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/dev/ieu-a-import/processed/ieu-a-1166/ieu-a-1166.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1166/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 08:26:51 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1166/ieu-a-1166.vcf.gz ...
Read summary statistics for 12776334 SNPs.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1286416 SNPs remain.
After merging with regression SNP LD, 1286416 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1257 (0.0233)
Lambda GC: 1.0594
Mean Chi^2: 1.0747
Intercept: 1.0208 (0.0072)
Ratio: 0.2786 (0.0957)
Analysis finished at Wed Feb  5 08:28:57 2020
Total time elapsed: 2.0m:5.63s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9594,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0.0058,
    "n": 21695,
    "n_snps": 12776334,
    "n_clumped_hits": 7,
    "n_p_sig": 345,
    "n_mono": 0,
    "n_ns": 3620,
    "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": 471749,
    "n_est": 21956.7296,
    "ratio_se_n": 1.006,
    "mean_diff": 0.0062,
    "ratio_diff": 2.2497,
    "sd_y_est1": 2.4062,
    "sd_y_est2": 2.4207,
    "r2_sum1": 0.0583,
    "r2_sum2": 0.0101,
    "r2_sum3": 0.0099,
    "r2_sum4": 0.0155,
    "ldsc_nsnp_merge_refpanel_ld": 1286416,
    "ldsc_nsnp_merge_regression_ld": 1286416,
    "ldsc_observed_scale_h2_beta": 0.1257,
    "ldsc_observed_scale_h2_se": 0.0233,
    "ldsc_intercept_beta": 1.0208,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.0594,
    "ldsc_mean_chisq": 1.0747,
    "ldsc_ratio": 0.2784
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 59 0 12776332 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 26 0 571 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 41 0 301 0 NA NA NA NA NA NA NA NA
numeric CHROM 1 0.9999999 NA NA NA NA NA 8.969244e+00 6.084074e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.885432e+07 5.610793e+07 1.15700e+03 3.259361e+07 6.988858e+07 1.147468e+08 2.492283e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -5.827100e-03 1.668343e-01 -3.58591e+00 -4.037790e-02 -3.145000e-04 3.874920e-02 1.688390e+00 ▁▁▁▇▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.163546e-01 1.165002e-01 1.86392e-02 2.778150e-02 6.068490e-02 1.766690e-01 1.173270e+00 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.935254e-01 2.903951e-01 0.00000e+00 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.935242e-01 2.903695e-01 0.00000e+00 2.403248e-01 4.912388e-01 7.448700e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.729926e-01 2.483046e-01 8.21000e-04 5.515500e-03 4.088330e-02 2.528310e-01 9.991340e-01 ▇▁▁▁▁
numeric AF_reference 471749 0.9630763 NA NA NA NA NA 1.790537e-01 2.438874e-01 0.00000e+00 4.193300e-03 5.890580e-02 2.687700e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 2.169500e+04 0.000000e+00 2.16950e+04 2.169500e+04 2.169500e+04 2.169500e+04 2.169500e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 55850 rs191890754 C G 0.0525832 0.215751 0.8100000 0.8074464 0.0050374 NA 21695
