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": "2019-10-27T07:19:33.416128",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004734/EBI-a-GCST004734_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-GCST004734/EBI-a-GCST004734.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004734/EBI-a-GCST004734_data.vcf.gz; Date=Sun Oct 27 07:23:56 2019",
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}
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-GCST004734/EBI-a-GCST004734.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-GCST004734/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-GCST004734/EBI-a-GCST004734.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 07:46:22 2019
Total time elapsed: 15.06sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9287,
"inflation_factor": 1.0322,
"mean_EFFECT": 6.9632e-06,
"n": "-Inf",
"n_snps": 2549725,
"n_clumped_hits": 6,
"n_p_sig": 102,
"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": 20700,
"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 | 2543022 | 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 | 2543038 | 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 | 8.582789e+00 | 5.661143e+00 | 1.000000 | 4.000000e+00 | 8.000000e+00 | 1.200000e+01 | 2.30000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.893562e+07 | 5.579496e+07 | 6689.000000 | 3.263609e+07 | 7.034726e+07 | 1.144542e+08 | 2.49219e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.000000e-06 | 7.408900e-03 | -0.421123 | -3.541000e-03 | -7.000000e-06 | 3.525000e-03 | 3.75026e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.952600e-03 | 4.232500e-03 | 0.003575 | 3.971000e-03 | 4.685000e-03 | 6.346000e-03 | 3.14344e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.946969e-01 | 2.903197e-01 | 0.000000 | 2.415989e-01 | 4.931693e-01 | 7.463779e-01 | 9.99968e-01 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.929730e-01 | 2.907648e-01 | 0.000000 | 2.390671e-01 | 4.908079e-01 | 7.450338e-01 | 9.99972e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.596413e-01 | 2.699429e-01 | 0.010001 | 1.253890e-01 | 2.957940e-01 | 5.565398e-01 | 9.89994e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 20700 | 0.9918601 | NA | NA | NA | NA | NA | NA | NA | 3.624201e-01 | 2.555532e-01 | 0.000000 | 1.473640e-01 | 3.011180e-01 | 5.469250e-01 | 1.00000e+00 | ▇▆▅▃▂ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 721290 | rs12565286 | G | C | -0.006132 | 0.012490 | 0.6253527 | 0.6234599 | 0.052682 | 0.0371406 | NA |
| 1 | 723819 | rs11804171 | T | A | -0.002594 | 0.012521 | 0.8367089 | 0.8358756 | 0.055861 | 0.1345850 | NA |
| 1 | 723891 | rs2977670 | G | C | -0.001515 | 0.010406 | 0.8848650 | 0.8842458 | 0.932639 | 0.7799520 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.006556 | 0.005405 | 0.2276392 | 0.2251486 | 0.787067 | 0.7182510 | NA |
| 1 | 754192 | rs3131968 | A | G | 0.006343 | 0.005841 | 0.2800722 | 0.2775037 | 0.861152 | 0.6785140 | NA |
| 1 | 761732 | rs2286139 | C | T | 0.009311 | 0.007284 | 0.2035968 | 0.2011503 | 0.771618 | 0.6257990 | NA |
| 1 | 768448 | rs12562034 | G | A | -0.004817 | 0.010755 | 0.6560123 | 0.6542364 | 0.088946 | 0.1918930 | NA |
| 1 | 775659 | rs2905035 | A | G | 0.003599 | 0.006057 | 0.5546244 | 0.5523860 | 0.861948 | 0.7450080 | NA |
