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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"file_date": "2019-10-26T09:27:59.669129",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003045/EBI-a-GCST003045_data.json --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/hg38/hg38.fa; 1.1.1",
"reference": "file:/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/b37/human_g1k_v37.fasta",
"bcftools_annotateVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
"bcftools_annotateCommand": "annotate -a /mnt/storage/home/gh13047/mr-eve/vcf-reference-datasets/dbsnp/dbsnp.v153.b37.vcf.gz -c ID -o /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003045/EBI-a-GCST003045.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003045/EBI-a-GCST003045_data.vcf.gz; Date=Sat Oct 26 09:42:37 2019",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST003045/ebi-a-GCST003045.vcf.gz; Date=Sat May 9 20:59:11 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/dev/ebi_gwas_import/processed/EBI-a-GCST003045/EBI-a-GCST003045.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003045/ldsc.txt \
--snplist /data/ref/snplist.gz \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Sat Oct 26 10:04:00 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003045/EBI-a-GCST003045.vcf.gz ...
and extracting SNPs specified in /data/ref/snplist.gz ...
Traceback (most recent call last):
File "./ldsc/ldsc.py", line 647, in <module>
sumstats.estimate_h2(args, log)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
args, log, args.h2)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 246, in _read_ld_sumstats
sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 165, in _read_sumstats
sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna, slh=args.snplist)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 85, in sumstats
x = read_vcf(fh, alleles, slh)
File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 161, in read_vcf
with gzip.open(slh) as f:
File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 34, in open
return GzipFile(filename, mode, compresslevel)
File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 94, in __init__
fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
IOError: [Errno 2] No such file or directory: '/data/ref/snplist.gz'
Analysis finished at Sat Oct 26 10:04:01 2019
Total time elapsed: 0.54sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9368,
"inflation_factor": 2.5662,
"mean_EFFECT": -0.0007,
"n": "-Inf",
"n_snps": 110944,
"n_clumped_hits": 87,
"n_p_sig": 3326,
"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": 756,
"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": "NA",
"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 | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| is_snpid_non_unique | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| n_p_sig | TRUE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | TRUE |
| ldsc_intercept_beta | TRUE |
