Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std plot

beta_std_plot

beta_std_plot

Metadata

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    "FORMAT.1": "<ID=ES,Number=A,Type=Float,Description=\"Effect size estimate relative to the alternative allele\">",
    "FORMAT.2": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
    "FORMAT.3": "<ID=ID,Number=1,Type=String,Description=\"Study variant identifier\">",
    "FORMAT.4": "<ID=LP,Number=A,Type=Float,Description=\"-log10 p-value for effect estimate\">",
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    "FORMAT.6": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
    "FORMAT.7": "<ID=SI,Number=A,Type=Float,Description=\"Accuracy score of summary data imputation\">",
    "FORMAT.8": "<ID=SS,Number=A,Type=Float,Description=\"Sample size used to estimate genetic effect\">",
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    "INFO.1": "<ID=ReverseComplementedAlleles,Number=0,Type=Flag,Description=\"The REF and the ALT alleles have been reverse complemented in liftover since the mapping from the previous reference to the current one was on the negative strand.\">",
    "INFO.2": "<ID=SwappedAlleles,Number=0,Type=Flag,Description=\"The REF and the ALT alleles have been swapped in liftover due to changes in the reference. It is possible that not all INFO annotations reflect this swap, and in the genotypes, only the GT, PL, and AD fields have been modified. You should check the TAGS_TO_REVERSE parameter that was used during the LiftOver to be sure.\">",
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    "META.7": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
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    "file_date": "2019-10-26T09:28:45.593381",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006090/EBI-a-GCST006090_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-GCST006090/EBI-a-GCST006090.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006090/EBI-a-GCST006090_data.vcf.gz; Date=Sat Oct 26 09:43:35 2019",
    "bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006090/ebi-a-GCST006090.vcf.gz; Date=Sun May 10 13:25:39 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-GCST006090/EBI-a-GCST006090.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-GCST006090/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 10:05:31 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006090/EBI-a-GCST006090.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 10:05:34 2019
Total time elapsed: 3.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8473,
    "inflation_factor": 1.016,
    "mean_EFFECT": 0.0006,
    "n": "-Inf",
    "n_snps": 548674,
    "n_clumped_hits": 2,
    "n_p_sig": 74,
    "n_mono": 0,
    "n_ns": 3945,
    "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": 5073,
    "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 between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations 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 expect ratio_se_n to 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 of diff and the mean of diff2 (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<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 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: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_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 4 38 0 547180 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 54 0 312 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 35 0 240 0 NA NA NA NA NA NA NA NA NA NA
logical N 547181 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 9.046100e+00 6.054004e+00 1.000e+00 4.000000e+00 8.000000e+00 1.400000e+01 2.300000e+01 ▇▅▅▃▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.622532e+07 5.668312e+07 3.587e+03 2.961052e+07 6.469502e+07 1.127275e+08 2.492107e+08 ▇▆▃▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 5.538000e-04 6.640940e-02 -1.829e+00 -3.979000e-02 3.264000e-04 4.054000e-02 2.582000e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.229500e-02 2.023090e-02 4.602e-02 4.958000e-02 5.574000e-02 6.861000e-02 1.254000e+00 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.979036e-01 2.893213e-01 0.000e+00 2.465000e-01 4.966003e-01 7.488003e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.979036e-01 2.893215e-01 0.000e+00 2.465192e-01 4.965873e-01 7.488146e-01 9.999971e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.687336e-01 2.456285e-01 3.305e-04 1.583400e-01 3.087860e-01 5.484140e-01 9.950140e-01 ▇▇▅▃▂
numeric AF_reference 5073 0.9907288 NA NA NA NA NA NA NA 3.395460e-01 2.296710e-01 1.997e-04 1.509580e-01 2.933310e-01 5.015970e-01 9.982030e-01 ▇▇▅▃▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 752566 rs3094315 G A -0.018810 0.06551 0.7740001 0.7740115 0.8448450 0.7182510 NA
1 990417 rs2465136 T C 0.038870 0.06451 0.5467994 0.5468133 0.1625320 0.3839860 NA
1 1018704 rs9442372 A G -0.033450 0.06014 0.5781000 0.5780726 0.8119550 0.6110220 NA
1 1036959 rs11579015 T C -0.070930 0.05780 0.2198002 0.2197615 0.2039570 0.1569490 NA
1 1046164 rs6666280 C T -0.070620 0.05795 0.2230001 0.2229821 0.2033310 0.2823480 NA
1 1062638 rs9442373 C A 0.016700 0.04763 0.7258001 0.7258739 0.5396500 0.5742810 NA
1 1216195 rs55834051 C A -0.005924 0.04890 0.9036000 0.9035760 0.6441210 0.3182910 NA
1 1217011 rs1262894 A C -0.028410 0.07245 0.6950003 0.6949603 0.1219990 0.2096650 NA
1 1217058 rs3753340 G A 0.267000 0.21280 0.2095999 0.2095880 0.0144225 0.0547125 NA
1 1218086 rs6603788 C T 0.027900 0.04837 0.5640999 0.5640720 0.6326950 0.4694490 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 155075318 rs28673993 A G 0.050830 0.04774 0.2870001 0.2870001 0.550435 0.494010 NA
23 155093036 rs5940611 A G -0.054520 0.04820 0.2581000 0.2580044 0.455459 0.518171 NA
23 155096106 rs7056030 C T -0.005369 0.04873 0.9123001 0.9122678 0.383539 0.509385 NA
23 155101861 rs5940431 T C 0.038220 0.04802 0.4261005 0.4260795 0.530342 0.646765 NA
23 155104608 rs5940618 G A -0.046040 0.04936 0.3508997 0.3509547 0.349194 0.339457 NA
23 155123237 rs4893101 C T 0.020290 0.04984 0.6839998 0.6839325 0.644919 0.592851 NA
23 155158490 rs5983829 A G 0.011030 0.05025 0.8262000 0.8262586 0.664322 0.637780 NA
23 155184866 rs2889416 T C 0.049250 0.05537 0.3737997 0.3737501 0.248887 0.370407 NA
23 155223705 rs7051412 T G 0.039450 0.05475 0.4711997 0.4711877 0.743987 0.552117 NA
23 155227607 rs3093457 T G 0.048730 0.05365 0.3636997 0.3637226 0.729838 0.512580 NA

