Investigating mobile element variations by statistical genetics
Abstract The integration of structural variations (SVs) in statistical genetics provides an opportunity to understand the genetic factors influencing complex human traits and disease. Recent advances in long-read technology and variant calling methods for short reads have improved the accurate disco...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2024-05-01
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| Series: | Human Genome Variation |
| Online Access: | https://doi.org/10.1038/s41439-024-00280-1 |
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| _version_ | 1832594877550952448 |
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| author | Shohei Kojima |
| author_facet | Shohei Kojima |
| author_sort | Shohei Kojima |
| collection | DOAJ |
| description | Abstract The integration of structural variations (SVs) in statistical genetics provides an opportunity to understand the genetic factors influencing complex human traits and disease. Recent advances in long-read technology and variant calling methods for short reads have improved the accurate discovery and genotyping of SVs, enabling their use in expression quantitative trait loci (eQTL) analysis and genome-wide association studies (GWAS). Mobile elements are DNA sequences that insert themselves into various genome locations. Insertional polymorphisms of mobile elements between humans, called mobile element variations (MEVs), contribute to approximately 25% of human SVs. We recently developed a variant caller that can accurately identify and genotype MEVs from biobank-scale short-read whole-genome sequencing (WGS) datasets and integrate them into statistical genetics. The use of MEVs in eQTL analysis and GWAS has a minimal impact on the discovery of genome loci associated with gene expression and disease; most disease-associated haplotypes can be identified by single nucleotide variations (SNVs). On the other hand, it helps make hypotheses about causal variants or effector variants. Focusing on MEVs, we identified multiple MEVs that contribute to differential gene expression and one of them is a potential cause of skin disease, emphasizing the importance of the integration of MEVs in medical genetics. Here, I will provide an overview of MEVs, MEV calling from WGS, and the integration of MEVs in statistical genetics. Finally, I will discuss the unanswered questions about MEVs, such as rare variants. |
| format | Article |
| id | doaj-art-034b307ec6d748528b0848a1928ef757 |
| institution | Kabale University |
| issn | 2054-345X |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Human Genome Variation |
| spelling | doaj-art-034b307ec6d748528b0848a1928ef7572025-01-19T12:15:50ZengNature Publishing GroupHuman Genome Variation2054-345X2024-05-011111610.1038/s41439-024-00280-1Investigating mobile element variations by statistical geneticsShohei Kojima0Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Center for Integrative Medical SciencesAbstract The integration of structural variations (SVs) in statistical genetics provides an opportunity to understand the genetic factors influencing complex human traits and disease. Recent advances in long-read technology and variant calling methods for short reads have improved the accurate discovery and genotyping of SVs, enabling their use in expression quantitative trait loci (eQTL) analysis and genome-wide association studies (GWAS). Mobile elements are DNA sequences that insert themselves into various genome locations. Insertional polymorphisms of mobile elements between humans, called mobile element variations (MEVs), contribute to approximately 25% of human SVs. We recently developed a variant caller that can accurately identify and genotype MEVs from biobank-scale short-read whole-genome sequencing (WGS) datasets and integrate them into statistical genetics. The use of MEVs in eQTL analysis and GWAS has a minimal impact on the discovery of genome loci associated with gene expression and disease; most disease-associated haplotypes can be identified by single nucleotide variations (SNVs). On the other hand, it helps make hypotheses about causal variants or effector variants. Focusing on MEVs, we identified multiple MEVs that contribute to differential gene expression and one of them is a potential cause of skin disease, emphasizing the importance of the integration of MEVs in medical genetics. Here, I will provide an overview of MEVs, MEV calling from WGS, and the integration of MEVs in statistical genetics. Finally, I will discuss the unanswered questions about MEVs, such as rare variants.https://doi.org/10.1038/s41439-024-00280-1 |
| spellingShingle | Shohei Kojima Investigating mobile element variations by statistical genetics Human Genome Variation |
| title | Investigating mobile element variations by statistical genetics |
| title_full | Investigating mobile element variations by statistical genetics |
| title_fullStr | Investigating mobile element variations by statistical genetics |
| title_full_unstemmed | Investigating mobile element variations by statistical genetics |
| title_short | Investigating mobile element variations by statistical genetics |
| title_sort | investigating mobile element variations by statistical genetics |
| url | https://doi.org/10.1038/s41439-024-00280-1 |
| work_keys_str_mv | AT shoheikojima investigatingmobileelementvariationsbystatisticalgenetics |