IEU GWAS information

Trait: HbA1c.

name	value
author	Howe LJ
consortium	Within family GWAS consortium
subcategory	NA
category	NA
build	HG19/GRCh37
population	European
trait	HbA1c
sex	Males and Females
group_name	public
year	2022
unit	SD
id	ieu-b-4841
ontology	EFO:0004541
note	Sibling estimate
sample_size	17724

Report information

name	value
gwas_id	ieu-b-4841
bcf_file	ieu-b-4841.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2022-01-05 15:12 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/b565680f-5e17-4165-9b90-f3d0f7c1e016/call-ldsc/inputs/-261044449/ieu-b-4841.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4841/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Jan  5 14:55:51 2022
Reading summary statistics from /data/cromwell-executions/qc/b565680f-5e17-4165-9b90-f3d0f7c1e016/call-ldsc/inputs/-261044449/ieu-b-4841.vcf.gz ...
Read summary statistics for 9695862 SNPs.
Dropped 158562 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, 1210242 SNPs remain.
After merging with regression SNP LD, 1210242 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.204 (0.0356)
Lambda GC: 1.0474
Mean Chi^2: 1.0702
Intercept: 0.9999 (0.0072)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Wed Jan  5 14:57:47 2022
Total time elapsed: 1.0m:55.16s

QC metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.0398,
    "mean_EFFECT": 0,
    "n": 17724,
    "n_snps": 9696819,
    "n_clumped_hits": 12,
    "n_p_sig": 365,
    "n_mono": 0,
    "n_ns": 1136879,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 9696819,
    "n_miss_AF_reference": 639022,
    "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": 1210242,
    "ldsc_nsnp_merge_regression_ld": 1210242,
    "ldsc_observed_scale_h2_beta": 0.204,
    "ldsc_observed_scale_h2_se": 0.0356,
    "ldsc_intercept_beta": 0.9999,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.0474,
    "ldsc_mean_chisq": 1.0702,
    "ldsc_ratio": -0.0014
}

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	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	logical.mean	logical.count	numeric.mean	numeric.sd	numeric.p0	numeric.p25	numeric.p50	numeric.p75	numeric.p100	numeric.hist
character	ID	580	0.9999402	3	58	0	9685280	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	101	0	46015	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	662	0	30812	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
logical	AF	9696819	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	8.676979e+00	5.777166e+00	1.0000	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.848897e+07	5.648206e+07	302.0000	3.210067e+07	6.873262e+07	1.142951e+08	2.492405e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.270000e-05	2.495200e-02	-0.3812	-1.170000e-02	0.000000e+00	1.180000e-02	2.849000e-01	▁▁▇▂▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	2.113520e-02	1.276880e-02	0.0085	1.160000e-02	1.560000e-02	2.700000e-02	1.344000e-01	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.934906e-01	2.904435e-01	0.0000	2.409001e-01	4.916001e-01	7.449995e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.934934e-01	2.904485e-01	0.0000	2.409052e-01	4.915291e-01	7.450650e-01	1.000000e+00	▇▇▇▇▇
numeric	AF_reference	639022	0.9340998	NA	NA	NA	NA	NA	NA	NA	2.599542e-01	2.537058e-01	0.0000	4.932110e-02	1.725240e-01	4.097440e-01	1.000000e+00	▇▃▂▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.600562e+04	2.526606e+03	10808.0000	1.641300e+04	1.723300e+04	1.772400e+04	1.772400e+04	▂▁▁▁▇

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	10177	rs1264289758	AC	A	0.0253	0.0150	0.0918798	0.0916675	NA	NA	10808
1	10352	rs1557426776	TA	T	0.0116	0.0147	0.4312995	0.4300444	NA	NA	10808
1	11008	rs575272151	C	G	0.0248	0.0254	0.3292001	0.3288772	NA	0.0880591	10808
1	11012	rs544419019	C	G	0.0248	0.0254	0.3292001	0.3288772	NA	0.0880591	10808
1	13110	rs540538026	G	A	0.0773	0.0401	0.0542600	0.0538948	NA	0.0267572	10808
1	13116	rs62635286	T	G	-0.0128	0.0175	0.4640000	0.4645174	NA	0.0970447	10808
1	13118	rs200579949	A	G	-0.0128	0.0175	0.4640000	0.4645174	NA	0.0970447	10808
1	13273	rs531730856	G	C	-0.0315	0.0214	0.1407000	0.1410310	NA	0.0950479	10808
1	14464	rs546169444	A	T	-0.0284	0.0205	0.1662999	0.1659406	NA	0.0958466	10808
1	14599	rs531646671	T	A	-0.0247	0.0172	0.1522001	0.1509891	NA	0.1475640	10808

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51233182	rs4040317	G	A	0.0019	0.0140	0.8937001	0.8920472	NA	0.2633790	10808
22	51233300	rs9616839	C	T	0.0012	0.0136	0.9292999	0.9296897	NA	0.3146960	10808
22	51233312	rs62240043	A	G	0.0009	0.0145	0.9527001	0.9505079	NA	0.2134580	10808
22	51233347	rs62240044	T	C	0.0009	0.0145	0.9527001	0.9505079	NA	0.2134580	10808
22	51234799	rs191117135	G	A	-0.0618	0.0524	0.2378998	0.2382432	NA	0.0059904	10808
22	51235959	rs200189535	T	C	-0.0007	0.0165	0.9676999	0.9661605	NA	0.1996810	10808
22	51235979	rs62240045	G	A	-0.0096	0.0164	0.5561001	0.5583017	NA	0.2400160	10808
22	51236013	rs200507571	AT	A	0.0050	0.0161	0.7558002	0.7561359	NA	NA	10808
22	51237063	rs3896457	T	C	-0.0086	0.0130	0.5076005	0.5082671	NA	0.2050720	12119
22	51237712	rs370652263	G	A	-0.0645	0.0299	0.0305999	0.0309908	NA	0.0690895	10808

