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_4924.vcf.gz --id UKB-b:4323 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4924.txt.gz --cohort_cases 2008 --cohort_controls 149051 --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": "2019-09-12T23:06:50.866976",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-4323/ukb-b-4323.vcf.gz; Date=Sun May 10 03:08:22 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-4323/UKB-b-4323_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4323/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4323/UKB-b-4323_data.vcf.gz ...
Read summary statistics for 3199002 SNPs.
Dropped 447 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, 790970 SNPs remain.
After merging with regression SNP LD, 790970 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0103 (0.0039)
Lambda GC: 1.0527
Mean Chi^2: 1.0534
Intercept: 1.019 (0.0102)
Ratio: 0.3554 (0.191)
Analysis finished at Thu Oct 17 14:44:54 2019
Total time elapsed: 44.39sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8321,
"inflation_factor": 1.0475,
"mean_EFFECT": -4.0771e-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": 25578,
"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": 790970,
"ldsc_nsnp_merge_regression_ld": 790970,
"ldsc_observed_scale_h2_beta": 0.0103,
"ldsc_observed_scale_h2_se": 0.0039,
"ldsc_intercept_beta": 1.019,
"ldsc_intercept_se": 0.0102,
"ldsc_lambda_gc": 1.0527,
"ldsc_mean_chisq": 1.0534,
"ldsc_ratio": 0.3558
}
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 | 3198558 | 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 | 3199002 | 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 | 8.661988e+00 | 5.772650e+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 | 7.854629e+07 | 5.670698e+07 | 828.0000000 | 3.160411e+07 | 6.893110e+07 | 1.147960e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -4.100000e-06 | 4.749000e-04 | -0.0025304 | -3.210000e-04 | -5.500000e-06 | 3.114000e-04 | 2.514100e-03 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.603000e-04 | 3.990000e-05 | 0.0004022 | 4.256000e-04 | 4.480000e-04 | 4.885000e-04 | 9.937000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.921688e-01 | 2.913240e-01 | 0.0000005 | 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 | 4.921699e-01 | 2.912992e-01 | 0.0000005 | 2.374141e-01 | 4.894627e-01 | 7.445012e-01 | 9.999993e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.272558e-01 | 1.835979e-01 | 0.1743030 | 2.662450e-01 | 3.932990e-01 | 5.695160e-01 | 8.256960e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 25578 | 0.9920044 | NA | NA | NA | NA | NA | NA | NA | 4.122333e-01 | 1.969912e-01 | 0.0000000 | 2.529950e-01 | 3.857830e-01 | 5.567090e-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 | -0.0003246 | 0.0007413 | 0.6600001 | 0.6614678 | 0.623770 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0004721 | 0.0007367 | 0.5199996 | 0.5216510 | 0.399208 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0002065 | 0.0007258 | 0.7800007 | 0.7760449 | 0.456181 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0006863 | 0.0008294 | 0.4100001 | 0.4079172 | 0.241265 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0009880 | 0.0005120 | 0.0539995 | 0.0536387 | 0.514841 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0007385 | 0.0006010 | 0.2200002 | 0.2191075 | 0.706127 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0007870 | 0.0004888 | 0.1100001 | 0.1074104 | 0.760056 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0000695 | 0.0005482 | 0.9000000 | 0.8991761 | 0.264841 | 0.0756789 | NA |
