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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"Gwas2VCF_command": "--data /data/cromwell-executions/qc/fdaae935-71a4-4807-99c7-7ddca4b175f6/call-vcf/inputs/-261043611/upload.txt.gz --id ieu-b-4902 --json /data/cromwell-executions/qc/fdaae935-71a4-4807-99c7-7ddca4b175f6/call-vcf/inputs/-261043611/ieu-b-4902_data.json --ref /data/cromwell-executions/qc/fdaae935-71a4-4807-99c7-7ddca4b175f6/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/fdaae935-71a4-4807-99c7-7ddca4b175f6/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-4902/ieu-b-4902.vcf.gz --alias alias.txt; 1.3.0",
"file_date": "2022-03-03T17:43:57.344578",
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}
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 /data/cromwell-executions/qc/fdaae935-71a4-4807-99c7-7ddca4b175f6/call-ldsc/inputs/-261043611/ieu-b-4902.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4902/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Thu Mar 3 17:52:13 2022
Reading summary statistics from /data/cromwell-executions/qc/fdaae935-71a4-4807-99c7-7ddca4b175f6/call-ldsc/inputs/-261043611/ieu-b-4902.vcf.gz ...
Read summary statistics for 0 SNPs.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
Traceback (most recent call last):
File "./ldsc/ldsc.py", line 647, in <module>
sumstats.estimate_h2(args, log)
File "/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
args, log, args.h2)
File "/ldsc/ldscore/sumstats.py", line 252, in _read_ld_sumstats
sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log)
File "/ldsc/ldscore/sumstats.py", line 238, in _merge_and_log
raise ValueError(msg.format(N=len(sumstats), F=noun))
ValueError: After merging with reference panel LD, 0 SNPs remain.
Analysis finished at Thu Mar 3 17:52:56 2022
Total time elapsed: 43.35sQC metrics
Metrics
Metrics
{
"af_correlation": 0.5873,
"inflation_factor": 0.9982,
"mean_EFFECT": -0,
"n": "-Inf",
"n_snps": 7002835,
"n_clumped_hits": 0,
"n_p_sig": 0,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 58038,
"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": 0,
"ldsc_nsnp_merge_regression_ld": "NA",
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": "NA",
"ldsc_intercept_se": "NA",
"ldsc_lambda_gc": "NA",
"ldsc_mean_chisq": "NA",
"ldsc_ratio": "NA"
}
Flags
| name | value |
|---|---|
| af_correlation | TRUE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | NA |
| n_p_sig | FALSE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | NA |
| ldsc_intercept_beta | NA |
