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\">",
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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.4": "<ID=TotalControls,Number=1,Type=Integer,Description=\"Total number of controls in the association study\">",
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    "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": "2019-10-26T21:49:20.622486",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005349/EBI-a-GCST005349_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-GCST005349/EBI-a-GCST005349.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005349/EBI-a-GCST005349_data.vcf.gz; Date=Sat Oct 26 22:10:03 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-GCST005349/ebi-a-GCST005349.vcf.gz; Date=Sat May  9 16:11:11 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-GCST005349/EBI-a-GCST005349.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-GCST005349/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 22:34:20 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST005349/EBI-a-GCST005349.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 22:35:38 2019
Total time elapsed: 1.0m:17.54s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9619,
    "inflation_factor": 1.0499,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 11932096,
    "n_clumped_hits": 22,
    "n_p_sig": 1511,
    "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": 328344,
    "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 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 3 59 0 11907744 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 11907794 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 8.808644e+00 5.939312e+00 1.0000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.821894e+07 5.584546e+07 828.0000 3.229913e+07 6.904715e+07 1.138348e+08 2.492331e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -5.640000e-05 5.671160e-02 -0.8806 -1.630000e-02 1.000000e-04 1.630000e-02 9.228000e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.091700e-02 3.888470e-02 0.0095 1.240000e-02 2.390000e-02 5.740000e-02 3.052000e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.914311e-01 2.907259e-01 0.0000 2.376999e-01 4.895003e-01 7.432998e-01 9.998000e-01 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.914300e-01 2.907247e-01 0.0000 2.376216e-01 4.895030e-01 7.433798e-01 9.996482e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.101337e-01 2.435458e-01 0.0050 2.900000e-02 1.031000e-01 3.094000e-01 9.948000e-01 ▇▂▁▁▁
numeric AF_reference 328344 0.9724261 NA NA NA NA NA NA NA 2.034532e-01 2.497457e-01 0.0000 1.537540e-02 8.905750e-02 3.123000e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 10583 rs58108140 G A -0.0367 0.0512 0.4736996 0.4734994 0.1739 NA NA
1 11012 rs544419019 C G -0.0194 0.0687 0.7778001 0.7776466 0.0873 0.0880591 NA
1 13116 rs62635286 T G 0.0187 0.0611 0.7591000 0.7595623 0.1901 0.0970447 NA
1 13118 rs200579949 A G 0.0187 0.0611 0.7591000 0.7595623 0.1901 0.0970447 NA
1 13273 rs531730856 G C 0.0026 0.0743 0.9718000 0.9720851 0.1315 0.0950479 NA
1 14599 rs531646671 T A 0.0043 0.0617 0.9447000 0.9444388 0.1835 0.1475640 NA
1 14604 rs541940975 A G 0.0043 0.0617 0.9447000 0.9444388 0.1835 0.1475640 NA
1 14930 rs75454623 A G -0.0740 0.0457 0.1058001 0.1053922 0.5010 0.4822280 NA