bcf preview

1   752566  rs3094315   G   A   .   PASS    AF=0.844845 ES:SE:LP:AF:ID  -0.01881:0.06551:0.111259:0.844845:rs3094315
1   990417  rs2465136   T   C   .   PASS    AF=0.162532 ES:SE:LP:AF:ID  0.03887:0.06451:0.262172:0.162532:rs2465136
1   1018704 rs9442372   A   G   .   PASS    AF=0.811955 ES:SE:LP:AF:ID  -0.03345:0.06014:0.237997:0.811955:rs9442372
1   1036959 rs11579015  T   C   .   PASS    AF=0.203957 ES:SE:LP:AF:ID  -0.07093:0.0578:0.657972:0.203957:rs11579015
1   1046164 rs6666280   C   T   .   PASS    AF=0.203331 ES:SE:LP:AF:ID  -0.07062:0.05795:0.651695:0.203331:rs6666280
1   1062638 rs9442373   C   A   .   PASS    AF=0.53965  ES:SE:LP:AF:ID  0.0167:0.04763:0.139183:0.53965:rs9442373
1   1216195 rs55834051  C   A   .   PASS    AF=0.644121 ES:SE:LP:AF:ID  -0.005924:0.0489:0.0440238:0.644121:rs55834051
1   1217011 rs1262894   A   C   .   PASS    AF=0.121999 ES:SE:LP:AF:ID  -0.02841:0.07245:0.158015:0.121999:rs1262894
1   1217058 rs3753340   G   A   .   PASS    AF=0.0144225    ES:SE:LP:AF:ID  0.267:0.2128:0.678609:0.0144225:rs3753340
1   1218086 rs6603788   C   T   .   PASS    AF=0.632695 ES:SE:LP:AF:ID  0.0279:0.04837:0.248644:0.632695:rs6603788
1   1220136 rs2144440   A   G   .   PASS    AF=0.652452 ES:SE:LP:AF:ID  0.009262:0.04885:0.0707855:0.652452:rs2144440
1   1220954 rs12751100  G   A   .   PASS    AF=0.63732  ES:SE:LP:AF:ID  0.02959:0.04854:0.265921:0.63732:rs12751100
1   1221138 rs11260578  G   A   .   PASS    AF=0.622697 ES:SE:LP:AF:ID  0.02001:0.04853:0.167427:0.622697:rs11260578
1   1222958 rs111819661 C   T   .   PASS    AF=0.0144934    ES:SE:LP:AF:ID  0.1352:0.213:0.279427:0.0144934:rs111819661
1   1223529 rs78481580  G   C   .   PASS    AF=0.182145 ES:SE:LP:AF:ID  -0.09008:0.06103:0.853872:0.182145:rs78481580
1   1225959 rs1570867   C   G   .   PASS    AF=0.922551 ES:SE:LP:AF:ID  0.02925:0.08891:0.129538:0.922551:rs1570867
1   1227244 rs2239609   G   A   .   PASS    AF=0.274785 ES:SE:LP:AF:ID  0.01477:0.05259:0.108574:0.274785:rs2239609
1   1278237 rs307361    T   C   .   PASS    AF=0.86929  ES:SE:LP:AF:ID  0.04447:0.07074:0.276052:0.86929:rs307361
1   1287040 rs182532    T   C   .   PASS    AF=0.869056 ES:SE:LP:AF:ID  0.04366:0.0708:0.269622:0.869056:rs182532
1   1295323 rs34389364  G   A   .   PASS    AF=0.808131 ES:SE:LP:AF:ID  0.007699:0.06078:0.0461437:0.808131:rs34389364
1   1314015 rs2649588   C   T   .   PASS    AF=0.826185 ES:SE:LP:AF:ID  0.07051:0.06349:0.573977:0.826185:rs2649588
1   1335218 rs2291889   G   A   .   PASS    AF=0.121077 ES:SE:LP:AF:ID  -0.09533:0.07217:0.729321:0.121077:rs2291889
1   1487059 rs1887284   G   A   .   PASS    AF=0.262417 ES:SE:LP:AF:ID  0.03232:0.05509:0.253833:0.262417:rs1887284
1   1489928 rs7366884   T   C   .   PASS    AF=0.716754 ES:SE:LP:AF:ID  -0.04568:0.0538:0.402524:0.716754:rs7366884
1   1500941 rs6603791   A   G   .   PASS    AF=0.824429 ES:SE:LP:AF:ID  -0.06553:0.06463:0.507799:0.824429:rs6603791
