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_5208.vcf.gz --id UKB-b:4535 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5208.txt.gz --cohort_controls 100983 --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-4535/UKB-b-4535_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4535/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4535/UKB-b-4535_data.vcf.gz ...
Read summary statistics for 9035029 SNPs.
Dropped 8866 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, 1287310 SNPs remain.
After merging with regression SNP LD, 1287310 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.034 (0.0051)
Lambda GC: 1.0771
Mean Chi^2: 1.0808
Intercept: 1.0142 (0.006)
Ratio: 0.1753 (0.074)
Analysis finished at Thu Oct 17 14:46:06 2019
Total time elapsed: 1.0m:39.48s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.948,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 167,
    "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": 94588,
    "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": 1287310,
    "ldsc_nsnp_merge_regression_ld": 1287310,
    "ldsc_observed_scale_h2_beta": 0.034,
    "ldsc_observed_scale_h2_se": 0.0051,
    "ldsc_intercept_beta": 1.0142,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0771,
    "ldsc_mean_chisq": 1.0808,
    "ldsc_ratio": 0.1757
}
 

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 1.000000 3 58 0 9026204 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 9035029 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 8.643024e+00 5.757961e+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 7.878462e+07 5.633536e+07 828.0000000 3.243388e+07 6.934929e+07 1.145317e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 1.470000e-05 1.489420e-02 -0.1721550 -5.814600e-03 3.350000e-05 5.864800e-03 1.995330e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.187600e-02 8.753400e-03 0.0042872 5.115200e-03 7.854300e-03 1.629470e-02 9.946820e-02 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.915581e-01 2.910936e-01 0.0000000 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.915568e-01 2.910680e-01 0.0000000 2.375701e-01 4.888821e-01 7.436667e-01 9.999996e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.196087e-01 2.584668e-01 0.0034660 2.021200e-02 1.007010e-01 3.462280e-01 9.965340e-01 ▇▂▁▁▁
numeric AF_reference 94588 0.989531 NA NA NA NA NA NA NA 2.199718e-01 2.503651e-01 0.0000000 1.757190e-02 1.182110e-01 3.446490e-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 0.0059403 0.0079197 0.4500005 0.4532100 0.623789 0.7821490 NA
1 54676 rs2462492 C T -0.0006599 0.0078666 0.9299999 0.9331487 0.398815 NA NA
1 86028 rs114608975 T C -0.0012142 0.0124836 0.9199999 0.9225158 0.104017 0.0277556 NA
1 91536 rs6702460 G T -0.0048036 0.0077374 0.5300002 0.5347157 0.455694 0.4207270 NA
1 234313 rs8179466 C T -0.0011698 0.0151534 0.9400001 0.9384646 0.074783 NA NA
1 534192 rs6680723 C T 0.0052983 0.0088444 0.5500004 0.5491326 0.240593 NA NA
1 546697 rs12025928 A G 0.0263006 0.0109633 0.0160000 0.0164414 0.912740 NA NA
1 693731 rs12238997 A G -0.0008120 0.0073570 0.9100000 0.9121188 0.117745 0.1417730 NA
1 705882 rs72631875 G A -0.0235337 0.0107553 0.0290001 0.0286621 0.067789 0.0315495 NA
1 706368 rs55727773 A G 0.0023008 0.0054545 0.6700003 0.6731646 0.514405 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0012770 0.0065995 0.8499999 0.8465716 0.137175 0.2052720 NA
22 51219387 rs9616832 T C 0.0063206 0.0085932 0.4600002 0.4620150 0.072723 0.0654952 NA
22 51219704 rs147475742 G A 0.0100846 0.0114323 0.3800004 0.3777148 0.041873 0.0473243 NA
22 51221190 rs369304721 G A 0.0004557 0.0115056 0.9699999 0.9684069 0.049131 NA NA
22 51221731 rs115055839 T C 0.0060126 0.0085960 0.4799997 0.4842610 0.072249 0.0625000 NA
22 51222100 rs114553188 G T -0.0087853 0.0100681 0.3800004 0.3828879 0.054415 0.0880591 NA
22 51223637 rs375798137 G A -0.0087722 0.0101211 0.3900004 0.3860933 0.054020 0.0788738 NA
22 51229805 rs9616985 T C 0.0069052 0.0086272 0.4199997 0.4234816 0.072116 0.0730831 NA
22 51232488 rs376461333 A G -0.0053880 0.0203901 0.7899998 0.7915901 0.019980 NA NA
22 51237063 rs3896457 T C 0.0008039 0.0052471 0.8800001 0.8782336 0.298181 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623789 ES:SE:LP:AF:ID  0.00594034:0.00791967:0.346787:0.623789:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398815 ES:SE:LP:AF:ID  -0.000659877:0.00786655:0.0315171:0.398815:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104017 ES:SE:LP:AF:ID  -0.00121422:0.0124836:0.0362122:0.104017:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455694 ES:SE:LP:AF:ID  -0.00480356:0.0077374:0.275724:0.455694:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074783 ES:SE:LP:AF:ID  -0.00116984:0.0151534:0.0268721:0.074783:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240593 ES:SE:LP:AF:ID  0.00529833:0.0088444:0.259637:0.240593:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91274  ES:SE:LP:AF:ID  