| 1 | 798400 | rs10900604 | A | G | -0.0008251 | 0.0005214 | 0.1100001 | 0.1134985 | 0.207852 | 0.4105430 | NA |
| 1 | 798959 | rs11240777 | G | A | -0.0008255 | 0.0005216 | 0.1100001 | 0.1134863 | 0.207686 | 0.4099440 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001870 | 0.0004808 | 0.6999999 | 0.6972873 | 0.712886 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000374 | 0.0004785 | 0.9400001 | 0.9377693 | 0.695238 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001609 | 0.0004824 | 0.7400005 | 0.7387726 | 0.714656 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0002052 | 0.0004482 | 0.6499995 | 0.6470149 | 0.380404 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0003597 | 0.0004367 | 0.4100001 | 0.4100984 | 0.449811 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001182 | 0.0004510 | 0.7899998 | 0.7931770 | 0.386255 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0001923 | 0.0005109 | 0.7099994 | 0.7066728 | 0.254555 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0004501 | 0.0005072 | 0.3700002 | 0.3749429 | 0.271271 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000671 | 0.0004808 | 0.8900000 | 0.8890841 | 0.331269 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001215 | 0.0004924 | 0.8100000 | 0.8051061 | 0.297711 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.62377 ES:SE:LP:AF:ID -0.000324592:0.000741268:0.180456:0.62377:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399208 ES:SE:LP:AF:ID -0.000472092:0.00073672:0.283997:0.399208:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456181 ES:SE:LP:AF:ID -0.000206466:0.000725774:0.107905:0.456181:rs6702460
1 534192 rs6680723 C T . PASS AF=0.241265 ES:SE:LP:AF:ID -0.000686348:0.000829359:0.387216:0.241265:rs6680723
1 706368 rs12029736 A G . PASS AF=0.514841 ES:SE:LP:AF:ID 0.000987951:0.00051196:1.26761:0.514841:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706127 ES:SE:LP:AF:ID 0.000738514:0.000600953:0.657577:0.706127:rs3115847
1 768253 rs2977608 A C . PASS AF=0.760056 ES:SE:LP:AF:ID 0.000786977:0.000488823:0.958607:0.760056:rs2977608
1 776546 rs12124819 A G . PASS AF=0.264841 ES:SE:LP:AF:ID -6.94556e-05:0.00054818:0.0457575:0.264841:rs12124819
1 798400 rs10900604 A G . PASS AF=0.207852 ES:SE:LP:AF:ID -0.000825132:0.000521357:0.958607:0.207852:rs10900604
1 798959 rs11240777 G A . PASS AF=0.207686 ES:SE:LP:AF:ID -0.000825516:0.000521582:0.958607:0.207686:rs11240777
1 808631 rs11240779 G A . PASS AF=0.770595 ES:SE:LP:AF:ID 0.000764464:0.00049575:0.920819:0.770595:rs11240779
1 808928 rs11240780 C T . PASS AF=0.770799 ES:SE:LP:AF:ID 0.000739841:0.00049654:0.853872:0.770799:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340117 ES:SE:LP:AF:ID -0.00037817:0.000704101:0.229148:0.340117:rs74461805
1 830181 rs28444699 A G . PASS AF=0.698145 ES:SE:LP:AF:ID 0.000346929:0.000470912:0.337242:0.698145:rs28444699
1 831489 rs4970385 C T . PASS AF=0.707001 ES:SE:LP:AF:ID 0.000248263:0.000462312:0.229148:0.707001:rs4970385
1 831909 rs9697642 C T . PASS AF=0.707061 ES:SE:LP:AF:ID 0.000251376:0.000462293:0.229148:0.707061:rs9697642
1 832066 rs9697380 G C . PASS AF=0.707246 ES:SE:LP:AF:ID 0.000240306:0.000462318:0.221849:0.707246:rs9697380
1 832318 rs4500250 C A . PASS AF=0.707279 ES:SE:LP:AF:ID 0.000238531:0.00046239:0.21467:0.707279:rs4500250
1 832398 rs4553118 T C . PASS AF=0.731781 ES:SE:LP:AF:ID 0.000199497:0.000475257:0.173925:0.731781:rs4553118