| 1 | 777122 | rs2980319 | A | T | 0.004143 | 0.006056 | 0.4962837 | 0.4939026 | 0.828615 | 0.7472040 | NA |
| 1 | 779322 | rs4040617 | A | G | -0.009559 | 0.006322 | 0.1325730 | 0.1305284 | 0.137702 | 0.2264380 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51196164 | rs8136603 | A | T | -0.004333 | 0.010240 | 0.6738915 | 0.6721898 | 0.058055 | 0.1427720 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.038264 | 0.011115 | 0.0006210 | 0.0005762 | 0.098665 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.009290 | 0.005682 | 0.1039080 | 0.1020516 | 0.380888 | 0.3724040 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.019297 | 0.009933 | 0.0533617 | 0.0520505 | 0.147409 | 0.1563500 | NA |
| 22 | 51217134 | rs117417021 | A | G | -0.009658 | 0.006443 | 0.1359800 | 0.1338759 | 0.418033 | 0.2671730 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.011358 | 0.009442 | 0.2315490 | 0.2290061 | 0.170236 | 0.2052720 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.002678 | 0.009740 | 0.7844983 | 0.7833557 | 0.060679 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.031865 | 0.013384 | 0.0179048 | 0.0172738 | 0.087922 | 0.0730831 | NA |
| 23 | 91415872 | rs6562597 | G | A | -0.024184 | 0.013675 | 0.0786104 | 0.0769803 | 0.019364 | 0.0021192 | NA |
| 23 | 118495837 | rs12882977 | G | A | -0.001858 | 0.003743 | 0.6216286 | 0.6196170 | 0.484853 | 0.2307280 | NA |
bcf preview
1 721290 rs12565286 G C . PASS AF=0.052682 ES:SE:LP:AF:ID -0.006132:0.01249:0.203875:0.052682:rs12565286
1 723819 rs11804171 T A . PASS AF=0.055861 ES:SE:LP:AF:ID -0.002594:0.012521:0.0774256:0.055861:rs11804171
1 723891 rs2977670 G C . PASS AF=0.932639 ES:SE:LP:AF:ID -0.001515:0.010406:0.053123:0.932639:rs2977670
1 752566 rs3094315 G A . PASS AF=0.787067 ES:SE:LP:AF:ID 0.006556:0.005405:0.642753:0.787067:rs3094315
1 754192 rs3131968 A G . PASS AF=0.861152 ES:SE:LP:AF:ID 0.006343:0.005841:0.55273:0.861152:rs3131968
1 761732 rs2286139 C T . PASS AF=0.771618 ES:SE:LP:AF:ID 0.009311:0.007284:0.691229:0.771618:rs2286139
1 768448 rs12562034 G A . PASS AF=0.088946 ES:SE:LP:AF:ID -0.004817:0.010755:0.183088:0.088946:rs12562034
1 775659 rs2905035 A G . PASS AF=0.861948 ES:SE:LP:AF:ID 0.003599:0.006057:0.256001:0.861948:rs2905035
1 777122 rs2980319 A T . PASS AF=0.828615 ES:SE:LP:AF:ID 0.004143:0.006056:0.30427:0.828615:rs2980319
1 779322 rs4040617 A G . PASS AF=0.137702 ES:SE:LP:AF:ID -0.009559:0.006322:0.877545:0.137702:rs4040617
1 780785 rs2977612 T A . PASS AF=0.861094 ES:SE:LP:AF:ID 0.005154:0.006508:0.365597:0.861094:rs2977612
1 785050 rs2905062 G A . PASS AF=0.859341 ES:SE:LP:AF:ID 0.008028:0.006305:0.687455:0.859341:rs2905062
1 785989 rs2980300 T C . PASS AF=0.793363 ES:SE:LP:AF:ID 0.007858:0.006196:0.683696:0.793363:rs2980300
1 798026 rs4951864 C T . PASS AF=0.902903 ES:SE:LP:AF:ID 0.003517:0.01003:0.138276:0.902903:rs4951864
1 798801 rs12132517 G A . PASS AF=0.102878 ES:SE:LP:AF:ID -0.00716:0.010034:0.32068:0.102878:rs12132517
1 798959 rs11240777 G A . PASS AF=0.251584 ES:SE:LP:AF:ID -0.003776:0.006275:0.259986:0.251584:rs11240777
1 888659 rs3748597 T C . PASS AF=0.940909 ES:SE:LP:AF:ID 0.01291:0.011533:0.57588:0.940909:rs3748597
1 918573 rs2341354 A G . PASS AF=0.542928 ES:SE:LP:AF:ID -0.001241:0.005844:0.0795156:0.542928:rs2341354