| 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 | 72 | 0.9999897 | 3 | 47 | 0 | 7000417 | 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 | 7002835 | 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 | 8.618705e+00 | 5.749463e+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 | 7.915377e+07 | 5.617346e+07 | 2801.0000000 | 3.284028e+07 | 6.984962e+07 | 1.148257e+08 | 2.492225e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.510000e-05 | 1.084465e-01 | -71.0575000 | -3.446770e-02 | -4.000000e-07 | 3.430090e-02 | 1.002450e+02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.455500e-02 | 8.061870e-02 | 0.0288957 | 3.389390e-02 | 4.746200e-02 | 8.385130e-02 | 5.908850e+01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.997003e-01 | 2.890308e-01 | 0.0000002 | 2.488502e-01 | 5.003858e-01 | 7.499287e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.996261e-01 | 2.890753e-01 | 0.0000002 | 2.487216e-01 | 5.003133e-01 | 7.498984e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.660951e-01 | 1.470647e-01 | 0.0096754 | 3.513900e-02 | 1.158300e-01 | 2.772200e-01 | 5.000000e-01 | ▇▃▂▂▂ |
| numeric | AF_reference | 58038 | 0.9917122 | NA | NA | NA | NA | NA | NA | NA | 2.437210e-01 | 2.514234e-01 | 0.0000000 | 3.953670e-02 | 1.501600e-01 | 3.793930e-01 | 1.000000e+00 | ▇▂▂▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 568800 | rs375217967 | G | A | 0.4043580 | 0.432310 | 0.3497172 | 0.3496117 | 0.016076 | 0.0778754 | NA |
| 1 | 569004 | rs9285835 | T | C | -0.2975590 | 0.379522 | 0.4331058 | 0.4330188 | 0.012532 | NA | NA |
| 1 | 569624 | rs6594035 | T | C | 0.6912730 | 0.907846 | 0.4464759 | 0.4463925 | 0.011422 | NA | NA |
| 1 | 717587 | rs144155419 | G | A | 0.0454052 | 0.366664 | 0.9014590 | 0.9014473 | 0.016514 | 0.0045927 | NA |
| 1 | 724849 | rs12126395 | C | A | 0.6386450 | 0.337894 | 0.0588830 | 0.0587479 | 0.030038 | NA | NA |
| 1 | 731453 | rs186002080 | G | A | 0.0370046 | 0.355649 | 0.9171410 | 0.9171312 | 0.015304 | 0.0059904 | NA |
| 1 | 732989 | rs369030935 | C | T | -0.0088524 | 0.206598 | 0.9658260 | 0.9658222 | 0.021775 | 0.0093850 | NA |
| 1 | 755240 | rs181660517 | T | G | -0.9690680 | 0.502478 | 0.0539150 | 0.0537833 | 0.011824 | 0.0025958 | NA |
| 1 | 769885 | rs200025137 | G | A | 0.2831470 | 0.746992 | 0.7046882 | 0.7046511 | 0.014613 | NA | NA |
| 1 | 770181 | rs146076599 | A | G | -0.3521900 | 0.483480 | 0.4664187 | 0.4663394 | 0.010521 | 0.0059904 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198569 | rs142671391 | G | C | 0.0477092 | 0.0697098 | 0.4937976 | 0.4937243 | 0.049827 | 0.1110220 | NA |
| 22 | 51198868 | rs185017671 | T | C | -0.1790450 | 0.1383590 | 0.1957830 | 0.1956443 | 0.012517 | 0.0045927 | NA |
| 22 | 51198998 | rs117815989 | C | A | -0.1797370 | 0.1383620 | 0.1940729 | 0.1939321 | 0.012531 | 0.0045927 | NA |
| 22 | 51208568 | rs148425445 | G | T | 0.0345051 | 0.0640416 | 0.5900869 | 0.5900304 | 0.055549 | 0.1160140 | NA |
| 22 | 51212614 | rs142823097 | C | T | 0.0796355 | 0.1453880 | 0.5839236 | 0.5838672 | 0.017949 | 0.0073882 | NA |
| 22 | 51217556 | rs187294336 | C | T | -0.2132690 | 0.1544120 | 0.1673709 | 0.1672271 | 0.010315 | 0.0023962 | NA |