1 832918 rs28765502 T C . PASS AF=0.292745 ES:SE:LP:AF:ID -0.000241412:0.000462369:0.221849:0.292745:rs28765502
1 833223 rs13303211 C T . PASS AF=0.235304 ES:SE:LP:AF:ID -0.00012452:0.000492446:0.09691:0.235304:rs13303211
1 833302 rs28752186 C T . PASS AF=0.235307 ES:SE:LP:AF:ID -0.000125171:0.000492429:0.09691:0.235307:rs28752186
1 833641 rs28594623 T C . PASS AF=0.238287 ES:SE:LP:AF:ID -0.000150495:0.000490956:0.119186:0.238287:rs28594623
1 833824 rs28484835 T C . PASS AF=0.235315 ES:SE:LP:AF:ID -0.00012402:0.000492436:0.09691:0.235315:rs28484835
1 833927 rs28593608 T C . PASS AF=0.210986 ES:SE:LP:AF:ID -9.13201e-05:0.000512136:0.0655015:0.210986:rs28593608
1 834198 rs28385272 T C . PASS AF=0.21088 ES:SE:LP:AF:ID -8.90061e-05:0.000512223:0.0655015:0.21088:rs28385272
1 834832 rs4411087 G C . PASS AF=0.235788 ES:SE:LP:AF:ID -0.000110868:0.000492066:0.0861861:0.235788:rs4411087
1 834928 rs4422949 A G . PASS AF=0.21155 ES:SE:LP:AF:ID -8.28119e-05:0.000511453:0.0604807:0.21155:rs4422949
1 834999 rs28570054 G A . PASS AF=0.211501 ES:SE:LP:AF:ID -0.000100993:0.000511574:0.0757207:0.211501:rs28570054
1 835499 rs4422948 A G . PASS AF=0.239825 ES:SE:LP:AF:ID 4.04608e-05:0.000488625:0.0315171:0.239825:rs4422948
1 836529 rs28731045 C G . PASS AF=0.212097 ES:SE:LP:AF:ID -0.000115102:0.000510914:0.0861861:0.212097:rs28731045
1 836896 rs28705752 T C . PASS AF=0.268235 ES:SE:LP:AF:ID -3.15084e-05:0.000471262:0.0222764:0.268235:rs28705752
1 836924 rs72890788 G A . PASS AF=0.212096 ES:SE:LP:AF:ID -0.000116417:0.000510958:0.0861861:0.212096:rs72890788
1 838387 rs4970384 T C . PASS AF=0.213161 ES:SE:LP:AF:ID -0.00012048:0.000509872:0.091515:0.213161:rs4970384
1 838555 rs4970383 C A . PASS AF=0.244903 ES:SE:LP:AF:ID -4.83986e-05:0.000485284:0.0362122:0.244903:rs4970383
1 839103 rs28562941 A G . PASS AF=0.268683 ES:SE:LP:AF:ID -1.01065e-05:0.000471676:0.00877392:0.268683:rs28562941
1 840753 rs4970382 T C . PASS AF=0.398903 ES:SE:LP:AF:ID -6.28578e-06:0.000426025:0.00436481:0.398903:rs4970382
1 841085 rs1574243 C G . PASS AF=0.235888 ES:SE:LP:AF:ID -4.80809e-05:0.000495808:0.0362122:0.235888:rs1574243
1 842013 rs7419119 T G . PASS AF=0.214133 ES:SE:LP:AF:ID -1.63647e-05:0.000510168:0.0132283:0.214133:rs7419119
1 842362 rs28540380 C T . PASS AF=0.234047 ES:SE:LP:AF:ID -7.62512e-07:0.000502868:-0:0.234047:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362603 ES:SE:LP:AF:ID -0.000495469:0.000528749:0.455932:0.362603:rs11516185
1 844300 rs61769713 C G . PASS AF=0.820001 ES:SE:LP:AF:ID -0.000238163:0.000543317:0.180456:0.820001:rs61769713
1 845283 rs7366404 G T . PASS AF=0.815527 ES:SE:LP:AF:ID -0.0002463:0.000538291:0.187087:0.815527:rs7366404
1 845635 rs117086422 C T . PASS AF=0.204997 ES:SE:LP:AF:ID 0.000235512:0.000517581:0.187087:0.204997:rs117086422
1 845938 rs57760052 G A . PASS AF=0.210722 ES:SE:LP:AF:ID 0.000146009:0.000512326:0.107905:0.210722:rs57760052
1 846078 rs28612348 C T . PASS AF=0.196275 ES:SE:LP:AF:ID 0.000249412:0.000525595:0.19382:0.196275:rs28612348
1 846338 rs4970334 A G . PASS AF=0.814895 ES:SE:LP:AF:ID -0.000252342:0.0005379:0.19382:0.814895:rs4970334
1 846398 rs58781670 G A . PASS AF=0.204206 ES:SE:LP:AF:ID 0.000281105:0.000519391:0.229148:0.204206:rs58781670
1 846489 rs4970333 T C . PASS AF=0.814979 ES:SE:LP:AF:ID -0.00025033:0.000538316:0.19382:0.814979:rs4970333
1 846808 rs4475691 C T . PASS AF=0.197892 ES:SE:LP:AF:ID 0.000228036:0.000523987:0.180456:0.197892:rs4475691