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": "2019-10-26T09:30:50.835418",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004885/EBI-a-GCST004885_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",
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"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-GCST004885/EBI-a-GCST004885.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004885/EBI-a-GCST004885_data.vcf.gz; Date=Sat Oct 26 09:44:13 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-GCST004885/EBI-a-GCST004885.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-GCST004885/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 10:07:04 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004885/EBI-a-GCST004885.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 10:07:04 2019
Total time elapsed: 0.58sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9135,
"inflation_factor": 1.0802,
"mean_EFFECT": -0.0207,
"n": "-Inf",
"n_snps": 504798,
"n_clumped_hits": 2,
"n_p_sig": 12,
"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": 12,
"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 | 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 | 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 | -2.070910e-02 | 3.462559e+00 | -7.08396e+02 | -8.490450e-02 | -2.995500e-03 | 7.696100e-02 | 7.083960e+02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.264317e-01 | 1.020235e+00 | -7.22854e+02 | -1.359730e-01 | -1.065530e-01 | -9.407450e-02 | 0.000000e+00 | ▁▁▁▁▇ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.876874e-01 | 2.919989e-01 | 0.00000e+00 | 2.315000e-01 | 4.833002e-01 | 7.411003e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.879297e-01 | 2.917765e-01 | 0.00000e+00 | 2.320073e-01 | 4.833645e-01 | 7.411466e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 12 | 0.9999761 | NA | NA | NA | NA | NA | NA | NA | 3.710420e-01 | 2.586242e-01 | 1.92300e-03 | 1.486000e-01 | 3.135000e-01 | 5.615000e-01 | 9.980770e-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 | -0.0016013 | -0.1486200 | 0.9913000 | 0.9914035 | 0.1027 | 0.191893 | NA |
| 1 | 1005806 | rs3934834 | C | T | 0.0188218 | -0.1224320 | 0.8781000 | 0.8778205 | 0.1654 | 0.223442 | NA |
| 1 | 1018704 | rs9442372 | A | G | -0.1124350 | -0.0911942 | 0.2141002 | 0.2176062 | 0.5196 | 0.611022 | NA |
| 1 | 1021415 | rs3737728 | A | G | -0.0601539 | -0.0996241 | 0.5427002 | 0.5459708 | 0.7028 | 0.812700 | NA |
| 1 | 1030565 | rs6687776 | C | T | -0.0089398 | -0.1222700 | 0.9414999 | 0.9417142 | 0.1641 | 0.306709 | NA |
| 1 | 1048955 | rs4970405 | A | G | 0.0657877 | -0.1420220 | 0.6431999 | 0.6432053 | 0.1152 | 0.110623 | NA |
| 1 | 1049950 | rs12726255 | A | G | -0.0185714 | -0.1295380 | 0.8860000 | 0.8860008 | 0.1431 | 0.289736 | NA |
| 1 | 1061166 | rs11807848 | T | C | 0.0459289 | -0.0919697 | 0.6179993 | 0.6175035 | 0.4216 | 0.329673 | NA |
| 1 | 1062638 | rs9442373 | C | A | -0.0497421 | -0.0909133 | 0.5865000 | 0.5842841 | 0.5412 | 0.574281 | NA |
| 1 | 1064979 | rs2298217 | C | T | -0.0340740 | -0.1272200 | 0.7889001 | 0.7888261 | 0.1473 | 0.164736 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154774707 | rs2305518 | C | T | -0.0421012 | -0.1060610 | 0.6887998 | 0.6914023 | 0.7549 | 0.643179 | NA |
