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=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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    "FORMAT.4": "<ID=SS,Number=A,Type=Float,Description=\"Sample size used to estimate genetic effect\">",
    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_6158_100.vcf.gz --id UKB-b:19752 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6158_100.txt.gz --cohort_cases 4289 --cohort_controls 10662 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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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/public/UKB-b-19752/UKB-b-19752_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19752/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:36 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19752/UKB-b-19752_data.vcf.gz ...
Read summary statistics for 4656184 SNPs.
Dropped 1132 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1065822 SNPs remain.
After merging with regression SNP LD, 1065822 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0409 (0.0403)
Lambda GC: 1.0044
Mean Chi^2: 1.0084
Intercept: 0.995 (0.0086)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:31 2019
Total time elapsed: 55.48s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8978,
    "inflation_factor": 1,
    "mean_EFFECT": 3.2321e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 39057,
    "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": 1065822,
    "ldsc_nsnp_merge_regression_ld": 1065822,
    "ldsc_observed_scale_h2_beta": 0.0409,
    "ldsc_observed_scale_h2_se": 0.0403,
    "ldsc_intercept_beta": 0.995,
    "ldsc_intercept_se": 0.0086,
    "ldsc_lambda_gc": 1.0044,
    "ldsc_mean_chisq": 1.0084,
    "ldsc_ratio": -0.5952
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq FALSE
n_p_sig FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio FALSE
ldsc_intercept_beta FALSE
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 58 0 4655061 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 4656184 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 8.663927e+00 5.766891e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.855742e+07 5.670186e+07 828.0000000 3.178076e+07 6.887456e+07 1.146648e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.200000e-06 6.645100e-03 -0.0452213 -4.274900e-03 7.300000e-06 4.300800e-03 4.342110e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.502300e-03 1.237600e-03 0.0050127 5.443300e-03 6.073100e-03 7.315600e-03 1.842150e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.987361e-01 2.891917e-01 0.0000003 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.987378e-01 2.891679e-01 0.0000003 2.469400e-01 4.988463e-01 7.489966e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.663749e-01 2.308343e-01 0.0816050 1.666918e-01 3.068500e-01 5.310310e-01 9.183950e-01 ▇▅▃▂▂
numeric AF_reference 39057 0.9916118 NA NA NA NA NA NA NA 3.583853e-01 2.296098e-01 0.0000000 1.689300e-01 3.073080e-01 5.199680e-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 -0.0056764 0.0093199 0.5400003 0.5424841 0.621207 0.7821490 NA
1 54676 rs2462492 C T -0.0080859 0.0092741 0.3800004 0.3832784 0.399983 NA NA
1 86028 rs114608975 T C 0.0326258 0.0148672 0.0280001 0.0282008 0.102682 0.0277556 NA
1 91536 rs6702460 G T -0.0022532 0.0091352 0.8100000 0.8051748 0.455967 0.4207270 NA
1 534192 rs6680723 C T 0.0083520 0.0102750 0.4199997 0.4163054 0.242887 NA NA
1 546697 rs12025928 A G -0.0204008 0.0131397 0.1199999 0.1205169 0.914845 NA NA
1 693731 rs12238997 A G 0.0059514 0.0087264 0.5000000 0.4952381 0.114841 0.1417730 NA
1 706368 rs55727773 A G -0.0070765 0.0064656 0.2700001 0.2737368 0.514509 0.2751600 NA
1 722670 rs116030099 T C -0.0035551 0.0106206 0.7400005 0.7378233 0.100148 0.0413339 NA
1 729679 rs4951859 C G 0.0018866 0.0075229 0.8000000 0.8019794 0.843062 0.6399760 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51198027 rs34939255 A G 0.0084819 0.0064480 0.1900002 0.1883628 0.252300 0.0984425 NA
22 51208537 rs72619593 G A -0.0029948 0.0084387 0.7199992 0.7226768 0.123350 0.1142170 NA
22 51210289 rs112565862 C T -0.0042204 0.0085362 0.6200004 0.6210127 0.130999 0.1018370 NA
22 51211106 rs9628250 T C 0.0072881 0.0063527 0.2500000 0.2512764 0.270853 0.1671330 NA
22 51211392 rs3888396 T C -0.0049240 0.0084626 0.5600000 0.5606614 0.133645 0.1641370 NA
22 51212875 rs2238837 A C 0.0036555 0.0060308 0.5400003 0.5444217 0.333067 0.3724040 NA
22 51213613 rs34726907 C T -0.0093150 0.0080002 0.2399999 0.2442853 0.126808 0.1727240 NA
22 51216564 rs9616970 T C -0.0089464 0.0079732 0.2599998 0.2618371 0.127206 0.1563500 NA
22 51219006 rs28729663 G A -0.0076769 0.0078190 0.3300000 0.3261836 0.136248 0.2052720 NA
22 51237063 rs3896457 T C 0.0049423 0.0061532 0.4199997 0.4218577 0.300574 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.621207 ES:SE:LP:AF:ID  -0.00567641:0.00931994:0.267606:0.621207:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399983 ES:SE:LP:AF:ID  -0.00808586:0.00927414:0.420216:0.399983:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.102682 ES:SE:LP:AF:ID  0.0326258:0.0148672:1.55284:0.102682:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455967 ES:SE:LP:AF:ID  -0.00225325:0.00913521:0.091515:0.455967:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.242887 ES:SE:LP:AF:ID  0.00835201:0.010275:0.376751:0.242887:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914845 ES:SE:LP:AF:ID  -0.0204008:0.0131397:0.920819:0.914845:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.114841 ES:SE:LP:AF:ID  0.00595141:0.00872639:0.30103:0.114841:rs12238997
