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": "2019-10-26T09:29:46.504813",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST001837/EBI-a-GCST001837_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-GCST001837/EBI-a-GCST001837.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST001837/EBI-a-GCST001837_data.vcf.gz; Date=Sat Oct 26 09:44:36 2019",
"bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST001837/ebi-a-GCST001837.vcf.gz; Date=Sat May 9 18:04:44 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-GCST001837/EBI-a-GCST001837.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-GCST001837/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-GCST001837/EBI-a-GCST001837.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 10:06:41 2019
Total time elapsed: 8.7sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9013,
"inflation_factor": 1.0841,
"mean_EFFECT": -0,
"n": "-Inf",
"n_snps": 1374543,
"n_clumped_hits": 0,
"n_p_sig": 0,
"n_mono": 9,
"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": 99,
"n_miss_AF_reference": 8797,
"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 | 33 | 0 | 1371260 | 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 | 1371262 | 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 | 8.412544e+00 | 5.579434e+00 | 1.0000e+00 | 4.000000e+00 | 7.000000e+00 | 1.200000e+01 | 2.300000e+01 | ▇▅▅▂▁ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.007957e+07 | 5.496651e+07 | 4.7011e+04 | 3.472252e+07 | 7.189479e+07 | 1.148189e+08 | 2.491707e+08 | ▇▇▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.670000e-05 | 1.856390e-02 | -1.9170e-01 | -1.130000e-02 | -1.000000e-04 | 1.120000e-02 | 2.000000e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.700080e-02 | 5.783500e-03 | 1.2600e-02 | 1.340000e-02 | 1.480000e-02 | 1.840000e-02 | 6.760000e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.860716e-01 | 2.913854e-01 | 3.6000e-06 | 2.300001e-01 | 4.824996e-01 | 7.385009e-01 | 9.993000e-01 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.860711e-01 | 2.913831e-01 | 3.8000e-06 | 2.298003e-01 | 4.824983e-01 | 7.388827e-01 | 9.987172e-01 | ▇▇▇▇▇ |
| numeric | AF | 99 | 0.9999278 | NA | NA | NA | NA | NA | NA | NA | 3.850052e-01 | 2.554785e-01 | 8.0000e-03 | 1.670000e-01 | 3.330000e-01 | 5.760000e-01 | 1.000000e+00 | ▇▇▅▃▂ |
| numeric | AF_reference | 8797 | 0.9935847 | NA | NA | NA | NA | NA | NA | NA | 3.856396e-01 | 2.402397e-01 | 1.9970e-04 | 1.837060e-01 | 3.380590e-01 | 5.638980e-01 | 9.970050e-01 | ▇▇▆▃▂ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1018562 | rs9442371 | C | T | 0.0134 | 0.0131 | 0.3043000 | 0.3063548 | 0.592 | 0.530152 | NA |
| 1 | 1018704 | rs9442372 | A | G | 0.0127 | 0.0131 | 0.3309002 | 0.3323129 | 0.602 | 0.611022 | NA |
| 1 | 1040026 | rs6671356 | T | C | 0.0101 | 0.0195 | 0.6028996 | 0.6044940 | 0.075 | 0.282947 | NA |
| 1 | 1048955 | rs4970405 | A | G | 0.0117 | 0.0208 | 0.5737002 | 0.5737754 | 0.052 | 0.110623 | NA |
| 1 | 1049950 | rs12726255 | A | G | 0.0043 | 0.0189 | 0.8208001 | 0.8200247 | 0.067 | 0.289736 | NA |