| 23 | 154776644 | rs5940479 | T | C | -0.0421012 | -0.1056630 | 0.6928002 | 0.6903001 | 0.7549 | 0.643444 | NA |
| 23 | 154777564 | rs5940484 | C | A | -0.0314987 | -0.1058770 | 0.7693997 | 0.7660827 | 0.7568 | 0.637086 | NA |
| 23 | 154830377 | rs5983658 | C | T | -0.0227395 | -0.1067170 | 0.8281000 | 0.8312628 | 0.7578 | 0.638411 | NA |
| 23 | 154892230 | rs553678 | A | G | -0.0285875 | -0.1057520 | 0.7853007 | 0.7869096 | 0.7577 | 0.636291 | NA |
| 23 | 154899846 | rs473491 | A | G | -0.0769610 | -0.0996702 | 0.4430003 | 0.4400217 | 0.7096 | 0.529007 | NA |
| 23 | 154929412 | rs557132 | C | T | 0.0237165 | -0.1069210 | 0.8269000 | 0.8244592 | 0.2384 | 0.356821 | NA |
| 23 | 155031838 | rs1764581 | A | G | -0.1195590 | -0.0909799 | 0.1886001 | 0.1888040 | 0.5236 | 0.504792 | NA |
| 23 | 155092794 | rs6567787 | C | T | 0.0449734 | -0.1088130 | 0.6801004 | 0.6793802 | 0.2192 | 0.305711 | NA |
| 23 | 155217992 | rs5983854 | C | A | -0.0544882 | -0.0905919 | 0.5466005 | 0.5475278 | 0.5465 | 0.588059 | NA |
bcf preview
1 768448 rs12562034 G A . PASS AF=0.1027 ES:SE:LP:AF:ID -0.00160128:-0.14862:0.00379489:0.1027:rs12562034
1 1005806 rs3934834 C T . PASS AF=0.1654 ES:SE:LP:AF:ID 0.0188218:-0.122432:0.056456:0.1654:rs3934834
1 1018704 rs9442372 A G . PASS AF=0.5196 ES:SE:LP:AF:ID -0.112435:-0.0911942:0.669383:0.5196:rs9442372
1 1021415 rs3737728 A G . PASS AF=0.7028 ES:SE:LP:AF:ID -0.0601539:-0.0996241:0.26544:0.7028:rs3737728
1 1030565 rs6687776 C T . PASS AF=0.1641 ES:SE:LP:AF:ID -0.00893984:-0.12227:0.0261797:0.1641:rs6687776
1 1048955 rs4970405 A G . PASS AF=0.1152 ES:SE:LP:AF:ID 0.0657877:-0.142022:0.191654:0.1152:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.1431 ES:SE:LP:AF:ID -0.0185714:-0.129538:0.0525663:0.1431:rs12726255
1 1061166 rs11807848 T C . PASS AF=0.4216 ES:SE:LP:AF:ID 0.0459289:-0.0919697:0.209012:0.4216:rs11807848
1 1062638 rs9442373 C A . PASS AF=0.5412 ES:SE:LP:AF:ID -0.0497421:-0.0909133:0.231732:0.5412:rs9442373
1 1064979 rs2298217 C T . PASS AF=0.1473 ES:SE:LP:AF:ID -0.034074:-0.12722:0.102978:0.1473:rs2298217
1 1066029 rs12145826 G A . PASS AF=0.08039 ES:SE:LP:AF:ID 0.0246926:-0.167051:0.0537934:0.08039:rs12145826
1 1087683 rs9442380 T C . PASS AF=0.93922 ES:SE:LP:AF:ID 0.199183:-0.189088:0.535361:0.93922:rs9442380
1 1090557 rs7553429 A C . PASS AF=0.03488 ES:SE:LP:AF:ID 0.202124:-0.246984:0.385103:0.03488:rs7553429
1 1094738 rs4970362 A G . PASS AF=0.6514 ES:SE:LP:AF:ID -0.00995033:-0.0959594:0.0361178:0.6514:rs4970362
1 1099342 rs9660710 A C . PASS AF=0.93874 ES:SE:LP:AF:ID 0.139262:-0.189371:0.335452:0.93874:rs9660710
1 1106473 rs4970420 G A . PASS AF=0.209 ES:SE:LP:AF:ID 0.103459:-0.111554:0.451488:0.209:rs4970420
1 1119858 rs1320565 C T . PASS AF=0.07143 ES:SE:LP:AF:ID -0.162519:-0.174317:0.455188:0.07143:rs1320565
1 1121794 rs11260549 G A . PASS AF=0.09266 ES:SE:LP:AF:ID -0.260326:-0.154603:1.03886:0.09266:rs11260549
1 1135242 rs9729550 A C . PASS AF=0.2344 ES:SE:LP:AF:ID -0.158527:-0.106739:0.863279:0.2344:rs9729550