1   706368  rs12029736  A   G   .   PASS    AF=0.514509 ES:SE:LP:AF:ID  -0.00707654:0.00646556:0.568636:0.514509:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.100148 ES:SE:LP:AF:ID  -0.00355511:0.0106206:0.130768:0.100148:rs116030099
1   729679  rs4951859   C   G   .   PASS    AF=0.843062 ES:SE:LP:AF:ID  0.00188663:0.00752286:0.09691:0.843062:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122234 ES:SE:LP:AF:ID  -7.39893e-06:0.00823033:-0:0.122234:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121272 ES:SE:LP:AF:ID  0.000742113:0.00824308:0.0315171:0.121272:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132194 ES:SE:LP:AF:ID  -0.00160808:0.00814231:0.0757207:0.132194:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838821 ES:SE:LP:AF:ID  0.00224934:0.0072856:0.119186:0.838821:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838462 ES:SE:LP:AF:ID  0.00262227:0.00727606:0.142668:0.838462:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.870233 ES:SE:LP:AF:ID  -0.0003932:0.00782524:0.0177288:0.870233:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129743 ES:SE:LP:AF:ID  0.000205004:0.00783275:0.00877392:0.129743:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869629 ES:SE:LP:AF:ID  -0.000495231:0.00781069:0.0222764:0.869629:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869748 ES:SE:LP:AF:ID  -0.000740674:0.00781374:0.0362122:0.869748:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86965  ES:SE:LP:AF:ID  -0.000573404:0.00781025:0.0268721:0.86965:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837892 ES:SE:LP:AF:ID  0.00184484:0.00725359:0.09691:0.837892:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838621 ES:SE:LP:AF:ID  0.00149329:0.00727692:0.0757207:0.838621:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839902 ES:SE:LP:AF:ID  0.00039206:0.00737554:0.0177288:0.839902:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869859 ES:SE:LP:AF:ID  -0.00129194:0.00779655:0.0604807:0.869859:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.869498 ES:SE:LP:AF:ID  -0.00144093:0.00777604:0.0705811:0.869498:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868253 ES:SE:LP:AF:ID  -0.000918265:0.00776359:0.0409586:0.868253:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869646 ES:SE:LP:AF:ID  -0.00124501:0.00778512:0.0604807:0.869646:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869657 ES:SE:LP:AF:ID  -0.0012301:0.00778572:0.0604807:0.869657:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869656 ES:SE:LP:AF:ID  -0.00125701:0.0077856:0.0604807:0.869656:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870049 ES:SE:LP:AF:ID  -0.00118134:0.00780693:0.0555173:0.870049:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838416 ES:SE:LP:AF:ID  0.000995701:0.00725553:0.05061:0.838416:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838535 ES:SE:LP:AF:ID  0.00106597:0.00726062:0.0555173:0.838535:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862429 ES:SE:LP:AF:ID  0.000435069:0.00776561:0.0177288:0.862429:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.707462 ES:SE:LP:AF:ID  0.0045284:0.00752405:0.259637:0.707462:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.104067 ES:SE:LP:AF:ID  0.00379263:0.00874375:0.180456:0.104067:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.757696 ES:SE:LP:AF:ID  -0.00650587:0.00615685:0.537602:0.757696:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.110067 ES:SE:LP:AF:ID  0.0111984:0.008408:0.744727:0.110067:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129288 ES:SE:LP:AF:ID  0.000194258:0.00782688:0.00877392:0.129288:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.86945  ES:SE:LP:AF:ID  -0.000555336:0.00779288:0.0268721:0.86945:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129593 ES:SE:LP:AF:ID  0.000302806:0.00781771:0.0132283:0.129593:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.869471 ES:SE:LP:AF:ID  -0.000696793:0.00779216:0.0315171:0.869471:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.263472 ES:SE:LP:AF:ID  -0.00838181:0.00692179:0.638272:0.263472:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870767 ES:SE:LP:AF:ID  0.00118718:0.00780915:0.0555173:0.870767:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.097924 ES:SE:LP:AF:ID  0.0144257:0.00897169:0.958607:0.097924:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.128257 ES:SE:LP:AF:ID  -0.00176665:0.00782685:0.0861861:0.128257:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128495 ES:SE:LP:AF:ID  -0.0013694:0.00781641:0.0655015:0.128495:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.869492 ES:SE:LP:AF:ID  0.0011189:0.00779121:0.05061:0.869492:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.100692 ES:SE:LP:AF:ID  0.00227708:0.00886819:0.09691:0.100692:rs61768199
1   782981  rs6594026   C   T   .   PASS    AF=0.129424 ES:SE:LP:AF:ID  -0.00107599:0.00781048:0.05061:0.129424:rs6594026
1   785050  rs2905062   G   A   .   PASS    AF=0.868793 ES:SE:LP:AF:ID  0.00138189:0.00778141:0.0655015:0.868793:rs2905062