IEU GWAS information

Trait: Age at menopause.

name	value
author	Howe LJ
consortium	Within family GWAS consortium
subcategory	NA
category	NA
build	HG19/GRCh37
population	European
trait	Age at menopause
sex	Males and Females
group_name	public
year	2022
nsnp	6972488
unit	years
id	ieu-b-4824
ontology	EFO:0004704
note	Population estimate
sample_size	11859

Report information

name	value
gwas_id	ieu-b-4824
bcf_file	ieu-b-4824.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2022-01-05 05:42 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/18912e94-02ef-4aae-b1f9-faed4a5c5eb8/call-ldsc/inputs/-261044508/ieu-b-4824.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4824/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Jan  5 05:29:47 2022
Reading summary statistics from /data/cromwell-executions/qc/18912e94-02ef-4aae-b1f9-faed4a5c5eb8/call-ldsc/inputs/-261044508/ieu-b-4824.vcf.gz ...
Read summary statistics for 6972433 SNPs.
Dropped 34341 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, 1168928 SNPs remain.
After merging with regression SNP LD, 1168928 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0934 (0.043)
Lambda GC: 1.0431
Mean Chi^2: 1.049
Intercept: 1.0269 (0.0071)
Ratio: 0.5491 (0.1443)
Analysis finished at Wed Jan  5 05:31:18 2022
Total time elapsed: 1.0m:30.56s

QC metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.047,
    "mean_EFFECT": 0.0014,
    "n": 11859,
    "n_snps": 6972488,
    "n_clumped_hits": 3,
    "n_p_sig": 51,
    "n_mono": 0,
    "n_ns": 242960,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 6972488,
    "n_miss_AF_reference": 137367,
    "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": 1168928,
    "ldsc_nsnp_merge_regression_ld": 1168928,
    "ldsc_observed_scale_h2_beta": 0.0934,
    "ldsc_observed_scale_h2_se": 0.043,
    "ldsc_intercept_beta": 1.0269,
    "ldsc_intercept_se": 0.0071,
    "ldsc_lambda_gc": 1.0431,
    "ldsc_mean_chisq": 1.049,
    "ldsc_ratio": 0.549
}

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	13	0.9999981	3	58	0	6972383	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	43	0	14371	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	44	0	5169	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
logical	AF	6972488	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	8.651440e+00	5.756353e+00	1.0000e+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	7.897793e+07	5.640312e+07	3.0200e+02	3.262478e+07	6.968164e+07	1.147591e+08	2.492223e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.433000e-03	1.024111e-01	-9.0600e-01	-5.540000e-02	9.000000e-04	5.780000e-02	1.060800e+00	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	9.218800e-02	3.935830e-02	5.3900e-02	6.110000e-02	7.560000e-02	1.129000e-01	2.490000e-01	▇▂▂▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.928291e-01	2.905292e-01	0.0000e+00	2.397000e-01	4.901003e-01	7.443000e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.928290e-01	2.905296e-01	0.0000e+00	2.397245e-01	4.900709e-01	7.443312e-01	1.000000e+00	▇▇▇▇▇
numeric	AF_reference	137367	0.9802987	NA	NA	NA	NA	NA	NA	NA	2.925070e-01	2.493129e-01	1.9970e-04	8.765970e-02	2.164540e-01	4.498800e-01	1.000000e+00	▇▃▂▂▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.142526e+04	5.490776e+02	1.0527e+04	1.093100e+04	1.185900e+04	1.185900e+04	1.185900e+04	▃▁▁▂▇

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	797440	rs58013264	T	C	-0.0865	0.0892	0.3319999	0.3321806	NA	0.189497	10931
1	798400	rs10900604	A	G	-0.1260	0.0733	0.0854791	0.0856211	NA	0.410543	10931
1	798959	rs11240777	G	A	-0.1260	0.0733	0.0854791	0.0856211	NA	0.409944	10931
1	801467	rs61768212	G	C	-0.0857	0.0887	0.3343998	0.3339551	NA	0.193091	10931
1	804759	rs7526310	C	T	-0.0891	0.0889	0.3162999	0.3162230	NA	0.193890	10931
1	808223	rs4951933	G	C	0.1209	0.0723	0.0947393	0.0944851	NA	0.452077	10527
1	808631	rs11240779	G	A	0.1206	0.0718	0.0931108	0.0930224	NA	0.453474	10527
1	808928	rs11240780	C	T	0.1242	0.0718	0.0837105	0.0836651	NA	0.452276	10527
1	845274	rs112856858	G	T	0.0407	0.0753	0.5887000	0.5888491	NA	0.374002	10527
1	845635	rs117086422	C	T	0.0497	0.0728	0.4947001	0.4948013	NA	0.158546	10931

