Detection of trait-associated structural variations using short-read sequencing.
Detection of trait-associated structural variations using short-read sequencing.
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DOI：
10.1016/j.xgen.2023.100328
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发表时间：
2023-06-14
期刊：
影响因子：
--
通讯作者：
Terao, Chikashi
中科院分区：
文献类型：
--
作者：
Kosugi, Shunichi;Kamatani, Yoichiro;Harada, Katsutoshi;Tomizuka, Kohei;Momozawa, Yukihide;Morisaki, Takayuki;Terao, Chikashi
基因组结构变异（SV）影响着不同生物的遗传和表型特征，但缺乏可靠的方法来检测SV阻碍了遗传分析。我们开发了一种计算算法（MOPline），其包括与高置信度SV调用选择相结合的缺失调用恢复和使用短读全基因组测序（WGS）数据的基因分型。使用3，672个高覆盖率的WGS数据集，MOPline稳定地检测到每个人16，000个SV，比以前的大规模项目高出1.7-3.3倍，同时表现出相当水平的统计质量指标。我们估算了181，622名日本人的42种疾病和60种数量性状的SV。全基因组关联研究与插补的SV揭示了41个排名靠前或接近排名靠前的全基因组显著SV，包括8个外显子SV与5个新的关联和丰富的移动的元件插入。这项研究表明，短读WGS数据可用于识别与各种性状相关的罕见和常见SV。开发MOPline以有效地从短读WGS数据中检测SV MOPline从3，258个BBJ WGS数据集中检测到每个个体16，122个SV构建BBJ SV面板以使用估算的SV估算181，622个日本个体GWAS中的SV，鉴定了41个与许多性状相关的排名最高的SV。MOPline是一种结构变异（SV）检测工具。MOPline具有灵活性和可扩展性，通过结合可靠的SV调用选择和缺失调用恢复算法，可以准确灵敏地从短读全基因组测序（WGS）数据中检测SV。随后使用181，622个单独的DNA微阵列数据集对MOPline在3，258个WGS数据集中检测到的SV进行插补。42种疾病和60个数量性状的GWAS与插补的SV确定了众多复杂性状的顶级SV-性状关联。
Genomic structural variation (SV) affects genetic and phenotypic characteristics in diverse organisms, but the lack of reliable methods to detect SV has hindered genetic analysis. We developed a computational algorithm (MOPline) that includes missing call recovery combined with high-confidence SV call selection and genotyping using short-read whole-genome sequencing (WGS) data. Using 3,672 high-coverage WGS datasets, MOPline stably detected ∼16,000 SVs per individual, which is over ∼1.7–3.3-fold higher than previous large-scale projects while exhibiting a comparable level of statistical quality metrics. We imputed SVs from 181,622 Japanese individuals for 42 diseases and 60 quantitative traits. A genome-wide association study with the imputed SVs revealed 41 top-ranked or nearly top-ranked genome-wide significant SVs, including 8 exonic SVs with 5 novel associations and enriched mobile element insertions. This study demonstrates that short-read WGS data can be used to identify rare and common SVs associated with a variety of traits. Development of MOPline to efficiently detect SVs from short-read WGS data MOPline detected 16,122 SVs per individual from 3,258 BBJ WGS datasets The BBJ SV panels were constructed to impute SVs in 181,622 Japanese individuals GWASs using the imputed SVs identified 41 top-ranked SVs associated with many traits Kosugi et al. have developed MOPline, a structural variation (SV) detection tool. MOPline is flexible and scalable to accurately and sensitively detect SVs from short-read whole-genome sequencing (WGS) data by combining reliable SV call selection and missing call recovery algorithms. SVs detected by MOPline in 3,258 WGS datasets were subsequently imputed using 181,622 individual DNA microarray datasets. GWASs for 42 diseases and 60 quantitative traits with the imputed SVs identified top-ranked SV-trait associations for numerous complex traits.
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