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-11447/UKB-b-11447_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11447/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11447/UKB-b-11447_data.vcf.gz ...
Read summary statistics for 2794118 SNPs.
Dropped 344 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, 700626 SNPs remain.
After merging with regression SNP LD, 700626 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0007 (0.0014)
Lambda GC: 1.183
Mean Chi^2: 1.1834
Intercept: 1.1763 (0.0109)
Ratio: 0.9609 (0.0597)
Analysis finished at Thu Oct 17 14:40:55 2019
Total time elapsed: 36.74s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.7994,
    "inflation_factor": 1.1999,
    "mean_EFFECT": -5.9073e-08,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 22190,
    "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": 700626,
    "ldsc_nsnp_merge_regression_ld": 700626,
    "ldsc_observed_scale_h2_beta": 0.0007,
    "ldsc_observed_scale_h2_se": 0.0014,
    "ldsc_intercept_beta": 1.1763,
    "ldsc_intercept_se": 0.0109,
    "ldsc_lambda_gc": 1.183,
    "ldsc_mean_chisq": 1.1834,
    "ldsc_ratio": 0.9613
}

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 1.0000000 4 58 0 2793777 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 2794118 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 8.660066e+00 5.770384e+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 7.856510e+07 5.665091e+07 5687.0000000 3.170459e+07 6.898552e+07 1.147401e+08 2.492013e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.000000e-07 1.469000e-04 -0.0007821 -9.890000e-05 -3.000000e-07 9.910000e-05 7.925000e-04 ▁▂▇▂▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.347000e-04 9.000000e-06 0.0001205 1.270000e-04 1.321000e-04 1.411000e-04 2.749000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.719567e-01 2.948913e-01 0.0000001 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.719558e-01 2.948649e-01 0.0000001 2.104098e-01 4.610742e-01 7.268454e-01 9.999997e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.427366e-01 1.657976e-01 0.2073460 2.970240e-01 4.153850e-01 5.739798e-01 7.926540e-01 ▇▆▅▃▃
numeric AF_reference 22190 0.9920583 NA NA NA NA NA NA NA 4.256066e-01 1.855092e-01 0.0001997 2.775560e-01 4.057510e-01 5.617010e-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 0.0001036 0.0002217 0.6400000 0.6402783 0.623765 0.7821490 NA
1 54676 rs2462492 C T 0.0001126 0.0002196 0.6100002 0.6082983 0.400401 NA NA
1 91536 rs6702460 G T -0.0001101 0.0002162 0.6100002 0.6107784 0.456846 0.4207270 NA
1 534192 rs6680723 C T 0.0001103 0.0002470 0.6600001 0.6552811 0.240959 NA NA
1 706368 rs55727773 A G -0.0000058 0.0001533 0.9699999 0.9700530 0.515645 0.2751600 NA
1 763394 rs369924889 G A -0.0001552 0.0001798 0.3900004 0.3879432 0.706753 0.6176120 NA
1 768253 rs2977608 A C -0.0001517 0.0001467 0.2999998 0.3011311 0.761297 0.4894170 NA
1 776546 rs12124819 A G -0.0000950 0.0001639 0.5600000 0.5622003 0.265385 0.0756789 NA
1 808631 rs11240779 G A -0.0000784 0.0001490 0.5999997 0.5988432 0.772619 0.4534740 NA
1 808928 rs11240780 C T -0.0000718 0.0001492 0.6300007 0.6301468 0.772847 0.4522760 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51180501 rs5770999 T C 0.0002530 0.0001444 0.0800000 0.0797477 0.713656 0.6369810 NA
22 51181919 rs9616825 G C 0.0002721 0.0001437 0.0580003 0.0582081 0.695470 0.6194090 NA
22 51182485 rs6009961 A G 0.0002774 0.0001448 0.0549997 0.0554698 0.715502 0.6383790 NA
22 51186143 rs2879914 T C 0.0002205 0.0001343 0.1000000 0.1005766 0.381825 0.2733630 NA
22 51186228 rs3865766 C T 0.0002420 0.0001309 0.0649995 0.0645482 0.451061 0.4532750 NA
22 51197266 rs61290853 A G 0.0002699 0.0001352 0.0460002 0.0458586 0.386333 0.4229230 NA
22 51198027 rs34939255 A G 0.0000518 0.0001530 0.7300002 0.7349276 0.254562 0.0984425 NA
22 51211106 rs9628250 T C 0.0000499 0.0001517 0.7400005 0.7423978 0.271547 0.1671330 NA
22 51212875 rs2238837 A C 0.0002745 0.0001441 0.0569994 0.0568416 0.331457 0.3724040 NA
22 51237063 rs3896457 T C 0.0002826 0.0001476 0.0549997 0.0554910 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  0.000103589:0.000221671:0.19382:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.00011255:0.000219608:0.21467:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  -0.000110053:0.00021623:0.21467:0.456846:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  0.000110267:0.000246993:0.180456:0.240959:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -5.75621e-06:0.000153328:0.0132283:0.515645:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  -0.000155199:0.000179763:0.408935:0.706753:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761297 ES:SE:LP:AF:ID  -0.000151708:0.000146718:0.522879:0.761297:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.265385 ES:SE:LP:AF:ID  -9.49862e-05:0.000163889:0.251812:0.265385:rs12124819
