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_6145_1.vcf.gz --id UKB-b:18162 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6145_1.txt.gz --cohort_cases 42866 --cohort_controls 416876 --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-18162/UKB-b-18162_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18162/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18162/UKB-b-18162_data.vcf.gz ...
Read summary statistics for 8020737 SNPs.
Dropped 6166 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, 1282531 SNPs remain.
After merging with regression SNP LD, 1282531 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0142 (0.0013)
Lambda GC: 1.1436
Mean Chi^2: 1.145
Intercept: 1.0185 (0.0061)
Ratio: 0.1278 (0.0419)
Analysis finished at Thu Oct 17 14:41:47 2019
Total time elapsed: 1.0m:28.48s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9431,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 9.7667e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 137,
    "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": 74909,
    "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": 1282531,
    "ldsc_nsnp_merge_regression_ld": 1282531,
    "ldsc_observed_scale_h2_beta": 0.0142,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.0185,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.1436,
    "ldsc_mean_chisq": 1.145,
    "ldsc_ratio": 0.1276
}

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 8014598 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 8020737 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 8.660644e+00 5.763453e+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 7.870358e+07 5.640873e+07 828.0000000 3.226831e+07 6.917389e+07 1.145380e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 9.800000e-06 1.552200e-03 -0.0147206 -7.258000e-04 3.300000e-06 7.392000e-04 2.245300e-02 ▁▇▆▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.291900e-03 7.810000e-04 0.0005847 6.777000e-04 9.457000e-04 1.701000e-03 7.209100e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.830062e-01 2.932425e-01 0.0000000 2.200002e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.830070e-01 2.932180e-01 0.0000000 2.246252e-01 4.765232e-01 7.371286e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.449627e-01 2.607037e-01 0.0081650 3.499900e-02 1.357210e-01 3.881520e-01 9.918350e-01 ▇▂▂▁▁
numeric AF_reference 74909 0.9906606 NA NA NA NA NA NA NA 2.442491e-01 2.525457e-01 0.0000000 3.674120e-02 1.511580e-01 3.833870e-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 0.0027661 0.0010760 0.0100000 0.0101515 0.623799 0.7821490 NA
1 54676 rs2462492 C T -0.0019349 0.0010659 0.0690001 0.0694831 0.400435 NA NA
1 86028 rs114608975 T C -0.0003681 0.0017045 0.8300000 0.8290233 0.103546 0.0277556 NA
1 91536 rs6702460 G T 0.0011319 0.0010496 0.2800000 0.2808819 0.456844 0.4207270 NA
1 234313 rs8179466 C T 0.0052882 0.0020696 0.0109999 0.0106158 0.074511 NA NA
1 534192 rs6680723 C T 0.0000315 0.0011990 0.9800000 0.9790169 0.240982 NA NA
1 546697 rs12025928 A G -0.0011354 0.0014960 0.4500005 0.4478610 0.913490 NA NA
1 693731 rs12238997 A G 0.0017616 0.0010045 0.0790005 0.0794647 0.116360 0.1417730 NA
1 705882 rs72631875 G A -0.0000140 0.0014726 0.9900000 0.9923882 0.067257 0.0315495 NA
1 706368 rs55727773 A G -0.0009793 0.0007443 0.1900002 0.1882619 0.515625 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0021539 0.0008980 0.0160000 0.0164620 0.137940 0.2052720 NA
22 51219387 rs9616832 T C 0.0013236 0.0011658 0.2599998 0.2562034 0.073713 0.0654952 NA
22 51219704 rs147475742 G A 0.0020208 0.0015620 0.2000000 0.1957639 0.041941 0.0473243 NA
22 51221190 rs369304721 G A 0.0019405 0.0015597 0.2099999 0.2134287 0.049706 NA NA
22 51221731 rs115055839 T C 0.0013641 0.0011665 0.2399999 0.2422639 0.073202 0.0625000 NA
22 51222100 rs114553188 G T 0.0030141 0.0013728 0.0280001 0.0281216 0.054476 0.0880591 NA
22 51223637 rs375798137 G A 0.0029802 0.0013794 0.0309999 0.0307378 0.054106 0.0788738 NA
22 51229805 rs9616985 T C 0.0012945 0.0011708 0.2700001 0.2688672 0.073038 0.0730831 NA
22 51232488 rs376461333 A G 0.0049694 0.0027570 0.0710003 0.0714686 0.020043 NA NA
22 51237063 rs3896457 T C 0.0007143 0.0007159 0.3200000 0.3184095 0.297924 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623799 ES:SE:LP:AF:ID  0.00276607:0.00107603:2:0.623799:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400435 ES:SE:LP:AF:ID  -0.00193491:0.00106591:1.16115:0.400435:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103546 ES:SE:LP:AF:ID  -0.00036809:0.00170448:0.0809219:0.103546:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456844 ES:SE:LP:AF:ID  0.00113188:0.00104965:0.552842:0.456844:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074511 ES:SE:LP:AF:ID  0.00528816:0.00206965:1.95861:0.074511:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240982 ES:SE:LP:AF:ID  3.15354e-05:0.001199:0.00877392:0.240982:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91349  ES:SE:LP:AF:ID  -0.00113542:0.00149597:0.346787:0.91349:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11636  ES:SE:LP:AF:ID  0.00176164:0.00100447:1.10237:0.11636:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067257 ES:SE:LP:AF:ID  -1.40489e-05:0.00147262:0.00436481:0.067257:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515625 ES:SE:LP:AF:ID  -0.000979333:0.000744322:0.721246:0.515625:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032997 ES:SE:LP:AF:ID  -0.00100066:0.00187662:0.229148:0.032997:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036611 ES:SE:LP:AF:ID  -0.00117314:0.00170463:0.309804:0.036611:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036726 ES:SE:LP:AF:ID  -0.0011854:0.00169821:0.309804:0.036726:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036428 ES:SE:LP:AF:ID  -0.0010606:0.00171036:0.267606:0.036428:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016395 ES:SE:LP:AF:ID  0.000901768:0.00263419:0.136677:0.016395:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036967 ES:SE:LP:AF:ID  -0.00110858:0.00169142:0.29243:0.036967:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037066 ES:SE:LP:AF:ID  -0.00118766:0.00168558:0.318759:0.037066:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101202 ES:SE:LP:AF:ID  -0.000604148:0.00122794:0.207608:0.101202:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959115 ES:SE:LP:AF:ID  -0.000274912:0.00162592:0.0604807:0.959115:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031442 ES:SE:LP:AF:ID  0.00248224:0.00295145:0.39794:0.031442:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053274 ES:SE:LP:AF:ID  -0.00139944:0.00234621:0.259637:0.053274:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036581 ES:SE:LP:AF:ID  -0.000886182:0.00169657:0.221849:0.036581:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036898 ES:SE:LP:AF:ID  -0.000992575:0.00168106:0.259637:0.036898:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.8432   ES:SE:LP:AF:ID  -0.00155136:0.000870626:1.12494:0.8432:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055952 ES:SE:LP:AF:ID  0.00096383:0.00140926:0.309804:0.055952:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122336 ES:SE:LP:AF:ID  0.00167219:0.000952848:1.10237:0.122336:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025701 ES:SE:LP:AF:ID  -0.00137511:0.00234492:0.251812:0.025701:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12158  ES:SE:LP:AF:ID  0.00171914:0.000953249:1.14874:0.12158:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132365 ES:SE:LP:AF:ID  0.00116573:0.000939574:0.677781:0.132365:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011137 ES:SE:LP:AF:ID  -9.58213e-05:0.00341624:0.00877392:0.011137:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036812 ES:SE:LP:AF:ID  -0.000897784:0.00166412:0.229148:0.036812:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838931 ES:SE:LP:AF:ID  -0.00105337:0.000843164:0.677781:0.838931:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838565 ES:SE:LP:AF:ID  -0.00110756:0.000842266:0.721246:0.838565:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869755 ES:SE:LP:AF:ID  -0.00139417:0.00090375:0.920819:0.869755:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129891 ES:SE:LP:AF:ID  0.00127886:0.0009056:0.79588:0.129891:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037323 ES:SE:LP:AF:ID  -0.00112798:0.00163589:0.309804:0.037323:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037566 ES:SE:LP:AF:ID  -0.00116688:0.00162556:0.327902:0.037566:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869101 ES:SE:LP:AF:ID  -0.00146559:0.000901991:1:0.869101:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869199 ES:SE:LP:AF:ID  -0.00146957:0.000902349:1:0.869199:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037526 ES:SE:LP:AF:ID  -0.00119827:0.00163257:0.337242:0.037526:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869104 ES:SE:LP:AF:ID  -0.0014814:0.000901972:1:0.869104:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838014 ES:SE:LP:AF:ID  -0.00102227:0.00083991:0.657577:0.838014:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037539 ES:SE:LP:AF:ID  -0.00125738:0.00163486:0.356547:0.037539:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838645 ES:SE:LP:AF:ID  -0.00105971:0.000842273:0.677781:0.838645:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013793 ES:SE:LP:AF:ID  -0.00127464:0.00293833:0.180456:0.013793:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839752 ES:SE:LP:AF:ID  -0.00103585:0.000853656:0.657577:0.839752:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869378 ES:SE:LP:AF:ID  -0.00138268:0.000900917:0.920819:0.869378:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868926 ES:SE:LP:AF:ID  -0.0013671:0.00089865:0.886057:0.868926:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867883 ES:SE:LP:AF:ID  -0.00157957:0.000896931:1.10791:0.867883:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869069 ES:SE:LP:AF:ID  -0.00139453:0.000899387:0.920819:0.869069:rs4951929