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_1660.vcf.gz --id UKB-b:14852 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1660.txt.gz --cohort_cases 1990 --cohort_controls 442028 --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": "2019-09-13T13:18:24.949171",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14852/ukb-b-14852.vcf.gz; Date=Sat May 9 13:38:04 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-14852/UKB-b-14852_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14852/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14852/UKB-b-14852_data.vcf.gz ...
Read summary statistics for 3177599 SNPs.
Dropped 442 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, 786347 SNPs remain.
After merging with regression SNP LD, 786347 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.001 (0.0011)
Lambda GC: 1.0276
Mean Chi^2: 1.027
Intercept: 1.0375 (0.009)
Ratio: 1.3894 (0.3321)
Analysis finished at Thu Oct 17 14:44:30 2019
Total time elapsed: 41.81sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8304,
"inflation_factor": 1,
"mean_EFFECT": -6.4844e-07,
"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": 25401,
"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": 786347,
"ldsc_nsnp_merge_regression_ld": 786347,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0375,
"ldsc_intercept_se": 0.009,
"ldsc_lambda_gc": 1.0276,
"ldsc_mean_chisq": 1.027,
"ldsc_ratio": 1.3889
}
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 | 3177160 | 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 | 3177599 | 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 | 8.662373e+00 | 5.772695e+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 | 7.854307e+07 | 5.670943e+07 | 828.0000000 | 3.159627e+07 | 6.892265e+07 | 1.147968e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.000000e-07 | 1.589000e-04 | -0.0008549 | -1.067000e-04 | -3.000000e-07 | 1.052000e-04 | 1.004100e-03 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.564000e-04 | 1.340000e-05 | 0.0001368 | 1.448000e-04 | 1.523000e-04 | 1.658000e-04 | 3.383000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.959886e-01 | 2.897199e-01 | 0.0000004 | 2.399999e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.959913e-01 | 2.896947e-01 | 0.0000004 | 2.437638e-01 | 4.956217e-01 | 7.462980e-01 | 9.999993e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.280814e-01 | 1.827156e-01 | 0.1758800 | 2.677470e-01 | 3.945310e-01 | 5.698980e-01 | 8.241200e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 25401 | 0.9920062 | NA | NA | NA | NA | NA | NA | NA | 4.129197e-01 | 1.964304e-01 | 0.0000000 | 2.541930e-01 | 3.867810e-01 | 5.569090e-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 | -0.0000183 | 0.0002517 | 0.9400001 | 0.9421693 | 0.623751 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001558 | 0.0002494 | 0.5300002 | 0.5321247 | 0.400416 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0003500 | 0.0002455 | 0.1499999 | 0.1540519 | 0.456856 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0000501 | 0.0002805 | 0.8600001 | 0.8583799 | 0.240909 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000425 | 0.0001741 | 0.8100000 | 0.8071764 | 0.515715 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0000514 | 0.0002042 | 0.8000000 | 0.8013169 | 0.706666 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0000150 | 0.0001667 | 0.9299999 | 0.9283007 | 0.761321 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0001085 | 0.0001862 | 0.5600000 | 0.5599892 | 0.265278 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | -0.0001653 | 0.0001778 | 0.3500000 | 0.3524585 | 0.206589 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | -0.0001786 | 0.0001779 | 0.3200000 | 0.3154640 | 0.206418 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -0.0000288 | 0.0001639 | 0.8600001 | 0.8605188 | 0.713707 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0000972 | 0.0001631 | 0.5500004 | 0.5511403 | 0.695495 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0000430 | 0.0001644 | 0.7899998 | 0.7934490 | 0.715542 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000532 | 0.0001525 | 0.7300002 | 0.7273602 | 0.382028 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001057 | 0.0001486 | 0.4799997 | 0.4769564 | 0.451293 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000536 | 0.0001534 | 0.7300002 | 0.7266506 | 0.386523 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000516 | 0.0001737 | 0.7700005 | 0.7664581 | 0.254383 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000518 | 0.0001722 | 0.7600007 | 0.7636554 | 0.271376 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000439 | 0.0001636 | 0.7899998 | 0.7882820 | 0.331617 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0001211 | 0.0001674 | 0.4700002 | 0.4695529 | 0.298161 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623751 ES:SE:LP:AF:ID -1.82603e-05:0.000251715:0.0268721:0.623751:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400416 ES:SE:LP:AF:ID -0.000155816:0.000249399:0.275724:0.400416:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456856 ES:SE:LP:AF:ID -0.000349954:0.000245519:0.823909:0.456856:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240909 ES:SE:LP:AF:ID -5.00553e-05:0.000280521:0.0655015:0.240909:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515715 ES:SE:LP:AF:ID 4.25041e-05:0.000174147:0.091515:0.515715:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706666 ES:SE:LP:AF:ID 5.13737e-05:0.000204153:0.09691:0.706666:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761321 ES:SE:LP:AF:ID -1.5e-05:0.000166698:0.0315171:0.761321:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265278 ES:SE:LP:AF:ID -0.00010851:0.000186169:0.251812:0.265278:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206589 ES:SE:LP:AF:ID -0.000165324:0.0001778:0.455932:0.206589:rs10900604
