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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}

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-5954/UKB-b-5954_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5954/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5954/UKB-b-5954_data.vcf.gz ...
Read summary statistics for 6018696 SNPs.
Dropped 2761 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, 1215960 SNPs remain.
After merging with regression SNP LD, 1215960 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0018 (0.0012)
Lambda GC: 1.0699
Mean Chi^2: 1.0749
Intercept: 1.0585 (0.0071)
Ratio: 0.7803 (0.0943)
Analysis finished at Thu Oct 17 14:46:40 2019
Total time elapsed: 59.47s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9248,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 3.1171e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 72,
    "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": 54475,
    "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": 1215960,
    "ldsc_nsnp_merge_regression_ld": 1215960,
    "ldsc_observed_scale_h2_beta": 0.0018,
    "ldsc_observed_scale_h2_se": 0.0012,
    "ldsc_intercept_beta": 1.0585,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.0699,
    "ldsc_mean_chisq": 1.0749,
    "ldsc_ratio": 0.781
}

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 1.000000 3 58 0 6015954 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 6018696 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 8.671447e+00 5.762306e+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 7.857806e+07 5.652050e+07 828.0000000 3.199058e+07 6.902236e+07 1.144950e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 3.100000e-06 5.069000e-04 -0.0044325 -2.972000e-04 3.900000e-06 3.035000e-04 5.271300e-03 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 4.639000e-04 1.568000e-04 0.0003030 3.350000e-04 4.028000e-04 5.555000e-04 1.590400e-03 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.887879e-01 2.917397e-01 0.0000000 2.300001e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.887900e-01 2.917135e-01 0.0000000 2.326248e-01 4.858725e-01 7.410100e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 3.110237e-01 2.535467e-01 0.0334170 9.607300e-02 2.286550e-01 4.762220e-01 9.665830e-01 ▇▃▂▂▁
numeric AF_reference 54475 0.990949 NA NA NA NA NA NA NA 3.075736e-01 2.466626e-01 0.0000000 1.050320e-01 2.364220e-01 4.668530e-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 -0.0000451 0.0005577 0.9400001 0.9355246 0.623852 0.7821490 NA
1 54676 rs2462492 C T 0.0002552 0.0005524 0.6400000 0.6440892 0.400429 NA NA
1 86028 rs114608975 T C 0.0000003 0.0008834 1.0000000 0.9997558 0.103537 0.0277556 NA
1 91536 rs6702460 G T -0.0000782 0.0005441 0.8900000 0.8856476 0.456952 0.4207270 NA
1 234313 rs8179466 C T 0.0000807 0.0010725 0.9400001 0.9400135 0.074530 NA NA
1 534192 rs6680723 C T 0.0000977 0.0006214 0.8800001 0.8750311 0.240963 NA NA
1 546697 rs12025928 A G 0.0016979 0.0007751 0.0280001 0.0284818 0.913441 NA NA
1 693731 rs12238997 A G 0.0006560 0.0005207 0.2099999 0.2077263 0.116306 0.1417730 NA
1 705882 rs72631875 G A -0.0011419 0.0007630 0.1299999 0.1344949 0.067322 0.0315495 NA
1 706368 rs55727773 A G 0.0000325 0.0003857 0.9299999 0.9328308 0.515684 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0002912 0.0006029 0.6300007 0.6291464 0.073589 0.0826677 NA
22 51219006 rs28729663 G A 0.0000055 0.0004655 0.9900000 0.9905281 0.137893 0.2052720 NA
22 51219387 rs9616832 T C 0.0002233 0.0006041 0.7099994 0.7116127 0.073721 0.0654952 NA
22 51219704 rs147475742 G A 0.0000847 0.0008096 0.9199999 0.9166994 0.041937 0.0473243 NA
22 51221190 rs369304721 G A 0.0007512 0.0008081 0.3500000 0.3525879 0.049719 NA NA
22 51221731 rs115055839 T C 0.0002682 0.0006045 0.6600001 0.6572425 0.073210 0.0625000 NA
22 51222100 rs114553188 G T 0.0001217 0.0007117 0.8600001 0.8642439 0.054455 0.0880591 NA
22 51223637 rs375798137 G A 0.0001165 0.0007151 0.8700001 0.8706221 0.054086 0.0788738 NA
22 51229805 rs9616985 T C 0.0003131 0.0006067 0.6100002 0.6057753 0.073045 0.0730831 NA
22 51237063 rs3896457 T C 0.0001684 0.0003709 0.6499995 0.6499377 0.298024 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623852 ES:SE:LP:AF:ID  -4.51186e-05:0.000557735:0.0268721:0.623852:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400429 ES:SE:LP:AF:ID  0.000255199:0.000552392:0.19382:0.400429:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103537 ES:SE:LP:AF:ID  2.70339e-07:0.000883374:-0:0.103537:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456952 ES:SE:LP:AF:ID  -7.82443e-05:0.000544067:0.05061:0.456952:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07453  ES:SE:LP:AF:ID  8.07065e-05:0.00107247:0.0268721:0.07453:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240963 ES:SE:LP:AF:ID  9.7729e-05:0.000621404:0.0555173:0.240963:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913441 ES:SE:LP:AF:ID  0.00169791:0.000775094:1.55284:0.913441:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116306 ES:SE:LP:AF:ID  0.00065603:0.000520724:0.677781:0.116306:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067322 ES:SE:LP:AF:ID  -0.00114194:0.000763018:0.886057:0.067322:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515684 ES:SE:LP:AF:ID  3.25103e-05:0.000385724:0.0315171:0.515684:rs12029736
