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.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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    "file_date": "2019-10-26T21:38:55.074554",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006810/EBI-a-GCST006810_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-GCST006810/EBI-a-GCST006810.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006810/EBI-a-GCST006810_data.vcf.gz; Date=Sat Oct 26 21:52:11 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-GCST006810/ebi-a-GCST006810.vcf.gz; Date=Sat May  9 18:39:09 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-GCST006810/EBI-a-GCST006810.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-GCST006810/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 22:17:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006810/EBI-a-GCST006810.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 22:18:00 2019
Total time elapsed: 5.66s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9385,
    "inflation_factor": 1.369,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 813688,
    "n_clumped_hits": 5,
    "n_p_sig": 380,
    "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": 6593,
    "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 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 5 33 0 810826 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 811256 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 1.270821e+00 4.443840e-01 1.0000e+00 1.000000e+00 1.000000e+00 2.000000e+00 2.000000e+00 ▇▁▁▁▃
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 1.019593e+08 7.806802e+07 1.0797e+04 3.580316e+07 7.354880e+07 1.778920e+08 2.492385e+08 ▇▆▂▃▅
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.330000e-05 2.331900e-03 -1.8900e-02 -1.200000e-03 3.500000e-06 1.210000e-03 2.160000e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.849300e-03 1.052700e-03 8.9600e-04 1.010000e-03 1.390000e-03 2.420000e-03 8.780000e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.530969e-01 3.005275e-01 0.0000e+00 1.800002e-01 4.299995e-01 7.099994e-01 1.000000e+00 ▇▆▆▅▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.528257e-01 3.005914e-01 0.0000e+00 1.804183e-01 4.326791e-01 7.130805e-01 1.000000e+00 ▇▆▆▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.537421e-01 2.619306e-01 1.0000e-02 4.000000e-02 1.500000e-01 4.100000e-01 9.900000e-01 ▇▂▂▁▁
numeric AF_reference 6593 0.9918731 NA NA NA NA NA NA NA 2.530637e-01 2.539207e-01 1.9970e-04 4.293130e-02 1.627400e-01 3.971650e-01 1.000000e+00 ▇▃▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C 0.001890 0.00163 0.2599998 0.2462487 0.620 0.7821490 NA
1 54676 rs2462492 C T -0.001520 0.00161 0.3300000 0.3451189 0.400 NA NA
1 86028 rs114608975 T C -0.003540 0.00258 0.1700000 0.1700345 0.100 0.0277556 NA
1 91536 rs6702460 G T 0.000698 0.00159 0.6600001 0.6606661 0.460 0.4207270 NA
1 234313 rs8179466 C T -0.006410 0.00313 0.0400000 0.0405675 0.070 NA NA
1 534192 rs6680723 C T 0.001270 0.00181 0.5400003 0.4828928 0.240 NA NA
1 546697 rs12025928 A G -0.004270 0.00226 0.0569994 0.0588409 0.914 NA NA
1 568800 rs375217967 G A 0.006600 0.00622 0.2800000 0.2886475 0.020 0.0778754 NA
1 693731 rs12238997 A G 0.000122 0.00152 0.9500000 0.9360280 0.120 0.1417730 NA
1 705882 rs72631875 G A 0.000163 0.00223 0.9500000 0.9417312 0.070 0.0315495 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
2 72236033 rs112683395 C T -2.62e-04 0.002020 0.9100000 0.8968014 0.05 0.0509185 NA
2 72236202 rs2420045 C T 2.31e-04 0.000920 0.7600007 0.8017469 0.52 0.5329470 NA
2 72236597 rs4852831 C T -8.60e-05 0.000920 0.9699999 0.9255236 0.52 0.5195690 NA
2 72236698 rs4852832 C T -5.16e-05 0.000920 1.0000000 0.9552725 0.52 0.5197680 NA
2 72236929 rs4852833 G A -5.27e-05 0.000919 1.0000000 0.9542704 0.52 0.5217650 NA
2 72236979 rs6726291 C T -4.28e-05 0.000919 0.9900000 0.9628541 0.52 0.5191690 NA
2 72237360 rs955788 G A 3.68e-05 0.000925 0.9300001 0.9682655 0.56 0.5642970 NA
2 91840589 rs1480083854 C T -1.14e-03 0.002020 0.5800000 0.5725116 0.19 NA NA
2 91862281 rs1195213840 G T 9.46e-04 0.001720 0.5999997 0.5823194 0.32 NA NA
2 91878678 rs9438265 A G -1.91e-04 0.001670 0.9199999 0.9089435 0.36 NA NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62 ES:SE:LP:AF:ID  0.00189:0.00163:0.585027:0.62:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.4  ES:SE:LP:AF:ID  -0.00152:0.00161:0.481486:0.4:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.1  ES:SE:LP:AF:ID  -0.00354:0.00258:0.769551:0.1:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.46 ES:SE:LP:AF:ID  0.000698:0.00159:0.180456:0.46:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07 ES:SE:LP:AF:ID  -0.00641:0.00313:1.39794:0.07:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24 ES:SE:LP:AF:ID  0.00127:0.00181:0.267606:0.24:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914    ES:SE:LP:AF:ID  -0.00427:0.00226:1.24413:0.914:rs12025928
