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_41204_I501.vcf.gz --id UKB-b:10555 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_I501.txt.gz --cohort_cases 2046 --cohort_controls 460964 --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": "2019-09-13T08:38:22.750916",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10555/ukb-b-10555.vcf.gz; Date=Sat May 9 18:06:15 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-10555/UKB-b-10555_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10555/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-10555/UKB-b-10555_data.vcf.gz ...
Read summary statistics for 3239801 SNPs.
Dropped 459 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, 799913 SNPs remain.
After merging with regression SNP LD, 799913 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0039 (0.0012)
Lambda GC: 1.0535
Mean Chi^2: 1.0515
Intercept: 1.0103 (0.0088)
Ratio: 0.201 (0.1719)
Analysis finished at Thu Oct 17 14:41:02 2019
Total time elapsed: 43.26sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8347,
"inflation_factor": 1.0475,
"mean_EFFECT": 1.5083e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 1,
"n_p_sig": 2,
"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": 25897,
"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": 799913,
"ldsc_nsnp_merge_regression_ld": 799913,
"ldsc_observed_scale_h2_beta": 0.0039,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0103,
"ldsc_intercept_se": 0.0088,
"ldsc_lambda_gc": 1.0535,
"ldsc_mean_chisq": 1.0515,
"ldsc_ratio": 0.2
}
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 | 3239345 | 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 | 3239801 | 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 | 8.660604e+00 | 5.771868e+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 | 7.853636e+07 | 5.670721e+07 | 828.0000000 | 3.159495e+07 | 6.892583e+07 | 1.147799e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.500000e-06 | 1.566000e-04 | -0.0008074 | -1.028000e-04 | 1.700000e-06 | 1.060000e-04 | 8.401000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.525000e-04 | 1.350000e-05 | 0.0001329 | 1.407000e-04 | 1.484000e-04 | 1.621000e-04 | 3.287000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.932653e-01 | 2.903112e-01 | 0.0000000 | 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 | 4.932655e-01 | 2.902854e-01 | 0.0000000 | 2.406842e-01 | 4.911682e-01 | 7.437599e-01 | 9.999984e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.257072e-01 | 1.853213e-01 | 0.1710660 | 2.632460e-01 | 3.911340e-01 | 5.690540e-01 | 8.289340e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 25897 | 0.9920066 | NA | NA | NA | NA | NA | NA | NA | 4.108693e-01 | 1.981386e-01 | 0.0000000 | 2.503990e-01 | 3.835860e-01 | 5.561100e-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 | 0.0000128 | 0.0002446 | 0.9599999 | 0.9581552 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001565 | 0.0002423 | 0.5199996 | 0.5183443 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001666 | 0.0002386 | 0.4899999 | 0.4850794 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002629 | 0.0002725 | 0.3300000 | 0.3347746 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001548 | 0.0001692 | 0.3599996 | 0.3602413 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0000072 | 0.0001984 | 0.9699999 | 0.9711339 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0001322 | 0.0001619 | 0.4100001 | 0.4142794 | 0.761297 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0003503 | 0.0001808 | 0.0530005 | 0.0527379 | 0.265385 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | 0.0003646 | 0.0001727 | 0.0350002 | 0.0347801 | 0.206591 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | 0.0003678 | 0.0001728 | 0.0329997 | 0.0332590 | 0.206420 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -0.0001044 | 0.0001592 | 0.5099998 | 0.5121913 | 0.713656 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0002514 | 0.0001584 | 0.1100001 | 0.1125529 | 0.695470 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0001045 | 0.0001597 | 0.5099998 | 0.5131079 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000757 | 0.0001481 | 0.6100002 | 0.6093629 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0000560 | 0.0001444 | 0.6999999 | 0.6983202 | 0.451061 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001237 | 0.0001490 | 0.4100001 | 0.4065375 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000251 | 0.0001687 | 0.8800001 | 0.8819136 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000181 | 0.0001673 | 0.9100000 | 0.9136569 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000011 | 0.0001590 | 0.9900000 | 0.9944570 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000490 | 0.0001627 | 0.7600007 | 0.7635420 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 1.28339e-05:0.000244601:0.0177288:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000156517:0.000242324:0.283997:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000166578:0.000238597:0.309804:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -0.000262878:0.000272542:0.481486:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000154791:0.000169188:0.443698:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 7.17778e-06:0.000198357:0.0132283:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -0.000132168:0.000161894:0.387216:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -0.000350302:0.000180842:1.27572:0.265385:rs12124819
