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\">",
    "FORMAT.5": "<ID=NC,Number=A,Type=Float,Description=\"Number of cases used to estimate genetic effect\">",
    "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\">",
    "INFO": "<ID=AF,Number=A,Type=Float,Description=\"Allele Frequency\">",
    "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.4": "<ID=TotalControls,Number=1,Type=Integer,Description=\"Total number of controls in the association study\">",
    "META.5": "<ID=TotalVariants,Number=1,Type=Integer,Description=\"Total number of variants in input\">",
    "META.6": "<ID=VariantsNotHarmonised,Number=1,Type=Integer,Description=\"Total number of variants that could not be harmonised\">",
    "META.7": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
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    "file_date": "2019-10-26T09:27:58.695809",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003044/EBI-a-GCST003044_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-GCST003044/EBI-a-GCST003044.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003044/EBI-a-GCST003044_data.vcf.gz; Date=Sat Oct 26 09:42:11 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-GCST003044/ebi-a-GCST003044.vcf.gz; Date=Sat May  9 19:57:39 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-GCST003044/EBI-a-GCST003044.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-GCST003044/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 10:04:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST003044/EBI-a-GCST003044.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 10:04:20 2019
Total time elapsed: 0.83s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9366,
    "inflation_factor": 2.9449,
    "mean_EFFECT": 0.0022,
    "n": "-Inf",
    "n_snps": 110583,
    "n_clumped_hits": 121,
    "n_p_sig": 6311,
    "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": 750,
    "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 TRUE
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 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 5 25 0 110201 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 110231 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 8.080585e+00 5.766350e+00 1.00000e+00 3.000000e+00 6.000000e+00 1.200000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.917961e+07 5.784865e+07 7.82320e+04 3.285255e+07 6.227839e+07 1.176941e+08 2.492107e+08 ▇▅▃▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.172300e-03 5.997810e-02 -1.26172e+00 -2.210850e-02 8.750000e-04 2.413640e-02 1.514640e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.530810e-02 2.734100e-02 1.18048e-02 1.325070e-02 1.635940e-02 2.559340e-02 8.952510e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 3.353607e-01 3.166618e-01 0.00000e+00 2.886690e-02 2.470808e-01 5.975758e-01 9.999835e-01 ▇▃▂▂▂
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 3.353607e-01 3.166618e-01 0.00000e+00 2.886730e-02 2.470819e-01 5.975764e-01 9.999835e-01 ▇▃▂▂▂
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.898938e-01 2.628059e-01 1.49000e-05 6.820500e-02 2.061000e-01 4.587000e-01 9.999700e-01 ▇▃▂▂▁
numeric AF_reference 750 0.9931961 NA NA NA NA NA NA NA 2.904501e-01 2.572371e-01 1.99700e-04 7.707670e-02 2.120610e-01 4.530750e-01 1.000000e+00 ▇▃▂▂▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 1118275 rs61733845 C T -0.0294999 0.0307510 0.3374007 0.3374000 0.044160 0.185703 NA
1 1120431 rs1320571 G A -0.0229954 0.0301098 0.4450348 0.4450352 0.046530 0.185304 NA
1 1135242 rs9729550 A C 0.0524943 0.0141053 0.0001980 0.0001980 0.258700 0.551917 NA
1 1140435 rs1815606 G T 0.0432499 0.0136608 0.0015456 0.0015456 0.317900 0.712061 NA
