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": "2019-10-26T09:40:34.005176",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004883/EBI-a-GCST004883_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-GCST004883/EBI-a-GCST004883.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004883/EBI-a-GCST004883_data.vcf.gz; Date=Sat Oct 26 09:45:36 2019",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST004883/ebi-a-GCST004883.vcf.gz; Date=Sun May 10 09:41:38 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-GCST004883/EBI-a-GCST004883.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-GCST004883/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 10:07:03 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004883/EBI-a-GCST004883.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 140, in read_vcf
o = [[rec.id, rec.samples[sample]['ES'][0]/rec.samples[sample]['SE'][0]] for rec in vcf_in.fetch()]
ZeroDivisionError: float division by zero
Analysis finished at Sat Oct 26 10:07:03 2019
Total time elapsed: 0.05sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9097,
"inflation_factor": 1.061,
"mean_EFFECT": -0.2949,
"n": "-Inf",
"n_snps": 504798,
"n_clumped_hits": 4,
"n_p_sig": 10,
"n_mono": 0,
"n_ns": 503110,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 316,
"n_miss_AF_reference": 2752,
"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 | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | TRUE |
| 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 | 30 | 0 | 503110 | 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 | 503110 | 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 | 9.145714e+00 | 6.102692e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.786048e+07 | 5.655991e+07 | 3.30120e+04 | 3.086719e+07 | 6.872085e+07 | 1.147535e+08 | 2.492107e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.948621e-01 | 1.775251e+01 | -7.08396e+02 | -1.252230e-01 | -3.807200e-03 | 1.142210e-01 | 7.083960e+02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.699127e-01 | 7.692084e+00 | -7.22854e+02 | -2.039500e-01 | -1.592090e-01 | -1.406570e-01 | 0.000000e+00 | ▁▁▁▁▇ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.907569e-01 | 2.909754e-01 | 0.00000e+00 | 2.366999e-01 | 4.871997e-01 | 7.424992e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.913162e-01 | 2.904822e-01 | 0.00000e+00 | 2.380446e-01 | 4.875323e-01 | 7.424663e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 316 | 0.9993719 | NA | NA | NA | NA | NA | NA | NA | 3.711571e-01 | 2.591256e-01 | 4.42500e-03 | 1.504000e-01 | 3.142000e-01 | 5.619000e-01 | 9.955750e-01 | ▇▆▅▃▂ |
| numeric | AF_reference | 2752 | 0.9945300 | NA | NA | NA | NA | NA | NA | NA | 3.736784e-01 | 2.437016e-01 | 1.99700e-04 | 1.697280e-01 | 3.202880e-01 | 5.511180e-01 | 9.968050e-01 | ▇▇▅▃▂ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 768448 | rs12562034 | G | A | -0.1029190 | -0.231518 | 0.6567000 | 0.6566523 | 0.09375 | 0.191893 | NA |
| 1 | 1005806 | rs3934834 | C | T | -0.0486652 | -0.186010 | 0.7933996 | 0.7936092 | 0.15620 | 0.223442 | NA |
| 1 | 1018704 | rs9442372 | A | G | -0.0657877 | -0.135547 | 0.6252994 | 0.6274283 | 0.53120 | 0.611022 | NA |
| 1 | 1021415 | rs3737728 | A | G | -0.0778865 | -0.146659 | 0.5951001 | 0.5953688 | 0.69910 | 0.812700 | NA |
| 1 | 1030565 | rs6687776 | C | T | -0.1724500 | -0.192760 | 0.3705996 | 0.3709818 | 0.14290 | 0.306709 | NA |