1 15211 rs78601809 T G 0.0114 0.0508 0.8222001 0.8224387 0.7263 0.6090260 NA
1 15850 rs575961614 G A 0.1624 0.1155 0.1596000 0.1597061 0.0173 0.0005990 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 148962417 rs11117540 G A -0.0288 0.0520 0.5793006 0.5796841 0.1182 0.1353640 NA
23 148964256 rs5983881 G C 0.0493 0.0422 0.2435000 0.2427074 0.3076 0.3226490 NA
23 148964929 rs147423702 C T 0.0310 0.0677 0.6473006 0.6470225 0.0467 0.0209272 NA
23 148973353 rs4893115 T G 0.0506 0.0466 0.2771999 0.2775512 0.1386 0.1549670 NA
23 148975006 rs7878544 T G 0.0532 0.0424 0.2096998 0.2095815 0.2655 NA NA
23 148977479 rs4893116 C T 0.0772 0.0474 0.1028999 0.1033782 0.1209 0.1324500 NA
23 148978414 rs11798728 A T -0.0304 0.0503 0.5455003 0.5455952 0.0898 0.1168210 NA
23 148999913 rs73244667 C T 0.0585 0.0480 0.2227999 0.2229391 0.1489 0.1594700 NA
23 153664698 rs6727 A C 0.0807 0.0476 0.0902298 0.0900037 0.3167 0.3144370 NA
23 154005148 rs1800533 G A -0.0814 0.0634 0.1996002 0.1991729 0.0829 0.1382780 NA

bcf preview

1   10583   rs58108140  G   A   .   PASS    AF=0.1739   ES:SE:LP:AF:ID  -0.0367:0.0512:0.324497:0.1739:rs58108140
1   11012   rs544419019 C   G   .   PASS    AF=0.0873   ES:SE:LP:AF:ID  -0.0194:0.0687:0.109132:0.0873:rs544419019
1   13116   rs62635286  T   G   .   PASS    AF=0.1901   ES:SE:LP:AF:ID  0.0187:0.0611:0.119701:0.1901:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.1901   ES:SE:LP:AF:ID  0.0187:0.0611:0.119701:0.1901:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.1315   ES:SE:LP:AF:ID  0.0026:0.0743:0.0124231:0.1315:rs531730856
1   14599   rs707680    T   A   .   PASS    AF=0.1835   ES:SE:LP:AF:ID  0.0043:0.0617:0.0247061:0.1835:rs707680
1   14604   rs541940975 A   G   .   PASS    AF=0.1835   ES:SE:LP:AF:ID  0.0043:0.0617:0.0247061:0.1835:rs541940975
1   14930   rs6682385   A   G   .   PASS    AF=0.501    ES:SE:LP:AF:ID  -0.074:0.0457:0.975514:0.501:rs6682385
1   15211   rs3982632   T   G   .   PASS    AF=0.7263   ES:SE:LP:AF:ID  0.0114:0.0508:0.0850225:0.7263:rs3982632
1   15850   rs575961614 G   A   .   PASS    AF=0.0173   ES:SE:LP:AF:ID  0.1624:0.1155:0.796967:0.0173:rs575961614
1   18849   rs533090414 C   G   .   PASS    AF=0.9777   ES:SE:LP:AF:ID  0.1107:0.1322:0.39545:0.9777:rs533090414
1   30923   rs806731    G   T   .   PASS    AF=0.4245   ES:SE:LP:AF:ID  -0.0071:0.0236:0.117532:0.4245:rs806731
1   51479   rs116400033 T   A   .   PASS    AF=0.2763   ES:SE:LP:AF:ID  0.0022:0.022:0.0357875:0.2763:rs116400033
1   54490   rs141149254 G   A   .   PASS    AF=0.2453   ES:SE:LP:AF:ID  -0.0057:0.0229:0.094744:0.2453:rs141149254
1   54676   rs2462492   C   T   .   PASS    AF=0.1407   ES:SE:LP:AF:ID  0.0182:0.041:0.183162:0.1407:rs2462492
1   55164   rs3091274   C   A   .   PASS    AF=0.935    ES:SE:LP:AF:ID  -0.0344:0.0462:0.340179:0.935:rs3091274
1   55299   rs10399749  C   T   .   PASS    AF=0.3954   ES:SE:LP:AF:ID  -0.022:0.0216:0.511873:0.3954:rs10399749
1   55326   rs3107975   T   C   .   PASS    AF=0.0177   ES:SE:LP:AF:ID  0.0418:0.0933:0.184223:0.0177:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.2519   ES:SE:LP:AF:ID  0.0522:0.057:0.444543:0.2519:rs28396308
1   58814   rs114420996 G   A   .   PASS    AF=0.0754   ES:SE:LP:AF:ID  0.0227:0.0481:0.195724:0.0754:rs114420996