| 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 | 5 | 25 | 0 | 110562 | 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 | 110592 | 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 | 8.078921e+00 | 5.763822e+00 | 1.00000e+00 | 3.000000e+00 | 6.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.924488e+07 | 5.782916e+07 | 7.82320e+04 | 3.292453e+07 | 6.230136e+07 | 1.176952e+08 | 2.492107e+08 | ▇▅▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.720000e-04 | 5.593840e-02 | -1.10304e+00 | -2.272750e-02 | -4.284000e-04 | 2.210290e-02 | 1.058540e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.683220e-02 | 2.952310e-02 | 1.23244e-02 | 1.375340e-02 | 1.704260e-02 | 2.679780e-02 | 9.404370e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.520886e-01 | 3.159340e-01 | 0.00000e+00 | 4.531820e-02 | 2.799210e-01 | 6.179015e-01 | 9.999573e-01 | ▇▃▂▂▂ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.520886e-01 | 3.159340e-01 | 0.00000e+00 | 4.531820e-02 | 2.799220e-01 | 6.179018e-01 | 9.999573e-01 | ▇▃▂▂▂ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.891431e-01 | 2.630214e-01 | 1.49000e-05 | 6.716750e-02 | 2.050000e-01 | 4.579000e-01 | 9.999700e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 756 | 0.9931641 | NA | NA | NA | NA | NA | NA | NA | 2.898029e-01 | 2.573625e-01 | 1.99700e-04 | 7.627800e-02 | 2.110620e-01 | 4.522760e-01 | 1.000000e+00 | ▇▃▂▂▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1118275 | rs61733845 | C | T | 0.0192020 | 0.0320018 | 0.5484866 | 0.5484871 | 0.044160 | 0.185703 | NA |
| 1 | 1120431 | rs1320571 | G | A | 0.0098475 | 0.0314857 | 0.7544614 | 0.7544620 | 0.046530 | 0.185304 | NA |
| 1 | 1135242 | rs9729550 | A | C | 0.0433147 | 0.0147194 | 0.0032537 | 0.0032537 | 0.258700 | 0.551917 | NA |
| 1 | 1140435 | rs1815606 | G | T | 0.0405531 | 0.0142165 | 0.0043371 | 0.0043372 | 0.317900 | 0.712061 | NA |
| 1 | 1163804 | rs7515488 | C | T | 0.0906364 | 0.0173110 | 0.0000002 | 0.0000002 | 0.148600 | 0.186901 | NA |
| 1 | 1165310 | rs11260562 | G | A | 0.0903556 | 0.0264339 | 0.0006304 | 0.0006304 | 0.055600 | 0.101837 | NA |
| 1 | 1173611 | rs6697886 | G | A | 0.0994688 | 0.0178792 | 0.0000000 | 0.0000000 | 0.137300 | 0.220647 | NA |
| 1 | 1194804 | rs11804831 | T | C | 0.0910417 | 0.0160841 | 0.0000000 | 0.0000000 | 0.185600 | 0.685903 | NA |
| 1 | 1218086 | rs6603788 | C | T | 0.0747598 | 0.0230269 | 0.0011678 | 0.0011678 | 0.076970 | 0.469449 | NA |
| 1 | 1227897 | rs3737721 | A | G | 0.0517328 | 0.0945477 | 0.5842679 | 0.5842682 | 0.002436 | 0.228035 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50966914 | rs470119 | T | C | -0.0557607 | 0.0127581 | 0.0000124 | 0.0000124 | 0.609800 | 0.5547120 | NA |
| 22 | 50971752 | rs131794 | A | C | -0.0685185 | 0.0151700 | 0.0000063 | 0.0000063 | 0.791900 | 0.8326680 | NA |
| 22 | 50988193 | rs131779 | A | G | -0.0208810 | 0.0136947 | 0.1273210 | 0.1273214 | 0.657900 | 0.5802720 | NA |
