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/798d6fe0-2256-4139-9ff6-1440feae5e32/call-vcf/inputs/562856130/upload.txt.gz --id ieu-b-15 --json /data/cromwell-executions/qc/798d6fe0-2256-4139-9ff6-1440feae5e32/call-vcf/inputs/562856130/ieu-b-15_data.json --ref /data/cromwell-executions/qc/798d6fe0-2256-4139-9ff6-1440feae5e32/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/798d6fe0-2256-4139-9ff6-1440feae5e32/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-15/ieu-b-15.vcf.gz --rm_chr_prefix --cohort_cases 3070 --cohort_controls 29677; 1.2.1",
"file_date": "2020-06-26T15:26:47.525205",
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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/798d6fe0-2256-4139-9ff6-1440feae5e32/call-ldsc/inputs/562856130/ieu-b-15.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-15/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Fri Jun 26 15:33:13 2020
Reading summary statistics from /data/cromwell-executions/qc/798d6fe0-2256-4139-9ff6-1440feae5e32/call-ldsc/inputs/562856130/ieu-b-15.vcf.gz ...
Read summary statistics for 4987251 SNPs.
Dropped 11902 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/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 /data/ref/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 950985 SNPs remain.
After merging with regression SNP LD, 950985 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0028 (0.0171)
Lambda GC: 1.0937
Mean Chi^2: 1.0912
Intercept: 1.0933 (0.0084)
Ratio: 1.0224 (0.0921)
Analysis finished at Fri Jun 26 15:33:51 2020
Total time elapsed: 37.33sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9302,
"inflation_factor": 1.0966,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 4987264,
"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": 29961,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 950985,
"ldsc_nsnp_merge_regression_ld": 950985,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0933,
"ldsc_intercept_se": 0.0084,
"ldsc_lambda_gc": 1.0937,
"ldsc_mean_chisq": 1.0912,
"ldsc_ratio": 1.023
}
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 | 1 | 0.9999998 | 3 | 35 | 0 | 4987260 | 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 | 4987264 | 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 | 8.645456e+00 | 5.871760e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.30000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.980134e+07 | 5.500905e+07 | 3.30120e+04 | 3.479413e+07 | 7.130747e+07 | 1.144010e+08 | 2.49219e+08 | ▇▇▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.293000e-04 | 4.729200e-03 | -4.84791e-02 | -2.460400e-03 | 1.020000e-04 | 2.643200e-03 | 5.32969e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.110500e-03 | 1.967900e-03 | 2.10030e-03 | 2.751000e-03 | 3.313500e-03 | 4.694600e-03 | 