IEU GWAS information

Trait: Age at first birth.

name	value
author	Howe LJ
consortium	Within family GWAS consortium
subcategory	NA
category	NA
build	HG19/GRCh37
population	European
trait	Age at first birth
sex	Males and Females
group_name	public
year	2022
unit	years
id	ieu-b-4819
ontology	EFO:0009101
note	Sibling estimate
sample_size	9999

Report information

name	value
gwas_id	ieu-b-4819
bcf_file	ieu-b-4819.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2022-01-05 04:51 UTC
build_version	master-74213fc-2021-03-26

Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

QQ plot

qq_plot

AF plot

af_plot

P-Z plot

pz_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 /data/cromwell-executions/qc/42a46ee9-c6e8-4176-b1ee-be8b00631f90/call-ldsc/inputs/-261044534/ieu-b-4819.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4819/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Jan  5 04:39:20 2022
Reading summary statistics from /data/cromwell-executions/qc/42a46ee9-c6e8-4176-b1ee-be8b00631f90/call-ldsc/inputs/-261044534/ieu-b-4819.vcf.gz ...
Read summary statistics for 6046988 SNPs.
Dropped 16724 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1152796 SNPs remain.
After merging with regression SNP LD, 1152796 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0093 (0.0359)
Lambda GC: 1.0189
Mean Chi^2: 1.0244
Intercept: 1.0219 (0.0066)
Ratio: 0.8968 (0.2693)
Analysis finished at Wed Jan  5 04:40:46 2022
Total time elapsed: 1.0m:25.64s

QC metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.0226,
    "mean_EFFECT": 0.0003,
    "n": 12542,
    "n_snps": 6047010,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 6047010,
    "n_miss_AF_reference": 39453,
    "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": 1152796,
    "ldsc_nsnp_merge_regression_ld": 1152796,
    "ldsc_observed_scale_h2_beta": 0.0093,
    "ldsc_observed_scale_h2_se": 0.0359,
    "ldsc_intercept_beta": 1.0219,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0189,
    "ldsc_mean_chisq": 1.0244,
    "ldsc_ratio": 0.8975
}

Flags

name	value
af_correlation	NA
inflation_factor	FALSE
n	FALSE
is_snpid_non_unique	TRUE
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	6	0.9999990	3	58	0	6046991	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	AF	6047010	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	8.630937e+00	5.740717e+00	1.000e+00	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.888937e+07	5.640061e+07	8.280e+02	3.241012e+07	6.951510e+07	1.147393e+08	2.492223e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	2.697000e-04	8.895520e-02	-7.505e-01	-5.000000e-02	4.000000e-04	5.040000e-02	7.880000e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	8.129460e-02	3.230720e-02	4.450e-02	5.670000e-02	6.800000e-02	9.580000e-02	2.270000e-01	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.960387e-01	2.900105e-01	1.000e-07	2.435000e-01	4.952004e-01	7.473998e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.960388e-01	2.900110e-01	1.000e-07	2.435256e-01	4.951945e-01	7.473981e-01	1.000000e+00	▇▇▇▇▇
numeric	AF_reference	39453	0.9934756	NA	NA	NA	NA	NA	NA	NA	3.043527e-01	2.467845e-01	1.997e-04	1.018370e-01	2.318290e-01	4.628590e-01	1.000000e+00	▇▅▂▂▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.163247e+04	1.108893e+03	7.088e+03	1.138900e+04	1.189700e+04	1.254200e+04	1.254200e+04	▁▁▁▁▇

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	797440	rs58013264	T	C	-0.1021	0.0925	0.2699001	0.2696869	NA	0.189497	9752
1	798400	rs10900604	A	G	-0.0930	0.0760	0.2209999	0.2210714	NA	0.410543	9983
1	798959	rs11240777	G	A	-0.0730	0.0678	0.2818000	0.2816160	NA	0.409944	11550
1	801467	rs61768212	G	C	-0.1094	0.0931	0.2398999	0.2399625	NA	0.193091	9555
1	804759	rs7526310	C	T	-0.1252	0.0930	0.1780000	0.1782262	NA	0.193890	9555
1	808631	rs11240779	G	A	0.0336	0.0720	0.6404998	0.6407384	NA	0.453474	9983
1	808928	rs11240780	C	T	0.0308	0.0722	0.6696995	0.6696759	NA	0.452276	9983
1	845635	rs117086422	C	T	-0.0091	0.0690	0.8954000	0.8950760	NA	0.158546	11550
1	845938	rs57760052	G	A	0.0126	0.0689	0.8552000	0.8548971	NA	0.363419	11550
1	846078	rs28612348	C	T	-0.0300	0.0713	0.6737007	0.6739323	NA	0.161741	11550

