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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_6140_3.vcf.gz --id UKB-b:4184 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6140_3.txt.gz --cohort_cases 3892 --cohort_controls 148971 --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-4184/UKB-b-4184_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4184/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:03 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4184/UKB-b-4184_data.vcf.gz ...
Read summary statistics for 4492319 SNPs.
Dropped 1028 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, 1039539 SNPs remain.
After merging with regression SNP LD, 1039539 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0053 (0.0033)
Lambda GC: 1.0541
Mean Chi^2: 1.0524
Intercept: 1.0352 (0.0073)
Ratio: 0.6715 (0.1401)
Analysis finished at Thu Oct 17 14:44:57 2019
Total time elapsed: 53.86s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8929,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 9.5611e-07,
    "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": 37407,
    "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": 1039539,
    "ldsc_nsnp_merge_regression_ld": 1039539,
    "ldsc_observed_scale_h2_beta": 0.0053,
    "ldsc_observed_scale_h2_se": 0.0033,
    "ldsc_intercept_beta": 1.0352,
    "ldsc_intercept_se": 0.0073,
    "ldsc_lambda_gc": 1.0541,
    "ldsc_mean_chisq": 1.0524,
    "ldsc_ratio": 0.6718
}

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 TRUE
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 4491298 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 4492319 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 8.657438e+00 5.765806e+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 7.859088e+07 5.672824e+07 828.0000000 3.172762e+07 6.893804e+07 1.147247e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.000000e-06 7.252000e-04 -0.0045074 -4.703000e-04 -5.000000e-07 4.744000e-04 5.080000e-03 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.971000e-04 1.226000e-04 0.0005499 5.916000e-04 6.551000e-04 7.785000e-04 2.013000e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.915673e-01 2.909893e-01 0.0000004 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.915665e-01 2.909655e-01 0.0000005 2.374011e-01 4.883255e-01 7.436179e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.732305e-01 2.267582e-01 0.0899290 1.766870e-01 3.166140e-01 5.366510e-01 9.100710e-01 ▇▅▃▂▂
numeric AF_reference 37407 0.9916731 NA NA NA NA NA NA NA 3.645631e-01 2.266461e-01 0.0000000 1.775160e-01 3.158950e-01 5.251600e-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 -0.0009210 0.0010138 0.3599996 0.3636078 0.623748 0.7821490 NA
1 54676 rs2462492 C T 0.0000888 0.0010070 0.9299999 0.9296946 0.399299 NA NA
1 86028 rs114608975 T C -0.0004704 0.0016050 0.7700005 0.7694663 0.103775 0.0277556 NA
1 91536 rs6702460 G T 0.0008986 0.0009922 0.3700002 0.3651508 0.456252 0.4207270 NA
1 534192 rs6680723 C T 0.0006174 0.0011339 0.5900000 0.5860796 0.241207 NA NA
1 693731 rs12238997 A G -0.0004995 0.0009452 0.5999997 0.5971376 0.116971 0.1417730 NA
1 706368 rs55727773 A G 0.0002283 0.0007000 0.7400005 0.7443198 0.514914 0.2751600 NA
1 722670 rs116030099 T C 0.0015054 0.0011529 0.1900002 0.1916453 0.101506 0.0413339 NA
1 729679 rs4951859 C G 0.0010170 0.0008181 0.2099999 0.2138338 0.841976 0.6399760 NA
1 731718 rs142557973 T C -0.0003725 0.0008970 0.6800001 0.6779525 0.122901 0.1543530 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51198027 rs34939255 A G 0.0001449 0.0006986 0.8400000 0.8356874 0.254582 0.0984425 NA
22 51208537 rs72619593 G A -0.0001868 0.0009320 0.8400000 0.8411401 0.121334 0.1142170 NA
22 51210289 rs112565862 C T -0.0001754 0.0009295 0.8499999 0.8502835 0.130384 0.1018370 NA
22 51211106 rs9628250 T C 0.0002422 0.0006935 0.7300002 0.7269136 0.271375 0.1671330 NA
22 51211392 rs3888396 T C -0.0001062 0.0009214 0.9100000 0.9082716 0.133000 0.1641370 NA
22 51212875 rs2238837 A C 0.0000163 0.0006575 0.9800000 0.9802277 0.331080 0.3724040 NA
22 51213613 rs34726907 C T -0.0009493 0.0008705 0.2800000 0.2754681 0.126733 0.1727240 NA
22 51216564 rs9616970 T C -0.0007639 0.0008667 0.3800004 0.3780666 0.127283 0.1563500 NA
22 51219006 rs28729663 G A -0.0009207 0.0008478 0.2800000 0.2774459 0.137059 0.2052720 NA
22 51237063 rs3896457 T C 0.0000616 0.0006732 0.9299999 0.9270895 0.297634 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623748 ES:SE:LP:AF:ID  -0.000921013:0.00101376:0.443698:0.623748:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399299 ES:SE:LP:AF:ID  8.88458e-05:0.00100699:0.0315171:0.399299:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103775 ES:SE:LP:AF:ID  -0.000470394:0.00160504:0.113509:0.103775:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456252 ES:SE:LP:AF:ID  0.000898552:0.000992224:0.431798:0.456252:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.241207 ES:SE:LP:AF:ID  0.000617427:0.00113388:0.229148:0.241207:rs6680723
1   693731  rs12238997  A   G   .   PASS    AF=0.116971 ES:SE:LP:AF:ID  -0.000499535:0.000945156:0.221849:0.116971:rs12238997