1.33366e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.864542e-01 | 2.917252e-01 | 3.00000e-07 | 2.300001e-01 | 4.799997e-01 | 7.400005e-01 | 1.00000e+00 | ▇▇▇▇▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.864558e-01 | 2.917010e-01 | 3.00000e-07 | 2.302116e-01 | 4.821082e-01 | 7.386856e-01 | 1.00000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.075316e-01 | 2.583824e-01 | 1.00004e-02 | 8.979180e-02 | 2.269570e-01 | 4.777680e-01 | 9.89988e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 29961 | 0.9939925 | NA | NA | NA | NA | NA | NA | NA | 3.087296e-01 | 2.469743e-01 | 1.99700e-04 | 1.044330e-01 | 2.382190e-01 | 4.696490e-01 | 1.00000e+00 | ▇▅▃▂▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1029805 | rs6689308 | A | G | -0.0042738 | 0.0035153 | 0.2200002 | 0.2240636 | 0.1626170 | 0.3156950 | NA |
| 1 | 1030565 | rs6687776 | C | T | -0.0035776 | 0.0035100 | 0.3100002 | 0.3080854 | 0.1625690 | 0.3067090 | NA |
| 1 | 1030633 | rs6678318 | G | A | -0.0035874 | 0.0035101 | 0.3100002 | 0.3067717 | 0.1625750 | 0.3065100 | NA |
| 1 | 1031973 | rs9651270 | C | T | -0.0032478 | 0.0035669 | 0.3599996 | 0.3625369 | 0.1593910 | 0.3107030 | NA |
| 1 | 1033596 | rs6604964 | T | C | -0.0032456 | 0.0035691 | 0.3599996 | 0.3631630 | 0.1592550 | 0.3117010 | NA |
| 1 | 1033670 | rs6604966 | T | C | -0.0034105 | 0.0035712 | 0.3400001 | 0.3395647 | 0.1591780 | 0.3158950 | NA |
| 1 | 1033680 | rs6604967 | T | A | -0.0033279 | 0.0035710 | 0.3500000 | 0.3513689 | 0.1591790 | 0.3117010 | NA |
| 1 | 1033994 | rs6698368 | C | T | -0.0033114 | 0.0035702 | 0.3500000 | 0.3536638 | 0.1592430 | 0.3115020 | NA |
| 1 | 1034200 | rs77977351 | T | C | -0.0032189 | 0.0035703 | 0.3700002 | 0.3672846 | 0.1592610 | 0.3115020 | NA |
| 1 | 1036601 | rs72910156 | C | T | -0.0043245 | 0.0085908 | 0.6100002 | 0.6146957 | 0.0233091 | 0.0399361 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 139314056 | rs5955415 | C | G | -0.0000435 | 0.0036469 | 0.9900000 | 0.9904776 | 0.096204 | 0.125033 | NA |
| 23 | 139314798 | rs62609008 | C | A | 0.0004330 | 0.0036611 | 0.9100000 | 0.9058621 | 0.095354 | 0.106755 | NA |
| 23 | 139315107 | rs62609010 | T | A | 0.0006119 | 0.0036582 | 0.8700001 | 0.8671614 | 0.095470 | 0.107020 | NA |
| 23 | 139316769 | rs73230748 | T | C | 0.0003861 | 0.0036520 | 0.9199999 | 0.9158041 | 0.095831 | 0.121060 | NA |
| 23 | 139317055 | rs62609011 | C | A | 0.0003620 | 0.0036514 | 0.9199999 | 0.9210353 | 0.095866 | 0.116026 | NA |
| 23 | 139317165 | rs28877369 | C | G | 0.0002460 | 0.0036534 | 0.9500000 | 0.9463227 | 0.095778 | 0.116291 | NA |
| 23 | 139317337 | rs55757628 | A | G | 0.0002388 | 0.0036582 | 0.9500000 | 0.9479605 | 0.095484 | 0.107285 | NA |
| 23 | 139319949 | rs140616281 | A | G | 0.0005025 | 0.0036607 | 0.8900000 | 0.8908217 | 0.095444 | 0.116291 | NA |