1 61743 rs184286948 G C 0.4606870 0.380380 0.2300001 0.2258482 0.0014630 0.0045926 21695
1 99719 rs183898652 C T 0.1569700 0.357586 0.6600001 0.6606823 0.0021039 0.0089856 21695
1 534540 rs183186584 G T 0.2012580 0.269942 0.4600002 0.4559332 0.0030403 0.0011981 21695
1 669228 rs187185296 G A 0.3079540 0.274025 0.2599998 0.2610906 0.0023126 0.0001997 21695
1 701131 rs185335630 A C -0.2445830 0.334882 0.4700002 0.4651727 0.0029933 0.0041933 21695
1 712583 rs142048655 T C -0.1135030 0.256969 0.6600001 0.6587069 0.0023907 0.0001997 21695
1 713337 rs184249808 G A -0.1623140 0.326125 0.6200004 0.6186921 0.0024171 0.0071885 21695
1 715211 rs184426933 C G 0.1057290 0.230002 0.6499995 0.6457407 0.0031305 NA 21695
1 720468 rs187315357 G A 0.1480720 0.242984 0.5400003 0.5422660 0.0037757 0.0039936 21695
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0449308 0.0692787 0.5199996 0.5166284 0.0395422 0.0309934 21695
23 154925045 rs509981 C T -0.0065531 0.0234804 0.7800007 0.7801762 0.2408650 0.3634440 21695
23 154925895 rs538470 C T 0.0028207 0.0245008 0.9100000 0.9083455 0.2444180 0.3634440 21695
23 154926315 rs145589925 A C 0.1887450 0.2006740 0.3500000 0.3469328 0.0066350 0.0037086 21695
23 154927185 rs185685661 T C -0.0269358 0.0390344 0.4899999 0.4901609 0.1402100 0.1796030 21695
23 154927199 rs645904 C T -0.0057607 0.0234077 0.8100000 0.8056037 0.2391490 0.3674170 21695
23 154927581 rs644138 G A -0.0088014 0.0230913 0.6999999 0.7030881 0.2847020 0.4635760 21695
23 154929412 rs557132 C T -0.0052999 0.0234682 0.8200001 0.8213304 0.2407430 0.3568210 21695
23 154930230 rs781880 A G -0.0059952 0.0235993 0.8000000 0.7994635 0.2408700 0.3618540 21695
NA 3696 rs17052208 C T 0.0921579 0.1585850 0.5600000 0.5611554 0.0077230 NA 21695

bcf preview

1   55850   rs191890754 C   G   .   PASS    AF=0.00503738   ES:SE:LP:AF:SS:ID   0.0525832:0.215751:0.091515:0.00503738:21695:rs191890754
1   61743   rs184286948 G   C   .   PASS    AF=0.00146299   ES:SE:LP:AF:SS:ID   0.460687:0.38038:0.638272:0.00146299:21695:rs184286948
1   99719   rs183898652 C   T   .   PASS    AF=0.0021039    ES:SE:LP:AF:SS:ID   0.15697:0.357586:0.180456:0.0021039:21695:rs183898652
1   534540  rs183186584 G   T   .   PASS    AF=0.00304029   ES:SE:LP:AF:SS:ID   0.201258:0.269942:0.337242:0.00304029:21695:rs183186584
1   669228  rs187185296 G   A   .   PASS    AF=0.0023126    ES:SE:LP:AF:SS:ID   0.307954:0.274025:0.585027:0.0023126:21695:rs187185296
1   701131  rs185335630 A   C   .   PASS    AF=0.00299333   ES:SE:LP:AF:SS:ID   -0.244583:0.334882:0.327902:0.00299333:21695:rs185335630
1   712583  rs142048655 T   C   .   PASS    AF=0.0023907    ES:SE:LP:AF:SS:ID   -0.113503:0.256969:0.180456:0.0023907:21695:rs142048655
