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": "2019-10-26T09:30:51.343573",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006094/EBI-a-GCST006094_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",
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"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-GCST006094/EBI-a-GCST006094.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006094/EBI-a-GCST006094_data.vcf.gz; Date=Sat Oct 26 09:44:13 2019",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006094/ebi-a-GCST006094.vcf.gz; Date=Sun May 10 10:44:51 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-GCST006094/EBI-a-GCST006094.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-GCST006094/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 10:06:33 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006094/EBI-a-GCST006094.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 10:06:36 2019
Total time elapsed: 3.21sQC metrics
Metrics
Metrics
{
"af_correlation": 0.848,
"inflation_factor": 1.0552,
"mean_EFFECT": -0.0009,
"n": "-Inf",
"n_snps": 558247,
"n_clumped_hits": 5,
"n_p_sig": 1995,
"n_mono": 0,
"n_ns": 4900,
"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": 5120,
"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 | FALSE |
| 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 | 4 | 38 | 0 | 556753 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 44 | 0 | 446 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 31 | 0 | 289 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 556754 | 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 | 8.794868e+00 | 6.068222e+00 | 1.000e+00 | 3.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.873843e+07 | 5.618585e+07 | 3.587e+03 | 3.133649e+07 | 7.104706e+07 | 1.136811e+08 | 2.492107e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -8.864000e-04 | 8.250320e-02 | -1.314e+00 | -3.514000e-02 | -5.980000e-05 | 3.502000e-02 | 1.253000e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.374220e-02 | 1.853650e-02 | 3.936e-02 | 4.238000e-02 | 4.775000e-02 | 5.896000e-02 | 1.156000e+00 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.910116e-01 | 2.919976e-01 | 0.000e+00 | 2.369000e-01 | 4.884004e-01 | 7.442006e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.910116e-01 | 2.919975e-01 | 0.000e+00 | 2.368555e-01 | 4.883896e-01 | 7.442267e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.670911e-01 | 2.465726e-01 | 4.100e-04 | 1.560800e-01 | 3.070395e-01 | 5.468070e-01 | 9.957020e-01 | ▇▆▅▃▂ |
| numeric | AF_reference | 5120 | 0.9908038 | NA | NA | NA | NA | NA | NA | NA | 3.380690e-01 | 2.301242e-01 | 1.997e-04 | 1.489620e-01 | 2.911340e-01 | 5.000000e-01 | 9.974040e-01 | ▇▇▅▃▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 752566 | rs3094315 | G | A | 0.024670 | 0.05626 | 0.6609993 | 0.6610240 | 0.8448450 | 0.7182510 | NA |
| 1 | 990417 | rs2465136 | T | C | -0.013910 | 0.05472 | 0.7993000 | 0.7993386 | 0.1625320 | 0.3839860 | NA |
| 1 | 1018704 | rs9442372 | A | G | 0.106800 | 0.05123 | 0.0371202 | 0.0370951 | 0.8119550 | 0.6110220 | NA |
| 1 | 1036959 | rs11579015 | T | C | -0.040900 | 0.04982 | 0.4117001 | 0.4116717 | 0.2039570 | 0.1569490 | NA |
| 1 | 1046164 | rs6666280 | C | T | -0.052270 | 0.04997 | 0.2955997 | 0.2955483 | 0.2033310 | 0.2823480 | NA |
| 1 | 1062638 | rs9442373 | C | A | 0.010400 | 0.04081 | 0.7989000 | 0.7988471 | 0.5396500 | 0.5742810 | NA |