| 23 | 139320136 | rs76621315 | C | G | 0.0000734 | 0.0036506 | 0.9800000 | 0.9839567 | 0.095958 | 0.114437 | NA |
| 23 | 139325995 | rs62609013 | G | A | 0.0000565 | 0.0036544 | 0.9900000 | 0.9876697 | 0.095753 | 0.101987 | NA |
bcf preview
1 1029805 rs891281851 A G . PASS AF=0.162617 ES:SE:LP:AF:ID -0.00427383:0.00351526:0.657577:0.162617:rs891281851
1 1030565 rs6687776 C T . PASS AF=0.162569 ES:SE:LP:AF:ID -0.00357761:0.00351005:0.508638:0.162569:rs6687776
1 1030633 rs6678318 G A . PASS AF=0.162575 ES:SE:LP:AF:ID -0.00358737:0.00351008:0.508638:0.162575:rs6678318
1 1031973 rs9651270 C T . PASS AF=0.159391 ES:SE:LP:AF:ID -0.00324782:0.00356691:0.443698:0.159391:rs9651270
1 1033596 rs6604964 T C . PASS AF=0.159255 ES:SE:LP:AF:ID -0.00324555:0.00356907:0.443698:0.159255:rs6604964
1 1033670 rs1370950991 T C . PASS AF=0.159178 ES:SE:LP:AF:ID -0.00341054:0.00357115:0.468521:0.159178:rs1370950991
1 1033680 rs1370950991 T A . PASS AF=0.159179 ES:SE:LP:AF:ID -0.00332793:0.00357098:0.455932:0.159179:rs1370950991
1 1033994 rs6698368 C T . PASS AF=0.159243 ES:SE:LP:AF:ID -0.00331141:0.00357023:0.455932:0.159243:rs6698368
1 1034200 rs77977351 T C . PASS AF=0.159261 ES:SE:LP:AF:ID -0.00321888:0.0035703:0.431798:0.159261:rs77977351
1 1036601 rs72910156 C T . PASS AF=0.0233091 ES:SE:LP:AF:ID -0.00432448:0.00859085:0.21467:0.0233091:rs72910156
1 1036860 rs11579922 A C . PASS AF=0.126353 ES:SE:LP:AF:ID -0.00506655:0.00391469:0.69897:0.126353:rs11579922
1 1036959 rs1162868282 T C . PASS AF=0.112757 ES:SE:LP:AF:ID -0.00466166:0.00407455:0.60206:0.112757:rs1162868282
1 1037303 rs11260592 T C . PASS AF=0.139358 ES:SE:LP:AF:ID -0.00543481:0.0037146:0.853872:0.139358:rs11260592
1 1037313 rs11260593 A G . PASS AF=0.139445 ES:SE:LP:AF:ID -0.00540132:0.00371452:0.823909:0.139445:rs11260593
1 1037367 rs11260594 G A . PASS AF=0.139343 ES:SE:LP:AF:ID -0.0055408:0.00371515:0.853872:0.139343:rs11260594
1 1038088 rs66622470 G C . PASS AF=0.139422 ES:SE:LP:AF:ID -0.00549121:0.00371291:0.853872:0.139422:rs66622470
1 1039098 rs11260595 C A . PASS AF=0.023594 ES:SE:LP:AF:ID -0.00634762:0.00852627:0.337242:0.023594:rs11260595
1 1039268 rs9329410 T C . PASS AF=0.139561 ES:SE:LP:AF:ID -0.00567984:0.00370975:0.886057:0.139561:rs9329410
1 1039817 rs1205065516 A G . PASS AF=0.139483 ES:SE:LP:AF:ID -0.00552941:0.00371157:0.853872:0.139483:rs1205065516
1 1040026 rs6671356 T C . PASS AF=0.139875 ES:SE:LP:AF:ID -0.00556195:0.00370427:0.886057:0.139875:rs6671356
1 1040472 rs6664124 C T . PASS AF=0.139784 ES:SE:LP:AF:ID -0.00551536:0.00370624:0.853872:0.139784:rs6664124
1 1040794 rs6687681 G A . PASS AF=0.139762 ES:SE:LP:AF:ID -0.00549642:0.00370656:0.853872:0.139762:rs6687681
1 1040824 rs6656379 T C . PASS AF=0.139914 ES:SE:LP:AF:ID -0.00548387:0.0037048:0.853872:0.139914:rs6656379
1 1040985 rs6697379 C G . PASS AF=0.139739 ES:SE:LP:AF:ID -0.00547155:0.00370666:0.853872:0.139739:rs6697379