1   1599161 rs6604981   A   G   .   PASS    AF=0.621014 ES:SE:LP:AF:ID  -0.006346:0.04895:0.0473044:0.621014:rs6604981
1   1647686 rs909823    A   C   .   PASS    AF=0.869063 ES:SE:LP:AF:ID  0.06228:0.0669:0.453581:0.869063:rs909823
1   1687625 rs34306661  T   C   .   PASS    AF=0.332064 ES:SE:LP:AF:ID  -0.006723:0.0502:0.0489054:0.332064:rs34306661
1   1706160 rs7531583   A   G   .   PASS    AF=0.70011  ES:SE:LP:AF:ID  0.02174:0.05188:0.170568:0.70011:rs7531583
1   1721479 rs2272908   C   T   .   PASS    AF=0.502984 ES:SE:LP:AF:ID  0.008136:0.04703:0.0641402:0.502984:rs2272908
1   1722585 rs3737626   C   A   .   PASS    AF=0.0381004    ES:SE:LP:AF:ID  -0.03923:0.1226:0.125576:0.0381004:rs3737626
1   1722932 rs3737628   C   T   .   PASS    AF=0.502721 ES:SE:LP:AF:ID  0.01158:0.04707:0.0938266:0.502721:rs3737628
1   1723031 rs9660180   G   A   .   PASS    AF=0.504401 ES:SE:LP:AF:ID  0.01382:0.04711:0.113904:0.504401:rs9660180
1   1725016 rs80220232  G   A   .   PASS    AF=0.066223 ES:SE:LP:AF:ID  -0.07153:0.09451:0.347657:0.066223:rs80220232
1   1725626 rs9970652   C   T   .   PASS    AF=0.206243 ES:SE:LP:AF:ID  -0.05454:0.05923:0.44721:0.206243:rs9970652
1   1733219 rs10907185  A   G   .   PASS    AF=0.710327 ES:SE:LP:AF:ID  -0.03936:0.05197:0.34785:0.710327:rs10907185
1   1734337 rs75622028  T   C   .   PASS    AF=0.103286 ES:SE:LP:AF:ID  0.05507:0.07719:0.322849:0.103286:rs75622028
1   1737900 rs17363334  C   T   .   PASS    AF=0.0685096    ES:SE:LP:AF:ID  0.02683:0.09439:0.110026:0.0685096:rs17363334
1   1738984 rs76117314  C   A   .   PASS    AF=0.0598594    ES:SE:LP:AF:ID  -0.03881:0.09865:0.158641:0.0598594:rs76117314
1   1745726 rs16825336  G   A   .   PASS    AF=0.266627 ES:SE:LP:AF:ID  0.009818:0.05353:0.0682879:0.266627:rs16825336
1   1746694 rs12742323  G   T   .   PASS    AF=0.207488 ES:SE:LP:AF:ID  -0.05774:0.05925:0.481881:0.207488:rs12742323
1   1747318 rs59787372  T   C   .   PASS    AF=0.0356951    ES:SE:LP:AF:ID  -0.02873:0.1269:0.0857626:0.0356951:rs59787372
1   1748734 rs2180311   T   C   .   PASS    AF=0.505353 ES:SE:LP:AF:ID  0.0153:0.04699:0.128019:0.505353:rs2180311
1   1752955 rs4648726   C   T   .   PASS    AF=0.843103 ES:SE:LP:AF:ID  -0.1326:0.0658:1.35684:0.843103:rs4648726
1   1759026 rs9786963   T   C   .   PASS    AF=0.908969 ES:SE:LP:AF:ID  -0.03988:0.08069:0.206838:0.908969:rs9786963
1   1759054 rs10907187  G   A   .   PASS    AF=0.128451 ES:SE:LP:AF:ID  -0.1139:0.06964:0.991826:0.128451:rs10907187
1   1759213 rs9786942   A   G   .   PASS    AF=0.505091 ES:SE:LP:AF:ID  0.006932:0.04705:0.0540885:0.505091:rs9786942
1   1760937 rs77726987  G   A   .   PASS    AF=0.0353723    ES:SE:LP:AF:ID  -0.01711:0.131:0.0476435:0.0353723:rs77726987
1   1765583 rs6603797   T   C   .   PASS    AF=0.908316 ES:SE:LP:AF:ID  -0.0501:0.08049:0.272784:0.908316:rs6603797
1   1766094 rs6663586   A   C   .   PASS    AF=0.195172 ES:SE:LP:AF:ID  0.01369:0.05973:0.0868752:0.195172:rs6663586