1   706368  rs12029736  A   G   .   PASS    AF=0.514914 ES:SE:LP:AF:ID  0.000228283:0.000699958:0.130768:0.514914:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.101506 ES:SE:LP:AF:ID  0.00150536:0.00115289:0.721246:0.101506:rs116030099
1   729679  rs4951859   C   G   .   PASS    AF=0.841976 ES:SE:LP:AF:ID  0.00101698:0.000818105:0.677781:0.841976:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122901 ES:SE:LP:AF:ID  -0.000372502:0.000897036:0.167491:0.122901:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.122123 ES:SE:LP:AF:ID  -0.000332316:0.000897471:0.148742:0.122123:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13332  ES:SE:LP:AF:ID  -0.00104359:0.000883115:0.619789:0.13332:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.837725 ES:SE:LP:AF:ID  0.00129334:0.000792193:1:0.837725:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837337 ES:SE:LP:AF:ID  0.00129736:0.000791304:1:0.837337:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868987 ES:SE:LP:AF:ID  0.000630897:0.000849368:0.337242:0.868987:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130713 ES:SE:LP:AF:ID  -0.000685675:0.00085105:0.376751:0.130713:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.868307 ES:SE:LP:AF:ID  0.000580672:0.000847657:0.309804:0.868307:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868414 ES:SE:LP:AF:ID  0.000556619:0.000848038:0.29243:0.868414:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.868303 ES:SE:LP:AF:ID  0.00057763:0.000847602:0.30103:0.868303:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836819 ES:SE:LP:AF:ID  0.00114624:0.000789247:0.823909:0.836819:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.837451 ES:SE:LP:AF:ID  0.00108615:0.000791438:0.769551:0.837451:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.83868  ES:SE:LP:AF:ID  0.00113869:0.000802263:0.79588:0.83868:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868622 ES:SE:LP:AF:ID  0.000680787:0.000846742:0.376751:0.868622:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868175 ES:SE:LP:AF:ID  0.000589483:0.000844633:0.309804:0.868175:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.86698  ES:SE:LP:AF:ID  0.000550102:0.000842644:0.29243:0.86698:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868317 ES:SE:LP:AF:ID  0.000597596:0.000845308:0.318759:0.868317:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868329 ES:SE:LP:AF:ID  0.000598473:0.00084537:0.318759:0.868329:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.868337 ES:SE:LP:AF:ID  0.000595406:0.000845394:0.318759:0.868337:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.868806 ES:SE:LP:AF:ID  0.000669145:0.000847767:0.366532:0.868806:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.837146 ES:SE:LP:AF:ID  0.000899949:0.000787474:0.60206:0.837146:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.837273 ES:SE:LP:AF:ID  0.000882084:0.000788016:0.585027:0.837273:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.861329 ES:SE:LP:AF:ID  0.000711944:0.000842263:0.39794:0.861329:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706268 ES:SE:LP:AF:ID  0.000656124:0.000822006:0.376751:0.706268:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105749 ES:SE:LP:AF:ID  -0.00085149:0.000944898:0.431798:0.105749:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.760137 ES:SE:LP:AF:ID  0.00077651:0.000668556:0.60206:0.760137:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106828 ES:SE:LP:AF:ID  -0.000701388:0.000920383:0.346787:0.106828:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.130285 ES:SE:LP:AF:ID  -0.000622252:0.000850829:0.337242:0.130285:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868243 ES:SE:LP:AF:ID  0.000601234:0.000846281:0.318759:0.868243:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.130392 ES:SE:LP:AF:ID  -0.000645272:0.000850192:0.346787:0.130392:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868264 ES:SE:LP:AF:ID  0.000598052:0.000846308:0.318759:0.868264:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.264886 ES:SE:LP:AF:ID  -0.000174154:0.000749447:0.0861861:0.264886:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.869409 ES:SE:LP:AF:ID  0.000614343:0.000848271:0.327902:0.869409:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.095454 ES:SE:LP:AF:ID  -8.46607e-05:0.000981375:0.0315171:0.095454:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.129275 ES:SE:LP:AF:ID  -0.000679036:0.000851442:0.366532:0.129275:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.129565 ES:SE:LP:AF:ID  -0.000717355:0.000850024:0.39794:0.129565:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.86811  ES:SE:LP:AF:ID  0.000625293:0.000846009:0.337242:0.86811:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.102399 ES:SE:LP:AF:ID  -0.000640436:0.000959054:0.30103:0.102399:rs61768199
1   782981  rs6594026   C   T   .   PASS    AF=0.130203 ES:SE:LP:AF:ID  -0.000693987:0.000849934:0.387216:0.130203:rs6594026
1   785050  rs2905062   G   A   .   PASS    AF=0.867776 ES:SE:LP:AF:ID  0.000659342:0.000845568:0.356547:0.867776:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.867715 ES:SE:LP:AF:ID  0.000626355:0.000846147:0.337242:0.867715:rs2980300