| 22 | 51220517 | rs9616980 | C | G | 0.0651226 | 0.1963180 | 0.7401334 | 0.7401007 | 0.011741 | 0.0027955 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0247724 | 0.0652036 | 0.7040395 | 0.7040020 | 0.053551 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0214240 | 0.0657286 | 0.7444971 | 0.7444649 | 0.053086 | 0.0788738 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0373195 | 0.1375340 | 0.7861492 | 0.7861238 | 0.020104 | NA | NA |
bcf preview
1 568800 rs375217967 G A . PASS AF=0.016076 ES:SE:LP:AF:ID 0.404358:0.43231:0.456283:0.016076:rs375217967
1 569004 rs9285835 T C . PASS AF=0.012532 ES:SE:LP:AF:ID -0.297559:0.379522:0.363406:0.012532:rs9285835
1 569624 rs6594035 T C . PASS AF=0.011422 ES:SE:LP:AF:ID 0.691273:0.907846:0.350202:0.011422:rs6594035
1 717587 rs144155419 G A . PASS AF=0.016514 ES:SE:LP:AF:ID 0.0454052:0.366664:0.045054:0.016514:rs144155419
1 724849 rs12126395 C A . PASS AF=0.030038 ES:SE:LP:AF:ID 0.638645:0.337894:1.23001:0.030038:rs12126395
1 731453 rs186002080 G A . PASS AF=0.015304 ES:SE:LP:AF:ID 0.0370046:0.355649:0.0375639:0.015304:rs186002080
1 732989 rs369030935 C T . PASS AF=0.021775 ES:SE:LP:AF:ID -0.00885243:0.206598:0.0151011:0.021775:rs369030935
1 755240 rs181660517 T G . PASS AF=0.011824 ES:SE:LP:AF:ID -0.969068:0.502478:1.26829:0.011824:rs181660517
1 769885 rs200025137 G A . PASS AF=0.014613 ES:SE:LP:AF:ID 0.283147:0.746992:0.152003:0.014613:rs200025137
1 770181 rs146076599 A G . PASS AF=0.010521 ES:SE:LP:AF:ID -0.35219:0.48348:0.331224:0.010521:rs146076599
1 772927 rs116390263 C T . PASS AF=0.016549 ES:SE:LP:AF:ID 0.479772:0.490157:0.484414:0.016549:rs116390263
1 773124 rs142049927 T C . PASS AF=0.013657 ES:SE:LP:AF:ID -0.450736:0.746251:0.262881:0.013657:rs142049927
1 773136 rs146709693 G T . PASS AF=0.013816 ES:SE:LP:AF:ID -0.51989:0.745095:0.313892:0.013816:rs146709693
1 774760 rs1387547637 C T . PASS AF=0.012828 ES:SE:LP:AF:ID 0.00645912:0.428562:0.00525361:0.012828:rs1387547637
1 775125 rs147281566 C T . PASS AF=0.012821 ES:SE:LP:AF:ID 0.251705:0.580474:0.177434:0.012821:rs147281566
1 776556 rs151160018 C T . PASS AF=0.010383 ES:SE:LP:AF:ID -1.19624:0.746046:0.962637:0.010383:rs151160018
1 782721 rs185280546 G A . PASS AF=0.012589 ES:SE:LP:AF:ID -0.793599:0.455714:1.08751:0.012589:rs185280546
1 783071 rs142849724 C T . PASS AF=0.017654 ES:SE:LP:AF:ID -0.0817466:0.381152:0.0808178:0.017654:rs142849724
1 783193 rs145767270 G C . PASS AF=0.012227 ES:SE:LP:AF:ID -0.812224:0.462411:1.10157:0.012227:rs145767270
1 783194 rs138555831 G T . PASS AF=0.01225 ES:SE:LP:AF:ID -0.812844:0.463148:1.10021:0.01225:rs138555831
1 787173 rs115093905 G T . PASS AF=0.030501 ES:SE:LP:AF:ID 0.24421:0.345465:0.319028:0.030501:rs115093905
1 793947 rs2519031 A G . PASS AF=0.020591 ES:SE:LP:AF:ID -0.351915:0.427345:0.386877:0.020591:rs2519031
1 799879 rs149363311 C T . PASS AF=0.012097 ES:SE:LP:AF:ID 0.333872:0.268958:0.668346:0.012097:rs149363311