1   568800  rs375217967 G   A   .   PASS    AF=0.02 ES:SE:LP:AF:ID  0.0066:0.00622:0.552842:0.02:rs375217967
1   693731  rs12238997  A   G   .   PASS    AF=0.12 ES:SE:LP:AF:ID  0.000122:0.00152:0.0222764:0.12:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.07 ES:SE:LP:AF:ID  0.000163:0.00223:0.0222764:0.07:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.52 ES:SE:LP:AF:ID  -0.000183:0.00113:0.0457575:0.52:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03 ES:SE:LP:AF:ID  -0.00064:0.00287:0.0705811:0.03:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000455:0.00261:0.0555173:0.04:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000242:0.0026:0.0268721:0.04:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000826:0.00261:0.113509:0.04:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.02 ES:SE:LP:AF:ID  0.00233:0.00407:0.221849:0.02:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000788:0.00259:0.107905:0.04:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.00056:0.00258:0.0757207:0.04:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.1  ES:SE:LP:AF:ID  -0.000308:0.00186:0.0757207:0.1:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.96 ES:SE:LP:AF:ID  0.00104:0.0025:0.161151:0.96:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03 ES:SE:LP:AF:ID  -0.00328:0.00445:0.327902:0.03:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.05 ES:SE:LP:AF:ID  -0.000581:0.00355:0.0655015:0.05:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000805:0.00259:0.113509:0.04:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000444:0.00257:0.0555173:0.04:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84 ES:SE:LP:AF:ID  5.13e-05:0.00132:0.0132283:0.84:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.06 ES:SE:LP:AF:ID  -0.00183:0.00212:0.431798:0.06:rs148120343
1   731453  rs186002080 G   A   .   PASS    AF=0.02 ES:SE:LP:AF:ID  -0.00304:0.00484:0.283997:0.02:rs186002080
1   731718  rs58276399  T   C   .   PASS    AF=0.12 ES:SE:LP:AF:ID  -0.000254:0.00144:0.0655015:0.12:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.03 ES:SE:LP:AF:ID  -0.00453:0.00353:0.677781:0.03:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12 ES:SE:LP:AF:ID  -0.000247:0.00144:0.0655015:0.12:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13 ES:SE:LP:AF:ID  -0.000739:0.00142:0.207608:0.13:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.00999999   ES:SE:LP:AF:ID  0.00543:0.00518:0.508638:0.00999999:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000487:0.00254:0.0604807:0.04:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.84 ES:SE:LP:AF:ID  -7.27e-05:0.00128:0.0177288:0.84:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.84 ES:SE:LP:AF:ID  -0.000168:0.00128:0.0457575:0.84:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.87 ES:SE:LP:AF:ID  -4.21e-05:0.00137:0.00877392:0.87:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.13 ES:SE:LP:AF:ID  0.000238:0.00137:0.0604807:0.13:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -3.86e-05:0.0025:0.0043648:0.04:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.04 ES:SE:LP:AF:ID  4.87e-05:0.00249:0.0132283:0.04:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.87 ES:SE:LP:AF:ID  -0.000188:0.00137:0.0457575:0.87:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.87 ES:SE:LP:AF:ID  -0.000223:0.00137:0.0555173:0.87:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000148:0.0025:0.0132283:0.04:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.87 ES:SE:LP:AF:ID  -0.000178:0.00137:0.0457575:0.87:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.84 ES:SE:LP:AF:ID  -0.000222:0.00127:0.0604807:0.84:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.04 ES:SE:LP:AF:ID  -0.000184:0.0025:0.0177288:0.04:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.84 ES:SE:LP:AF:ID  -0.000186:0.00128:0.05061:0.84:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.00999999   ES:SE:LP:AF:ID  -0.00501:0.00441:0.60206:0.00999999:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.84 ES:SE:LP:AF:ID  4.56e-05:0.00129:0.0177288:0.84:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.87 ES:SE:LP:AF:ID  5.99e-05:0.00137:0.0177288:0.87:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.87 ES:SE:LP:AF:ID  7.37e-05:0.00136:0.0222764:0.87:rs3131962