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": "2019-10-27T07:19:15.217397",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004732/EBI-a-GCST004732_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-GCST004732/EBI-a-GCST004732.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004732/EBI-a-GCST004732_data.vcf.gz; Date=Sun Oct 27 07:23:56 2019",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST004732/ebi-a-GCST004732.vcf.gz; Date=Sun May 10 09:27:07 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-GCST004732/EBI-a-GCST004732.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-GCST004732/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 Sun Oct 27 07:46:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004732/EBI-a-GCST004732.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 Sun Oct 27 07:46:23 2019
Total time elapsed: 15.94sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9292,
"inflation_factor": 1.0364,
"mean_EFFECT": -0.0001,
"n": "-Inf",
"n_snps": 2526818,
"n_clumped_hits": 5,
"n_p_sig": 230,
"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": 20464,
"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 | FALSE |
| 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 | 3 | 42 | 0 | 2520208 | 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 | 2520223 | 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 | 8.583631e+00 | 5.660414e+00 | 1.000e+00 | 4.000000e+00 | 8.000000e+00 | 1.200000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.893510e+07 | 5.579597e+07 | 6.689e+03 | 3.263925e+07 | 7.033844e+07 | 1.144649e+08 | 2.492190e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.000000e-05 | 2.392400e-02 | -1.177e+00 | -1.017500e-02 | -2.700000e-05 | 1.010700e-02 | 1.323000e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.839020e-02 | 1.527060e-02 | 9.967e-03 | 1.119600e-02 | 1.330000e-02 | 1.874500e-02 | 1.227420e+00 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.939655e-01 | 2.905656e-01 | 0.000e+00 | 2.405000e-01 | 4.923002e-01 | 7.453993e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.961031e-01 | 2.901918e-01 | 0.000e+00 | 2.435443e-01 | 4.951073e-01 | 7.473736e-01 | 9.999888e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.589570e-01 | 2.699609e-01 | 1.010e-02 | 1.248000e-01 | 2.947000e-01 | 5.555000e-01 | 9.899000e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 20464 | 0.9918801 | NA | NA | NA | NA | NA | NA | NA | 3.617531e-01 | 2.555875e-01 | 0.000e+00 | 1.467650e-01 | 3.001200e-01 | 5.459270e-01 | 1.000000e+00 | ▇▆▃▃▂ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 721290 | rs12565286 | G | C | -0.031458 | 0.032669 | 0.4536000 | 0.3355820 | 0.0507 | 0.0371406 | NA |
| 1 | 723819 | rs11804171 | T | A | -0.020646 | 0.032638 | 0.7399000 | 0.5270109 | 0.0543 | 0.1345850 | NA |
| 1 | 723891 | rs2977670 | G | C | 0.014683 | 0.026671 | 0.7507995 | 0.5819607 | 0.9314 | 0.7799520 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.031947 | 0.014024 | 0.0201502 | 0.0227252 | 0.7833 | 0.7182510 | NA |
| 1 | 754192 | rs3131968 | A | G | 0.042238 | 0.015943 | 0.0104501 | 0.0080656 | 0.8600 | 0.6785140 | NA |
| 1 | 761732 | rs2286139 | C | T | 0.059191 | 0.022549 | 0.0066681 | 0.0086650 | 0.7566 | 0.6257990 | NA |