| 1 | 1053452 | rs4970409 | G | A | 0.0063 | 0.0208 | 0.7614002 | 0.7619778 | 0.050 | 0.136182 | NA |
| 1 | 1097335 | rs9442385 | T | G | -0.0257 | 0.0263 | 0.3291001 | 0.3284769 | 0.942 | 0.834665 | NA |
| 1 | 1129672 | rs11260554 | G | T | -0.0034 | 0.0201 | 0.8645000 | 0.8656753 | 0.125 | 0.254393 | NA |
| 1 | 1156131 | rs2887286 | T | C | 0.0018 | 0.0179 | 0.9218000 | 0.9199008 | 0.183 | 0.511182 | NA |
| 1 | 1158277 | rs3813199 | G | A | 0.0157 | 0.0217 | 0.4681004 | 0.4693713 | 0.100 | 0.124201 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51101938 | rs6010044 | A | C | 0.0147 | 0.0161 | 0.3597999 | 0.3612197 | 0.245 | 0.2390180 | NA |
| 22 | 51103692 | rs8138460 | A | G | 0.0149 | 0.0129 | 0.2471997 | 0.2480746 | 0.442 | 0.2951280 | NA |
| 22 | 51104680 | rs9616906 | G | A | 0.0176 | 0.0128 | 0.1695001 | 0.1691314 | 0.450 | 0.2689700 | NA |
| 22 | 51105556 | rs9616812 | C | T | 0.0093 | 0.0128 | 0.4685004 | 0.4674940 | 0.483 | 0.3628190 | NA |
| 22 | 51109992 | rs9628185 | T | C | 0.0084 | 0.0128 | 0.5117997 | 0.5116633 | 0.483 | 0.4053510 | NA |
| 22 | 51150473 | rs5770820 | G | A | -0.0471 | 0.0155 | 0.0024160 | 0.0023759 | 0.190 | 0.2462060 | NA |
| 22 | 51163138 | rs715586 | C | T | 0.0352 | 0.0184 | 0.0557096 | 0.0557425 | 0.150 | 0.0902556 | NA |
| 22 | 51175626 | rs3810648 | A | G | 0.0681 | 0.0280 | 0.0149599 | 0.0150098 | 0.067 | 0.1084270 | NA |
| 23 | 51666786 | rs14115 | A | G | 0.0735 | 0.0315 | 0.0194998 | 0.0196307 | 0.025 | NA | NA |
| 23 | 118495837 | rs12882977 | G | A | 0.0135 | 0.0128 | 0.2939998 | 0.2915683 | 0.509 | 0.2307280 | NA |
bcf preview
1 1018562 rs9442371 C T . PASS AF=0.592 ES:SE:LP:AF:ID 0.0134:0.0131:0.516698:0.592:rs9442371
1 1018704 rs9442372 A G . PASS AF=0.602 ES:SE:LP:AF:ID 0.0127:0.0131:0.480303:0.602:rs9442372
1 1040026 rs6671356 T C . PASS AF=0.075 ES:SE:LP:AF:ID 0.0101:0.0195:0.219755:0.075:rs6671356
1 1048955 rs4970405 A G . PASS AF=0.052 ES:SE:LP:AF:ID 0.0117:0.0208:0.241315:0.052:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.067 ES:SE:LP:AF:ID 0.0043:0.0189:0.0857626:0.067:rs12726255
1 1053452 rs4970409 G A . PASS AF=0.05 ES:SE:LP:AF:ID 0.0063:0.0208:0.118387:0.05:rs4970409
1 1097335 rs9442385 T G . PASS AF=0.942 ES:SE:LP:AF:ID -0.0257:0.0263:0.482672:0.942:rs9442385
1 1129672 rs11260554 G T . PASS AF=0.125 ES:SE:LP:AF:ID -0.0034:0.0201:0.063235:0.125:rs11260554
1 1156131 rs2887286 T C . PASS AF=0.183 ES:SE:LP:AF:ID 0.0018:0.0179:0.0353633:0.183:rs2887286
1 1158277 rs3813199 G A . PASS AF=0.1 ES:SE:LP:AF:ID 0.0157:0.0217:0.329661:0.1:rs3813199
1 1162435 rs3766186 C A . PASS AF=0.1 ES:SE:LP:AF:ID 0.0157:0.022:0.322941:0.1:rs3766186
1 1165310 rs11260562 G A . PASS AF=0.052 ES:SE:LP:AF:ID -0.0654:0.0295:1.57988:0.052:rs11260562
1 1211292 rs6685064 C T . PASS AF=0.1 ES:SE:LP:AF:ID -0.0539:0.0254:1.4675:0.1:rs6685064
1 1462766 rs9439462 C T . PASS AF=0.042 ES:SE:LP:AF:ID -0.0364:0.0357:0.511873:0.042:rs9439462
1 1477244 rs7290 T C . PASS AF=0.25 ES:SE:LP:AF:ID 0.0067:0.0142:0.195111:0.25:rs7290
1 1478153 rs3766180 T C . PASS AF=0.242 ES:SE:LP:AF:ID 0.007:0.0142:0.208309:0.242:rs3766180
1 1478180 rs3766178 T C . PASS AF=0.267 ES:SE:LP:AF:ID 0.0081:0.0142:0.244735:0.267:rs3766178