1 1041700 rs6604968 A G . PASS AF=0.139888 ES:SE:LP:AF:ID -0.00543827:0.00370556:0.853872:0.139888:rs6604968
1 1041786 rs6604969 T C . PASS AF=0.139899 ES:SE:LP:AF:ID -0.0054378:0.0037052:0.853872:0.139899:rs6604969
1 1042483 rs12733365 C T . PASS AF=0.139759 ES:SE:LP:AF:ID -0.0055029:0.00370618:0.853872:0.139759:rs12733365
1 1042527 rs1486993720 G C . PASS AF=0.112551 ES:SE:LP:AF:ID -0.00472912:0.00407564:0.60206:0.112551:rs1486993720
1 1042673 rs897825316 C T . PASS AF=0.140897 ES:SE:LP:AF:ID -0.00527933:0.00371717:0.79588:0.140897:rs897825316
1 1042927 rs4970354 G T . PASS AF=0.139747 ES:SE:LP:AF:ID -0.00548811:0.00370593:0.853872:0.139747:rs4970354
1 1043053 rs4970355 A G . PASS AF=0.13968 ES:SE:LP:AF:ID -0.00542878:0.00370666:0.853872:0.13968:rs4970355
1 1045473 rs11586034 G A . PASS AF=0.111729 ES:SE:LP:AF:ID -0.00501011:0.00409128:0.657577:0.111729:rs11586034
1 1046073 rs11590188 C A . PASS AF=0.138081 ES:SE:LP:AF:ID -0.00595424:0.00372863:0.958607:0.138081:rs11590188
1 1046164 rs386627439 C T . PASS AF=0.139722 ES:SE:LP:AF:ID -0.00548097:0.00370644:0.853872:0.139722:rs386627439
1 1046717 rs34820586 G C . PASS AF=0.112609 ES:SE:LP:AF:ID -0.00479603:0.00407474:0.619789:0.112609:rs34820586
1 1046861 rs12723165 G A . PASS AF=0.1126 ES:SE:LP:AF:ID -0.00478755:0.0040747:0.619789:0.1126:rs12723165
1 1047374 rs12743678 T A . PASS AF=0.139374 ES:SE:LP:AF:ID -0.00537703:0.00371028:0.823909:0.139374:rs12743678
1 1048501 rs7518814 G A . PASS AF=0.138091 ES:SE:LP:AF:ID -0.00591167:0.00372867:0.958607:0.138091:rs7518814
1 1048955 rs4970405 A G . PASS AF=0.104326 ES:SE:LP:AF:ID -0.00631321:0.00421657:0.886057:0.104326:rs4970405
1 1048989 rs4970406 A G . PASS AF=0.113394 ES:SE:LP:AF:ID -0.00414247:0.00406805:0.508638:0.113394:rs4970406
1 1049083 rs4970407 C A . PASS AF=0.11256 ES:SE:LP:AF:ID -0.00451554:0.00407685:0.568636:0.11256:rs4970407
1 1049950 rs12726255 A G . PASS AF=0.138388 ES:SE:LP:AF:ID -0.00449607:0.00373081:0.638272:0.138388:rs12726255
1 1052946 rs12755848 G T . PASS AF=0.111522 ES:SE:LP:AF:ID -0.00497422:0.00410396:0.638272:0.111522:rs12755848
1 1053452 rs4970409 G A . PASS AF=0.111524 ES:SE:LP:AF:ID -0.00499597:0.00410367:0.657577:0.111524:rs4970409
1 1053670 rs4970410 G A . PASS AF=0.137213 ES:SE:LP:AF:ID -0.00491876:0.00375077:0.721246:0.137213:rs4970410
1 1053724 rs4970411 A G . PASS AF=0.136835 ES:SE:LP:AF:ID -0.00482397:0.00375646:0.69897:0.136835:rs4970411
1 1054552 rs12567697 G A . PASS AF=0.110596 ES:SE:LP:AF:ID -0.00485723:0.00412075:0.619789:0.110596:rs12567697
1 1054893 rs4970412 T C . PASS AF=0.137102 ES:SE:LP:AF:ID -0.0046279:0.00375281:0.657577:0.137102:rs4970412
1 1055653 rs34808604 C G . PASS AF=0.110742 ES:SE:LP:AF:ID -0.00484075:0.00411745:0.619789:0.110742:rs34808604
1 1055797 rs76744376 A G . PASS AF=0.11129 ES:SE:LP:AF:ID -0.0042303:0.00411119:0.522879:0.11129:rs76744376