| 1 | 768448 | rs12562034 | G | A | -0.030060 | 0.032678 | 0.6934993 | 0.3576329 | 0.0909 | 0.1918930 | NA |
| 1 | 775659 | rs2905035 | A | G | 0.035611 | 0.016621 | 0.0386403 | 0.0321508 | 0.8608 | 0.7450080 | NA |
| 1 | 777122 | rs2980319 | A | T | 0.035995 | 0.016604 | 0.0360903 | 0.0301700 | 0.8206 | 0.7472040 | NA |
| 1 | 779322 | rs4040617 | A | G | -0.049388 | 0.017490 | 0.0059220 | 0.0047460 | 0.1386 | 0.2264380 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51196164 | rs8136603 | A | T | -0.070703 | 0.033829 | 0.0120199 | 0.0366168 | 0.0596 | 0.1427720 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.065405 | 0.029578 | 0.0172500 | 0.0270170 | 0.0985 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.022537 | 0.016108 | 0.2312001 | 0.1617775 | 0.3837 | 0.3724040 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.073543 | 0.028385 | 0.0054760 | 0.0095722 | 0.1526 | 0.1563500 | NA |
| 22 | 51217134 | rs117417021 | A | G | -0.025794 | 0.017902 | 0.1287001 | 0.1496286 | 0.4192 | 0.2671730 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.048702 | 0.028077 | 0.0960904 | 0.0828140 | 0.1814 | 0.2052720 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.078818 | 0.032847 | 0.0107199 | 0.0164153 | 0.0622 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.080465 | 0.037141 | 0.0371296 | 0.0302750 | 0.0901 | 0.0730831 | NA |
| 23 | 91415872 | rs6562597 | G | A | -0.117303 | 0.059704 | 0.1405999 | 0.0494440 | 0.0196 | 0.0021192 | NA |
| 23 | 118495837 | rs12882977 | G | A | 0.010512 | 0.010727 | 0.5994997 | 0.3271073 | 0.4813 | 0.2307280 | NA |
bcf preview
1 721290 rs12565286 G C . PASS AF=0.0507 ES:SE:LP:AF:ID -0.031458:0.032669:0.343327:0.0507:rs12565286
1 723819 rs11804171 T A . PASS AF=0.0543 ES:SE:LP:AF:ID -0.020646:0.032638:0.130827:0.0543:rs11804171
1 723891 rs2977670 G C . PASS AF=0.9314 ES:SE:LP:AF:ID 0.014683:0.026671:0.124476:0.9314:rs2977670
1 752566 rs3094315 G A . PASS AF=0.7833 ES:SE:LP:AF:ID 0.031947:0.014024:1.69572:0.7833:rs3094315
1 754192 rs3131968 A G . PASS AF=0.86 ES:SE:LP:AF:ID 0.042238:0.015943:1.98088:0.86:rs3131968
1 761732 rs2286139 C T . PASS AF=0.7566 ES:SE:LP:AF:ID 0.059191:0.022549:2.176:0.7566:rs2286139
1 768448 rs12562034 G A . PASS AF=0.0909 ES:SE:LP:AF:ID -0.03006:0.032678:0.158954:0.0909:rs12562034
1 775659 rs2905035 A G . PASS AF=0.8608 ES:SE:LP:AF:ID 0.035611:0.016621:1.41296:0.8608:rs2905035
1 777122 rs2980319 A T . PASS AF=0.8206 ES:SE:LP:AF:ID 0.035995:0.016604:1.44261:0.8206:rs2980319
1 779322 rs4040617 A G . PASS AF=0.1386 ES:SE:LP:AF:ID -0.049388:0.01749:2.22753:0.1386:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86 ES:SE:LP:AF:ID 0.043689:0.018186:1.61137:0.86:rs2977612
1 785050 rs2905062 G A . PASS AF=0.8586 ES:SE:LP:AF:ID 0.051699:0.017469:2.30601:0.8586:rs2905062
1 785989 rs2980300 T C . PASS AF=0.7806 ES:SE:LP:AF:ID 0.047987:0.017259:1.96297:0.7806:rs2980300
1 798026 rs4951864 C T . PASS AF=0.9042 ES:SE:LP:AF:ID 0.002015:0.031414:0.00952795:0.9042:rs4951864
1 798801 rs12132517 G A . PASS AF=0.1012 ES:SE:LP:AF:ID -0.003144:0.032192:0.014888:0.1012:rs12132517
1 798959 rs11240777 G A . PASS AF=0.2609 ES:SE:LP:AF:ID -0.017319:0.018003:0.425621:0.2609:rs11240777
1 888659 rs3748597 T C . PASS AF=0.9404 ES:SE:LP:AF:ID 0.06971:0.046809:0.926648:0.9404:rs3748597
