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": "2019-10-26T09:28:53.694205",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004884/EBI-a-GCST004884_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-GCST004884/EBI-a-GCST004884.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004884/EBI-a-GCST004884_data.vcf.gz; Date=Sat Oct 26 09:43:12 2019",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST004884/ebi-a-GCST004884.vcf.gz; Date=Sun May 10 10:50:21 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-GCST004884/EBI-a-GCST004884.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-GCST004884/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 10:05:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004884/EBI-a-GCST004884.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 10:05:17 2019
Total time elapsed: 0.22sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9059,
"inflation_factor": 1.0591,
"mean_EFFECT": -1.2261,
"n": "-Inf",
"n_snps": 504798,
"n_clumped_hits": 3,
"n_p_sig": 3,
"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": 1215,
"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 | TRUE |
| 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 | 1.000000 | 3 | 30 | 0 | 503110 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 503110 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | 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 | 1.000000 | 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 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | -1.226136e+00 | 3.479179e+01 | -7.08396e+02 | -1.638180e-01 | -7.729800e-03 | 1.441000e-01 | 7.083960e+02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | -4.976005e-01 | 1.359002e+01 | -7.22854e+02 | -2.611920e-01 | -2.050645e-01 | -1.808980e-01 | 0.000000e+00 | ▁▁▁▁▇ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.907815e-01 | 2.902162e-01 | 0.00000e+00 | 2.371002e-01 | 4.876003e-01 | 7.418746e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.915180e-01 | 2.896726e-01 | 0.00000e+00 | 2.392206e-01 | 4.882026e-01 | 7.419180e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 1215 | 0.997585 | NA | NA | NA | NA | NA | NA | NA | 3.712710e-01 | 2.602175e-01 | 7.24600e-03 | 1.471000e-01 | 3.134000e-01 | 5.635000e-01 | 9.927540e-01 | ▇▆▅▃▂ |
| numeric | AF_reference | 2752 | 0.994530 | 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 | -0.2646170 | -0.316169 | 0.4012999 | 0.4026218 | 0.08088 | 0.191893 | NA |
| 1 | 1005806 | rs3934834 | C | T | 0.1527210 | -0.227834 | 0.5030000 | 0.5026557 | 0.18460 | 0.223442 | NA |
| 1 | 1018704 | rs9442372 | A | G | -0.2515370 | -0.175509 | 0.1497001 | 0.1518048 | 0.48480 | 0.611022 | NA |
| 1 | 1021415 | rs3737728 | A | G | -0.1831550 | -0.184731 | 0.3197003 | 0.3214568 | 0.67650 | 0.812700 | NA |
| 1 | 1030565 | rs6687776 | C | T | -0.0456252 | -0.239477 | 0.8487000 | 0.8489015 | 0.15910 | 0.306709 | NA |
| 1 | 1048955 | rs4970405 | A | G | -0.1430620 | -0.293363 | 0.6254002 | 0.6257892 | 0.09559 | 0.110623 | NA |
| 1 | 1049950 | rs12726255 | A | G | -0.0923346 | -0.254857 | 0.7171005 | 0.7171281 | 0.13430 | 0.289736 | NA |
| 1 | 1061166 | rs11807848 | T | C | -0.0308717 | -0.177278 | 0.8617000 | 0.8617532 | 0.40300 | 0.329673 | NA |