1 798959 rs11240777 G A . PASS AF=0.206418 ES:SE:LP:AF:ID -0.000178556:0.000177876:0.49485:0.206418:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772633 ES:SE:LP:AF:ID 0.000128893:0.000169227:0.346787:0.772633:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772864 ES:SE:LP:AF:ID 0.000140294:0.000169512:0.387216:0.772864:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340411 ES:SE:LP:AF:ID -0.000660189:0.000238812:2.24413:0.340411:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697217 ES:SE:LP:AF:ID -0.000110806:0.000159718:0.309804:0.697217:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705386 ES:SE:LP:AF:ID -3.82322e-05:0.000156825:0.091515:0.705386:rs4970385
1 831909 rs9697642 C T . PASS AF=0.70543 ES:SE:LP:AF:ID -3.92141e-05:0.000156819:0.09691:0.70543:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705616 ES:SE:LP:AF:ID -4.11667e-05:0.000156825:0.102373:0.705616:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705643 ES:SE:LP:AF:ID -4.16743e-05:0.000156842:0.102373:0.705643:rs4500250
1 832398 rs4553118 T C . PASS AF=0.73009 ES:SE:LP:AF:ID -3.86314e-05:0.000161123:0.091515:0.73009:rs4553118
1 832918 rs28765502 T C . PASS AF=0.29439 ES:SE:LP:AF:ID 4.1586e-05:0.000156834:0.102373:0.29439:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236708 ES:SE:LP:AF:ID 3.24203e-05:0.000167016:0.0705811:0.236708:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236698 ES:SE:LP:AF:ID 3.25338e-05:0.000167015:0.0705811:0.236698:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239767 ES:SE:LP:AF:ID -3.04553e-06:0.00016648:0.00436481:0.239767:rs28594623
1 833824 rs28484835 T C . PASS AF=0.2367 ES:SE:LP:AF:ID 3.25209e-05:0.000167014:0.0705811:0.2367:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212457 ES:SE:LP:AF:ID 2.97188e-05:0.000173596:0.0655015:0.212457:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212352 ES:SE:LP:AF:ID 3.48072e-05:0.000173627:0.0757207:0.212352:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237186 ES:SE:LP:AF:ID 3.6704e-05:0.000166887:0.0809219:0.237186:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212997 ES:SE:LP:AF:ID 3.41933e-05:0.00017338:0.0757207:0.212997:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212958 ES:SE:LP:AF:ID 4.36364e-05:0.000173415:0.09691:0.212958:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID 7.72838e-05:0.000165717:0.19382:0.241162:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213577 ES:SE:LP:AF:ID 3.82241e-05:0.000173161:0.0809219:0.213577:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269517 ES:SE:LP:AF:ID 7.39711e-05:0.000159905:0.19382:0.269517:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213546 ES:SE:LP:AF:ID 3.80985e-05:0.000173183:0.0809219:0.213546:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214613 ES:SE:LP:AF:ID 4.49076e-05:0.000172857:0.09691:0.214613:rs4970384
1 838555 rs4970383 C A . PASS AF=0.24625 ES:SE:LP:AF:ID 6.71564e-05:0.000164585:0.167491:0.24625:rs4970383
1 839103 rs28562941 A G . PASS AF=0.27002 ES:SE:LP:AF:ID 7.58142e-05:0.000160014:0.19382:0.27002:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400289 ES:SE:LP:AF:ID -0.00012139:0.000144679:0.39794:0.400289:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237185 ES:SE:LP:AF:ID 2.88614e-05:0.000168038:0.0655015:0.237185:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215421 ES:SE:LP:AF:ID 4.62199e-05:0.000172966:0.102373:0.215421:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235356 ES:SE:LP:AF:ID 0.000117517:0.000170609:0.309804:0.235356:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362614 ES:SE:LP:AF:ID -1.18347e-05:0.0001796:0.0222764:0.362614:rs11516185
1 844300 rs61769713 C G . PASS AF=0.818739 ES:SE:LP:AF:ID 0.000173628:0.000184564:0.455932:0.818739:rs61769713
1 845283 rs7366404 G T . PASS AF=0.814424 ES:SE:LP:AF:ID 0.000187575:0.00018294:0.508638:0.814424:rs7366404
1 845635 rs117086422 C T . PASS AF=0.205416 ES:SE:LP:AF:ID -1.11605e-05:0.000175943:0.0222764:0.205416:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210834 ES:SE:LP:AF:ID -2.11852e-05:0.000174255:0.0457575:0.210834:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196789 ES:SE:LP:AF:ID -8.59938e-05:0.000178637:0.200659:0.196789:rs28612348
1 846338 rs4970334 A G . PASS AF=0.813754 ES:SE:LP:AF:ID 0.000188132:0.000182783:0.522879:0.813754:rs4970334
1 846398 rs58781670 G A . PASS AF=0.204422 ES:SE:LP:AF:ID 4.83261e-05:0.000176553:0.107905:0.204422:rs58781670
1 846489 rs4970333 T C . PASS AF=0.813845 ES:SE:LP:AF:ID 0.000187751:0.000182909:0.522879:0.813845:rs4970333
1 846808 rs4475691 C T . PASS AF=0.198386 ES:SE:LP:AF:ID -0.00010036:0.000178125:0.244125:0.198386:rs4475691