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": "2019-10-26T21:46:45.058474",
"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006940/EBI-a-GCST006940_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-GCST006940/EBI-a-GCST006940.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006940/EBI-a-GCST006940_data.vcf.gz; Date=Sat Oct 26 22:06:54 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-GCST006940/EBI-a-GCST006940.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-GCST006940/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 22:31:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006940/EBI-a-GCST006940.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 22:32:44 2019
Total time elapsed: 1.0m:12.55sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9589,
"inflation_factor": 1.3384,
"mean_EFFECT": -0,
"n": "-Inf",
"n_snps": 10824976,
"n_clumped_hits": 96,
"n_p_sig": 7886,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 429028,
"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 | TRUE |
| 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 | TRUE |
| 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 | 47 | 0 | 10809307 | 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 | 10809308 | 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 | 8.526276e+00 | 5.701006e+00 | 1.0000000 | 4.000000e+00 | 7.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.942164e+07 | 5.593376e+07 | 828.0000000 | 3.358340e+07 | 7.035975e+07 | 1.147893e+08 | 2.492251e+08 | ▇▇▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.970000e-05 | 2.013610e-02 | -0.4260280 | -4.690300e-03 | 1.140000e-05 | 4.669500e-03 | 4.479940e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.233080e-02 | 1.541640e-02 | 0.0022938 | 2.865700e-03 | 5.611800e-03 | 1.586700e-02 | 1.213030e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.517766e-01 | 3.017806e-01 | 0.0000000 | 1.785998e-01 | 4.352001e-01 | 7.130008e-01 | 9.996000e-01 | ▇▆▆▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.517766e-01 | 3.017805e-01 | 0.0000000 | 1.786253e-01 | 4.353904e-01 | 7.128736e-01 | 9.992021e-01 | ▇▆▆▅▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.801870e-01 | 2.533999e-01 | 0.0000894 | 5.471700e-03 | 4.879600e-02 | 2.651730e-01 | 9.998900e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 429028 | 0.9603094 | NA | NA | NA | NA | NA | NA | NA | 1.908489e-01 | 2.453555e-01 | 0.0000000 | 1.337860e-02 | 7.488020e-02 | 2.825480e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 715265 | rs12184267 | C | T | 0.0067870 | 0.0061476 | 0.2696001 | 0.2695934 | 0.0366377 | 0.0275559 | NA |
| 1 | 715367 | rs12184277 | A | G | 0.0056186 | 0.0061339 | 0.3595001 | 0.3596664 | 0.0367695 | 0.0281550 | NA |
| 1 | 717485 | rs12184279 | C | A | 0.0071992 | 0.0061584 | 0.2422998 | 0.2424036 | 0.0364631 | NA | NA |
| 1 | 720381 | rs116801199 | G | T | 0.0069178 | 0.0061003 | 0.2567001 | 0.2567949 | 0.0371104 | 0.0359425 | NA |
| 1 | 721290 | rs12565286 | G | C | 0.0071606 | 0.0060941 | 0.2402000 | 0.2399951 | 0.0372080 | 0.0371406 | NA |
| 1 | 723891 | rs2977670 | G | C | -0.0033249 | 0.0053198 | 0.5322994 | 0.5319712 | 0.9503520 | 0.7799520 | NA |
| 1 | 726794 | rs28454925 | C | G | 0.0063346 | 0.0061382 | 0.3018999 | 0.3020715 | 0.0366130 | 0.0279553 | NA |
| 1 | 729632 | rs116720794 | C | T | 0.0069565 | 0.0061130 | 0.2549999 | 0.2551203 | 0.0368931 | 0.0099840 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0011780 | 0.0031247 | 0.7064005 | 0.7061728 | 0.8316140 | 0.6399760 | NA |