1 1163804 rs7515488 C T 0.0840669 0.0167779 0.0000005 0.0000005 0.148600 0.186901 NA
1 1165310 rs11260562 G A 0.0821914 0.0253050 0.0011621 0.0011621 0.055600 0.101837 NA
1 1173611 rs6697886 G A 0.0872903 0.0174247 0.0000005 0.0000005 0.137300 0.220647 NA
1 1194804 rs11804831 T C 0.0644855 0.0156228 0.0000366 0.0000366 0.185600 0.685903 NA
1 1218086 rs6603788 C T 0.0278539 0.0227151 0.2201132 0.2201127 0.076970 0.469449 NA
1 1227897 rs3737721 A G 0.1086210 0.0906502 0.2308235 0.2308223 0.002436 0.228035 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 50960682 rs140524 C T 0.0213042 0.0161293 0.1865559 0.1865552 0.1709 0.230232 NA
22 50966914 rs470119 T C -0.0445357 0.0123360 0.0003059 0.0003059 0.6098 0.554712 NA
22 50971752 rs131794 A C -0.0455758 0.0146924 0.0019222 0.0019222 0.7919 0.832668 NA
22 50988193 rs131779 A G -0.0241081 0.0131541 0.0668405 0.0668406 0.6579 0.580272 NA
22 50999182 rs140518 C T -0.0176681 0.0132186 0.1813506 0.1813503 0.6966 0.765575 NA
22 51078251 rs4040041 C T -0.0075038 0.0125603 0.5502233 0.5502230 0.3731 0.466653 NA
22 51094926 rs9616810 C T -0.0036593 0.0147696 0.8043223 0.8043215 0.2186 0.222444 NA
22 51105556 rs9616812 C T 0.0053232 0.0124593 0.6692001 0.6692011 0.4832 0.362819 NA
22 51109992 rs9628185 T C 0.0090266 0.0124944 0.4700153 0.4700149 0.4843 0.405351 NA
22 51156666 rs9628187 C T -0.0018783 0.0157831 0.9052720 0.9052718 0.2032 0.129992 NA

bcf preview

1   1118275 rs61733845  C   T   .   PASS    AF=0.04416  ES:SE:LP:AF:ID  -0.0294999:0.030751:0.471854:0.04416:rs61733845
1   1120431 rs1320571   G   A   .   PASS    AF=0.04653  ES:SE:LP:AF:ID  -0.0229954:0.0301098:0.351606:0.04653:rs1320571
1   1135242 rs9729550   A   C   .   PASS    AF=0.2587   ES:SE:LP:AF:ID  0.0524943:0.0141053:3.7034:0.2587:rs9729550
1   1140435 rs1815606   G   T   .   PASS    AF=0.3179   ES:SE:LP:AF:ID  0.0432499:0.0136608:2.81089:0.3179:rs1815606
1   1163804 rs7515488   C   T   .   PASS    AF=0.1486   ES:SE:LP:AF:ID  0.0840669:0.0167779:6.26544:0.1486:rs7515488
1   1165310 rs11260562  G   A   .   PASS    AF=0.0556   ES:SE:LP:AF:ID  0.0821914:0.025305:2.93477:0.0556:rs11260562
1   1173611 rs6697886   G   A   .   PASS    AF=0.1373   ES:SE:LP:AF:ID  0.0872903:0.0174247:6.26323:0.1373:rs6697886
1   1194804 rs11804831  T   C   .   PASS    AF=0.1856   ES:SE:LP:AF:ID  0.0644855:0.0156228:4.43594:0.1856:rs11804831
1   1218086 rs6603788   C   T   .   PASS    AF=0.07697  ES:SE:LP:AF:ID  0.0278539:0.0227151:0.657354:0.07697:rs6603788
1   1227897 rs3737721   A   G   .   PASS    AF=0.002436 ES:SE:LP:AF:ID  0.108621:0.0906502:0.63672:0.002436:rs3737721
1   1231656 rs1749951   G   A   .   PASS    AF=0.04028  ES:SE:LP:AF:ID  0.0289805:0.031906:0.439237:0.04028:rs1749951
1   1233941 rs1739855   T   C   .   PASS    AF=0.07333  ES:SE:LP:AF:ID  0.046083:0.0229599:1.34932:0.07333:rs1739855
1   1241529 rs1536168   A   G   .   PASS    AF=0.95394  ES:SE:LP:AF:ID  -0.0388531:0.0294021:0.729658:0.95394:rs1536168
1   1242468 rs2274264   G   A   .   PASS    AF=0.002467 ES:SE:LP:AF:ID  0.106205:0.09604:0.570579:0.002467:rs2274264
1   1247494 rs12103 T   C   .   PASS    AF=0.8166   ES:SE:LP:AF:ID  -0.0788611:0.0157611:6.24954:0.8166:rs12103
1   1249187 rs12142199  G   A   .   PASS    AF=0.8026   ES:SE:LP:AF:ID  -0.0646391:0.0154665:4.53399:0.8026:rs12142199
1   1254255 rs62623580  G   A   .   PASS    AF=0.007606 ES:SE:LP:AF:ID  0.0960413:0.067431:0.811459:0.007606:rs62623580
1   1335790 rs1240708   A   G   .   PASS    AF=0.1737   ES:SE:LP:AF:ID  0.0745538:0.0160139:5.49069:0.1737:rs1240708
1   1493727 rs880051    G   A   .   PASS    AF=0.232    ES:SE:LP:AF:ID  0.0593995:0.0141283:4.58189:0.232:rs880051