1   713337  rs184249808 G   A   .   PASS    AF=0.0024171    ES:SE:LP:AF:SS:ID   -0.162314:0.326125:0.207608:0.0024171:21695:rs184249808
1   715211  rs184426933 C   G   .   PASS    AF=0.00313049   ES:SE:LP:AF:SS:ID   0.105729:0.230002:0.187087:0.00313049:21695:rs184426933
1   720468  rs187315357 G   A   .   PASS    AF=0.00377569   ES:SE:LP:AF:SS:ID   0.148072:0.242984:0.267606:0.00377569:21695:rs187315357
1   723379  rs181223789 T   A   .   PASS    AF=0.00241745   ES:SE:LP:AF:SS:ID   -0.10523:0.255483:0.167491:0.00241745:21695:rs181223789
1   748332  rs182373484 T   C   .   PASS    AF=0.00411936   ES:SE:LP:AF:SS:ID   -0.0787442:0.255195:0.119186:0.00411936:21695:rs182373484
1   748524  rs144265613 C   T   .   PASS    AF=0.0030819    ES:SE:LP:AF:SS:ID   0.0436232:0.286665:0.0555173:0.0030819:21695:rs144265613
1   755435  rs184270342 T   G   .   PASS    AF=0.00143177   ES:SE:LP:AF:SS:ID   -0.413904:0.370849:0.585027:0.00143177:21695:rs184270342
1   770281  rs188583355 T   A   .   PASS    AF=0.00215254   ES:SE:LP:AF:SS:ID   -0.132155:0.316405:0.167491:0.00215254:21695:rs188583355
1   772172  rs141427868 C   G   .   PASS    AF=0.00137714   ES:SE:LP:AF:SS:ID   -0.391195:0.495921:0.366532:0.00137714:21695:rs141427868
1   775426  rs2905037   G   A   .   PASS    AF=0.998058 ES:SE:LP:AF:SS:ID   -0.309156:0.337105:0.443698:0.998058:21695:rs2905037
1   783632  rs193023236 G   A   .   PASS    AF=0.0030342    ES:SE:LP:AF:SS:ID   -0.0646743:0.293781:0.0809219:0.0030342:21695:rs193023236
1   789256  rs3131939   T   C   .   PASS    AF=0.989984 ES:SE:LP:AF:SS:ID   -0.0586266:0.168758:0.136677:0.989984:21695:rs3131939
1   791932  rs149451138 C   T   .   PASS    AF=0.0035666    ES:SE:LP:AF:SS:ID   -0.0741884:0.294185:0.09691:0.0035666:21695:rs149451138
1   794319  rs186495636 G   A   .   PASS    AF=0.00144783   ES:SE:LP:AF:SS:ID   -0.0978606:0.417753:0.091515:0.00144783:21695:rs186495636
1   794368  rs191334345 G   A   .   PASS    AF=0.0018618    ES:SE:LP:AF:SS:ID   -0.0582047:0.29426:0.0757207:0.0018618:21695:rs191334345
1   794787  rs183239437 A   G   .   PASS    AF=0.00199079   ES:SE:LP:AF:SS:ID   0.209973:0.314771:0.30103:0.00199079:21695:rs183239437
1   795131  rs187620961 G   A   .   PASS    AF=0.00216635   ES:SE:LP:AF:SS:ID   -0.131977:0.314146:0.173925:0.00216635:21695:rs187620961
1   796298  rs143060708 C   G   .   PASS    AF=0.00302765   ES:SE:LP:AF:SS:ID   0.131182:0.268631:0.200659:0.00302765:21695:rs143060708
1   799346  rs184260340 C   A   .   PASS    AF=0.00265105   ES:SE:LP:AF:SS:ID   -0.163526:0.351612:0.19382:0.00265105:21695:rs184260340
1   799389  rs187038627 G   A   .   PASS    AF=0.0091864    ES:SE:LP:AF:SS:ID   0.00346481:0.173015:0.00877392:0.0091864:21695:rs187038627
1   801136  rs140609992 C   T   .   PASS    AF=0.00129571   ES:SE:LP:AF:SS:ID   0.41069:0.391782:0.537602:0.00129571:21695:rs140609992
1   801848  rs114911728 C   A   .   PASS    AF=0.00507533   ES:SE:LP:AF:SS:ID   -0.398248:0.214299:1.20066:0.00507533:21695:rs114911728