| 1 | 1048955 | rs4970405 | A | G | -0.1743530 | -0.231317 | 0.4503996 | 0.4510050 | 0.09292 | 0.110623 | NA |
| 1 | 1049950 | rs12726255 | A | G | -0.1749490 | -0.204021 | 0.3903004 | 0.3911659 | 0.12500 | 0.289736 | NA |
| 1 | 1061166 | rs11807848 | T | C | 0.0953102 | -0.135825 | 0.4833002 | 0.4828580 | 0.43360 | 0.329673 | NA |
| 1 | 1062638 | rs9442373 | C | A | -0.0880109 | -0.136154 | 0.5159006 | 0.5180158 | 0.53150 | 0.574281 | NA |
| 1 | 1064979 | rs2298217 | C | T | -0.2340780 | -0.203816 | 0.2496002 | 0.2507717 | 0.12390 | 0.164736 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154774707 | rs2305518 | C | T | -0.0806579 | -0.155410 | 0.6053994 | 0.6037602 | 0.7478 | 0.643179 | NA |
| 23 | 154776644 | rs5940479 | T | C | -0.0797350 | -0.155534 | 0.6080006 | 0.6081939 | 0.7478 | 0.643444 | NA |
| 23 | 154777564 | rs5940484 | C | A | -0.0797350 | -0.155534 | 0.6088005 | 0.6081939 | 0.7478 | 0.637086 | NA |
| 23 | 154830377 | rs5983658 | C | T | -0.0769610 | -0.155558 | 0.6203003 | 0.6207826 | 0.7478 | 0.638411 | NA |
| 23 | 154892230 | rs553678 | A | G | -0.0934903 | -0.156100 | 0.5462004 | 0.5492310 | 0.7455 | 0.636291 | NA |
| 23 | 154899846 | rs473491 | A | G | -0.0411419 | -0.149949 | 0.7820995 | 0.7837983 | 0.7168 | 0.529007 | NA |
| 23 | 154929412 | rs557132 | C | T | 0.0980337 | -0.155454 | 0.5279004 | 0.5282836 | 0.2522 | 0.356821 | NA |
| 23 | 155031838 | rs1764581 | A | G | -0.0353671 | -0.135448 | 0.7956000 | 0.7940061 | 0.5446 | 0.504792 | NA |
| 23 | 155092794 | rs6567787 | C | T | -0.1048050 | -0.169781 | 0.5366004 | 0.5370400 | 0.1947 | 0.305711 | NA |
| 23 | 155217992 | rs5983854 | C | A | -0.0276152 | -0.135067 | 0.8358001 | 0.8379975 | 0.5531 | 0.588059 | NA |
bcf preview
1 768448 rs12562034 G A . PASS AF=0.09375 ES:SE:LP:AF:ID -0.102919:-0.231518:0.182633:0.09375:rs12562034
1 1005806 rs3934834 C T . PASS AF=0.1562 ES:SE:LP:AF:ID -0.0486652:-0.18601:0.100508:0.1562:rs3934834
1 1018704 rs9442372 A G . PASS AF=0.5312 ES:SE:LP:AF:ID -0.0657877:-0.135547:0.203912:0.5312:rs9442372
1 1021415 rs3737728 A G . PASS AF=0.6991 ES:SE:LP:AF:ID -0.0778865:-0.146659:0.22541:0.6991:rs3737728
1 1030565 rs6687776 C T . PASS AF=0.1429 ES:SE:LP:AF:ID -0.17245:-0.19276:0.431095:0.1429:rs6687776
1 1048955 rs4970405 A G . PASS AF=0.09292 ES:SE:LP:AF:ID -0.174353:-0.231317:0.346402:0.09292:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.125 ES:SE:LP:AF:ID -0.174949:-0.204021:0.408601:0.125:rs12726255
1 1061166 rs11807848 T C . PASS AF=0.4336 ES:SE:LP:AF:ID 0.0953102:-0.135825:0.315783:0.4336:rs11807848
1 1062638 rs9442373 C A . PASS AF=0.5315 ES:SE:LP:AF:ID -0.0880109:-0.136154:0.287434:0.5315:rs9442373
1 1064979 rs2298217 C T . PASS AF=0.1239 ES:SE:LP:AF:ID -0.234078:-0.203816:0.602755:0.1239:rs2298217
1 1066029 rs12145826 G A . PASS AF=0.04911 ES:SE:LP:AF:ID -0.501866:-0.311449:0.985479:0.04911:rs12145826
1 1087683 rs9442380 T C . PASS AF=0.91518 ES:SE:LP:AF:ID -0.159565:-0.243104:0.292685:0.91518:rs9442380
1 1090557 rs7553429 A C . PASS AF=0.02679 ES:SE:LP:AF:ID -0.0701006:-0.418013:0.0620811:0.02679:rs7553429
1 1094738 rs4970362 A G . PASS AF=0.5721 ES:SE:LP:AF:ID -0.344299:-0.13738:1.93256:0.5721:rs4970362
1 1099342 rs9660710 A C . PASS AF=0.92661 ES:SE:LP:AF:ID -0.0544882:-0.262651:0.0778976:0.92661:rs9660710
1 1106473 rs4970420 G A . PASS AF=0.1351 ES:SE:LP:AF:ID -0.421747:-0.197926:1.49716:0.1351:rs4970420
