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-4943/UKB-b-4943_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4943/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:49 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4943/UKB-b-4943_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0229 (0.0017)
Lambda GC: 1.2023
Mean Chi^2: 1.2319
Intercept: 1.034 (0.0083)
Ratio: 0.1468 (0.0357)
Analysis finished at Thu Oct 17 14:46:31 2019
Total time elapsed: 1.0m:42.19s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 4.6989e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 184,
    "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": 184849,
    "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": 1289166,
    "ldsc_nsnp_merge_regression_ld": 1289166,
    "ldsc_observed_scale_h2_beta": 0.0229,
    "ldsc_observed_scale_h2_se": 0.0017,
    "ldsc_intercept_beta": 1.034,
    "ldsc_intercept_se": 0.0083,
    "ldsc_lambda_gc": 1.2023,
    "ldsc_mean_chisq": 1.2319,
    "ldsc_ratio": 0.1466
}

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 9837196 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 9851866 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 8.622825e+00 5.748290e+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 7.886027e+07 5.628334e+07 828.0000000 3.259061e+07 6.948835e+07 1.145912e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 5.000000e-07 3.672300e-03 -0.0880553 -1.159700e-03 -8.300000e-06 1.143400e-03 7.642950e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.600800e-03 2.462900e-03 0.0007277 8.907000e-04 1.493600e-03 3.446600e-03 3.810430e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.778314e-01 2.953171e-01 0.0000000 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.778318e-01 2.952918e-01 0.0000000 2.162573e-01 4.703111e-01 7.339795e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035075e-01 2.568646e-01 0.0009700 1.316800e-02 7.791400e-02 3.164410e-01 9.990280e-01 ▇▂▁▁▁
numeric AF_reference 184849 0.9812372 NA NA NA NA NA NA NA 2.068392e-01 2.482924e-01 0.0000000 1.198080e-02 9.984030e-02 3.202880e-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 -0.0002468 0.0013387 0.8499999 0.8537171 0.623742 0.7821490 NA
1 54676 rs2462492 C T 0.0040688 0.0013262 0.0022000 0.0021557 0.400435 NA NA
1 86028 rs114608975 T C -0.0022020 0.0021199 0.2999998 0.2989377 0.103544 0.0277556 NA
1 91536 rs6702460 G T 0.0015446 0.0013062 0.2399999 0.2369860 0.456878 0.4207270 NA
1 234313 rs8179466 C T -0.0000526 0.0025750 0.9800000 0.9837100 0.074497 NA NA
1 534192 rs6680723 C T -0.0017695 0.0014915 0.2399999 0.2354707 0.240995 NA NA
1 546697 rs12025928 A G 0.0029105 0.0018605 0.1199999 0.1177333 0.913445 NA NA
1 693731 rs12238997 A G -0.0012878 0.0012500 0.2999998 0.3029010 0.116326 0.1417730 NA
1 705882 rs72631875 G A -0.0031814 0.0018314 0.0819993 0.0823594 0.067288 0.0315495 NA
1 706368 rs55727773 A G 0.0000204 0.0009262 0.9800000 0.9824135 0.515725 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0014339 0.0019424 0.4600002 0.4603711 0.041992 0.0473243 NA
22 51219766 rs182321900 C T -0.0011431 0.0091166 0.9000000 0.9002186 0.001914 NA NA
22 51220146 rs868950473 C T -0.0036376 0.0090252 0.6899999 0.6869154 0.001965 NA NA
22 51221190 rs369304721 G A -0.0014120 0.0019405 0.4700002 0.4668343 0.049714 NA NA
22 51221731 rs115055839 T C -0.0009582 0.0014513 0.5099998 0.5091243 0.073210 0.0625000 NA
22 51222100 rs114553188 G T -0.0011359 0.0017088 0.5099998 0.5062047 0.054455 0.0880591 NA
22 51223637 rs375798137 G A -0.0011981 0.0017170 0.4899999 0.4853265 0.054086 0.0788738 NA
22 51229805 rs9616985 T C -0.0008815 0.0014566 0.5500004 0.5450461 0.073050 0.0730831 NA
22 51232488 rs376461333 A G 0.0002110 0.0034311 0.9500000 0.9509760 0.020049 NA NA
22 51237063 rs3896457 T C 0.0008343 0.0008911 0.3500000 0.3491594 0.297820 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623742 ES:SE:LP:AF:ID  -0.000246821:0.00133867:0.0705811:0.623742:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400435 ES:SE:LP:AF:ID  0.00406877:0.00132624:2.65758:0.400435:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103544 ES:SE:LP:AF:ID  -0.00220197:0.0021199:0.522879:0.103544:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456878 ES:SE:LP:AF:ID  0.00154464:0.00130619:0.619789:0.456878:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074497 ES:SE:LP:AF:ID  -5.25766e-05:0.00257503:0.00877392:0.074497:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240995 ES:SE:LP:AF:ID  -0.00176948:0.00149149:0.619789:0.240995:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913445 ES:SE:LP:AF:ID  0.00291049:0.0018605:0.920819:0.913445:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116326 ES:SE:LP:AF:ID  -0.00128778:0.00124999:0.522879:0.116326:rs12238997
