IEU GWAS information

Trait: Wellbeing.

name	value
author	Howe LJ
consortium	Within family GWAS consortium
subcategory	NA
category	NA
build	HG19/GRCh37
population	European
trait	Wellbeing
sex	Males and Females
group_name	public
year	2022
nsnp	7465362
unit	SD
id	ieu-b-4852
ontology	EFO:0007869
note	Population estimate
sample_size	63392

Report information

name	value
gwas_id	ieu-b-4852
bcf_file	ieu-b-4852.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2022-01-05 18:33 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/68ad021c-dc1e-479a-aead-55a177e7a7d8/call-ldsc/inputs/-261044417/ieu-b-4852.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4852/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Jan  5 18:19:40 2022
Reading summary statistics from /data/cromwell-executions/qc/68ad021c-dc1e-479a-aead-55a177e7a7d8/call-ldsc/inputs/-261044417/ieu-b-4852.vcf.gz ...
Read summary statistics for 7464957 SNPs.
Dropped 36368 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, 1191073 SNPs remain.
After merging with regression SNP LD, 1191073 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0534 (0.0094)
Lambda GC: 1.1534
Mean Chi^2: 1.1517
Intercept: 1.0849 (0.0084)
Ratio: 0.5596 (0.0556)
Analysis finished at Wed Jan  5 18:21:16 2022
Total time elapsed: 1.0m:35.56s

QC metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.1448,
    "mean_EFFECT": 0.0002,
    "n": 63392,
    "n_snps": 7465013,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 252389,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 7465013,
    "n_miss_AF_reference": 148441,
    "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": 1191073,
    "ldsc_nsnp_merge_regression_ld": 1191073,
    "ldsc_observed_scale_h2_beta": 0.0534,
    "ldsc_observed_scale_h2_se": 0.0094,
    "ldsc_intercept_beta": 1.0849,
    "ldsc_intercept_se": 0.0084,
    "ldsc_lambda_gc": 1.1534,
    "ldsc_mean_chisq": 1.1517,
    "ldsc_ratio": 0.5597
}

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	0.9999983	3	58	0	7464896	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	43	0	14895	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	44	0	5389	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
logical	AF	7465013	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	8.646251e+00	5.753221e+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	7.898340e+07	5.636960e+07	3.0200e+02	3.269918e+07	6.968910e+07	1.147299e+08	2.492223e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.569000e-04	1.233850e-02	-1.5070e-01	-6.000000e-03	2.000000e-04	6.300000e-03	1.318000e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.018670e-02	5.520800e-03	5.3000e-03	6.200000e-03	7.800000e-03	1.230000e-02	3.650000e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.784802e-01	2.942506e-01	1.0000e-07	2.184002e-01	4.705004e-01	7.330996e-01	1.000000e+00	▇▇▆▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.784778e-01	2.942693e-01	1.0000e-07	2.184092e-01	4.706258e-01	7.330394e-01	1.000000e+00	▇▇▆▆▆
numeric	AF_reference	148441	0.9801151	NA	NA	NA	NA	NA	NA	NA	2.779184e-01	2.510102e-01	1.9970e-04	7.208470e-02	1.968850e-01	4.309110e-01	1.000000e+00	▇▃▂▂▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	5.785016e+04	7.280751e+03	3.8076e+04	5.560400e+04	5.964500e+04	6.339200e+04	6.339200e+04	▁▁▁▁▇

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	797440	rs58013264	T	C	-0.0202	0.0090	0.0251200	0.0248038	NA	0.189497	50819
1	798400	rs10900604	A	G	-0.0028	0.0076	0.7124001	0.7125593	NA	0.410543	50819
1	798959	rs11240777	G	A	-0.0013	0.0073	0.8603000	0.8586584	NA	0.409944	54860
1	801467	rs61768212	G	C	-0.0195	0.0090	0.0306902	0.0302603	NA	0.193091	50819
1	804759	rs7526310	C	T	-0.0187	0.0090	0.0386999	0.0377298	NA	0.193890	50819
1	808223	rs4951933	G	C	-0.0025	0.0081	0.7552000	0.7575939	NA	0.452077	42733
1	808631	rs11240779	G	A	-0.0005	0.0073	0.9478999	0.9453931	NA	0.453474	50819
1	808928	rs11240780	C	T	-0.0006	0.0073	0.9367000	0.9344942	NA	0.452276	50819
1	845274	rs112856858	G	T	0.0210	0.0085	0.0129500	0.0134891	NA	0.374002	42733
1	845635	rs117086422	C	T	0.0160	0.0074	0.0306302	0.0306057	NA	0.158546	54860