1 918573 rs2341354 A G . PASS AF=0.537 ES:SE:LP:AF:ID -0.015638:0.01781:0.374688:0.537:rs2341354
1 926431 rs4970403 A T . PASS AF=0.9139 ES:SE:LP:AF:ID -0.02239:0.056682:0.136737:0.9139:rs4970403
1 947034 rs2465126 G A . PASS AF=0.8784 ES:SE:LP:AF:ID -0.006274:0.0479:0.0563077:0.8784:rs2465126
1 962210 rs3128126 A G . PASS AF=0.4407 ES:SE:LP:AF:ID 0.012787:0.018988:0.355168:0.4407:rs3128126
1 990380 rs3121561 C T . PASS AF=0.2927 ES:SE:LP:AF:ID 0.033945:0.01862:1.11702:0.2927:rs3121561
1 990417 rs2465136 T C . PASS AF=0.3469 ES:SE:LP:AF:ID 0.016093:0.020409:0.479255:0.3469:rs2465136
1 1003629 rs4075116 C T . PASS AF=0.7254 ES:SE:LP:AF:ID -0.017346:0.012874:0.949234:0.7254:rs4075116
1 1005806 rs3934834 C T . PASS AF=0.1653 ES:SE:LP:AF:ID 0.006061:0.019002:0.06778:0.1653:rs3934834
1 1017170 rs3766193 C G . PASS AF=0.539 ES:SE:LP:AF:ID -0.014388:0.012822:1.14673:0.539:rs3766193
1 1017197 rs3766192 C T . PASS AF=0.5486 ES:SE:LP:AF:ID -0.016032:0.011651:1.30086:0.5486:rs3766192
1 1017587 rs3766191 C T . PASS AF=0.158 ES:SE:LP:AF:ID 0.008853:0.018819:0.00489154:0.158:rs3766191
1 1018562 rs9442371 C T . PASS AF=0.5574 ES:SE:LP:AF:ID -0.017184:0.011316:1.38648:0.5574:rs9442371
1 1018704 rs9442372 A G . PASS AF=0.5581 ES:SE:LP:AF:ID -0.017787:0.011294:1.48986:0.5581:rs9442372
1 1021346 rs10907177 A G . PASS AF=0.1616 ES:SE:LP:AF:ID 0.012742:0.019055:0.095934:0.1616:rs10907177
1 1021415 rs3737728 A G . PASS AF=0.7095 ES:SE:LP:AF:ID -0.014739:0.012657:1.40252:0.7095:rs3737728
1 1021583 rs10907178 A C . PASS AF=0.1628 ES:SE:LP:AF:ID 0.013025:0.019073:0.0748943:0.1628:rs10907178
1 1021695 rs9442398 A G . PASS AF=0.7178 ES:SE:LP:AF:ID -0.014695:0.013244:1.07314:0.7178:rs9442398
1 1022037 rs6701114 C T . PASS AF=0.5404 ES:SE:LP:AF:ID -0.016271:0.012721:1.142:0.5404:rs6701114
1 1026707 rs4074137 C A . PASS AF=0.59 ES:SE:LP:AF:ID -0.007833:0.01679:0.0130492:0.59:rs4074137
1 1030565 rs6687776 C T . PASS AF=0.1628 ES:SE:LP:AF:ID -0.004666:0.018447:0.036165:0.1628:rs6687776
1 1030633 rs6678318 G A . PASS AF=0.1418 ES:SE:LP:AF:ID -0.002303:0.020384:0.056555:0.1418:rs6678318
1 1031540 rs9651273 A G . PASS AF=0.6778 ES:SE:LP:AF:ID -0.006706:0.016047:0.138645:0.6778:rs9651273
1 1036959 rs11579015 T C . PASS AF=0.0788 ES:SE:LP:AF:ID -0.01795:0.021229:0.738975:0.0788:rs11579015
1 1039098 rs11260595 C A . PASS AF=0.034 ES:SE:LP:AF:ID 0.097251:0.06403:1.05325:0.034:rs11260595
1 1040026 rs6671356 T C . PASS AF=0.1328 ES:SE:LP:AF:ID -0.009347:0.020418:0.127552:0.1328:rs6671356
1 1041700 rs6604968 A G . PASS AF=0.1929 ES:SE:LP:AF:ID -0.009983:0.026699:0.0406247:0.1929:rs6604968
1 1046164 rs6666280 C T . PASS AF=0.0972 ES:SE:LP:AF:ID -0.018513:0.022114:0.647817:0.0972:rs6666280
1 1048955 rs4970405 A G . PASS AF=0.0901 ES:SE:LP:AF:ID -0.0165:0.018756:0.563837:0.0901:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.0942 ES:SE:LP:AF:ID -0.015538:0.021811:0.505011:0.0942:rs12726255
1 1053452 rs4970409 G A . PASS AF=0.0753 ES:SE:LP:AF:ID -0.010689:0.023956:0.543786:0.0753:rs4970409
1 1060608 rs17160824 G A . PASS AF=0.0815 ES:SE:LP:AF:ID -0.012766:0.02424:0.564315:0.0815:rs17160824
1 1061115 rs17160826 T C . PASS AF=0.0745 ES:SE:LP:AF:ID -0.011767:0.024246:0.572189:0.0745:rs17160826
1 1061152 rs12748370 T C . PASS AF=0.0744 ES:SE:LP:AF:ID -0.011768:0.024379:0.575118:0.0744:rs12748370