1   802671  rs191771620 T   G   .   PASS    AF=0.003486 ES:SE:LP:AF:SS:ID   -0.0255166:0.26479:0.0362122:0.003486:21695:rs191771620
1   803192  rs190240504 C   T   .   PASS    AF=0.00267073   ES:SE:LP:AF:SS:ID   0.482798:0.235367:1.39794:0.00267073:21695:rs190240504
1   809001  rs184951212 G   A   .   PASS    AF=0.00322111   ES:SE:LP:AF:SS:ID   0.102691:0.223117:0.187087:0.00322111:21695:rs184951212
1   810470  rs181485325 C   A   .   PASS    AF=0.00120633   ES:SE:LP:AF:SS:ID   0.399523:0.40257:0.49485:0.00120633:21695:rs181485325
1   810777  rs143204076 C   G   .   PASS    AF=0.0130197    ES:SE:LP:AF:SS:ID   0.176165:0.14619:0.638272:0.0130197:21695:rs143204076
1   812717  rs151199172 G   T   .   PASS    AF=0.00173359   ES:SE:LP:AF:SS:ID   -0.000129399:0.310028:-0:0.00173359:21695:rs151199172
1   819065  rs188466450 C   T   .   PASS    AF=0.0068528    ES:SE:LP:AF:SS:ID   -0.114936:0.197512:0.251812:0.0068528:21695:rs188466450
1   819467  rs187253977 T   A   .   PASS    AF=0.00151854   ES:SE:LP:AF:SS:ID   0.235188:0.410432:0.244125:0.00151854:21695:rs187253977
1   823790  rs143626389 G   A   .   PASS    AF=0.0209886    ES:SE:LP:AF:SS:ID   -0.0454468:0.11779:0.154902:0.0209886:21695:rs143626389
1   824357  rs80134645  C   T   .   PASS    AF=0.00289833   ES:SE:LP:AF:SS:ID   -0.0759278:0.223426:0.136677:0.00289833:21695:rs80134645
1   824666  rs140725574 C   A   .   PASS    AF=0.0047316    ES:SE:LP:AF:SS:ID   0.00112997:0.214502:-0:0.0047316:21695:rs140725574
1   830791  rs184031231 C   T   .   PASS    AF=0.00704278   ES:SE:LP:AF:SS:ID   0.2246:0.18875:0.638272:0.00704278:21695:rs184031231
1   834240  rs188158544 G   A   .   PASS    AF=0.00218954   ES:SE:LP:AF:SS:ID   -0.416578:0.366908:0.585027:0.00218954:21695:rs188158544
1   834830  rs116452738 G   A   .   PASS    AF=0.0121869    ES:SE:LP:AF:SS:ID   -0.00581173:0.139033:0.0132283:0.0121869:21695:rs116452738
1   837096  rs144333274 C   T   .   PASS    AF=0.00684682   ES:SE:LP:AF:SS:ID   0.205644:0.189995:0.552842:0.00684682:21695:rs144333274
1   837214  rs72631888  G   C   .   PASS    AF=0.0099436    ES:SE:LP:AF:SS:ID   0.035221:0.170152:0.0757207:0.0099436:21695:rs72631888
1   839682  rs189624598 G   A   .   PASS    AF=0.0222536    ES:SE:LP:AF:SS:ID   -0.0150174:0.108899:0.05061:0.0222536:21695:rs189624598
1   841003  rs149367433 G   A   .   PASS    AF=0.00444051   ES:SE:LP:AF:SS:ID   0.102873:0.244847:0.173925:0.00444051:21695:rs149367433
1   842473  rs80143686  C   T   .   PASS    AF=0.0037148    ES:SE:LP:AF:SS:ID   -0.208433:0.20797:0.49485:0.0037148:21695:rs80143686
1   842690  rs71628922  G   A   .   PASS    AF=0.00231224   ES:SE:LP:AF:SS:ID   -0.201295:0.362447:0.236572:0.00231224:21695:rs71628922
1   846589  rs141482909 C   G   .   PASS    AF=0.00107391   ES:SE:LP:AF:SS:ID   0.242472:0.422611:0.244125:0.00107391:21695:rs141482909