1 1479333 rs7533 A G . PASS AF=0.246 ES:SE:LP:AF:ID 0.0069:0.0142:0.203495:0.246:rs7533
1 1497201 rs3766169 A C . PASS AF=0.246 ES:SE:LP:AF:ID 0.0056:0.0142:0.158015:0.246:rs3766169
1 1499298 rs9439468 A G . PASS AF=0.31 ES:SE:LP:AF:ID -0.0119:0.0136:0.417709:0.31:rs9439468
1 1500941 rs6603791 A G . PASS AF=0.3 ES:SE:LP:AF:ID -0.0125:0.0136:0.445996:0.3:rs6603791
1 1505255 rs6603793 C T . PASS AF=0.3 ES:SE:LP:AF:ID -0.0116:0.0136:0.405166:0.3:rs6603793
1 1509034 rs7520996 T C . PASS AF=0.28 ES:SE:LP:AF:ID -0.0152:0.0141:0.549905:0.28:rs7520996
1 1510801 rs7519837 C T . PASS AF=0.292 ES:SE:LP:AF:ID -0.0137:0.0141:0.479385:0.292:rs7519837
1 1706160 rs7531583 A G . PASS AF=0.783 ES:SE:LP:AF:ID 0.0073:0.0156:0.193413:0.783:rs7531583
1 1708801 rs12044597 A G . PASS AF=0.517 ES:SE:LP:AF:ID -0.0005:0.0129:0.0146634:0.517:rs12044597
1 1721479 rs2272908 C T . PASS AF=0.517 ES:SE:LP:AF:ID -0.0002:0.0129:0.00607904:0.517:rs2272908
1 1722932 rs3737628 C T . PASS AF=0.508 ES:SE:LP:AF:ID -0.0008:0.0132:0.022505:0.508:rs3737628
1 1723031 rs9660180 G A . PASS AF=0.5 ES:SE:LP:AF:ID -0.0012:0.0129:0.0334828:0.5:rs9660180
1 1748734 rs2180311 T C . PASS AF=0.517 ES:SE:LP:AF:ID 0.0007:0.0129:0.0180003:0.517:rs2180311
1 1759054 rs10907187 G A . PASS AF=0.275 ES:SE:LP:AF:ID 0.0074:0.0143:0.219467:0.275:rs10907187
1 1776269 rs4648727 C A . PASS AF=0.458 ES:SE:LP:AF:ID -0.0028:0.0129:0.0818123:0.458:rs4648727
1 1778090 rs12126768 T G . PASS AF=0.186 ES:SE:LP:AF:ID -0.0151:0.0171:0.423659:0.186:rs12126768
1 1778469 rs6687065 A G . PASS AF=0.517 ES:SE:LP:AF:ID 0.0005:0.0129:0.0142144:0.517:rs6687065
1 1783201 rs12402876 G A . PASS AF=0.275 ES:SE:LP:AF:ID 0.0077:0.0143:0.227752:0.275:rs12402876
1 1787378 rs12125422 G A . PASS AF=0.192 ES:SE:LP:AF:ID -0.0162:0.0171:0.46382:0.192:rs12125422
1 1797947 rs9660106 A G . PASS AF=0.517 ES:SE:LP:AF:ID 0.0004:0.0129:0.0116194:0.517:rs9660106
1 1801034 rs4648592 G A . PASS AF=0.275 ES:SE:LP:AF:ID 0.008:0.0143:0.237772:0.275:rs4648592
1 1805391 rs10907193 G A . PASS AF=0.275 ES:SE:LP:AF:ID 0.0077:0.0143:0.230106:0.275:rs10907193
1 1810090 rs7525092 C T . PASS AF=0.275 ES:SE:LP:AF:ID 0.0077:0.0143:0.229074:0.275:rs7525092
1 1812688 rs6603803 A G . PASS AF=0.517 ES:SE:LP:AF:ID 0.0008:0.0129:0.0229627:0.517:rs6603803
1 1818129 rs16824526 A C . PASS AF=0.192 ES:SE:LP:AF:ID -0.0168:0.0171:0.485187:0.192:rs16824526
1 1888193 rs3820011 C A . PASS AF=0.292 ES:SE:LP:AF:ID -0.0042:0.0143:0.113622:0.292:rs3820011
1 2024064 rs2459994 C T . PASS AF=0.116 ES:SE:LP:AF:ID 0.0302:0.0194:0.922996:0.116:rs2459994
1 2027901 rs7513222 G A . PASS AF=0.292 ES:SE:LP:AF:ID 0.0156:0.0143:0.559878:0.292:rs7513222
1 2033256 rs908742 G A . PASS AF=0.339 ES:SE:LP:AF:ID 0.0029:0.0139:0.0771189:0.339:rs908742
1 2039719 rs1878752 G A . PASS AF=0.911 ES:SE:LP:AF:ID 0.0003:0.0197:0.00572685:0.911:rs1878752
1 2040763 rs4648805 G A . PASS AF=0.89 ES:SE:LP:AF:ID 0.0003:0.0197:0.00572685:0.89:rs4648805
1 2040898 rs4648807 T C . PASS AF=0.625 ES:SE:LP:AF:ID 0.0015:0.0133:0.039672:0.625:rs4648807
1 2040936 rs4648808 T C . PASS AF=0.89 ES:SE:LP:AF:ID 0.0005:0.0196:0.008641:0.89:rs4648808