0.0263006:0.0109633:1.79588:0.91274:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117745 ES:SE:LP:AF:ID  -0.000811968:0.00735702:0.0409586:0.117745:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067789 ES:SE:LP:AF:ID  -0.0235337:0.0107553:1.5376:0.067789:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514405 ES:SE:LP:AF:ID  0.00230076:0.00545451:0.173925:0.514405:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033688 ES:SE:LP:AF:ID  -0.00406367:0.0136032:0.113509:0.033688:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037345 ES:SE:LP:AF:ID  -0.00398457:0.0123737:0.124939:0.037345:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037425 ES:SE:LP:AF:ID  -0.00330929:0.0123344:0.102373:0.037425:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03711  ES:SE:LP:AF:ID  -0.00488251:0.0124211:0.161151:0.03711:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016675 ES:SE:LP:AF:ID  0.0279285:0.0191944:0.823909:0.016675:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037686 ES:SE:LP:AF:ID  -0.00359647:0.0122808:0.113509:0.037686:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03777  ES:SE:LP:AF:ID  -0.00439443:0.0122431:0.142668:0.03777:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101223 ES:SE:LP:AF:ID  -0.00269672:0.00902593:0.113509:0.101223:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958024 ES:SE:LP:AF:ID  0.00519543:0.0117772:0.180456:0.958024:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031836 ES:SE:LP:AF:ID  -0.0488021:0.0215348:1.63827:0.031836:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052555 ES:SE:LP:AF:ID  0.00492305:0.0174211:0.107905:0.052555:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037236 ES:SE:LP:AF:ID  -0.00267005:0.0123286:0.0809219:0.037236:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037557 ES:SE:LP:AF:ID  -0.00108254:0.012223:0.0315171:0.037557:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840864 ES:SE:LP:AF:ID  -0.00109053:0.00637149:0.0655015:0.840864:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056197 ES:SE:LP:AF:ID  -0.0133853:0.0103676:0.69897:0.056197:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123726 ES:SE:LP:AF:ID  6.24892e-05:0.00698338:0.00436481:0.123726:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025879 ES:SE:LP:AF:ID  0.0340246:0.017148:1.3279:0.025879:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122922 ES:SE:LP:AF:ID  -0.000360076:0.00698734:0.0177288:0.122922:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13374  ES:SE:LP:AF:ID  -0.00110637:0.00688599:0.0604807:0.13374:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011213 ES:SE:LP:AF:ID  -0.00642828:0.024973:0.09691:0.011213:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005981 ES:SE:LP:AF:ID  0.0643363:0.0314777:1.38722:0.005981:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037508 ES:SE:LP:AF:ID  0.000310261:0.0120918:0.00877392:0.037508:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836664 ES:SE:LP:AF:ID  -0.000184577:0.00616531:0.00877392:0.836664:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836215 ES:SE:LP:AF:ID  -0.000243068:0.00615801:0.0132283:0.836215:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867985 ES:SE:LP:AF:ID  0.00104018:0.00660629:0.0604807:0.867985:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131706 ES:SE:LP:AF:ID  -0.000282367:0.00662003:0.0132283:0.131706:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037948 ES:SE:LP:AF:ID  -0.00208141:0.0119024:0.0655015:0.037948:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038195 ES:SE:LP:AF:ID  -0.0025349:0.011829:0.0809219:0.038195:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867267 ES:SE:LP:AF:ID  0.00112756:0.0065928:0.0655015:0.867267:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86737  ES:SE:LP:AF:ID  0.00143237:0.00659588:0.0809219:0.86737:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038158 ES:SE:LP:AF:ID  -0.00213896:0.0118746:0.0655015:0.038158:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867265 ES:SE:LP:AF:ID  0.00119338:0.0065924:0.0655015:0.867265:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005078 ES:SE:LP:AF:ID  -0.0166449:0.0342032:0.200659:0.005078:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005046 ES:SE:LP:AF:ID  -0.0166253:0.0342982:0.200659:0.005046:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835764 ES:SE:LP:AF:ID  -4.11421e-05:0.00614504:0.00436481:0.835764:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038174 ES:SE:LP:AF:ID  -0.00244999:0.0118908:0.0757207:0.038174:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836369 ES:SE:LP:AF:ID  -0.000454625:0.00616137:0.0268721:0.836369:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013184 ES:SE:LP:AF:ID  0.00727193:0.0221009:0.130768:0.013184:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005522 ES:SE:LP:AF:ID  0.0257641:0.0333497:0.356547:0.005522:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837677 ES:SE:LP:AF:ID  -1.95248e-05:0.00624568:-0:0.837677:rs3131965