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": "2019-10-26T21:44:19.015689",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004904/EBI-a-GCST004904_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-GCST004904/EBI-a-GCST004904.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004904/EBI-a-GCST004904_data.vcf.gz; Date=Sat Oct 26 21:58:00 2019",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST004904/ebi-a-GCST004904.vcf.gz; Date=Sun May 10 12:32:53 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-GCST004904/EBI-a-GCST004904.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-GCST004904/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 22:23:21 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004904/EBI-a-GCST004904.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 22:24:00 2019
Total time elapsed: 38.92sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8843,
"inflation_factor": 1.44,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 5952516,
"n_clumped_hits": 67,
"n_p_sig": 5075,
"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": 19,
"n_miss_AF_reference": 68224,
"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 | FALSE |
| 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 | 3 | 57 | 0 | 5940313 | 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 | 5940315 | 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 | 8.548647e+00 | 5.693248e+00 | 1.000000 | 4.000000e+00 | 7.000000e+00 | 1.200000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.931908e+07 | 5.621613e+07 | 828.000000 | 3.287988e+07 | 7.017075e+07 | 1.149981e+08 | 2.492026e+08 | ▇▇▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.720000e-05 | 8.809800e-03 | -0.266500 | -4.246000e-03 | 6.450000e-05 | 4.345000e-03 | 5.140000e-01 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.325900e-03 | 4.582800e-03 | 0.003527 | 3.854000e-03 | 4.679000e-03 | 6.990000e-03 | 4.043000e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.409122e-01 | 3.038299e-01 | 0.000000 | 1.630001e-01 | 4.183001e-01 | 7.039001e-01 | 1.000000e+00 | ▇▆▅▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.409122e-01 | 3.038299e-01 | 0.000000 | 1.629643e-01 | 4.182746e-01 | 7.039413e-01 | 9.999997e-01 | ▇▆▅▅▅ |
| numeric | AF | 19 | 0.9999968 | NA | NA | NA | NA | NA | NA | NA | 3.151725e-01 | 2.705347e-01 | 0.000100 | 8.310000e-02 | 2.337000e-01 | 4.970000e-01 | 9.999000e-01 | ▇▃▂▂▂ |
| numeric | AF_reference | 68224 | 0.9885151 | NA | NA | NA | NA | NA | NA | NA | 3.009431e-01 | 2.413757e-01 | 0.000000 | 1.022360e-01 | 2.346250e-01 | 4.586660e-01 | 1.000000e+00 | ▇▅▃▂▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 751756 | rs143225517 | T | C | 0.0002037 | 0.005048 | 0.9678000 | 0.9678120 | 0.1517 | 0.2422120 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0002009 | 0.004899 | 0.9673000 | 0.9672892 | 0.8438 | 0.7182510 | NA |
| 1 | 753405 | rs3115860 | C | A | -0.0002146 | 0.005262 | 0.9675001 | 0.9674689 | 0.8385 | 0.7517970 | NA |
| 1 | 845635 | rs117086422 | C | T | 0.0016340 | 0.005477 | 0.7653992 | 0.7654448 | 0.1402 | 0.1585460 | NA |
| 1 | 846078 | rs28612348 | C | T | 0.0017210 | 0.005483 | 0.7537008 | 0.7536127 | 0.1419 | 0.1617410 | NA |
| 1 | 846808 | rs4475691 | C | T | 0.0013900 | 0.005094 | 0.7848994 | 0.7849531 | 0.1411 | 0.2547920 | NA |