1   1497824 rs2296716   C   T   .   PASS    AF=0.1208   ES:SE:LP:AF:ID  0.0381788:0.0184257:1.41723:0.1208:rs2296716
1   1611995 rs4074196   A   G   .   PASS    AF=0.4414   ES:SE:LP:AF:ID  -0.000456669:0.0138093:0.0116109:0.4414:rs4074196
1   1706160 rs7531583   A   G   .   PASS    AF=0.77 ES:SE:LP:AF:ID  -0.0339188:0.0140192:1.80843:0.77:rs7531583
1   1721479 rs2272908   C   T   .   PASS    AF=0.4974   ES:SE:LP:AF:ID  -0.0401344:0.0120482:3.06306:0.4974:rs2272908
1   1723031 rs9660180   G   A   .   PASS    AF=0.4974   ES:SE:LP:AF:ID  -0.0418833:0.0120656:3.28571:0.4974:rs9660180
1   1781220 rs6681938   T   C   .   PASS    AF=0.2996   ES:SE:LP:AF:ID  -0.00356077:0.0132235:0.103629:0.2996:rs6681938
1   1838516 rs2377037   C   A   .   PASS    AF=0.2747   ES:SE:LP:AF:ID  0.0245855:0.0137239:1.13535:0.2747:rs2377037
1   1840038 rs2474461   T   C   .   PASS    AF=0.95331  ES:SE:LP:AF:ID  -0.0367077:0.0287288:0.696061:0.95331:rs2474461
1   1853288 rs1884454   G   T   .   PASS    AF=0.2694   ES:SE:LP:AF:ID  0.0229567:0.0137751:1.01951:0.2694:rs1884454
1   1855319 rs2295362   C   T   .   PASS    AF=0.0452   ES:SE:LP:AF:ID  0.0228883:0.0295255:0.358309:0.0452:rs2295362
1   1871337 rs16824543  G   A   .   PASS    AF=0.04241  ES:SE:LP:AF:ID  0.0115429:0.0306901:0.150683:0.04241:rs16824543
1   1873625 rs12758705  G   A   .   PASS    AF=0.2651   ES:SE:LP:AF:ID  0.022732:0.0139577:0.985514:0.2651:rs12758705
1   1881070 rs4648596   G   A   .   PASS    AF=0.04073  ES:SE:LP:AF:ID  0.0162246:0.0319478:0.21356:0.04073:rs4648596
1   1888193 rs3820011   C   A   .   PASS    AF=0.2698   ES:SE:LP:AF:ID  0.0285245:0.0141515:1.35816:0.2698:rs3820011
1   2024064 rs2459994   C   T   .   PASS    AF=0.1824   ES:SE:LP:AF:ID  0.0400149:0.0163225:1.84694:0.1824:rs2459994
1   2146966 rs7512482   T   C   .   PASS    AF=0.1674   ES:SE:LP:AF:ID  -0.0281917:0.0173789:0.979782:0.1674:rs7512482
1   2202774 rs6673129   C   T   .   PASS    AF=0.1651   ES:SE:LP:AF:ID  0.0053222:0.0174129:0.119259:0.1651:rs6673129
1   2229478 rs12562937  C   T   .   PASS    AF=0.1998   ES:SE:LP:AF:ID  0.00655724:0.0161572:0.164398:0.1998:rs12562937
1   2283896 rs2840528   A   G   .   PASS    AF=0.4158   ES:SE:LP:AF:ID  -0.00913153:0.0126387:0.327918:0.4158:rs2840528
1   2290143 rs34587196  G   A   .   PASS    AF=0.01202  ES:SE:LP:AF:ID  0.0411307:0.0561654:0.333504:0.01202:rs34587196
1   2404256 rs2494626   C   T   .   PASS    AF=0.2884   ES:SE:LP:AF:ID  -0.00666996:0.0146293:0.18813:0.2884:rs2494626
1   2407781 rs78504402  C   T   .   PASS    AF=0.06862  ES:SE:LP:AF:ID  0.0518998:0.0252018:1.40385:0.06862:rs78504402
1   2408471 rs115996655 G   A   .   PASS    AF=0.007814 ES:SE:LP:AF:ID  -0.0967139:0.0693296:0.78776:0.007814:rs115996655
1   2408834 rs11588930  G   A   .   PASS    AF=0.101    ES:SE:LP:AF:ID  -0.00162999:0.021933:0.0265218:0.101:rs11588930
1   2409892 rs12727342  A   G   .   PASS    AF=0.6229   ES:SE:LP:AF:ID  0.0123899:0.0132409:0.456662:0.6229:rs12727342
1   2410789 rs11799501  C   T   .   PASS    AF=0.5834   ES:SE:LP:AF:ID  0.0293994:0.0131663:1.59254:0.5834:rs11799501
1   2412293 rs12731186  C   T   .   PASS    AF=0.1211   ES:SE:LP:AF:ID  -0.0110222:0.0190713:0.249261:0.1211:rs12731186
1   2413166 rs115810747 A   G   .   PASS    AF=0.04094  ES:SE:LP:AF:ID  0.0419527:0.0335342:0.675884:0.04094:rs115810747
1   2414928 rs4995304   G   A   .   PASS    AF=0.5732   ES:SE:LP:AF:ID  0.0283814:0.0131517:1.50965:0.5732:rs4995304
1   2415108 rs114637672 T   C   .   PASS    AF=0.02017  ES:SE:LP:AF:ID  0.0353923:0.0438156:0.377546:0.02017:rs114637672
1   2415497 rs61763948  T   C   .   PASS    AF=0.5737   ES:SE:LP:AF:ID  0.025031:0.0130884:1.25322:0.5737:rs61763948