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-1167/ieu-a-1167.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-1167/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 08:41:02 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1167/ieu-a-1167.vcf.gz ...
Read summary statistics for 12799369 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, 1286476 SNPs remain.
After merging with regression SNP LD, 1286476 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.4714 (0.0465)
Lambda GC: 1.165
Mean Chi^2: 1.2442
Intercept: 1.041 (0.0083)
Ratio: 0.168 (0.0338)
Analysis finished at Wed Feb  5 08:43:07 2020
Total time elapsed: 2.0m:4.96s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9594,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0.0006,
    "n": 22286,
    "n_snps": 12799369,
    "n_clumped_hits": 45,
    "n_p_sig": 4131,
    "n_mono": 0,
    "n_ns": 3621,
    "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": 473738,
    "n_est": 22613.0738,
    "ratio_se_n": 1.0073,
    "mean_diff": 0.0012,
    "ratio_diff": 1.2104,
    "sd_y_est1": 1.4819,
    "sd_y_est2": 1.4927,
    "r2_sum1": 0.2452,
    "r2_sum2": 0.1116,
    "r2_sum3": 0.11,
    "r2_sum4": 0.1664,
    "ldsc_nsnp_merge_refpanel_ld": 1286476,
    "ldsc_nsnp_merge_regression_ld": 1286476,
    "ldsc_observed_scale_h2_beta": 0.4714,
    "ldsc_observed_scale_h2_se": 0.0465,
    "ldsc_intercept_beta": 1.041,
    "ldsc_intercept_se": 0.0083,
    "ldsc_lambda_gc": 1.165,
    "ldsc_mean_chisq": 1.2442,
    "ldsc_ratio": 0.1679
}

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 TRUE
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 12799367 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 8.970202e+00 6.084919e+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 7.885526e+07 5.611037e+07 1.15700e+03 3.259167e+07 6.988970e+07 1.147533e+08 2.492283e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -6.306000e-04 1.016291e-01 -1.57436e+00 -2.512230e-02 -6.210000e-05 2.491920e-02 1.220130e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 7.080000e-02 7.038340e-02 1.13103e-02 1.688190e-02 3.700170e-02 1.079060e-01 3.776620e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.840871e-01 2.930979e-01 0.00000e+00 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.840890e-01 2.930722e-01 0.00000e+00 2.264461e-01 4.785846e-01 7.378084e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.727068e-01 2.482047e-01 7.29400e-04 5.472200e-03 4.059660e-02 2.521860e-01 9.991230e-01 ▇▁▁▁▁
numeric AF_reference 473738 0.9629874 NA NA NA NA NA 1.787739e-01 2.438074e-01 0.00000e+00 4.193300e-03 5.850640e-02 2.681710e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 2.228600e+04 0.000000e+00 2.22860e+04 2.228600e+04 2.228600e+04 2.228600e+04 2.228600e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 55850 rs191890754 C G 0.0129399 0.132686 0.9199999 0.9223113 0.0050374 NA 22286
1 61743 rs184286948 G C 0.0346218 0.262689 0.9000000 0.8951443 0.0014630 0.0045926 22286
1 88188 rs148331237 C A 0.1399740 0.112295 0.2099999 0.2125865 0.0081978 0.0033946 22286
1 99719 rs183898652 C T 0.5539930 0.208067 0.0077999 0.0077546 0.0021039 0.0089856 22286
1 534540 rs183186584 G T 0.2110620 0.161542 0.1900002 0.1913671 0.0030403 0.0011981 22286
1 669228 rs187185296 G A 0.1185950 0.171319 0.4899999 0.4887825 0.0023126 0.0001997 22286
1 701131 rs185335630 A C 0.1972530 0.178150 0.2700001 0.2681946 0.0029933 0.0041933 22286
1 712583 rs142048655 T C -0.0782731 0.156191 0.6200004 0.6162747 0.0023907 0.0001997 22286
1 713337 rs184249808 G A -0.1398160 0.199188 0.4799997 0.4827229 0.0024171 0.0071885 22286
1 715211 rs184426933 C G 0.0056199 0.146402 0.9699999 0.9693795 0.0031305 NA 22286
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0109874 0.0413238 0.7899998 0.7903274 0.0395422 0.0309934 22286