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51167244	rs143213926	AG	A	0.0343	0.1266	0.7864009	0.7864433	NA	0.0319489	10527
22	51171497	rs2301584	G	A	0.1218	0.0758	0.1081999	0.1080850	NA	0.2533950	10527
22	51177257	rs73174437	C	T	-0.0373	0.1689	0.8252000	0.8252164	NA	0.0091853	10527
22	51180878	rs6010069	A	G	-0.0071	0.0777	0.9267001	0.9271930	NA	0.1589460	10527
22	51180959	rs6010070	A	G	0.0055	0.0777	0.9439000	0.9435688	NA	0.2056710	10527
22	51181685	rs34756634	G	A	-0.0271	0.0851	0.7499995	0.7501445	NA	0.7118610	10527
22	51181687	rs9616955	A	T	0.0261	0.1259	0.8360000	0.8357698	NA	0.0485224	10527
22	51181759	rs13056621	A	G	-0.0343	0.0900	0.7030998	0.7031208	NA	0.6761180	10527
22	51182090	rs28516879	G	A	-0.0089	0.1159	0.9390000	0.9387903	NA	0.0467252	10527
22	51185848	rs3865764	G	A	0.0886	0.1425	0.5339001	0.5341034	NA	0.9776360	10527

bcf preview

1   797440  rs58013264  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0865:0.0892:0.478862:10931:rs58013264
1   798400  rs10900604  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.126:0.0733:1.06814:10931:rs10900604
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.126:0.0733:1.06814:10931:rs11240777
1   801467  rs61768212  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0857:0.0887:0.475734:10931:rs61768212
1   804759  rs7526310   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0891:0.0889:0.499901:10931:rs7526310
1   808223  rs1557576983    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.1209:0.0723:1.02347:10527:rs1557576983
1   808631  rs11240779  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.1206:0.0718:1.031:10527:rs11240779
1   808928  rs1247187939    C   T   .   PASS    .   ES:SE:LP:SS:ID  0.1242:0.0718:1.07722:10527:rs1247187939
1   845274  rs1324283754    G   T   .   PASS    .   ES:SE:LP:SS:ID  0.0407:0.0753:0.230106:10527:rs1324283754
1   845635  rs117086422 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0497:0.0728:0.305658:10931:rs117086422
1   845938  rs57760052  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0648:0.0724:0.43086:10931:rs57760052
1   846078  rs778265812 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0317:0.0744:0.174185:10931:rs778265812
1   846398  rs58781670  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0595:0.0739:0.376027:10931:rs58781670
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0354:0.0743:0.198459:10931:rs4475691
1   846864  rs1269142199    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0271:0.0744:0.145512:10931:rs1269142199
1   847228  rs3905286   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0284:0.073:0.156767:10931:rs3905286
1   847491  rs1158719307    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0539:0.0718:0.344189:10931:rs1158719307
1   848090  rs4246505   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0535:0.0719:0.340274:10931:rs4246505
1   848445  rs1156895099    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0658:0.072:0.442854:10931:rs1156895099
1   848456  rs11507767  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0674:0.0719:0.457673:10931:rs11507767
1   848738  rs3829741   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0535:0.0719:0.340274:10931:rs3829741
1   850062  rs28723578  A   T   .   PASS    .   ES:SE:LP:SS:ID  0.0496:0.0718:0.310159:10931:rs28723578
1   850123  rs28622257  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0512:0.072:0.321937:10931:rs28622257
1   851190  rs28609852  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0493:0.0718:0.307506:10931:rs28609852
1   851204  rs28552953  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0288:0.0711:0.164183:10931:rs28552953
1   852664  rs28605311  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0413:0.0726:0.244888:10931:rs28605311
1   852758  rs4970462   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0391:0.0725:0.229369:10931:rs4970462
1   853239  rs4970380   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0391:0.0725:0.229369:10931:rs4970380
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0451:0.0724:0.273191:10931:rs7537756
1   856436  rs34105146  TG  T   .   PASS    .   ES:SE:LP:SS:ID  0.0568:0.0754:0.345823:10527:rs34105146
1   856476  rs4040605   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0117:0.0613:0.0710412:10931:rs4040605
1   858040  rs4970460   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.0585:0.0708:0.388808:10931:rs4970460
1   858051  rs4970459   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0566:0.0708:0.372839:10931:rs4970459
1   858801  rs7418179   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.091:0.0697:0.717151:10931:rs7418179
1   859685  rs111572704 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0568:0.0734:0.357931:10527:rs111572704
1   860416  rs61464428  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0903:0.0697:0.709298:10931:rs61464428
1   860688  rs60837925  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0866:0.0698:0.66736:10931:rs60837925
1   861630  rs2879816   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0895:0.0697:0.700493:10931:rs2879816
1   862866  rs3892970   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0914:0.0697:0.721475:10931:rs3892970
1   864002  rs1806501   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0406:0.0705:0.248567:10931:rs1806501
1   864938  rs1185651409    G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0652:0.0685:0.467118:10931:rs1185651409
1   865219  rs75551395  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0381:0.0705:0.23018:10931:rs75551395
1   866893  rs2880024   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0194:0.0595:0.128077:10931:rs2880024
1   866938  rs74047407  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0544:0.0691:0.365624:10931:rs74047407
1   867635  rs76964081  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0447:0.0704:0.279593:10931:rs76964081
1   869303  rs113171913 C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0035:0.0671:0.0184079:10527:rs113171913
1   870645  rs28576697  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0437:0.0641:0.305395:10931:rs28576697
1   870651  rs76294260  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.2576:0.1984:0.711751:10527:rs76294260
1   871334  rs4072383   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0343:0.0643:0.226726:10931:rs4072383
1   872352  rs1445142858    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0126:0.0652:0.0722191:10931:rs1445142858

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bcf preview