1 801536 rs79373928 T G . PASS AF=0.013709 ES:SE:LP:AF:ID -0.395382:0.455293:0.41424:0.013709:rs79373928
1 807994 rs192742681 C T . PASS AF=0.016807 ES:SE:LP:AF:ID 0.145169:0.297535:0.203657:0.016807:rs192742681
1 810777 rs143204076 C G . PASS AF=0.022274 ES:SE:LP:AF:ID -0.453844:0.354281:0.698271:0.022274:rs143204076
1 812670 rs4970340 A C . PASS AF=0.011804 ES:SE:LP:AF:ID 0.194755:0.296967:0.290718:0.011804:rs4970340
1 814870 rs149628520 T G . PASS AF=0.012136 ES:SE:LP:AF:ID -1.47207:0.66449:1.5712:0.012136:rs149628520
1 821452 rs138748533 G A . PASS AF=0.012585 ES:SE:LP:AF:ID -0.242394:0.792264:0.119374:0.012585:rs138748533
1 828539 rs149168804 A T . PASS AF=0.019671 ES:SE:LP:AF:ID -0.703529:0.328863:1.48777:0.019671:rs149168804
1 832359 rs9697294 C T . PASS AF=0.020904 ES:SE:LP:AF:ID 0.055447:0.162957:0.134483:0.020904:rs9697294
1 834056 rs28482280 A C . PASS AF=0.02293 ES:SE:LP:AF:ID 0.0867512:0.156326:0.237326:0.02293:rs28482280
1 834263 rs143031236 C T . PASS AF=0.019725 ES:SE:LP:AF:ID 0.0664767:0.165914:0.161968:0.019725:rs143031236
1 834956 rs7518581 G A . PASS AF=0.022651 ES:SE:LP:AF:ID 0.0970907:0.158:0.26845:0.022651:rs7518581
1 836684 rs74460547 G A . PASS AF=0.021134 ES:SE:LP:AF:ID -0.0594548:0.242279:0.0935728:0.021134:rs74460547
1 837214 rs72631888 G C . PASS AF=0.016147 ES:SE:LP:AF:ID 0.500317:0.242629:1.40537:0.016147:rs72631888
1 837657 rs149737509 G C . PASS AF=0.018657 ES:SE:LP:AF:ID 0.157096:0.198845:0.366947:0.018657:rs149737509
1 838665 rs28678693 T C . PASS AF=0.022006 ES:SE:LP:AF:ID 0.0824025:0.161949:0.214007:0.022006:rs28678693
1 838732 rs952157075 G A . PASS AF=0.021909 ES:SE:LP:AF:ID 0.0785181:0.162079:0.201957:0.021909:rs952157075
1 838890 rs28437697 A G . PASS AF=0.022031 ES:SE:LP:AF:ID 0.084028:0.161819:0.219233:0.022031:rs28437697
1 838916 rs28539852 A T . PASS AF=0.0222 ES:SE:LP:AF:ID 0.0824251:0.16173:0.21442:0.0222:rs28539852
1 839461 rs78238606 T C . PASS AF=0.021873 ES:SE:LP:AF:ID 0.0737662:0.162228:0.18751:0.021873:rs78238606
1 839495 rs113341842 G T . PASS AF=0.021822 ES:SE:LP:AF:ID 0.072321:0.162238:0.183223:0.021822:rs113341842
1 839528 rs115928763 A G . PASS AF=0.021885 ES:SE:LP:AF:ID 0.0745047:0.162312:0.18959:0.021885:rs115928763
1 839529 rs386627407 T G . PASS AF=0.021873 ES:SE:LP:AF:ID 0.0767388:0.162292:0.196289:0.021873:rs386627407
1 846236 rs138796872 G A . PASS AF=0.011191 ES:SE:LP:AF:ID -1.30477:0.685296:1.24374:0.011191:rs138796872
1 846465 rs60454217 C T . PASS AF=0.019727 ES:SE:LP:AF:ID -0.196171:0.329749:0.258088:0.019727:rs60454217
1 846543 rs79396034 G T . PASS AF=0.017995 ES:SE:LP:AF:ID -0.329859:0.353821:0.454323:0.017995:rs79396034
1 848223 rs4246504 G A . PASS AF=0.013842 ES:SE:LP:AF:ID 0.706555:0.207445:3.17301:0.013842:rs4246504
1 853394 rs142336952 G A . PASS AF=0.013512 ES:SE:LP:AF:ID 0.0880365:0.211996:0.168781:0.013512:rs142336952