| 1 | 752478 | rs146277091 | G | A | 0.0070329 | 0.0061103 | 0.2497998 | 0.2497325 | 0.0367696 | 0.0277556 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0007515 | 0.0057370 | 0.8954000 | 0.8957753 | 0.0427541 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0095869 | 0.0200983 | 0.6335998 | 0.6333624 | 0.0032748 | NA | NA |
| 22 | 51220025 | rs148808236 | G | A | -0.0233402 | 0.0275238 | 0.3962999 | 0.3964376 | 0.0017399 | 0.0215655 | NA |
| 22 | 51220146 | rs868950473 | C | T | 0.0003199 | 0.0199955 | 0.9869000 | 0.9872344 | 0.0033097 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0036206 | 0.0044047 | 0.4111999 | 0.4110759 | 0.0732027 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0111041 | 0.0049973 | 0.0262500 | 0.0262829 | 0.0568181 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0109078 | 0.0050382 | 0.0303599 | 0.0303869 | 0.0558736 | 0.0788738 | NA |
| 22 | 51224208 | rs116656403 | G | A | -0.0241487 | 0.0274417 | 0.3789998 | 0.3788592 | 0.0017501 | 0.0209665 | NA |
| 22 | 51224267 | rs138354270 | G | A | -0.0061148 | 0.0275442 | 0.8243000 | 0.8243140 | 0.0017376 | 0.0209665 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0032859 | 0.0043988 | 0.4551002 | 0.4550641 | 0.0735865 | 0.0730831 | NA |
bcf preview
1 715265 rs12184267 C T . PASS AF=0.0366377 ES:SE:LP:AF:ID 0.00678698:0.00614763:0.56928:0.0366377:rs12184267
1 715367 rs12184277 A G . PASS AF=0.0367695 ES:SE:LP:AF:ID 0.00561864:0.00613388:0.444301:0.0367695:rs12184277
1 717485 rs12184279 C A . PASS AF=0.0364631 ES:SE:LP:AF:ID 0.00719917:0.0061584:0.615647:0.0364631:rs12184279
1 720381 rs116801199 G T . PASS AF=0.0371104 ES:SE:LP:AF:ID 0.00691778:0.00610034:0.590574:0.0371104:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037208 ES:SE:LP:AF:ID 0.00716055:0.00609409:0.619427:0.037208:rs12565286
1 723891 rs2977670 G C . PASS AF=0.950352 ES:SE:LP:AF:ID -0.00332488:0.00531981:0.273844:0.950352:rs2977670
1 726794 rs28454925 C G . PASS AF=0.036613 ES:SE:LP:AF:ID 0.00633463:0.0061382:0.520137:0.036613:rs28454925
1 729632 rs116720794 C T . PASS AF=0.0368931 ES:SE:LP:AF:ID 0.00695654:0.00611295:0.59346:0.0368931:rs116720794
1 729679 rs4951859 C G . PASS AF=0.831614 ES:SE:LP:AF:ID 0.001178:0.00312466:0.150949:0.831614:rs4951859
1 752478 rs146277091 G A . PASS AF=0.0367696 ES:SE:LP:AF:ID 0.00703294:0.00611029:0.602408:0.0367696:rs146277091
1 752566 rs3094315 G A . PASS AF=0.829189 ES:SE:LP:AF:ID -0.000820925:0.00305177:0.103474:0.829189:rs3094315
1 752721 rs3131972 A G . PASS AF=0.826243 ES:SE:LP:AF:ID -0.000432882:0.00302715:0.0523703:0.826243:rs3131972
1 753405 rs3115860 C A . PASS AF=0.860653 ES:SE:LP:AF:ID 0.000385455:0.00332289:0.041962:0.860653:rs3115860
1 753541 rs2073813 G A . PASS AF=0.140455 ES:SE:LP:AF:ID -0.00122633:0.00331441:0.147886:0.140455:rs2073813
1 754063 rs12184312 G T . PASS AF=0.0371833 ES:SE:LP:AF:ID 0.00628358:0.00606523:0.522879:0.0371833:rs12184312
1 754105 rs12184325 C T . PASS AF=0.0374306 ES:SE:LP:AF:ID 0.00710013:0.00604267:0.61997:0.0374306:rs12184325
1 754182 rs3131969 A G . PASS AF=0.857265 ES:SE:LP:AF:ID 0.000802941:0.00329074:0.0931803:0.857265:rs3131969
1 754192 rs3131968 A G . PASS AF=0.857357 ES:SE:LP:AF:ID 0.000911823:0.00329178:0.106793:0.857357:rs3131968
1 754211 rs12184313 G A . PASS AF=0.0374202 ES:SE:LP:AF:ID 0.00727768:0.00604961:0.640544:0.0374202:rs12184313