| 1 | 846864 | rs950122 | G | C | 0.0013890 | 0.005094 | 0.7851000 | 0.7851040 | 0.1411 | 0.2228430 | NA |
| 1 | 847228 | rs3905286 | C | T | 0.0010790 | 0.005153 | 0.8342000 | 0.8341418 | 0.1391 | 0.2426120 | NA |
| 1 | 847491 | rs28407778 | G | A | 0.0010370 | 0.005155 | 0.8405000 | 0.8405704 | 0.1394 | 0.2519970 | NA |
| 1 | 847983 | rs79932038 | C | T | 0.0010670 | 0.011400 | 0.9254000 | 0.9254297 | 0.0318 | 0.0015974 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51165390 | rs76268556 | C | T | -0.0059120 | 0.007511 | 0.4312002 | 0.4312162 | 0.0772 | 0.0515176 | NA |
| 22 | 51165664 | rs8137951 | G | A | 0.0063150 | 0.003687 | 0.0867401 | 0.0867540 | 0.3924 | 0.4063500 | NA |
| 22 | 51169045 | rs8140772 | C | T | -0.0058160 | 0.006333 | 0.3584000 | 0.3584283 | 0.0903 | 0.0632987 | NA |
| 22 | 51171497 | rs2301584 | G | A | 0.0094490 | 0.004108 | 0.0214501 | 0.0214399 | 0.2496 | 0.2533950 | NA |
| 22 | 51171667 | rs41281537 | G | A | -0.0053350 | 0.006210 | 0.3903004 | 0.3902863 | 0.0910 | 0.0577077 | NA |
| 22 | 51171693 | rs756638 | G | A | -0.0040040 | 0.005603 | 0.4748003 | 0.4748456 | 0.1129 | 0.3049120 | NA |
| 22 | 51172460 | rs5770824 | T | C | -0.0058610 | 0.006397 | 0.3595001 | 0.3595564 | 0.0984 | 0.0684904 | NA |
| 22 | 51175626 | rs3810648 | A | G | 0.0015460 | 0.017330 | 0.9289001 | 0.9289154 | 0.0104 | 0.1084270 | NA |
| 22 | 51178090 | rs2285395 | G | A | 0.0005625 | 0.017410 | 0.9741999 | 0.9742256 | 0.0103 | 0.0666933 | NA |
| 23 | 147407824 | rs28859988 | G | A | -0.0087770 | 0.009116 | 0.3356000 | 0.3356416 | 0.0414 | NA | NA |
bcf preview
1 751756 rs28527770 T C . PASS AF=0.1517 ES:SE:LP:AF:ID 0.0002037:0.005048:0.0142144:0.1517:rs28527770
1 752566 rs3094315 G A . PASS AF=0.8438 ES:SE:LP:AF:ID -0.0002009:0.004899:0.0144388:0.8438:rs3094315
1 753405 rs3115860 C A . PASS AF=0.8385 ES:SE:LP:AF:ID -0.0002146:0.005262:0.014349:0.8385:rs3115860
1 845635 rs117086422 C T . PASS AF=0.1402 ES:SE:LP:AF:ID 0.001634:0.005477:0.116112:0.1402:rs117086422
1 846078 rs28612348 C T . PASS AF=0.1419 ES:SE:LP:AF:ID 0.001721:0.005483:0.122801:0.1419:rs28612348
1 846808 rs4475691 C T . PASS AF=0.1411 ES:SE:LP:AF:ID 0.00139:0.005094:0.105186:0.1411:rs4475691
1 846864 rs950122 G C . PASS AF=0.1411 ES:SE:LP:AF:ID 0.001389:0.005094:0.105075:0.1411:rs950122
1 847228 rs3905286 C T . PASS AF=0.1391 ES:SE:LP:AF:ID 0.001079:0.005153:0.0787298:0.1391:rs3905286
1 847491 rs28407778 G A . PASS AF=0.1394 ES:SE:LP:AF:ID 0.001037:0.005155:0.0754623:0.1394:rs28407778
1 847983 rs79932038 C T . PASS AF=0.0318 ES:SE:LP:AF:ID 0.001067:0.0114:0.0336705:0.0318:rs79932038
1 848090 rs4246505 G A . PASS AF=0.1389 ES:SE:LP:AF:ID 0.001002:0.005153:0.0727323:0.1389:rs4246505
1 848445 rs4626817 G A . PASS AF=0.1389 ES:SE:LP:AF:ID 0.0009995:0.005153:0.072527:0.1389:rs4626817
1 848456 rs11507767 A G . PASS AF=0.1389 ES:SE:LP:AF:ID 0.0009969:0.005153:0.0723217:0.1389:rs11507767
1 848738 rs3829741 C T . PASS AF=0.1392 ES:SE:LP:AF:ID 0.0009567:0.005154:0.0692037:0.1392:rs3829741
1 850062 rs28723578 A T . PASS AF=0.1389 ES:SE:LP:AF:ID 0.001013:0.00523:0.072373:0.1389:rs28723578
1 850123 rs28622257 C T . PASS AF=0.1372 ES:SE:LP:AF:ID 0.0009068:0.005216:0.0644927:0.1372:rs28622257
1 850373 rs151325546 A G . PASS AF=0.0375 ES:SE:LP:AF:ID 0.001014:0.009578:0.0382942:0.0375:rs151325546