| 1 | 1216195 | rs55834051 | C | A | 0.070180 | 0.04222 | 0.0965006 | 0.0964636 | 0.6441210 | 0.3182910 | NA |
| 1 | 1217011 | rs1262894 | A | C | -0.001658 | 0.06213 | 0.9787000 | 0.9787102 | 0.1219990 | 0.2096650 | NA |
| 1 | 1217058 | rs3753340 | G | A | -0.167000 | 0.17570 | 0.3418999 | 0.3418665 | 0.0144225 | 0.0547125 | NA |
| 1 | 1218086 | rs6603788 | C | T | 0.063490 | 0.04183 | 0.1290001 | 0.1290623 | 0.6326950 | 0.4694490 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 155075318 | rs28673993 | A | G | -0.053170 | 0.04094 | 0.1940001 | 0.1940367 | 0.550435 | 0.494010 | NA |
| 23 | 155093036 | rs5940611 | A | G | 0.029840 | 0.04107 | 0.4675004 | 0.4674928 | 0.455459 | 0.518171 | NA |
| 23 | 155096106 | rs7056030 | C | T | 0.002818 | 0.04163 | 0.9459999 | 0.9460312 | 0.383539 | 0.509385 | NA |
| 23 | 155101861 | rs5940431 | T | C | -0.013690 | 0.04088 | 0.7377004 | 0.7377137 | 0.530342 | 0.646765 | NA |
| 23 | 155104608 | rs5940618 | G | A | 0.015230 | 0.04245 | 0.7198003 | 0.7197634 | 0.349194 | 0.339457 | NA |
| 23 | 155123237 | rs4893101 | C | T | 0.026290 | 0.04252 | 0.5364003 | 0.5363794 | 0.644919 | 0.592851 | NA |
| 23 | 155158490 | rs5983829 | A | G | 0.015640 | 0.04284 | 0.7149999 | 0.7150522 | 0.664322 | 0.637780 | NA |
| 23 | 155184866 | rs2889416 | T | C | -0.054460 | 0.04687 | 0.2451997 | 0.2452609 | 0.248887 | 0.370407 | NA |
| 23 | 155223705 | rs7051412 | T | G | 0.085780 | 0.04665 | 0.0659796 | 0.0659447 | 0.743987 | 0.552117 | NA |
| 23 | 155227607 | rs3093457 | T | G | 0.091790 | 0.04601 | 0.0460299 | 0.0460428 | 0.729838 | 0.512580 | NA |
bcf preview
1 752566 rs3094315 G A . PASS AF=0.844845 ES:SE:LP:AF:ID 0.02467:0.05626:0.179799:0.844845:rs3094315
1 990417 rs2465136 T C . PASS AF=0.162532 ES:SE:LP:AF:ID -0.01391:0.05472:0.0972902:0.162532:rs2465136
1 1018704 rs9442372 A G . PASS AF=0.811955 ES:SE:LP:AF:ID 0.1068:0.05123:1.43039:0.811955:rs9442372
1 1036959 rs11579015 T C . PASS AF=0.203957 ES:SE:LP:AF:ID -0.0409:0.04982:0.385419:0.203957:rs11579015
1 1046164 rs6666280 C T . PASS AF=0.203331 ES:SE:LP:AF:ID -0.05227:0.04997:0.529296:0.203331:rs6666280
1 1062638 rs9442373 C A . PASS AF=0.53965 ES:SE:LP:AF:ID 0.0104:0.04081:0.0975076:0.53965:rs9442373
1 1216195 rs55834051 C A . PASS AF=0.644121 ES:SE:LP:AF:ID 0.07018:0.04222:1.01547:0.644121:rs55834051
1 1217011 rs1262894 A C . PASS AF=0.121999 ES:SE:LP:AF:ID -0.001658:0.06213:0.00935041:0.121999:rs1262894
1 1217058 rs3753340 G A . PASS AF=0.0144225 ES:SE:LP:AF:ID -0.167:0.1757:0.466101:0.0144225:rs3753340
1 1218086 rs6603788 C T . PASS AF=0.632695 ES:SE:LP:AF:ID 0.06349:0.04183:0.88941:0.632695:rs6603788
1 1220136 rs2144440 A G . PASS AF=0.652452 ES:SE:LP:AF:ID 0.05881:0.04217:0.787546:0.652452:rs2144440
1 1220954 rs12751100 G A . PASS AF=0.63732 ES:SE:LP:AF:ID 0.06544:0.04201:0.92336:0.63732:rs12751100
1 1221138 rs11260578 G A . PASS AF=0.622697 ES:SE:LP:AF:ID 0.07472:0.0419:1.12779:0.622697:rs11260578
1 1222958 rs111819661 C T . PASS AF=0.0144934 ES:SE:LP:AF:ID -0.2419:0.1791:0.752518:0.0144934:rs111819661
1 1223529 rs78481580 G C . PASS AF=0.182145 ES:SE:LP:AF:ID 0.007914:0.05299:0.0548762:0.182145:rs78481580
1 1225959 rs1570867 C G . PASS AF=0.922551 ES:SE:LP:AF:ID 0.02241:0.0754:0.115601:0.922551:rs1570867
1 1227244 rs2239609 G A . PASS AF=0.274785 ES:SE:LP:AF:ID -0.05954:0.04499:0.731188:0.274785:rs2239609