| 22 | 50999182 | rs140518 | C | T | -0.0408692 | 0.0137477 | 0.0029509 | 0.0029509 | 0.696600 | 0.7655750 | NA |
| 22 | 51078251 | rs4040041 | C | T | 0.0088169 | 0.0132207 | 0.5048345 | 0.5048343 | 0.373100 | 0.4666530 | NA |
| 22 | 51094926 | rs9616810 | C | T | 0.0411207 | 0.0151862 | 0.0067736 | 0.0067737 | 0.218600 | 0.2224440 | NA |
| 22 | 51105556 | rs9616812 | C | T | -0.0223395 | 0.0128789 | 0.0828171 | 0.0828150 | 0.483200 | 0.3628190 | NA |
| 22 | 51109992 | rs9628185 | T | C | -0.0289028 | 0.0130478 | 0.0267498 | 0.0267500 | 0.484300 | 0.4053510 | NA |
| 22 | 51134186 | rs8135777 | A | G | -0.4247410 | 0.3102890 | 0.1710437 | 0.1710443 | 0.000446 | 0.0273562 | NA |
| 22 | 51156666 | rs9628187 | C | T | -0.0061069 | 0.0163022 | 0.7079523 | 0.7079536 | 0.203200 | 0.1299920 | NA |
bcf preview
1 1118275 rs61733845 C T . PASS AF=0.04416 ES:SE:LP:AF:ID 0.019202:0.0320018:0.260834:0.04416:rs61733845
1 1120431 rs1320571 G A . PASS AF=0.04653 ES:SE:LP:AF:ID 0.00984751:0.0314857:0.122363:0.04653:rs1320571
1 1135242 rs9729550 A C . PASS AF=0.2587 ES:SE:LP:AF:ID 0.0433147:0.0147194:2.48762:0.2587:rs9729550
1 1140435 rs1815606 G T . PASS AF=0.3179 ES:SE:LP:AF:ID 0.0405531:0.0142165:2.3628:0.3179:rs1815606
1 1163804 rs7515488 C T . PASS AF=0.1486 ES:SE:LP:AF:ID 0.0906364:0.017311:6.78439:0.1486:rs7515488
1 1165310 rs11260562 G A . PASS AF=0.0556 ES:SE:LP:AF:ID 0.0903556:0.0264339:3.20036:0.0556:rs11260562
1 1173611 rs6697886 G A . PASS AF=0.1373 ES:SE:LP:AF:ID 0.0994688:0.0178792:7.57742:0.1373:rs6697886
1 1194804 rs11804831 T C . PASS AF=0.1856 ES:SE:LP:AF:ID 0.0910417:0.0160841:7.82086:0.1856:rs11804831
1 1218086 rs6603788 C T . PASS AF=0.07697 ES:SE:LP:AF:ID 0.0747598:0.0230269:2.93262:0.07697:rs6603788
1 1227897 rs3737721 A G . PASS AF=0.002436 ES:SE:LP:AF:ID 0.0517328:0.0945477:0.233388:0.002436:rs3737721
1 1231656 rs1749951 G A . PASS AF=0.04028 ES:SE:LP:AF:ID 0.062385:0.0329014:1.23699:0.04028:rs1749951
1 1233941 rs1739855 T C . PASS AF=0.07333 ES:SE:LP:AF:ID 0.0662944:0.0237983:2.27233:0.07333:rs1739855
1 1241529 rs1536168 A G . PASS AF=0.95394 ES:SE:LP:AF:ID -0.0688144:0.0302075:1.64353:0.95394:rs1536168
1 1242468 rs2274264 G A . PASS AF=0.002467 ES:SE:LP:AF:ID 0.0503525:0.100315:0.210625:0.002467:rs2274264
1 1247494 rs12103 T C . PASS AF=0.8166 ES:SE:LP:AF:ID -0.0995588:0.0162944:9.00158:0.8166:rs12103
1 1249187 rs12142199 G A . PASS AF=0.8026 ES:SE:LP:AF:ID -0.0983383:0.0159317:9.17249:0.8026:rs12142199
1 1254255 rs62623580 G A . PASS AF=0.007606 ES:SE:LP:AF:ID 0.115117:0.0702762:0.993929:0.007606:rs62623580
1 1335790 rs1240708 A G . PASS AF=0.1737 ES:SE:LP:AF:ID 0.0958588:0.0164435:8.25517:0.1737:rs1240708
1 1493727 rs880051 G A . PASS AF=0.232 ES:SE:LP:AF:ID 0.0561188:0.0148341:3.80998:0.232:rs880051
1 1497824 rs2296716 C T . PASS AF=0.1208 ES:SE:LP:AF:ID 0.0331484:0.019684:1.03538:0.1208:rs2296716
1 1611995 rs4074196 A G . PASS AF=0.4414 ES:SE:LP:AF:ID -0.0129163:0.0144783:0.429071:0.4414:rs4074196
