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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_20155.vcf.gz --id UKB-b:16311 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20155.txt.gz --cohort_controls 105451 --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-16311/UKB-b-16311_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16311/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:59 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16311/UKB-b-16311_data.vcf.gz ...
Read summary statistics for 9059054 SNPs.
Dropped 8987 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, 1287314 SNPs remain.
After merging with regression SNP LD, 1287314 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1343 (0.007)
Lambda GC: 1.2497
Mean Chi^2: 1.293
Intercept: 1.0124 (0.008)
Ratio: 0.0423 (0.0274)
Analysis finished at Thu Oct 17 14:46:32 2019
Total time elapsed: 1.0m:33.56s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9478,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 8,
    "n_p_sig": 1538,
    "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": 96071,
    "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": 1287314,
    "ldsc_nsnp_merge_regression_ld": 1287314,
    "ldsc_observed_scale_h2_beta": 0.1343,
    "ldsc_observed_scale_h2_se": 0.007,
    "ldsc_intercept_beta": 1.0124,
    "ldsc_intercept_se": 0.008,
    "ldsc_lambda_gc": 1.2497,
    "ldsc_mean_chisq": 1.293,
    "ldsc_ratio": 0.0423
}

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 TRUE
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 1.000000 3 58 0 9050109 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 9059054 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.641622e+00 5.757937e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.879322e+07 5.633742e+07 828.0000000 3.244155e+07 6.935352e+07 1.145365e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 1.181000e-04 1.505030e-02 -0.1709610 -6.043100e-03 5.480000e-05 6.197600e-03 1.537680e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.162850e-02 8.627500e-03 0.0041719 4.977100e-03 7.665500e-03 1.595640e-02 9.486520e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.718527e-01 2.963754e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.718520e-01 2.963511e-01 0.0000000 2.080086e-01 4.626278e-01 7.279152e-01 9.999998e-01 ▇▆▆▆▆
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.190112e-01 2.584116e-01 0.0033200 1.990600e-02 9.983700e-02 3.452780e-01 9.966800e-01 ▇▂▁▁▁
numeric AF_reference 96071 0.989395 NA NA NA NA NA NA NA 2.194032e-01 2.502920e-01 0.0000000 1.717250e-02 1.174120e-01 3.438500e-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 -0.0027687 0.0076544 0.7199992 0.7175689 0.623912 0.7821490 NA
1 54676 rs2462492 C T 0.0044147 0.0075946 0.5600000 0.5610430 0.399584 NA NA
1 86028 rs114608975 T C -0.0083688 0.0121629 0.4899999 0.4914142 0.103445 0.0277556 NA
1 91536 rs6702460 G T -0.0015786 0.0074737 0.8300000 0.8327155 0.456363 0.4207270 NA
1 234313 rs8179466 C T -0.0263857 0.0148304 0.0749998 0.0752130 0.074157 NA NA
1 534192 rs6680723 C T -0.0020697 0.0085203 0.8100000 0.8080741 0.241080 NA NA
1 546697 rs12025928 A G -0.0244700 0.0106292 0.0210000 0.0213268 0.913055 NA NA
1 693731 rs12238997 A G 0.0146755 0.0071506 0.0400000 0.0401347 0.116878 0.1417730 NA
1 705882 rs72631875 G A 0.0066664 0.0104203 0.5199996 0.5223325 0.067801 0.0315495 NA
1 706368 rs55727773 A G -0.0029985 0.0053035 0.5700002 0.5718166 0.515813 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0053590 0.0064345 0.4000000 0.4049258 0.136708 0.2052720 NA
22 51219387 rs9616832 T C -0.0007484 0.0083699 0.9299999 0.9287554 0.072619 0.0654952 NA
22 51219704 rs147475742 G A 0.0027232 0.0112418 0.8100000 0.8085979 0.041215 0.0473243 NA
22 51221190 rs369304721 G A -0.0040215 0.0111705 0.7199992 0.7188391 0.049209 NA NA
22 51221731 rs115055839 T C -0.0006431 0.0083779 0.9400001 0.9388100 0.072081 0.0625000 NA
22 51222100 rs114553188 G T 0.0127924 0.0098163 0.1900002 0.1925117 0.054406 0.0880591 NA
22 51223637 rs375798137 G A 0.0115412 0.0098678 0.2399999 0.2421669 0.054029 0.0788738 NA
22 51229805 rs9616985 T C -0.0003701 0.0084075 0.9599999 0.9648893 0.071911 0.0730831 NA
22 51232488 rs376461333 A G 0.0186465 0.0198061 0.3500000 0.3464731 0.019867 NA NA