1 798400 rs10900604 A G . PASS AF=0.206591 ES:SE:LP:AF:ID 0.000364571:0.000172708:1.45593:0.206591:rs10900604
1 798959 rs11240777 G A . PASS AF=0.20642 ES:SE:LP:AF:ID 0.000367843:0.000172782:1.48149:0.20642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772619 ES:SE:LP:AF:ID -0.00029847:0.000164364:1.16115:0.772619:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772847 ES:SE:LP:AF:ID -0.000294707:0.00016464:1.13668:0.772847:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID -0.000213743:0.000231986:0.443698:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID -0.00015975:0.000155211:0.522879:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID -0.000131167:0.000152402:0.408935:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID -0.000134963:0.000152397:0.420216:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID -0.000128002:0.000152404:0.39794:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID -0.000128422:0.00015242:0.39794:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID -0.000186176:0.000156573:0.638272:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID 0.000127835:0.000152413:0.39794:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID 1.69531e-05:0.000162267:0.0362122:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID 1.73882e-05:0.000162269:0.0409586:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID 3.79117e-05:0.000161748:0.091515:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID 1.74149e-05:0.000162267:0.0409586:0.236693:rs28484835
1 833927 rs28593608 T C . PASS AF=0.212421 ES:SE:LP:AF:ID 8.12651e-05:0.000168655:0.200659:0.212421:rs28593608
1 834198 rs28385272 T C . PASS AF=0.212316 ES:SE:LP:AF:ID 7.46805e-05:0.000168684:0.180456:0.212316:rs28385272
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID 1.03457e-05:0.000162143:0.0222764:0.237178:rs4411087
1 834928 rs4422949 A G . PASS AF=0.212962 ES:SE:LP:AF:ID 6.70125e-05:0.000168444:0.161151:0.212962:rs4422949
1 834999 rs28570054 G A . PASS AF=0.212924 ES:SE:LP:AF:ID 6.57725e-05:0.000168479:0.154902:0.212924:rs28570054
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID 1.36589e-05:0.000161011:0.0315171:0.241162:rs4422948
1 836529 rs28731045 C G . PASS AF=0.213543 ES:SE:LP:AF:ID 9.52433e-05:0.000168231:0.244125:0.213543:rs28731045
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID 3.70055e-05:0.000155363:0.091515:0.269511:rs28705752
1 836924 rs72890788 G A . PASS AF=0.213513 ES:SE:LP:AF:ID 9.48789e-05:0.000168251:0.244125:0.213513:rs72890788
1 838387 rs4970384 T C . PASS AF=0.214587 ES:SE:LP:AF:ID 0.000100589:0.000167927:0.259637:0.214587:rs4970384
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID 9.06395e-05:0.000159904:0.244125:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID 4.21375e-05:0.000155473:0.102373:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 0.000130674:0.000140578:0.455932:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID 8.22851e-05:0.000163286:0.21467:0.237102:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215391 ES:SE:LP:AF:ID 7.73695e-05:0.00016804:0.187087:0.215391:rs7419119
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID 7.85551e-05:0.000165732:0.19382:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -0.00035405:0.000174507:1.37675:0.362606:rs11516185
1 844300 rs61769713 C G . PASS AF=0.818811 ES:SE:LP:AF:ID -6.89288e-05:0.00017935:0.154902:0.818811:rs61769713
1 845283 rs7366404 G T . PASS AF=0.814497 ES:SE:LP:AF:ID -9.32117e-05:0.00017777:0.221849:0.814497:rs7366404
1 845635 rs117086422 C T . PASS AF=0.205429 ES:SE:LP:AF:ID 0.000246338:0.000170897:0.823909:0.205429:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210864 ES:SE:LP:AF:ID 0.000255924:0.000169253:0.886057:0.210864:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196785 ES:SE:LP:AF:ID 0.000238219:0.000173516:0.769551:0.196785:rs28612348
1 846338 rs4970334 A G . PASS AF=0.813828 ES:SE:LP:AF:ID -9.79083e-05:0.000177619:0.236572:0.813828:rs4970334
1 846398 rs58781670 G A . PASS AF=0.204447 ES:SE:LP:AF:ID 0.000211717:0.000171481:0.657577:0.204447:rs58781670
1 846489 rs4970333 T C . PASS AF=0.81392 ES:SE:LP:AF:ID -8.93546e-05:0.000177741:0.207608:0.81392:rs4970333
1 846808 rs4475691 C T . PASS AF=0.198378 ES:SE:LP:AF:ID 0.000235945:0.000173023:0.769551:0.198378:rs4475691