1 1706160 rs7531583 A G . PASS AF=0.77 ES:SE:LP:AF:ID -0.0411673:0.0147257:2.28566:0.77:rs7531583
1 1721479 rs2272908 C T . PASS AF=0.4974 ES:SE:LP:AF:ID -0.0583209:0.0125715:5.45608:0.4974:rs2272908
1 1723031 rs9660180 G A . PASS AF=0.4974 ES:SE:LP:AF:ID -0.0585612:0.012573:5.49515:0.4974:rs9660180
1 1781220 rs6681938 T C . PASS AF=0.2996 ES:SE:LP:AF:ID -0.04139:0.0142526:2.4337:0.2996:rs6681938
1 1838516 rs2377037 C A . PASS AF=0.2747 ES:SE:LP:AF:ID 0.0408188:0.0142903:2.36807:0.2747:rs2377037
1 1840038 rs2474461 T C . PASS AF=0.95331 ES:SE:LP:AF:ID -0.121137:0.0293899:4.42476:0.95331:rs2474461
1 1853288 rs1884454 G T . PASS AF=0.2694 ES:SE:LP:AF:ID 0.0391229:0.0143493:2.1937:0.2694:rs1884454
1 1855319 rs2295362 C T . PASS AF=0.0452 ES:SE:LP:AF:ID 0.0744716:0.0304297:1.84189:0.0452:rs2295362
1 1871337 rs16824543 G A . PASS AF=0.04241 ES:SE:LP:AF:ID 0.0706185:0.0313929:1.61118:0.04241:rs16824543
1 1873625 rs12758705 G A . PASS AF=0.2651 ES:SE:LP:AF:ID 0.037465:0.0145693:1.99458:0.2651:rs12758705
1 1881070 rs4648596 G A . PASS AF=0.04073 ES:SE:LP:AF:ID 0.0764288:0.0329815:1.68854:0.04073:rs4648596
1 1888193 rs3820011 C A . PASS AF=0.2698 ES:SE:LP:AF:ID 0.0342737:0.0145362:1.73559:0.2698:rs3820011
1 2024064 rs2459994 C T . PASS AF=0.1824 ES:SE:LP:AF:ID 0.0207617:0.0169543:0.656127:0.1824:rs2459994
1 2146966 rs7512482 T C . PASS AF=0.1674 ES:SE:LP:AF:ID -0.021892:0.0178823:0.655871:0.1674:rs7512482
1 2202774 rs6673129 C T . PASS AF=0.1651 ES:SE:LP:AF:ID 0.0231766:0.0179545:0.70607:0.1651:rs6673129
1 2229478 rs12562937 C T . PASS AF=0.1998 ES:SE:LP:AF:ID 0.0451627:0.0164414:2.22066:0.1998:rs12562937
1 2283896 rs2840528 A G . PASS AF=0.4158 ES:SE:LP:AF:ID 0.0162644:0.0131999:0.661766:0.4158:rs2840528
1 2290143 rs34587196 G A . PASS AF=0.01202 ES:SE:LP:AF:ID 0.0886126:0.0597007:0.860957:0.01202:rs34587196
1 2404256 rs2494626 C T . PASS AF=0.2884 ES:SE:LP:AF:ID -0.00187853:0.0153883:0.0443895:0.2884:rs2494626
1 2407781 rs78504402 C T . PASS AF=0.06862 ES:SE:LP:AF:ID -0.0437856:0.0270384:0.977309:0.06862:rs78504402
1 2408471 rs115996655 G A . PASS AF=0.007814 ES:SE:LP:AF:ID -0.002633:0.0681198:0.0136012:0.007814:rs115996655
1 2408834 rs11588930 G A . PASS AF=0.101 ES:SE:LP:AF:ID 0.0279766:0.0232268:0.641306:0.101:rs11588930
1 2409892 rs12727342 A G . PASS AF=0.6229 ES:SE:LP:AF:ID 0.0354679:0.0138191:1.98841:0.6229:rs12727342
1 2410789 rs11799501 C T . PASS AF=0.5834 ES:SE:LP:AF:ID 0.0444544:0.0137203:2.92261:0.5834:rs11799501
1 2412293 rs12731186 C T . PASS AF=0.1211 ES:SE:LP:AF:ID 0.0329436:0.0196216:1.03075:0.1211:rs12731186
1 2413166 rs115810747 A G . PASS AF=0.04094 ES:SE:LP:AF:ID 0.0181816:0.0355888:0.215073:0.04094:rs115810747
1 2414928 rs4995304 G A . PASS AF=0.5732 ES:SE:LP:AF:ID 0.051575:0.0136601:3.7969:0.5732:rs4995304
1 2415108 rs114637672 T C . PASS AF=0.02017 ES:SE:LP:AF:ID -0.0883633:0.0474919:1.20203:0.02017:rs114637672
1 2415497 rs61763948 T C . PASS AF=0.5737 ES:SE:LP:AF:ID 0.0532894:0.0135465:4.07774:0.5737:rs61763948