1 1119858 rs1320565 C T . PASS AF=0.07965 ES:SE:LP:AF:ID -0.0447882:-0.248255:0.0670699:0.07965:rs1320565
1 1121794 rs11260549 G A . PASS AF=0.1018 ES:SE:LP:AF:ID -0.15642:-0.222161:0.318036:0.1018:rs11260549
1 1135242 rs9729550 A C . PASS AF=0.2857 ES:SE:LP:AF:ID 0.108854:-0.14956:0.331428:0.2857:rs9729550
1 1152631 rs11721 C A . PASS AF=0.1 ES:SE:LP:AF:ID 0.220741:-0.227493:0.480435:0.1:rs11721
1 1156131 rs2887286 T C . PASS AF=0.1473 ES:SE:LP:AF:ID -0.0802344:-0.190442:0.171791:0.1473:rs2887286
1 1158277 rs3813199 G A . PASS AF=0.1195 ES:SE:LP:AF:ID 0.234281:-0.207637:0.588717:0.1195:rs3813199
1 1162435 rs3766186 C A . PASS AF=0.1205 ES:SE:LP:AF:ID 0.267734:-0.208003:0.705975:0.1205:rs3766186
1 1163804 rs7515488 C T . PASS AF=0.1835 ES:SE:LP:AF:ID 0.225541:-0.177056:0.695294:0.1835:rs7515488
1 1172907 rs715643 C T . PASS AF=0.04054 ES:SE:LP:AF:ID -0.527802:-0.34261:0.924088:0.04054:rs715643
1 1176597 rs6675798 T C . PASS AF=0.1261 ES:SE:LP:AF:ID 0.314811:-0.204993:0.909742:0.1261:rs6675798
1 1192515 rs7524470 A G . PASS AF=0.03604 ES:SE:LP:AF:ID 0.0843411:-0.364165:0.0881502:0.03604:rs7524470
1 1194804 rs11804831 T C . PASS AF=0.1909 ES:SE:LP:AF:ID 0.0778865:-0.173353:0.185686:0.1909:rs11804831
1 1211292 rs6685064 C T . PASS AF=0.05357 ES:SE:LP:AF:ID -0.2589:-0.299336:0.413863:0.05357:rs6685064
1 1310924 rs2765033 T C . PASS AF=0.97768 ES:SE:LP:AF:ID -0.0695261:-0.457498:0.0557148:0.97768:rs2765033
1 1314015 rs2649588 C T . PASS AF=0.94196 ES:SE:LP:AF:ID 0.0857758:-0.288638:0.115545:0.94196:rs2649588
1 1425700 rs819980 T C . PASS AF=0.07589 ES:SE:LP:AF:ID -0.0112632:-0.255049:0.0156527:0.07589:rs819980
1 1462766 rs9439462 C T . PASS AF=0.04018 ES:SE:LP:AF:ID 0.183155:-0.344469:0.22614:0.04018:rs9439462
1 1478180 rs3766178 T C . PASS AF=0.3214 ES:SE:LP:AF:ID 0.173113:-0.144636:0.636952:0.3214:rs3766178
1 1485984 rs2031709 C A . PASS AF=0.01339 ES:SE:LP:AF:ID -0.026036:-0.587395:0.0156527:0.01339:rs2031709
1 1486834 rs3128342 C A . PASS AF=0.4513 ES:SE:LP:AF:ID 0.0535408:-0.135142:0.159831:0.4513:rs3128342
1 1497824 rs2296716 C T . PASS AF=0.1239 ES:SE:LP:AF:ID 0.076961:-0.204325:0.150581:0.1239:rs2296716
1 1505255 rs6603793 C T . PASS AF=0.3584 ES:SE:LP:AF:ID 0.161268:-0.14019:0.603278:0.3584:rs6603793
1 1706136 rs6603811 T C . PASS AF=0.95575 ES:SE:LP:AF:ID 0.245006:-0.326402:0.345342:0.95575:rs6603811
1 1706160 rs7531583 A G . PASS AF=0.7788 ES:SE:LP:AF:ID 0.107029:-0.161882:0.29388:0.7788:rs7531583
1 1745726 rs16825336 G A . PASS AF=0.07965 ES:SE:LP:AF:ID -0.144679:-0.248084:0.252278:0.07965:rs16825336
1 1748734 rs2180311 T C . PASS AF=0.4956 ES:SE:LP:AF:ID 0.0168571:-0.134386:0.0464336:0.4956:rs2180311
1 1781220 rs6681938 T C . PASS AF=0.3142 ES:SE:LP:AF:ID 0.0554347:-0.144753:0.154654:0.3142:rs6681938
1 1793111 rs10907192 A G . PASS AF=0.96396 ES:SE:LP:AF:ID 0.407217:-0.362764:0.587708:0.96396:rs10907192
1 1801034 rs4648592 G A . PASS AF=0.2768 ES:SE:LP:AF:ID 0.0506931:-0.150867:0.133122:0.2768:rs4648592
1 1810090 rs7525092 C T . PASS AF=0.2793 ES:SE:LP:AF:ID 0.0610951:-0.151308:0.163106:0.2793:rs7525092
1 1844046 rs2474460 C T . PASS AF=0.5 ES:SE:LP:AF:ID 0.0741794:-0.134945:0.23545:0.5:rs2474460
1 1873625 rs12758705 G A . PASS AF=0.308 ES:SE:LP:AF:ID 0.221542:-0.146286:0.889074:0.308:rs12758705
1 1874581 rs2803329 A G . PASS AF=0.8468 ES:SE:LP:AF:ID 0.353964:-0.18819:1.23366:0.8468:rs2803329
1 1888193 rs3820011 C A . PASS AF=0.3333 ES:SE:LP:AF:ID 0.270027:-0.143928:1.22243:0.3333:rs3820011