1 851190 rs28609852 G A . PASS AF=0.1389 ES:SE:LP:AF:ID 0.0008615:0.005145:0.0619809:0.1389:rs28609852
1 851204 rs28552953 G C . PASS AF=0.1389 ES:SE:LP:AF:ID 0.0008571:0.005144:0.0616304:0.1389:rs28552953
1 852664 rs28605311 C T . PASS AF=0.1388 ES:SE:LP:AF:ID 0.0008434:0.005143:0.0606305:0.1388:rs28605311
1 852758 rs4970462 G C . PASS AF=0.1388 ES:SE:LP:AF:ID 0.0008409:0.005143:0.0604308:0.1388:rs4970462
1 853089 rs78738176 G C . PASS AF=0.0378 ES:SE:LP:AF:ID 0.0009705:0.009464:0.0370154:0.0378:rs78738176
1 853239 rs4970380 A G . PASS AF=0.1388 ES:SE:LP:AF:ID 0.000833:0.005142:0.0598323:0.1388:rs4970380
1 853596 rs191666748 A G . PASS AF=0.0378 ES:SE:LP:AF:ID 0.0009743:0.009446:0.0372046:0.0378:rs191666748
1 854250 rs7537756 A G . PASS AF=0.1766 ES:SE:LP:AF:ID 0.0009044:0.004646:0.0727837:0.1766:rs7537756
1 854793 rs80174979 A C . PASS AF=0.0378 ES:SE:LP:AF:ID 0.0009723:0.009435:0.0372046:0.0378:rs80174979
1 857177 rs28409649 T C . PASS AF=0.4594 ES:SE:LP:AF:ID 0.01057:0.003702:2.36815:0.4594:rs28409649
1 858040 rs4970460 C A . PASS AF=0.1732 ES:SE:LP:AF:ID -0.001647:0.005513:0.116225:0.1732:rs4970460
1 858051 rs4970459 C T . PASS AF=0.1732 ES:SE:LP:AF:ID -0.001658:0.005514:0.117134:0.1732:rs4970459
1 858801 rs7418179 A G . PASS AF=0.4063 ES:SE:LP:AF:ID -0.01183:0.003862:2.66134:0.4063:rs7418179
1 859404 rs71509444 C G . PASS AF=0.5782 ES:SE:LP:AF:ID -0.01142:0.003926:2.43997:0.5782:rs71509444
1 859690 rs71509445 C G . PASS AF=0.5313 ES:SE:LP:AF:ID -0.01089:0.003869:2.31016:0.5313:rs71509445
1 859701 rs71509446 C G . PASS AF=0.5038 ES:SE:LP:AF:ID -0.01134:0.004007:2.33301:0.5038:rs71509446
1 860416 rs61464428 G A . PASS AF=0.4175 ES:SE:LP:AF:ID -0.01204:0.003879:2.71738:0.4175:rs61464428
1 860461 rs57465118 G A . PASS AF=0.5388 ES:SE:LP:AF:ID -0.01053:0.003666:2.38955:0.5388:rs57465118
1 860521 rs57924093 C A . PASS AF=0.5353 ES:SE:LP:AF:ID -0.01043:0.003634:2.38806:0.5353:rs57924093
1 860688 rs60837925 G A . PASS AF=0.4283 ES:SE:LP:AF:ID -0.01197:0.003937:2.62801:0.4283:rs60837925
1 860778 rs61338526 A G . PASS AF=0.5071 ES:SE:LP:AF:ID -0.01088:0.003825:2.35301:0.5071:rs61338526
1 860857 rs141034733 G A . PASS AF=0.0457 ES:SE:LP:AF:ID 0.0003655:0.009681:0.013273:0.0457:rs141034733
1 861008 rs28521172 G C . PASS AF=0.5404 ES:SE:LP:AF:ID -0.01038:0.003592:2.41296:0.5404:rs28521172
1 861630 rs2879816 G A . PASS AF=0.4066 ES:SE:LP:AF:ID -0.01162:0.003781:2.67551:0.4066:rs2879816
1 861808 rs13302982 A G . PASS AF=0.5404 ES:SE:LP:AF:ID -0.01036:0.003586:2.41398:0.5404:rs13302982
1 862072 rs76842830 C T . PASS AF=0.0374 ES:SE:LP:AF:ID 0.001209:0.009726:0.045227:0.0374:rs76842830
1 862093 rs13303291 T C . PASS AF=0.5403 ES:SE:LP:AF:ID -0.01049:0.00363:2.41184:0.5403:rs13303291
1 862124 rs13303101 A G . PASS AF=0.54 ES:SE:LP:AF:ID -0.01076:0.003733:2.40461:0.54:rs13303101
1 862383 rs6680268 C T . PASS AF=0.5397 ES:SE:LP:AF:ID -0.01098:0.003814:2.39957:0.5397:rs6680268
1 862389 rs6693546 A G . PASS AF=0.5397 ES:SE:LP:AF:ID -0.01099:0.003816:2.39979:0.5397:rs6693546
1 863124 rs4040604 G T . PASS AF=0.5421 ES:SE:LP:AF:ID -0.01153:0.004003:2.40143:0.5421:rs4040604
1 876499 rs4372192 A G . PASS AF=0.9472 ES:SE:LP:AF:ID 0.008195:0.009274:0.423774:0.9472:rs4372192
1 879676 rs6605067 G A . PASS AF=0.9453 ES:SE:LP:AF:ID 0.007936:0.009125:0.415104:0.9453:rs6605067