23 154925045 rs509981 C T 0.0089260 0.0142539 0.5300002 0.5311747 0.2408650 0.3634440 22286
23 154925895 rs538470 C T 0.0081333 0.0149076 0.5900000 0.5853532 0.2444180 0.3634440 22286
23 154926315 rs145589925 A C -0.0476282 0.1269980 0.7099994 0.7076373 0.0066350 0.0037086 22286
23 154927185 rs185685661 T C -0.0057595 0.0237039 0.8100000 0.8080240 0.1402100 0.1796030 22286
23 154927199 rs645904 C T 0.0088380 0.0142149 0.5300002 0.5341092 0.2391490 0.3674170 22286
23 154927581 rs644138 G A 0.0094821 0.0140086 0.5000000 0.4984840 0.2847020 0.4635760 22286
23 154929412 rs557132 C T 0.0083700 0.0142544 0.5600000 0.5570776 0.2407430 0.3568210 22286
23 154930230 rs781880 A G 0.0084751 0.0143352 0.5500004 0.5543818 0.2408700 0.3618540 22286
NA 3696 rs17052208 C T -0.0346202 0.1018500 0.7300002 0.7339216 0.0077230 NA 22286

bcf preview

1   55850   rs191890754 C   G   .   PASS    AF=0.00503738   ES:SE:LP:AF:SS:ID   0.0129399:0.132686:0.0362122:0.00503738:22286:rs191890754
1   61743   rs184286948 G   C   .   PASS    AF=0.00146299   ES:SE:LP:AF:SS:ID   0.0346218:0.262689:0.0457575:0.00146299:22286:rs184286948
1   88188   rs148331237 C   A   .   PASS    AF=0.00819782   ES:SE:LP:AF:SS:ID   0.139974:0.112295:0.677781:0.00819782:22286:rs148331237
1   99719   rs183898652 C   T   .   PASS    AF=0.0021039    ES:SE:LP:AF:SS:ID   0.553993:0.208067:2.10791:0.0021039:22286:rs183898652
1   534540  rs183186584 G   T   .   PASS    AF=0.00304029   ES:SE:LP:AF:SS:ID   0.211062:0.161542:0.721246:0.00304029:22286:rs183186584
1   669228  rs187185296 G   A   .   PASS    AF=0.0023126    ES:SE:LP:AF:SS:ID   0.118595:0.171319:0.309804:0.0023126:22286:rs187185296
1   701131  rs185335630 A   C   .   PASS    AF=0.00299333   ES:SE:LP:AF:SS:ID   0.197253:0.17815:0.568636:0.00299333:22286:rs185335630
1   712583  rs142048655 T   C   .   PASS    AF=0.0023907    ES:SE:LP:AF:SS:ID   -0.0782731:0.156191:0.207608:0.0023907:22286:rs142048655
1   713337  rs184249808 G   A   .   PASS    AF=0.0024171    ES:SE:LP:AF:SS:ID   -0.139816:0.199188:0.318759:0.0024171:22286:rs184249808
1   715211  rs184426933 C   G   .   PASS    AF=0.00313049   ES:SE:LP:AF:SS:ID   0.00561987:0.146402:0.0132283:0.00313049:22286:rs184426933
1   720468  rs187315357 G   A   .   PASS    AF=0.00377569   ES:SE:LP:AF:SS:ID   0.0869255:0.153596:0.244125:0.00377569:22286:rs187315357
1   723379  rs181223789 T   A   .   PASS    AF=0.00241745   ES:SE:LP:AF:SS:ID   -0.0755014:0.155522:0.200659:0.00241745:22286:rs181223789
1   748332  rs182373484 T   C   .   PASS    AF=0.00411936   ES:SE:LP:AF:SS:ID   -0.219244:0.156308:0.79588:0.00411936:22286:rs182373484
1   748524  rs144265613 C   T   .   PASS    AF=0.0030819    ES:SE:LP:AF:SS:ID   0.19396:0.166274:0.619789:0.0030819:22286:rs144265613
1   755435  rs184270342 T   G   .   PASS    AF=0.00143177   ES:SE:LP:AF:SS:ID   -0.533605:0.227657:1.72125:0.00143177:22286:rs184270342
1   767811  rs140378911 G   C   .   PASS    AF=0.0229196    ES:SE:LP:AF:SS:ID   0.0736404:0.068786:0.552842:0.0229196:22286:rs140378911
1   770281  rs188583355 T   A   .   PASS    AF=0.00215254   ES:SE:LP:AF:SS:ID   -0.212317:0.197114:0.552842:0.00215254:22286:rs188583355
1   772172  rs141427868 C   G   .   PASS    AF=0.00137714   ES:SE:LP:AF:SS:ID   -0.112732:0.274706:0.167491:0.00137714:22286:rs141427868
1   775426  rs2905037   G   A   .   PASS    AF=0.998058 ES:SE:LP:AF:SS:ID   0.088834:0.222029:0.161151:0.998058:22286:rs2905037
1   783632  rs193023236 G   A   .   PASS    AF=0.0030342    ES:SE:LP:AF:SS:ID   -0.208689:0.186379:0.585027:0.0030342:22286:rs193023236
1   789256  rs3131939   T   C   .   PASS    AF=0.989984 ES:SE:LP:AF:SS:ID   0.00535632:0.101701:0.0177288:0.989984:22286:rs3131939
1   791932  rs149451138 C   T   .   PASS    AF=0.0035666    ES:SE:LP:AF:SS:ID   0.0435135:0.171453:0.09691:0.0035666:22286:rs149451138