1 1152631 rs11721 C A . PASS AF=0.07198 ES:SE:LP:AF:ID -0.138458:-0.174407:0.369572:0.07198:rs11721
1 1156131 rs2887286 T C . PASS AF=0.1523 ES:SE:LP:AF:ID -0.040822:-0.125949:0.127436:0.1523:rs2887286
1 1158277 rs3813199 G A . PASS AF=0.09073 ES:SE:LP:AF:ID -0.0726782:-0.156544:0.191992:0.09073:rs3813199
1 1162435 rs3766186 C A . PASS AF=0.08755 ES:SE:LP:AF:ID -0.0889405:-0.159633:0.238373:0.08755:rs3766186
1 1163804 rs7515488 C T . PASS AF=0.128 ES:SE:LP:AF:ID -0.200526:-0.135696:0.857611:0.128:rs7515488
1 1172907 rs715643 C T . PASS AF=0.0415 ES:SE:LP:AF:ID -0.503354:-0.226486:1.60783:0.0415:rs715643
1 1176597 rs6675798 T C . PASS AF=0.06977 ES:SE:LP:AF:ID -0.339818:-0.176103:1.28001:0.06977:rs6675798
1 1192515 rs7524470 A G . PASS AF=0.03113 ES:SE:LP:AF:ID -0.0668879:-0.25988:0.0985417:0.03113:rs7524470
1 1194804 rs11804831 T C . PASS AF=0.164 ES:SE:LP:AF:ID -0.106472:-0.123517:0.410274:0.164:rs11804831
1 1211292 rs6685064 C T . PASS AF=0.07004 ES:SE:LP:AF:ID 0.0266419:-0.177244:0.0552213:0.07004:rs6685064
1 1310924 rs2765033 T C . PASS AF=0.98456 ES:SE:LP:AF:ID 0.305982:-0.362983:0.400772:0.98456:rs2765033
1 1314015 rs2649588 C T . PASS AF=0.93992 ES:SE:LP:AF:ID 0.0489802:-0.189688:0.0989778:0.93992:rs2649588
1 1425700 rs819980 T C . PASS AF=0.06667 ES:SE:LP:AF:ID -0.150823:-0.181384:0.392438:0.06667:rs819980
1 1462766 rs9439462 C T . PASS AF=0.01946 ES:SE:LP:AF:ID -0.563172:-0.324056:1.10646:0.01946:rs9439462
1 1478180 rs3766178 T C . PASS AF=0.2793 ES:SE:LP:AF:ID -0.027474:-0.100917:0.104909:0.2793:rs3766178
1 1485984 rs2031709 C A . PASS AF=0.01765 ES:SE:LP:AF:ID 0.253867:-0.346805:0.3347:0.01765:rs2031709
1 1486834 rs3128342 C A . PASS AF=0.4202 ES:SE:LP:AF:ID -0.0730009:-0.0915524:0.371305:0.4202:rs3128342
1 1497824 rs2296716 C T . PASS AF=0.1231 ES:SE:LP:AF:ID 0.0695261:-0.137159:0.212256:0.1231:rs2296716
1 1505255 rs6603793 C T . PASS AF=0.3074 ES:SE:LP:AF:ID -0.0686001:-0.0978857:0.315334:0.3074:rs6603793
1 1706136 rs6603811 T C . PASS AF=0.95736 ES:SE:LP:AF:ID 0.283823:-0.222288:0.699405:0.95736:rs6603811
1 1706160 rs7531583 A G . PASS AF=0.8081 ES:SE:LP:AF:ID 0.286616:-0.114229:1.92738:0.8081:rs7531583
1 1745726 rs16825336 G A . PASS AF=0.0888 ES:SE:LP:AF:ID -0.0258308:-0.158266:0.0603809:0.0888:rs16825336
1 1748734 rs2180311 T C . PASS AF=0.5079 ES:SE:LP:AF:ID 0.0667236:-0.0910967:0.33208:0.5079:rs2180311
1 1781220 rs6681938 T C . PASS AF=0.3281 ES:SE:LP:AF:ID 0.119559:-0.096437:0.668775:0.3281:rs6681938
1 1793111 rs10907192 A G . PASS AF=0.95577 ES:SE:LP:AF:ID 0.193827:-0.218019:0.428408:0.95577:rs10907192
1 1801034 rs4648592 G A . PASS AF=0.2891 ES:SE:LP:AF:ID 0.111541:-0.100061:0.57659:0.2891:rs4648592
1 1810090 rs7525092 C T . PASS AF=0.2925 ES:SE:LP:AF:ID 0.125751:-0.100308:0.678402:0.2925:rs7525092
1 1844046 rs2474460 C T . PASS AF=0.4883 ES:SE:LP:AF:ID 0.0276152:-0.0906247:0.118615:0.4883:rs2474460
1 1873625 rs12758705 G A . PASS AF=0.2925 ES:SE:LP:AF:ID 0.147558:-0.100114:0.853872:0.2925:rs12758705
1 1874581 rs2803329 A G . PASS AF=0.8405 ES:SE:LP:AF:ID 0.305575:-0.122923:1.89997:0.8405:rs2803329
1 1888193 rs3820011 C A . PASS AF=0.3016 ES:SE:LP:AF:ID 0.123986:-0.0988596:0.677574:0.3016:rs3820011