1   59040   rs62637815  T   C   .   PASS    AF=0.0824   ES:SE:LP:AF:ID  0.0586:0.0692:0.400881:0.0824:rs62637815
1   61743   rs184286948 G   C   .   PASS    AF=0.0082   ES:SE:LP:AF:ID  -0.2031:0.2365:0.408379:0.0082:rs184286948
1   61920   rs62637820  G   A   .   PASS    AF=0.0286   ES:SE:LP:AF:ID  0.022:0.1431:0.0565055:0.0286:rs62637820
1   61987   rs76735897  A   G   .   PASS    AF=0.4148   ES:SE:LP:AF:ID  -0.0163:0.02:0.381743:0.4148:rs76735897
1   61989   rs77573425  G   C   .   PASS    AF=0.4148   ES:SE:LP:AF:ID  -0.0172:0.02:0.409604:0.4148:rs77573425
1   62777   rs3844233   A   T   .   PASS    AF=0.4338   ES:SE:LP:AF:ID  -0.0545:0.0465:0.617803:0.4338:rs3844233
1   63671   rs80011619  G   A   .   PASS    AF=0.1224   ES:SE:LP:AF:ID  -0.0078:0.0558:0.0512937:0.1224:rs80011619
1   64649   rs181431124 A   C   .   PASS    AF=0.0241   ES:SE:LP:AF:ID  -0.0672:0.1308:0.216597:0.0241:rs181431124
1   66162   rs62639105  A   T   .   PASS    AF=0.4214   ES:SE:LP:AF:ID  -0.021:0.0204:0.519849:0.4214:rs62639105
1   66507   rs12401368  T   A   .   PASS    AF=0.1398   ES:SE:LP:AF:ID  0.0543:0.0306:1.12004:0.1398:rs12401368
1   69428   rs140739101 T   G   .   PASS    AF=0.0445   ES:SE:LP:AF:ID  0.1514:0.1111:0.762456:0.0445:rs140739101
1   69511   rs2691305   A   G   .   PASS    AF=0.4488   ES:SE:LP:AF:ID  0.0002:0.0218:0.00383871:0.4488:rs2691305
1   73841   rs143773730 C   T   .   PASS    AF=0.1716   ES:SE:LP:AF:ID  0.0334:0.0434:0.354283:0.1716:rs143773730
1   74790   rs13328700  C   G   .   PASS    AF=0.0367   ES:SE:LP:AF:ID  0.0911:0.1181:0.355857:0.0367:rs13328700
1   74792   rs13328684  G   A   .   PASS    AF=0.0367   ES:SE:LP:AF:ID  0.0911:0.1181:0.355857:0.0367:rs13328684
1   77462   rs2462497   G   A   .   PASS    AF=0.0698   ES:SE:LP:AF:ID  0.0241:0.0664:0.144299:0.0698:rs2462497
1   79050   rs2949413   G   T   .   PASS    AF=0.6186   ES:SE:LP:AF:ID  0.0239:0.0224:0.543634:0.6186:rs2949413
1   79137   rs143777184 A   T   .   PASS    AF=0.0083   ES:SE:LP:AF:ID  -0.1472:0.1824:0.376854:0.0083:rs143777184
1   82103   rs2020400   T   C   .   PASS    AF=0.9329   ES:SE:LP:AF:ID  -0.0343:0.0502:0.306625:0.9329:rs2020400
1   82163   rs139113303 G   A   .   PASS    AF=0.0666   ES:SE:LP:AF:ID  -0.0486:0.0587:0.389659:0.0666:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.1043   ES:SE:LP:AF:ID  -0.0088:0.037:0.0902836:0.1043:rs149189449
1   82734   rs4030331   T   C   .   PASS    AF=0.1855   ES:SE:LP:AF:ID  0.0339:0.0284:0.63339:0.1855:rs4030331
1   84002   rs28850140  G   A   .   PASS    AF=0.074    ES:SE:LP:AF:ID  0.0569:0.0552:0.519131:0.074:rs28850140
1   86028   rs114608975 T   C   .   PASS    AF=0.1023   ES:SE:LP:AF:ID  0.0199:0.0364:0.232918:0.1023:rs114608975
1   86065   rs116504101 G   C   .   PASS    AF=0.1058   ES:SE:LP:AF:ID  0.0006:0.0368:0.0054629:0.1058:rs116504101
1   86331   rs115209712 A   G   .   PASS    AF=0.1521   ES:SE:LP:AF:ID  -0.0185:0.037:0.210067:0.1521:rs115209712
1   87409   rs139490478 C   T   .   PASS    AF=0.0686   ES:SE:LP:AF:ID  -0.0489:0.0582:0.397072:0.0686:rs139490478
1   87647   rs146836579 T   C   .   PASS    AF=0.0206   ES:SE:LP:AF:ID  0.3257:0.1656:1.30751:0.0206:rs146836579
1   88169   rs940550    C   T   .   PASS    AF=0.1417   ES:SE:LP:AF:ID  -0.0141:0.0384:0.14685:0.1417:rs940550
1   88172   rs940551    G   A   .   PASS    AF=0.0762   ES:SE:LP:AF:ID  0.0011:0.045:0.00877392:0.0762:rs940551