1   793470  rs184843908 G   A   .   PASS    AF=0.0103438    ES:SE:LP:AF:SS:ID   0.0765038:0.0999269:0.356547:0.0103438:22286:rs184843908
1   794319  rs186495636 G   A   .   PASS    AF=0.00144783   ES:SE:LP:AF:SS:ID   -0.112207:0.251713:0.180456:0.00144783:22286:rs186495636
1   794368  rs191334345 G   A   .   PASS    AF=0.0018618    ES:SE:LP:AF:SS:ID   -0.207259:0.1888:0.568636:0.0018618:22286:rs191334345
1   794787  rs183239437 A   G   .   PASS    AF=0.00199079   ES:SE:LP:AF:SS:ID   -0.134411:0.213907:0.275724:0.00199079:22286:rs183239437
1   795131  rs187620961 G   A   .   PASS    AF=0.00216635   ES:SE:LP:AF:SS:ID   -0.210528:0.195659:0.552842:0.00216635:22286:rs187620961
1   796298  rs143060708 C   G   .   PASS    AF=0.00302765   ES:SE:LP:AF:SS:ID   -0.00295117:0.169775:0.00436481:0.00302765:22286:rs143060708
1   799346  rs184260340 C   A   .   PASS    AF=0.00265105   ES:SE:LP:AF:SS:ID   -0.0529924:0.205473:0.09691:0.00265105:22286:rs184260340
1   799389  rs187038627 G   A   .   PASS    AF=0.0091864    ES:SE:LP:AF:SS:ID   -0.020028:0.105229:0.0705811:0.0091864:22286:rs187038627
1   801136  rs140609992 C   T   .   PASS    AF=0.00129571   ES:SE:LP:AF:SS:ID   0.184198:0.255877:0.327902:0.00129571:22286:rs140609992
1   801848  rs114911728 C   A   .   PASS    AF=0.00507533   ES:SE:LP:AF:SS:ID   0.0406442:0.119562:0.136677:0.00507533:22286:rs114911728
1   802671  rs191771620 T   G   .   PASS    AF=0.003486 ES:SE:LP:AF:SS:ID   -0.105431:0.162487:0.283997:0.003486:22286:rs191771620
1   803192  rs190240504 C   T   .   PASS    AF=0.00267073   ES:SE:LP:AF:SS:ID   0.197995:0.158546:0.677781:0.00267073:22286:rs190240504
1   809001  rs184951212 G   A   .   PASS    AF=0.00322111   ES:SE:LP:AF:SS:ID   0.00555397:0.142021:0.0132283:0.00322111:22286:rs184951212
1   810470  rs181485325 C   A   .   PASS    AF=0.00120633   ES:SE:LP:AF:SS:ID   -0.0956631:0.287397:0.130768:0.00120633:22286:rs181485325
1   810777  rs143204076 C   G   .   PASS    AF=0.0130197    ES:SE:LP:AF:SS:ID   0.0163376:0.0907484:0.0655015:0.0130197:22286:rs143204076
1   812717  rs151199172 G   T   .   PASS    AF=0.00173359   ES:SE:LP:AF:SS:ID   -0.0520346:0.186881:0.107905:0.00173359:22286:rs151199172
1   819065  rs188466450 C   T   .   PASS    AF=0.0068528    ES:SE:LP:AF:SS:ID   0.00126479:0.114704:0.00436481:0.0068528:22286:rs188466450
1   819467  rs187253977 T   A   .   PASS    AF=0.00151854   ES:SE:LP:AF:SS:ID   0.0806664:0.254641:0.124939:0.00151854:22286:rs187253977
1   823790  rs143626389 G   A   .   PASS    AF=0.0209886    ES:SE:LP:AF:SS:ID   -0.0330597:0.07178:0.187087:0.0209886:22286:rs143626389
1   824357  rs80134645  C   T   .   PASS    AF=0.00289833   ES:SE:LP:AF:SS:ID   -0.0397642:0.134536:0.113509:0.00289833:22286:rs80134645
1   824666  rs140725574 C   A   .   PASS    AF=0.0047316    ES:SE:LP:AF:SS:ID   0.0441243:0.132094:0.130768:0.0047316:22286:rs140725574
1   830791  rs184031231 C   T   .   PASS    AF=0.00704278   ES:SE:LP:AF:SS:ID   0.0673976:0.120226:0.236572:0.00704278:22286:rs184031231
1   834240  rs188158544 G   A   .   PASS    AF=0.00218954   ES:SE:LP:AF:SS:ID   -0.26051:0.21365:0.657577:0.00218954:22286:rs188158544
1   834830  rs116452738 G   A   .   PASS    AF=0.0121869    ES:SE:LP:AF:SS:ID   -0.130716:0.086199:0.886057:0.0121869:22286:rs116452738
1   837096  rs144333274 C   T   .   PASS    AF=0.00684682   ES:SE:LP:AF:SS:ID   0.0685584:0.120482:0.244125:0.00684682:22286:rs144333274
1   837214  rs72631888  G   C   .   PASS    AF=0.0099436    ES:SE:LP:AF:SS:ID   -0.0644811:0.104546:0.267606:0.0099436:22286:rs72631888
1   839682  rs189624598 G   A   .   PASS    AF=0.0222536    ES:SE:LP:AF:SS:ID   0.0255955:0.0641586:0.161151:0.0222536:22286:rs189624598
1   842473  rs80143686  C   T   .   PASS    AF=0.0037148    ES:SE:LP:AF:SS:ID   -0.199115:0.129441:0.920819:0.0037148:22286:rs80143686