1 754334 rs3131967 T C . PASS AF=0.857369 ES:SE:LP:AF:ID 0.000717629:0.00329187:0.0822845:0.857369:rs3131967
1 754503 rs3115859 G A . PASS AF=0.826634 ES:SE:LP:AF:ID -0.000288797:0.00303997:0.034328:0.826634:rs3115859
1 754629 rs10454459 A G . PASS AF=0.0374193 ES:SE:LP:AF:ID 0.00716543:0.00605189:0.626169:0.0374193:rs10454459
1 754964 rs3131966 C T . PASS AF=0.827271 ES:SE:LP:AF:ID -0.00018284:0.00304733:0.021135:0.827271:rs3131966
1 755775 rs3131965 A G . PASS AF=0.831031 ES:SE:LP:AF:ID -0.000390999:0.00312799:0.0455163:0.831031:rs3131965
1 755890 rs3115858 A T . PASS AF=0.860232 ES:SE:LP:AF:ID 0.00034103:0.00331097:0.0372519:0.860232:rs3115858
1 756604 rs3131962 A G . PASS AF=0.85979 ES:SE:LP:AF:ID -0.000320439:0.00330349:0.0350336:0.85979:rs3131962
1 757640 rs3115853 G A . PASS AF=0.854521 ES:SE:LP:AF:ID 0.000120479:0.00325618:0.0130045:0.854521:rs3115853
1 757734 rs4951929 C T . PASS AF=0.859889 ES:SE:LP:AF:ID -0.000188415:0.00330553:0.0202696:0.859889:rs4951929
1 757936 rs4951862 C A . PASS AF=0.859935 ES:SE:LP:AF:ID -0.000178526:0.00330603:0.0190881:0.859935:rs4951862
1 758144 rs3131956 A G . PASS AF=0.859942 ES:SE:LP:AF:ID -0.000168612:0.00330611:0.0182267:0.859942:rs3131956
1 758626 rs3131954 C T . PASS AF=0.860464 ES:SE:LP:AF:ID 0.000112686:0.00331429:0.0120211:0.860464:rs3131954
1 759036 rs114525117 G A . PASS AF=0.0374737 ES:SE:LP:AF:ID 0.00490998:0.00603933:0.380489:0.0374737:rs114525117
1 760912 rs1048488 C T . PASS AF=0.82959 ES:SE:LP:AF:ID -0.000468583:0.00306264:0.0561594:0.82959:rs1048488
1 760998 rs148828841 C A . PASS AF=0.0369479 ES:SE:LP:AF:ID 0.00541039:0.00609278:0.426548:0.0369479:rs148828841
1 761147 rs3115850 T C . PASS AF=0.829712 ES:SE:LP:AF:ID -0.000523946:0.00306401:0.0633355:0.829712:rs3115850
1 761606 rs377377186 G A . PASS AF=0.0358125 ES:SE:LP:AF:ID 0.00297493:0.00621071:0.19942:0.0358125:rs377377186
1 761732 rs2286139 C T . PASS AF=0.848492 ES:SE:LP:AF:ID 0.000471327:0.00327311:0.0529095:0.848492:rs2286139
1 761881 rs374493323 A C . PASS AF=0.0342849 ES:SE:LP:AF:ID 0.00434475:0.00636127:0.305658:0.0342849:rs374493323
1 766007 rs61768174 A C . PASS AF=0.105478 ES:SE:LP:AF:ID -0.000540854:0.0037822:0.0524683:0.105478:rs61768174
1 767096 rs115991721 A G . PASS AF=0.00259125 ES:SE:LP:AF:ID -0.00178236:0.0225615:0.0281677:0.00259125:rs115991721
1 768253 rs2977608 A C . PASS AF=0.744125 ES:SE:LP:AF:ID -0.0016468:0.00263489:0.274333:0.744125:rs2977608
1 768448 rs12562034 G A . PASS AF=0.111263 ES:SE:LP:AF:ID 0.00294719:0.00364751:0.377682:0.111263:rs12562034
1 768680 rs140605359 C T . PASS AF=0.00057169 ES:SE:LP:AF:ID -0.0496728:0.047993:0.522156:0.00057169:rs140605359
1 768819 rs12562811 C T . PASS AF=0.0121867 ES:SE:LP:AF:ID 0.00677896:0.0104937:0.285503:0.0121867:rs12562811
1 769223 rs60320384 C G . PASS AF=0.135131 ES:SE:LP:AF:ID -0.000157933:0.00336028:0.016509:0.135131:rs60320384
1 770504 rs150664068 C G . PASS AF=0.000562164 ES:SE:LP:AF:ID -0.0436554:0.0483984:0.434979:0.000562164:rs150664068
1 771823 rs2977605 T C . PASS AF=0.859693 ES:SE:LP:AF:ID -0.000251339:0.0033071:0.0271032:0.859693:rs2977605
1 771967 rs59066358 G A . PASS AF=0.136281 ES:SE:LP:AF:ID -8.70553e-05:0.00334828:0.00912859:0.136281:rs59066358
1 772755 rs2905039 A C . PASS AF=0.859685 ES:SE:LP:AF:ID -0.000347242:0.00330707:0.0379623:0.859685:rs2905039
1 776556 rs151160018 C T . PASS AF=0.00833475 ES:SE:LP:AF:ID -0.00267785:0.0126314:0.0799811:0.00833475:rs151160018