bcf preview

1   10177   rs1264289758    AC  A   .   PASS    .   ES:SE:LP:SS:ID  0.0253:0.015:1.03678:10808:rs1264289758
1   10352   rs1557426776    TA  T   .   PASS    .   ES:SE:LP:SS:ID  0.0116:0.0147:0.365221:10808:rs1557426776
1   11008   rs575272151 C   G   .   PASS    .   ES:SE:LP:SS:ID  0.0248:0.0254:0.48254:10808:rs575272151
1   11012   rs544419019 C   G   .   PASS    .   ES:SE:LP:SS:ID  0.0248:0.0254:0.48254:10808:rs544419019
1   13110   rs540538026 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0773:0.0401:1.26552:10808:rs540538026
1   13116   rs62635286  T   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0128:0.0175:0.333482:10808:rs62635286
1   13118   rs62028691  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0128:0.0175:0.333482:10808:rs62028691
1   13273   rs531730856 G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0315:0.0214:0.851706:10808:rs531730856
1   14464   rs546169444 A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0284:0.0205:0.779108:10808:rs546169444
1   14599   rs707680    T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0247:0.0172:0.817585:10808:rs707680
1   14604   rs1418508701    A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0247:0.0172:0.817585:10808:rs1418508701
1   14930   rs6682385   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0087:0.0151:0.246723:10808:rs6682385
1   14933   rs199856693 G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0222:0.0459:0.201695:10808:rs199856693
1   15211   rs3982632   T   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0036:0.0161:0.0833355:10808:rs3982632
1   15820   rs1316988498    G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0057:0.0158:0.144663:10808:rs1316988498
1   15903   rs557514207 GC  G   .   PASS    .   ES:SE:LP:SS:ID  -0.0274:0.0143:1.26273:10808:rs557514207
1   28590   rs1344649620    T   TTGG    .   PASS    .   ES:SE:LP:SS:ID  0.1056:0.0478:1.56719:10808:rs1344649620
1   30923   rs1165072081    G   T   .   PASS    .   ES:SE:LP:SS:ID  0.0746:0.0303:1.86107:10808:rs1165072081
1   47159   rs540662756 T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0031:0.0335:0.0328734:10808:rs540662756
1   49298   rs10399793  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0144:0.0155:0.449527:11040:rs10399793
1   49554   rs539322794 A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0241:0.0284:0.403403:10808:rs539322794
1   51479   rs116400033 T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0224:0.0173:0.706859:10808:rs116400033
1   54490   rs141149254 G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0196:0.0202:0.480829:10808:rs141149254
1   54676   rs2462492   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0047:0.0157:0.117646:11040:rs2462492
1   54712   rs573184866 TTTTC   T   .   PASS    .   ES:SE:LP:SS:ID  0.0028:0.0139:0.0761345:10808:rs573184866
1   54716   rs1166278911    C   T   .   PASS    .   ES:SE:LP:SS:ID  0:0.015:0.000130308:10808:rs1166278911
1   55545   rs28396308  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0098:0.0159:0.270835:10808:rs28396308
1   58814   rs114420996 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0184:0.0288:0.281664:10808:rs114420996
1   59040   rs62637815  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0217:0.0297:0.331428:10808:rs62637815
1   60351   rs62637817  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0207:0.031:0.296967:10808:rs62637817
1   62777   rs3844233   A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0168:0.015:0.578232:10808:rs3844233
1   63268   rs28664618  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0122:0.0148:0.386898:10808:rs28664618
1   63671   rs80011619  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0058:0.0199:0.113961:10808:rs80011619
1   63735   rs61158452  C   CCTA    .   PASS    .   ES:SE:LP:SS:ID  -0.0099:0.0146:0.301117:10808:rs61158452
1   64931   rs62639104  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.021:0.0313:0.298432:10808:rs62639104
1   68082   rs367789441 T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0173:0.031:0.238598:10808:rs367789441
1   69428   rs140739101 T   G   .   PASS    .   ES:SE:LP:SS:ID  0.0874:0.0463:1.23062:10808:rs140739101
1   69761   rs200505207 A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0377:0.03:0.681102:10808:rs200505207
1   69897   rs200676709 T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.018:0.0155:0.61048:10808:rs200676709
1   74790   rs13328700  C   G   .   PASS    .   ES:SE:LP:SS:ID  0.0097:0.0437:0.0842837:10808:rs13328700
1   74792   rs1335672253    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0097:0.0437:0.0842837:10808:rs1335672253
1   76838   rs563953605 T   G   .   PASS    .   ES:SE:LP:SS:ID  0.0248:0.0323:0.353596:10808:rs563953605
1   76854   rs367666799 A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0119:0.0295:0.162917:10808:rs367666799
1   77866   rs563593912 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0253:0.0323:0.36191:10808:rs563593912
1   77874   rs62641297  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0253:0.0323:0.36191:10808:rs62641297
1   81260   rs571136476 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0028:0.0488:0.0201786:10808:rs571136476
1   81587   rs536406113 C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0209:0.0335:0.27368:10808:rs536406113
1   82163   rs139113303 G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0017:0.0295:0.020816:10808:rs139113303
1   82609   rs149189449 C   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0017:0.0295:0.020816:10808:rs149189449
1   83514   rs201754587 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0023:0.0151:0.0573472:10808:rs201754587

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Summary {data-width=650}

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bcf preview