1 1278237 rs307361 T C . PASS AF=0.86929 ES:SE:LP:AF:ID 0.0001591:0.06064:0.000912977:0.86929:rs307361
1 1287040 rs182532 T C . PASS AF=0.869056 ES:SE:LP:AF:ID 0.00627:0.06068:0.0372993:0.869056:rs182532
1 1295323 rs34389364 G A . PASS AF=0.808131 ES:SE:LP:AF:ID 0.03006:0.05202:0.24926:0.808131:rs34389364
1 1314015 rs2649588 C T . PASS AF=0.826185 ES:SE:LP:AF:ID 0.02061:0.05432:0.152181:0.826185:rs2649588
1 1335218 rs2291889 G A . PASS AF=0.121077 ES:SE:LP:AF:ID 0.02346:0.06254:0.150212:0.121077:rs2291889
1 1487059 rs1887284 G A . PASS AF=0.262417 ES:SE:LP:AF:ID -0.01827:0.04662:0.157953:0.262417:rs1887284
1 1489928 rs7366884 T C . PASS AF=0.716754 ES:SE:LP:AF:ID -0.02588:0.04589:0.242073:0.716754:rs7366884
1 1500941 rs6603791 A G . PASS AF=0.824429 ES:SE:LP:AF:ID 0.04793:0.05482:0.41805:0.824429:rs6603791
1 1599161 rs6604981 A G . PASS AF=0.621014 ES:SE:LP:AF:ID -0.005108:0.04166:0.0446009:0.621014:rs6604981
1 1647686 rs909823 A C . PASS AF=0.869063 ES:SE:LP:AF:ID 0.005476:0.05797:0.0339991:0.869063:rs909823
1 1687625 rs34306661 T C . PASS AF=0.332064 ES:SE:LP:AF:ID -0.01241:0.0431:0.111596:0.332064:rs34306661
1 1706160 rs7531583 A G . PASS AF=0.70011 ES:SE:LP:AF:ID 0.006349:0.04432:0.0525173:0.70011:rs7531583
1 1721479 rs2272908 C T . PASS AF=0.502984 ES:SE:LP:AF:ID 0.01405:0.0405:0.137511:0.502984:rs2272908
1 1722585 rs3737626 C A . PASS AF=0.0381004 ES:SE:LP:AF:ID -0.0203:0.1054:0.0719628:0.0381004:rs3737626
1 1722932 rs3737628 C T . PASS AF=0.502721 ES:SE:LP:AF:ID 0.0124:0.0405:0.119529:0.502721:rs3737628
1 1723031 rs9660180 G A . PASS AF=0.504401 ES:SE:LP:AF:ID 0.01514:0.04058:0.149293:0.504401:rs9660180
1 1725016 rs80220232 G A . PASS AF=0.066223 ES:SE:LP:AF:ID 0.01963:0.08057:0.0928575:0.066223:rs80220232
1 1725626 rs9970652 C T . PASS AF=0.206243 ES:SE:LP:AF:ID -0.01835:0.05064:0.14436:0.206243:rs9970652
1 1733219 rs10907185 A G . PASS AF=0.710327 ES:SE:LP:AF:ID -0.007986:0.04456:0.0666646:0.710327:rs10907185
1 1734337 rs75622028 T C . PASS AF=0.103286 ES:SE:LP:AF:ID -0.01229:0.06557:0.0699174:0.103286:rs75622028
1 1737900 rs17363334 C T . PASS AF=0.0685096 ES:SE:LP:AF:ID 0.0632:0.08063:0.363412:0.0685096:rs17363334
1 1738984 rs76117314 C A . PASS AF=0.0598594 ES:SE:LP:AF:ID 0.006934:0.08448:0.0293742:0.0598594:rs76117314
1 1745726 rs16825336 G A . PASS AF=0.266627 ES:SE:LP:AF:ID -0.01049:0.04569:0.0870344:0.266627:rs16825336
1 1746694 rs12742323 G T . PASS AF=0.207488 ES:SE:LP:AF:ID -0.03197:0.05063:0.277613:0.207488:rs12742323
1 1747318 rs59787372 T C . PASS AF=0.0356951 ES:SE:LP:AF:ID 0.006924:0.1108:0.022185:0.0356951:rs59787372
1 1748734 rs2180311 T C . PASS AF=0.505353 ES:SE:LP:AF:ID 0.01039:0.04049:0.0982149:0.505353:rs2180311
1 1752955 rs4648726 C T . PASS AF=0.843103 ES:SE:LP:AF:ID -0.000889:0.05528:0.00559485:0.843103:rs4648726
1 1759026 rs9786963 T C . PASS AF=0.908969 ES:SE:LP:AF:ID -0.001273:0.06978:0.00638744:0.908969:rs9786963
1 1759054 rs10907187 G A . PASS AF=0.128451 ES:SE:LP:AF:ID 0.0007742:0.06089:0.00440868:0.128451:rs10907187
1 1759213 rs9786942 A G . PASS AF=0.505091 ES:SE:LP:AF:ID 0.01439:0.04053:0.141162:0.505091:rs9786942
1 1760937 rs77726987 G A . PASS AF=0.0353723 ES:SE:LP:AF:ID 0.02614:0.1125:0.0881502:0.0353723:rs77726987
1 1765583 rs6603797 T C . PASS AF=0.908316 ES:SE:LP:AF:ID -0.01181:0.06917:0.0632852:0.908316:rs6603797
1 1766094 rs6663586 A C . PASS AF=0.195172 ES:SE:LP:AF:ID 0.002393:0.05109:0.0165541:0.195172:rs6663586