1   808631  rs11240779  G   A   .   PASS    AF=0.772619 ES:SE:LP:AF:ID  -7.83605e-05:0.000148956:0.221849:0.772619:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772847 ES:SE:LP:AF:ID  -7.18462e-05:0.000149207:0.200659:0.772847:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340396 ES:SE:LP:AF:ID  0.000238484:0.000210239:0.585027:0.340396:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697255 ES:SE:LP:AF:ID  2.22888e-05:0.000140661:0.0604807:0.697255:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705397 ES:SE:LP:AF:ID  3.66764e-05:0.000138115:0.102373:0.705397:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705442 ES:SE:LP:AF:ID  4.0295e-05:0.000138111:0.113509:0.705442:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705627 ES:SE:LP:AF:ID  3.93405e-05:0.000138117:0.107905:0.705627:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705655 ES:SE:LP:AF:ID  3.83557e-05:0.000138132:0.107905:0.705655:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730129 ES:SE:LP:AF:ID  8.12053e-06:0.000141895:0.0222764:0.730129:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294377 ES:SE:LP:AF:ID  -3.87965e-05:0.000138125:0.107905:0.294377:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236703 ES:SE:LP:AF:ID  -0.000184113:0.000147056:0.677781:0.236703:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236691 ES:SE:LP:AF:ID  -0.000183653:0.000147057:0.677781:0.236691:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.23975  ES:SE:LP:AF:ID  -0.000164599:0.000146586:0.585027:0.23975:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.236693 ES:SE:LP:AF:ID  -0.000178136:0.000147056:0.638272:0.236693:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212421 ES:SE:LP:AF:ID  -0.000163616:0.000152845:0.552842:0.212421:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.212316 ES:SE:LP:AF:ID  -0.000164485:0.000152872:0.552842:0.212316:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.237178 ES:SE:LP:AF:ID  -0.000172617:0.000146943:0.619789:0.237178:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.212962 ES:SE:LP:AF:ID  -0.000150153:0.000152654:0.481486:0.212962:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.212924 ES:SE:LP:AF:ID  -0.00015109:0.000152685:0.49485:0.212924:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241162 ES:SE:LP:AF:ID  -0.000124932:0.000145918:0.408935:0.241162:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213543 ES:SE:LP:AF:ID  -0.000136126:0.00015246:0.431798:0.213543:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269511 ES:SE:LP:AF:ID  -0.000128699:0.000140799:0.443698:0.269511:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213513 ES:SE:LP:AF:ID  -0.000136764:0.000152479:0.431798:0.213513:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.214587 ES:SE:LP:AF:ID  -0.000118847:0.000152185:0.366532:0.214587:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.246204 ES:SE:LP:AF:ID  -0.000112914:0.000144914:0.356547:0.246204:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.270019 ES:SE:LP:AF:ID  -0.000121274:0.000140899:0.408935:0.270019:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400124 ES:SE:LP:AF:ID  8.79998e-06:0.0001274:0.0268721:0.400124:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.237102 ES:SE:LP:AF:ID  -0.000125021:0.000147979:0.39794:0.237102:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215391 ES:SE:LP:AF:ID  -0.000117566:0.000152287:0.356547:0.215391:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.235323 ES:SE:LP:AF:ID  -0.000194295:0.000150196:0.69897:0.235323:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362606 ES:SE:LP:AF:ID  0.000141742:0.000158149:0.431798:0.362606:rs11516185
1   845938  rs57760052  G   A   .   PASS    AF=0.210864 ES:SE:LP:AF:ID  0.000100385:0.000153387:0.29243:0.210864:rs57760052
1   847491  rs28407778  G   A   .   PASS    AF=0.214198 ES:SE:LP:AF:ID  -2.34402e-05:0.000152363:0.0555173:0.214198:rs28407778
1   848090  rs4246505   G   A   .   PASS    AF=0.212513 ES:SE:LP:AF:ID  -6.12e-05:0.000152764:0.161151:0.212513:rs4246505
1   848445  rs4626817   G   A   .   PASS    AF=0.209296 ES:SE:LP:AF:ID  -4.02242e-05:0.000154269:0.102373:0.209296:rs4626817
1   848456  rs11507767  A   G   .   PASS    AF=0.209245 ES:SE:LP:AF:ID  -3.93587e-05:0.000154297:0.09691:0.209245:rs11507767
1   848738  rs3829741   C   T   .   PASS    AF=0.212338 ES:SE:LP:AF:ID  -6.79211e-05:0.000152901:0.180456:0.212338:rs3829741
1   850062  rs28723578  A   T   .   PASS    AF=0.214408 ES:SE:LP:AF:ID  -1.80446e-05:0.000152222:0.0409586:0.214408:rs28723578
1   850123  rs28622257  C   T   .   PASS    AF=0.212773 ES:SE:LP:AF:ID  -5.51311e-05:0.00015261:0.142668:0.212773:rs28622257
1   850218  rs6664536   T   A   .   PASS    AF=0.590331 ES:SE:LP:AF:ID  -8.82251e-05:0.000127028:0.309804:0.590331:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603723 ES:SE:LP:AF:ID  -5.80262e-05:0.000127741:0.187087:0.603723:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603942 ES:SE:LP:AF:ID  -8.09171e-05:0.000127723:0.275724:0.603942:rs6657440