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51177257	rs73174437	C	T	0.0007	0.0165	0.9672000	0.9661605	NA	0.0091853	55194
22	51178607	rs6010067	G	C	-0.0212	0.0236	0.3701996	0.3690229	NA	0.0814696	42733
22	51180878	rs6010069	A	G	0.0166	0.0088	0.0587895	0.0592460	NA	0.1589460	43730
22	51180959	rs6010070	A	G	0.0165	0.0088	0.0594895	0.0607927	NA	0.2056710	43730
22	51181685	rs34756634	G	A	0.0072	0.0102	0.4820000	0.4802613	NA	0.7118610	43730
22	51181687	rs9616955	A	T	-0.0117	0.0144	0.4147000	0.4165048	NA	0.0485224	42733
22	51181759	rs13056621	A	G	0.0037	0.0088	0.6726993	0.6741534	NA	0.6761180	58944
22	51182090	rs28516879	G	A	-0.0112	0.0116	0.3350001	0.3342858	NA	0.0467252	57947
22	51185848	rs3865764	G	A	0.0182	0.0163	0.2636999	0.2641806	NA	0.9776360	52107
22	51188319	rs139620215	T	C	0.0033	0.0300	0.9135001	0.9124094	NA	0.0037939	40322

bcf preview

1   797440  rs58013264  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0202:0.009:1.59998:50819:rs58013264
1   798400  rs10900604  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0028:0.0076:0.147276:50819:rs10900604
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0013:0.0073:0.0653501:54860:rs11240777
1   801467  rs61768212  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0195:0.009:1.513:50819:rs61768212
1   804759  rs7526310   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0187:0.009:1.41229:50819:rs7526310
1   808223  rs1557576983    G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0025:0.0081:0.121938:42733:rs1557576983
1   808631  rs11240779  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0005:0.0073:0.0232375:50819:rs11240779
1   808928  rs1247187939    C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0006:0.0073:0.0283995:50819:rs1247187939
1   845274  rs1324283754    G   T   .   PASS    .   ES:SE:LP:SS:ID  0.021:0.0085:1.88773:42733:rs1324283754
1   845635  rs117086422 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.016:0.0074:1.51385:54860:rs117086422
1   845938  rs57760052  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.017:0.0074:1.67162:54860:rs57760052
1   846078  rs778265812 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0158:0.0077:1.39761:54860:rs778265812
1   846236  rs138796872 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0204:0.0252:0.379552:40322:rs138796872
1   846398  rs58781670  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.015:0.0075:1.34863:54860:rs58781670
1   846465  rs60454217  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0268:0.0242:0.572027:40322:rs60454217
1   846543  rs79396034  G   T   .   PASS    .   ES:SE:LP:SS:ID  0.0301:0.0248:0.648977:40322:rs79396034
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0154:0.0077:1.344:54860:rs4475691
1   846864  rs1269142199    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0158:0.0077:1.4023:54860:rs1269142199
1   847228  rs3905286   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0152:0.0076:1.35566:54860:rs3905286
1   847491  rs1158719307    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0153:0.0074:1.40143:54860:rs1158719307
1   848090  rs4246505   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0151:0.0075:1.36785:54860:rs4246505
1   848445  rs1156895099    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0151:0.0075:1.36432:54860:rs1156895099
1   848456  rs11507767  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0153:0.0075:1.39696:54860:rs11507767
1   848738  rs3829741   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0151:0.0075:1.36673:54860:rs3829741
1   850062  rs28723578  A   T   .   PASS    .   ES:SE:LP:SS:ID  0.0154:0.0074:1.41828:54860:rs28723578
1   850123  rs28622257  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0153:0.0075:1.39019:54860:rs28622257
1   851190  rs28609852  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0156:0.0074:1.44334:54860:rs28609852
1   851204  rs28552953  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0136:0.0074:1.1829:54860:rs28552953
1   852664  rs28605311  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0154:0.0075:1.38405:54860:rs28605311
1   852758  rs4970462   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0154:0.0075:1.38945:54860:rs4970462
1   853239  rs4970380   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0154:0.0075:1.38711:54860:rs4970380
1   853805  rs3748591   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.017:0.0218:0.361211:40322:rs3748591
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0155:0.0075:1.40373:54860:rs7537756
1   854429  rs72902552  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.017:0.0218:0.361211:40322:rs72902552
1   856436  rs34105146  TG  T   .   PASS    .   ES:SE:LP:SS:ID  0.0147:0.0086:1.05296:42733:rs34105146
1   856476  rs4040605   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0063:0.0065:0.473919:50819:rs4040605
1   857177  rs386627408 T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0288:0.0216:0.737075:40322:rs386627408
1   858040  rs4970460   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.0205:0.0077:2.13531:50819:rs4970460
1   858051  rs4970459   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0205:0.0077:2.12442:50819:rs4970459
1   858801  rs7418179   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.0074:2.15933:50819:rs7418179
1   859685  rs111572704 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.019:0.0085:1.60485:42733:rs111572704
1   859913  rs1187056171    A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0144:0.0234:0.26817:40322:rs1187056171
1   860416  rs61464428  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0197:0.0074:2.10657:50819:rs61464428
1   860461  rs57465118  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0174:0.0212:0.385103:40322:rs57465118
1   860521  rs57924093  C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0174:0.0212:0.385103:40322:rs57924093
1   860688  rs60837925  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.0074:2.15206:50819:rs60837925
1   861008  rs28521172  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0174:0.0212:0.385103:40322:rs28521172
1   861630  rs2879816   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0198:0.0074:2.11594:50819:rs2879816
1   861808  rs13302982  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0174:0.0212:0.385103:40322:rs13302982
1   862093  rs1210451934    T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0177:0.0212:0.395234:40322:rs1210451934

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Head and tail

bcf preview