1 926431 rs4970403 A T . PASS AF=0.921422 ES:SE:LP:AF:ID 0.001861:0.012829:0.0529135:0.921422:rs4970403
1 947034 rs2465126 G A . PASS AF=0.890545 ES:SE:LP:AF:ID 0.005789:0.011895:0.201775:0.890545:rs2465126
1 962210 rs3128126 A G . PASS AF=0.440072 ES:SE:LP:AF:ID 0.003045:0.007038:0.175862:0.440072:rs3128126
1 990380 rs3121561 C T . PASS AF=0.292513 ES:SE:LP:AF:ID 0.007829:0.00623:0.675113:0.292513:rs3121561
1 990417 rs2465136 T C . PASS AF=0.341994 ES:SE:LP:AF:ID -0.001431:0.005963:0.0907655:0.341994:rs2465136
1 998501 rs3813193 G C . PASS AF=0.179329 ES:SE:LP:AF:ID -0.001636:0.007936:0.0769986:0.179329:rs3813193
1 1003629 rs4075116 C T . PASS AF=0.724551 ES:SE:LP:AF:ID -0.003219:0.004892:0.289979:0.724551:rs4075116
1 1005806 rs3934834 C T . PASS AF=0.158808 ES:SE:LP:AF:ID -0.000369:0.007325:0.0177315:0.158808:rs3934834
1 1017170 rs3766193 C G . PASS AF=0.539491 ES:SE:LP:AF:ID -0.000662:0.004527:0.0534156:0.539491:rs3766193
1 1017197 rs3766192 C T . PASS AF=0.548414 ES:SE:LP:AF:ID -0.001262:0.004208:0.116078:0.548414:rs3766192
1 1017587 rs3766191 C T . PASS AF=0.153068 ES:SE:LP:AF:ID -0.001293:0.006648:0.0723253:0.153068:rs3766191
1 1018562 rs9442371 C T . PASS AF=0.557052 ES:SE:LP:AF:ID -0.001492:0.0041:0.144253:0.557052:rs9442371
1 1018704 rs9442372 A G . PASS AF=0.557515 ES:SE:LP:AF:ID -0.001982:0.004101:0.200132:0.557515:rs9442372
1 1021346 rs10907177 A G . PASS AF=0.157391 ES:SE:LP:AF:ID -0.001212:0.007362:0.0604987:0.157391:rs10907177
1 1021415 rs3737728 A G . PASS AF=0.708991 ES:SE:LP:AF:ID -0.004559:0.00457:0.493296:0.708991:rs3737728
1 1021583 rs10907178 A C . PASS AF=0.158343 ES:SE:LP:AF:ID -0.001361:0.006569:0.0774017:0.158343:rs10907178
1 1021695 rs9442398 A G . PASS AF=0.717426 ES:SE:LP:AF:ID -0.003322:0.005097:0.286609:0.717426:rs9442398
1 1022037 rs6701114 C T . PASS AF=0.540585 ES:SE:LP:AF:ID -0.001975:0.00453:0.177414:0.540585:rs6701114
1 1026707 rs4074137 C A . PASS AF=0.585939 ES:SE:LP:AF:ID -0.005225:0.006719:0.357303:0.585939:rs4074137
1 1030565 rs6687776 C T . PASS AF=0.158946 ES:SE:LP:AF:ID -0.004576:0.006381:0.322692:0.158946:rs6687776
1 1030633 rs6678318 G A . PASS AF=0.142572 ES:SE:LP:AF:ID -0.001263:0.006922:0.0675166:0.142572:rs6678318
1 1031540 rs9651273 A G . PASS AF=0.68173 ES:SE:LP:AF:ID -0.004382:0.006505:0.298562:0.68173:rs9651273
1 1036959 rs11579015 T C . PASS AF=0.083297 ES:SE:LP:AF:ID -0.004674:0.007201:0.285178:0.083297:rs11579015
1 1039098 rs11260595 C A . PASS AF=0.033967 ES:SE:LP:AF:ID 0.006698:0.014089:0.196294:0.033967:rs11260595
1 1040026 rs6671356 T C . PASS AF=0.134423 ES:SE:LP:AF:ID -0.002832:0.006828:0.167502:0.134423:rs6671356
1 1041700 rs6604968 A G . PASS AF=0.186226 ES:SE:LP:AF:ID -0.001932:0.007165:0.103168:0.186226:rs6604968
1 1046164 rs6666280 C T . PASS AF=0.102477 ES:SE:LP:AF:ID -0.003619:0.007087:0.213586:0.102477:rs6666280
1 1048955 rs4970405 A G . PASS AF=0.092497 ES:SE:LP:AF:ID -0.003134:0.006388:0.203671:0.092497:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.100788 ES:SE:LP:AF:ID -0.001115:0.007834:0.0518796:0.100788:rs12726255
1 1053452 rs4970409 G A . PASS AF=0.080496 ES:SE:LP:AF:ID -0.00235:0.007779:0.117028:0.080496:rs4970409
1 1060174 rs7548798 C T . PASS AF=0.337528 ES:SE:LP:AF:ID 0.004812:0.00784:0.266356:0.337528:rs7548798
1 1060608 rs17160824 G A . PASS AF=0.087406 ES:SE:LP:AF:ID -0.002022:0.007975:0.0964407:0.087406:rs17160824