22 51237063 rs3896457 T C 0.0055289 0.0050896 0.2800000 0.2773411 0.298137 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623912 ES:SE:LP:AF:ID  -0.00276869:0.00765445:0.142668:0.623912:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399584 ES:SE:LP:AF:ID  0.00441467:0.00759457:0.251812:0.399584:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103445 ES:SE:LP:AF:ID  -0.00836882:0.0121629:0.309804:0.103445:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456363 ES:SE:LP:AF:ID  -0.0015786:0.00747372:0.0809219:0.456363:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074157 ES:SE:LP:AF:ID  -0.0263857:0.0148304:1.12494:0.074157:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24108  ES:SE:LP:AF:ID  -0.00206967:0.00852027:0.091515:0.24108:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913055 ES:SE:LP:AF:ID  -0.02447:0.0106292:1.67778:0.913055:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116878 ES:SE:LP:AF:ID  0.0146755:0.00715055:1.39794:0.116878:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067801 ES:SE:LP:AF:ID  0.00666643:0.0104203:0.283997:0.067801:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515813 ES:SE:LP:AF:ID  -0.0029985:0.00530353:0.244125:0.515813:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032844 ES:SE:LP:AF:ID  0.0194657:0.0134152:0.823909:0.032844:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.0365   ES:SE:LP:AF:ID  0.0156401:0.012168:0.69897:0.0365:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036611 ES:SE:LP:AF:ID  0.0154021:0.0121227:0.69897:0.036611:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03631  ES:SE:LP:AF:ID  0.0164551:0.0122123:0.744727:0.03631:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01659  ES:SE:LP:AF:ID  -0.00641111:0.0186606:0.136677:0.01659:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036848 ES:SE:LP:AF:ID  0.0135436:0.0120748:0.585027:0.036848:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036962 ES:SE:LP:AF:ID  0.0148572:0.0120335:0.657577:0.036962:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102014 ES:SE:LP:AF:ID  -0.00360787:0.00870498:0.167491:0.102014:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959417 ES:SE:LP:AF:ID  -0.0183875:0.0116318:0.958607:0.959417:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031233 ES:SE:LP:AF:ID  0.0302144:0.0212738:0.79588:0.031233:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053294 ES:SE:LP:AF:ID  -0.021873:0.0167075:0.721246:0.053294:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036464 ES:SE:LP:AF:ID  0.0159338:0.012107:0.721246:0.036464:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036757 ES:SE:LP:AF:ID  0.014314:0.0120003:0.638272:0.036757:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843058 ES:SE:LP:AF:ID  -0.0134773:0.0062085:1.52288:0.843058:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056009 ES:SE:LP:AF:ID  0.010145:0.0100472:0.508638:0.056009:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122876 ES:SE:LP:AF:ID  0.0124827:0.00678845:1.18046:0.122876:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024701 ES:SE:LP:AF:ID  -0.0239406:0.0170373:0.79588:0.024701:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122111 ES:SE:LP:AF:ID  0.0130156:0.00679221:1.25964:0.122111:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132587 ES:SE:LP:AF:ID  0.0156619:0.00669415:1.72125:0.132587:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011113 ES:SE:LP:AF:ID  0.0435538:0.0244384:1.12494:0.011113:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005601 ES:SE:LP:AF:ID  0.0337765:0.031844:0.537602:0.005601:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036683 ES:SE:LP:AF:ID  0.0153772:0.0118768:0.69897:0.036683:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838666 ES:SE:LP:AF:ID  -0.0129502:0.00601227:1.50864:0.838666:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838314 ES:SE:LP:AF:ID  -0.0129884:0.00600586:1.50864:0.838314:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86916  ES:SE:LP:AF:ID  -0.0120248:0.00643724:1.20761:0.86916:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130499 ES:SE:LP:AF:ID  0.0114278:0.00645092:1.11919:0.130499:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037193 ES:SE:LP:AF:ID  0.0171521:0.0116764:0.853872:0.037193:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037445 ES:SE:LP:AF:ID  0.0166051:0.0116:0.823909:0.037445:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86851  ES:SE:LP:AF:ID  -0.0118628:0.00642532:1.18709:0.86851:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868614 ES:SE:LP:AF:ID  -0.0121661:0.00642863:1.23657:0.868614:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037396 ES:SE:LP:AF:ID  0.01715:0.011653:0.853872:0.037396:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868513 ES:SE:LP:AF:ID  -0.0118719:0.00642511:1.18709:0.868513:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005275 ES:SE:LP:AF:ID  -0.00447188:0.0324803:0.05061:0.005275:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005247 ES:SE:LP:AF:ID  -0.00645315:0.0325376:0.0757207:0.005247:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837719 ES:SE:LP:AF:ID  -0.0129995:0.00598874:1.52288:0.837719:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037427 ES:SE:LP:AF:ID  0.0170148:0.0116671:0.853872:0.037427:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838301 ES:SE:LP:AF:ID  -0.0130372:0.00600464:1.52288:0.838301:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013669 ES:SE:LP:AF:ID  -0.00329819:0.0210089:0.0555173:0.013669:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005406 ES:SE:LP:AF:ID  0.0163575:0.0326878:0.207608:0.005406:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839454 ES:SE:LP:AF:ID  -0.0138484:0.00608505:1.63827:0.839454:rs3131965