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51213613	rs34726907	C	T	0.1124	0.0986	0.2540002	0.2543032	NA	0.1727240	7948
22	51216564	rs9616970	T	C	0.1034	0.0985	0.2938001	0.2938348	NA	0.1563500	7948
22	51217954	rs9616974	G	A	0.1348	0.1256	0.2833003	0.2831597	NA	0.0621006	7751
22	51218224	rs9616975	C	A	0.1348	0.1256	0.2833003	0.2831597	NA	0.0619010	7751
22	51218377	rs2519461	G	C	0.1446	0.1258	0.2503001	0.2503731	NA	0.0826677	7751
22	51219387	rs9616832	T	C	0.1345	0.1256	0.2838997	0.2842324	NA	0.0654952	7751
22	51221731	rs115055839	T	C	0.1347	0.1261	0.2855999	0.2854304	NA	0.0625000	7751
22	51222100	rs114553188	G	T	-0.0064	0.1518	0.9666000	0.9663706	NA	0.0880591	7787
22	51223637	rs375798137	G	A	-0.0344	0.1529	0.8220000	0.8219920	NA	0.0788738	7787
22	51229805	rs9616985	T	C	0.1381	0.1262	0.2737997	0.2738257	NA	0.0730831	7751

bcf preview

1   797440  rs58013264  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.1021:0.0925:0.568797:9752:rs58013264
1   798400  rs10900604  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.093:0.076:0.655608:9983:rs10900604
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.073:0.0678:0.550059:11550:rs11240777
1   801467  rs61768212  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.1094:0.0931:0.61997:9555:rs61768212
1   804759  rs7526310   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.1252:0.093:0.74958:9555:rs7526310
1   808631  rs11240779  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0336:0.072:0.193481:9983:rs11240779
1   808928  rs1247187939    C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0308:0.0722:0.17412:9983:rs1247187939
1   845635  rs117086422 C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0091:0.069:0.0479829:11550:rs117086422
1   845938  rs57760052  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0126:0.0689:0.0679323:11550:rs57760052
1   846078  rs778265812 C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.03:0.0713:0.171533:11550:rs778265812
1   846398  rs58781670  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0201:0.0694:0.112383:11550:rs58781670
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0306:0.071:0.17633:11550:rs4475691
1   846864  rs1269142199    G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0401:0.0713:0.241391:11550:rs1269142199
1   847228  rs3905286   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0026:0.0699:0.0128255:11550:rs3905286
1   847491  rs1158719307    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0185:0.0687:0.103694:11550:rs1158719307
1   848090  rs4246505   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0082:0.0688:0.0431595:11550:rs4246505
1   848445  rs1156895099    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0211:0.0688:0.119815:11550:rs1156895099
1   848456  rs11507767  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0207:0.0688:0.117077:11550:rs11507767
1   848738  rs3829741   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0096:0.0688:0.050854:11550:rs3829741
1   850062  rs28723578  A   T   .   PASS    .   ES:SE:LP:SS:ID  0.0126:0.0686:0.0684404:11550:rs28723578
1   850123  rs28622257  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0079:0.0689:0.0414361:11550:rs28622257
1   851190  rs28609852  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0188:0.0687:0.105684:11550:rs28609852
1   851204  rs28552953  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.022:0.0679:0.127145:11550:rs28552953
1   852664  rs28605311  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0187:0.0695:0.103253:11550:rs28605311
1   852758  rs4970462   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0178:0.0695:0.098106:11550:rs4970462
1   853239  rs4970380   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0186:0.0695:0.102923:11550:rs4970380
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0211:0.0693:0.118615:11550:rs7537756
1   856476  rs4040605   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0454:0.0635:0.32413:9983:rs4040605
1   858040  rs4970460   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.1357:0.0764:1.12085:9983:rs4970460
1   858051  rs4970459   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.1343:0.0763:1.1048:9983:rs4970459
1   858801  rs7418179   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.1614:0.0735:1.55191:9983:rs7418179
1   860416  rs61464428  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.1789:0.0734:1.82798:9983:rs61464428
1   860688  rs60837925  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.1804:0.0734:1.85263:9983:rs60837925
1   861630  rs2879816   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.1799:0.0735:1.84436:9983:rs2879816
1   862866  rs3892970   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.1763:0.0732:1.79697:9983:rs3892970
1   864002  rs1806501   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.1747:0.0754:1.68952:9983:rs1806501
1   864938  rs1185651409    G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.151:0.0724:1.43121:9983:rs1185651409
1   865219  rs75551395  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.13:0.0753:1.075:9983:rs75551395
1   866893  rs2880024   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0211:0.0614:0.135845:9983:rs2880024
1   866938  rs74047407  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.1588:0.0733:1.51999:9983:rs74047407
1   867635  rs76964081  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.1422:0.0751:1.23381:9983:rs76964081
1   870645  rs28576697  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0491:0.0673:0.331707:9983:rs28576697
1   871334  rs4072383   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0567:0.0672:0.399245:9983:rs4072383
1   872352  rs1445142858    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0425:0.0686:0.270835:9983:rs1445142858
1   873558  rs1110052   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0379:0.0669:0.24306:9983:rs1110052
1   875770  rs4970379   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0168:0.0619:0.104688:9983:rs4970379
1   876499  rs755331663 A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0481:0.1315:0.146119:9689:rs755331663
1   877147  rs114982608 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0452:0.0698:0.286341:9983:rs114982608
1   879676  rs6605067   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0286:0.1296:0.0832829:9689:rs6605067
1   879687  rs2839  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0459:0.1315:0.138406:9689:rs2839

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bcf preview