| 1 | 1062638 | rs9442373 | C | A | 0.0908004 | -0.177431 | 0.6083997 | 0.6088256 | 0.57580 | 0.574281 | NA |
| 1 | 1064979 | rs2298217 | C | T | -0.1751870 | -0.254262 | 0.4903995 | 0.4908221 | 0.13040 | 0.164736 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154774707 | rs2305518 | C | T | -0.1275130 | -0.198417 | 0.5207005 | 0.5204502 | 0.7388 | 0.643179 | NA |
| 23 | 154776644 | rs5940479 | T | C | -0.1266330 | -0.198561 | 0.5225995 | 0.5236341 | 0.7388 | 0.643444 | NA |
| 23 | 154777564 | rs5940484 | C | A | -0.1070590 | -0.197774 | 0.5898995 | 0.5882871 | 0.7426 | 0.637086 | NA |
| 23 | 154830377 | rs5983658 | C | T | -0.0852598 | -0.200110 | 0.6710007 | 0.6700607 | 0.7463 | 0.638411 | NA |
| 23 | 154892230 | rs553678 | A | G | -0.0898407 | -0.197140 | 0.6503004 | 0.6485911 | 0.7464 | 0.636291 | NA |
| 23 | 154899846 | rs473491 | A | G | -0.0944007 | -0.190037 | 0.6179993 | 0.6193660 | 0.7059 | 0.529007 | NA |
| 23 | 154929412 | rs557132 | C | T | 0.1449660 | -0.198379 | 0.4641005 | 0.4649302 | 0.2612 | 0.356821 | NA |
| 23 | 155031838 | rs1764581 | A | G | 0.0520304 | -0.173960 | 0.7649006 | 0.7648683 | 0.5662 | 0.504792 | NA |
| 23 | 155092794 | rs6567787 | C | T | 0.2358620 | -0.197288 | 0.2310000 | 0.2318834 | 0.2536 | 0.305711 | NA |
| 23 | 155217992 | rs5983854 | C | A | 0.0550900 | -0.174470 | 0.7525009 | 0.7521875 | 0.5735 | 0.588059 | NA |
bcf preview
1 768448 rs12562034 G A . PASS AF=0.08088 ES:SE:LP:AF:ID -0.264617:-0.316169:0.396531:0.08088:rs12562034
1 1005806 rs3934834 C T . PASS AF=0.1846 ES:SE:LP:AF:ID 0.152721:-0.227834:0.298432:0.1846:rs3934834
1 1018704 rs9442372 A G . PASS AF=0.4848 ES:SE:LP:AF:ID -0.251537:-0.175509:0.824778:0.4848:rs9442372
1 1021415 rs3737728 A G . PASS AF=0.6765 ES:SE:LP:AF:ID -0.183155:-0.184731:0.495257:0.6765:rs3737728
1 1030565 rs6687776 C T . PASS AF=0.1591 ES:SE:LP:AF:ID -0.0456252:-0.239477:0.0712458:0.1591:rs6687776
1 1048955 rs4970405 A G . PASS AF=0.09559 ES:SE:LP:AF:ID -0.143062:-0.293363:0.203842:0.09559:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.1343 ES:SE:LP:AF:ID -0.0923346:-0.254857:0.14442:0.1343:rs12726255
1 1061166 rs11807848 T C . PASS AF=0.403 ES:SE:LP:AF:ID -0.0308717:-0.177278:0.0646439:0.403:rs11807848
1 1062638 rs9442373 C A . PASS AF=0.5758 ES:SE:LP:AF:ID 0.0908004:-0.177431:0.215811:0.5758:rs9442373
1 1064979 rs2298217 C T . PASS AF=0.1304 ES:SE:LP:AF:ID -0.175187:-0.254262:0.30945:0.1304:rs2298217
1 1066029 rs12145826 G A . PASS AF=0.0303 ES:SE:LP:AF:ID -1.0043:-0.508897:1.40044:0.0303:rs12145826
1 1087683 rs9442380 T C . PASS AF=0.93182 ES:SE:LP:AF:ID 0.0764492:-0.347329:0.0831777:0.93182:rs9442380
1 1090557 rs7553429 A C . PASS AF=0.03788 ES:SE:LP:AF:ID 0.288182:-0.459903:0.275888:0.03788:rs7553429
1 1094738 rs4970362 A G . PASS AF=0.5746 ES:SE:LP:AF:ID -0.334327:-0.175878:1.24749:0.5746:rs4970362
1 1099342 rs9660710 A C . PASS AF=0.93443 ES:SE:LP:AF:ID 0.0666741:-0.367898:0.0674755:0.93443:rs9660710
1 1106473 rs4970420 G A . PASS AF=0.2121 ES:SE:LP:AF:ID 0.122218:-0.214329:0.245575:0.2121:rs4970420
1 1119858 rs1320565 C T . PASS AF=0.08088 ES:SE:LP:AF:ID -0.027988:-0.316519:0.0317973:0.08088:rs1320565
1 1121794 rs11260549 G A . PASS AF=0.09559 ES:SE:LP:AF:ID -0.226023:-0.293219:0.356547:0.09559:rs11260549