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1   714596  rs149887893 T   C   .   PASS    AF=0.03296  ES:SE:LP:AF:ID  0.00070524:0.00233621:0.119186:0.03296:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036577 ES:SE:LP:AF:ID  -0.000144433:0.00212174:0.0222764:0.036577:rs12184267
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1   717485  rs12184279  C   A   .   PASS    AF=0.036399 ES:SE:LP:AF:ID  -3.43212e-05:0.0021287:0.00436481:0.036399:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016393 ES:SE:LP:AF:ID  -0.00094444:0.00327794:0.113509:0.016393:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036936 ES:SE:LP:AF:ID  -3.8908e-05:0.00210522:0.00436481:0.036936:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037031 ES:SE:LP:AF:ID  -5.43773e-05:0.00209801:0.00877392:0.037031:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101249 ES:SE:LP:AF:ID  0.000152349:0.00152734:0.0362122:0.101249:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959123 ES:SE:LP:AF:ID  -0.000728044:0.00202325:0.142668:0.959123:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031444 ES:SE:LP:AF:ID  -6.49741e-05:0.00367131:0.00436481:0.031444:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053242 ES:SE:LP:AF:ID  0.000242318:0.00292145:0.0315171:0.053242:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036546 ES:SE:LP:AF:ID  -0.000103252:0.00211155:0.0177288:0.036546:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036857 ES:SE:LP:AF:ID  -0.000267117:0.00209249:0.0457575:0.036857:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843231 ES:SE:LP:AF:ID  0.000485258:0.00108359:0.187087:0.843231:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055972 ES:SE:LP:AF:ID  -0.00138644:0.00175326:0.366532:0.055972:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122311 ES:SE:LP:AF:ID  -0.00123445:0.00118578:0.522879:0.122311:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025731 ES:SE:LP:AF:ID  0.00223478:0.00291673:0.356547:0.025731:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121554 ES:SE:LP:AF:ID  -0.00122449:0.00118628:0.522879:0.121554:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132347 ES:SE:LP:AF:ID  -0.00075063:0.00116923:0.283997:0.132347:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011155 ES:SE:LP:AF:ID  0.00147728:0.00424268:0.136677:0.011155:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005739 ES:SE:LP:AF:ID  0.00167874:0.00546799:0.119186:0.005739:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00229  ES:SE:LP:AF:ID  0.00672892:0.00917419:0.337242:0.00229:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.00103  ES:SE:LP:AF:ID  0.0369085:0.015087:1.85387:0.00103:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036788 ES:SE:LP:AF:ID  -0.000221225:0.00207075:0.0409586:0.036788:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838963 ES:SE:LP:AF:ID  0.000201174:0.00104929:0.0705811:0.838963:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838589 ES:SE:LP:AF:ID  0.000232582:0.00104815:0.0861861:0.838589:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869772 ES:SE:LP:AF:ID  0.00047072:0.0011246:0.167491:0.869772:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129865 ES:SE:LP:AF:ID  -0.000489603:0.00112693:0.180456:0.129865:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037292 ES:SE:LP:AF:ID  -2.24095e-05:0.00203596:0.00436481:0.037292:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037533 ES:SE:LP:AF:ID  -5.00819e-05:0.00202322:0.00877392:0.037533:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869116 ES:SE:LP:AF:ID  0.000514832:0.00112241:0.187087:0.869116:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869217 ES:SE:LP:AF:ID  0.000495716:0.00112287:0.180456:0.869217:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037496 ES:SE:LP:AF:ID  -1.55737e-05:0.00203183:0.00436481:0.037496:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869119 ES:SE:LP:AF:ID  0.000531643:0.00112239:0.19382:0.869119:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00514  ES:SE:LP:AF:ID  0.00463701:0.00575288:0.376751:0.00514:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005105 ES:SE:LP:AF:ID  0.00462841:0.00576815:0.376751:0.005105:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838061 ES:SE:LP:AF:ID  0.000291696:0.00104537:0.107905:0.838061:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037508 ES:SE:LP:AF:ID  -9.81841e-05:0.00203474:0.0177288:0.037508:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838696 ES:SE:LP:AF:ID  0.000329092:0.00104833:0.124939:0.838696:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013794 ES:SE:LP:AF:ID  0.000911071:0.00365601:0.09691:0.013794:rs181660517