836924 rs72890788 G A . PASS AF=0.212655 ES:SE:LP:AF:ID 0.003493:0.00497169:0.318759:0.212655:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214028 ES:SE:LP:AF:ID 0.0030669:0.00495777:0.267606:0.214028:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246288 ES:SE:LP:AF:ID 0.00233156:0.00469503:0.207608:0.246288:rs4970383
1 839103 rs28562941 A G . PASS AF=0.271405 ES:SE:LP:AF:ID 0.000962714:0.00457864:0.0809219:0.271405:rs28562941
1 840753 rs4970382 T C . PASS AF=0.399164 ES:SE:LP:AF:ID -0.00465472:0.0041105:0.585027:0.399164:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237782 ES:SE:LP:AF:ID 0.00269115:0.00478:0.244125:0.237782:rs1574243
1 842013 rs7419119 T G . PASS AF=0.214375 ES:SE:LP:AF:ID 0.00326709:0.00495737:0.29243:0.214375:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235345 ES:SE:LP:AF:ID 0.00198594:0.00488155:0.167491:0.235345:rs28540380
1 843405 rs11516185 A G . PASS AF=0.360996 ES:SE:LP:AF:ID -0.00160257:0.00513045:0.124939:0.360996:rs11516185
1 844300 rs61769713 C G . PASS AF=0.819076 ES:SE:LP:AF:ID 0.00426585:0.00523934:0.376751:0.819076:rs61769713
1 845283 rs7366404 G T . PASS AF=0.813447 ES:SE:LP:AF:ID 0.00438495:0.00517454:0.39794:0.813447:rs7366404
1 845635 rs117086422 C T . PASS AF=0.204001 ES:SE:LP:AF:ID 0.000814378:0.00503399:0.0604807:0.204001:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210102 ES:SE:LP:AF:ID 0.000878146:0.00497255:0.0655015:0.210102:rs57760052
1 846078 rs28612348 C T . PASS AF=0.19446 ES:SE:LP:AF:ID 0.00184222:0.00512357:0.142668:0.19446:rs28612348
1 846338 rs4970334 A G . PASS AF=0.812532 ES:SE:LP:AF:ID 0.00443693:0.00516942:0.408935:0.812532:rs4970334
1 846398 rs58781670 G A . PASS AF=0.203322 ES:SE:LP:AF:ID 0.000734514:0.00503703:0.0555173:0.203322:rs58781670
1 846489 rs4970333 T C . PASS AF=0.812657 ES:SE:LP:AF:ID 0.00441041:0.0051716:0.408935:0.812657:rs4970333
1 846808 rs4475691 C T . PASS AF=0.196374 ES:SE:LP:AF:ID 0.00269925:0.00510655:0.221849:0.196374:rs4475691