1   715265  rs12184267  C   T   .   PASS    AF=0.0366   ES:SE:LP:AF:ID  0.000372631:0.000883488:0.173925:0.0366:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036716 ES:SE:LP:AF:ID  0.000435941:0.000880149:0.207608:0.036716:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036418 ES:SE:LP:AF:ID  0.000328224:0.000886453:0.148742:0.036418:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036955 ES:SE:LP:AF:ID  0.000326575:0.000876663:0.148742:0.036955:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03705  ES:SE:LP:AF:ID  0.000265838:0.000873671:0.119186:0.03705:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10124  ES:SE:LP:AF:ID  -0.00017921:0.000636165:0.107905:0.10124:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959136 ES:SE:LP:AF:ID  -0.000613011:0.000842755:0.327902:0.959136:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.053272 ES:SE:LP:AF:ID  4.34431e-05:0.00121571:0.0132283:0.053272:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03657  ES:SE:LP:AF:ID  0.000443612:0.000879313:0.21467:0.03657:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036888 ES:SE:LP:AF:ID  0.000415209:0.000871291:0.200659:0.036888:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843263 ES:SE:LP:AF:ID  -0.000541085:0.00045128:0.638272:0.843263:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055912 ES:SE:LP:AF:ID  0.000270328:0.000730674:0.148742:0.055912:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122288 ES:SE:LP:AF:ID  0.000389967:0.000493959:0.366532:0.122288:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121532 ES:SE:LP:AF:ID  0.000409192:0.000494165:0.387216:0.121532:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132293 ES:SE:LP:AF:ID  0.000359263:0.0004871:0.337242:0.132293:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036809 ES:SE:LP:AF:ID  0.000465492:0.000862378:0.229148:0.036809:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83899  ES:SE:LP:AF:ID  -0.000559744:0.000437041:0.69897:0.83899:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83862  ES:SE:LP:AF:ID  -0.000584588:0.000436572:0.744727:0.83862:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869809 ES:SE:LP:AF:ID  -0.00048958:0.000468497:0.522879:0.869809:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.12984  ES:SE:LP:AF:ID  0.000467591:0.000469457:0.49485:0.12984:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037318 ES:SE:LP:AF:ID  0.000279551:0.000847781:0.130768:0.037318:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037558 ES:SE:LP:AF:ID  0.000276103:0.000842473:0.130768:0.037558:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869155 ES:SE:LP:AF:ID  -0.000503773:0.000467582:0.552842:0.869155:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869253 ES:SE:LP:AF:ID  -0.000522828:0.000467764:0.585027:0.869253:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037519 ES:SE:LP:AF:ID  0.000283246:0.00084607:0.130768:0.037519:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869156 ES:SE:LP:AF:ID  -0.000505604:0.000467572:0.552842:0.869156:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838072 ES:SE:LP:AF:ID  -0.000536347:0.000435356:0.657577:0.838072:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037531 ES:SE:LP:AF:ID  0.000291211:0.000847284:0.136677:0.037531:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838704 ES:SE:LP:AF:ID  -0.000554109:0.000436586:0.69897:0.838704:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839812 ES:SE:LP:AF:ID  -0.000618827:0.000442483:0.79588:0.839812:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869436 ES:SE:LP:AF:ID  -0.000458048:0.000467032:0.481486:0.869436:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868979 ES:SE:LP:AF:ID  -0.00040686:0.000465853:0.420216:0.868979:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867936 ES:SE:LP:AF:ID  -0.00045328:0.000464965:0.481486:0.867936:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869124 ES:SE:LP:AF:ID  -0.00042985:0.000466235:0.443698:0.869124:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869132 ES:SE:LP:AF:ID  -0.000428961:0.000466271:0.443698:0.869132:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.86914  ES:SE:LP:AF:ID  -0.000430453:0.000466282:0.443698:0.86914:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869621 ES:SE:LP:AF:ID  -0.000451196:0.000467569:0.481486:0.869621:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.037588 ES:SE:LP:AF:ID  0.000348255:0.000842248:0.167491:0.037588:rs114525117
1   760912  rs1048488   C   T   .   PASS    AF=0.838349 ES:SE:LP:AF:ID  -0.00052372:0.000434531:0.638272:0.838349:rs1048488
1   760998  rs148828841 C   A   .   PASS    AF=0.037022 ES:SE:LP:AF:ID  0.000392007:0.000858036:0.187087:0.037022:rs148828841