1 1135242 rs9729550 A C . PASS AF=0.2615 ES:SE:LP:AF:ID -0.0127813:-0.200783:0.0225508:0.2615:rs9729550
1 1152631 rs11721 C A . PASS AF=0.06923 ES:SE:LP:AF:ID -0.180443:-0.347391:0.219683:0.06923:rs11721
1 1156131 rs2887286 T C . PASS AF=0.1136 ES:SE:LP:AF:ID -0.378628:-0.275595:0.777284:0.1136:rs2887286
1 1158277 rs3813199 G A . PASS AF=0.1045 ES:SE:LP:AF:ID 0.0834216:-0.284241:0.1143:0.1045:rs3813199
1 1162435 rs3766186 C A . PASS AF=0.1061 ES:SE:LP:AF:ID 0.123102:-0.284561:0.17757:0.1061:rs3766186
1 1163804 rs7515488 C T . PASS AF=0.1667 ES:SE:LP:AF:ID 0.108854:-0.23497:0.19233:0.1667:rs7515488
1 1172907 rs715643 C T . PASS AF=0.02308 ES:SE:LP:AF:ID -1.10927:-0.585402:1.33489:0.02308:rs715643
1 1176597 rs6675798 T C . PASS AF=0.09091 ES:SE:LP:AF:ID -0.0521358:-0.304687:0.0634863:0.09091:rs6675798
1 1192515 rs7524470 A G . PASS AF=0.0303 ES:SE:LP:AF:ID -0.0945305:-0.510809:0.069051:0.0303:rs7524470
1 1194804 rs11804831 T C . PASS AF=0.1418 ES:SE:LP:AF:ID -0.278128:-0.24899:0.581036:0.1418:rs11804831
1 1211292 rs6685064 C T . PASS AF=0.03788 ES:SE:LP:AF:ID -0.621943:-0.457754:0.776764:0.03788:rs6685064
1 1310924 rs2765033 T C . PASS AF=0.992424 ES:SE:LP:AF:ID 1.02638:-1.00624:0.542421:0.992424:rs2765033
1 1314015 rs2649588 C T . PASS AF=0.9697 ES:SE:LP:AF:ID 0.764643:-0.509252:0.906228:0.9697:rs2649588
1 1425700 rs819980 T C . PASS AF=0.06818 ES:SE:LP:AF:ID -0.126811:-0.347258:0.145755:0.06818:rs819980
1 1462766 rs9439462 C T . PASS AF=0.0303 ES:SE:LP:AF:ID -0.108922:-0.51069:0.080399:0.0303:rs9439462
1 1478180 rs3766178 T C . PASS AF=0.2652 ES:SE:LP:AF:ID -0.0989368:-0.198379:0.209012:0.2652:rs3766178
1 1485984 rs2031709 C A . PASS AF=0.02941 ES:SE:LP:AF:ID 0.777029:-0.514647:0.915066:0.02941:rs2031709
1 1486834 rs3128342 C A . PASS AF=0.4179 ES:SE:LP:AF:ID -0.0825124:-0.176278:0.194092:0.4179:rs3128342
1 1497824 rs2296716 C T . PASS AF=0.07246 ES:SE:LP:AF:ID -0.516676:-0.329834:0.94577:0.07246:rs2296716
1 1505255 rs6603793 C T . PASS AF=0.2754 ES:SE:LP:AF:ID -0.223769:-0.19178:0.615826:0.2754:rs6603793
1 1706136 rs6603811 T C . PASS AF=0.96377 ES:SE:LP:AF:ID 0.453343:-0.457524:0.497846:0.96377:rs6603811
1 1706160 rs7531583 A G . PASS AF=0.7609 ES:SE:LP:AF:ID 0.00601807:-0.201019:0.0105502:0.7609:rs7531583
1 1745726 rs16825336 G A . PASS AF=0.1087 ES:SE:LP:AF:ID 0.198031:-0.275489:0.326887:0.1087:rs16825336
1 1748734 rs2180311 T C . PASS AF=0.4632 ES:SE:LP:AF:ID -0.112497:-0.173099:0.287687:0.4632:rs2180311
1 1781220 rs6681938 T C . PASS AF=0.25 ES:SE:LP:AF:ID -0.262144:-0.199203:0.728158:0.25:rs6681938
1 1793111 rs10907192 A G . PASS AF=0.97101 ES:SE:LP:AF:ID 0.63224:-0.509322:0.68403:0.97101:rs10907192
1 1801034 rs4648592 G A . PASS AF=0.291 ES:SE:LP:AF:ID 0.121332:-0.191622:0.278189:0.291:rs4648592
1 1810090 rs7525092 C T . PASS AF=0.3047 ES:SE:LP:AF:ID 0.183987:-0.193382:0.4675:0.3047:rs7525092
1 1844046 rs2474460 C T . PASS AF=0.4328 ES:SE:LP:AF:ID -0.195893:-0.175432:0.579055:0.4328:rs2474460
1 1873625 rs12758705 G A . PASS AF=0.2803 ES:SE:LP:AF:ID 0.0880109:-0.194956:0.186419:0.2803:rs12758705
1 1874581 rs2803329 A G . PASS AF=0.7769 ES:SE:LP:AF:ID -0.107957:-0.212336:0.215026:0.7769:rs2803329
1 1888193 rs3820011 C A . PASS AF=0.3385 ES:SE:LP:AF:ID 0.293416:-0.186728:0.93968:0.3385:rs3820011