Development of the combinatorial analysis methods to understand missing heritability
| Project/Area Number |
15H01717
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Life / Health / Medical informatics
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| Research Institution | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
Sese Jun 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 招聘研究員 (40361539)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥43,290,000 (Direct Cost: ¥33,300,000、Indirect Cost: ¥9,990,000)
Fiscal Year 2018: ¥10,530,000 (Direct Cost: ¥8,100,000、Indirect Cost: ¥2,430,000)
Fiscal Year 2017: ¥10,530,000 (Direct Cost: ¥8,100,000、Indirect Cost: ¥2,430,000)
Fiscal Year 2016: ¥10,530,000 (Direct Cost: ¥8,100,000、Indirect Cost: ¥2,430,000)
Fiscal Year 2015: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
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| Keywords | 統計的有意性 / 組み合わせ / GWAS / ミッシングヘリタビリティ / 多重検定 / 相乗効果 / 生存解析 / 変異 / ゲノム / 構造変異 |
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| Outline of Final Research Achievements |
We developed softwares to understand the reason why missing heritability appears in GWAS analysis by extending Limitless-Arity Muliple-testing Procedure (LAMP). We performed four researches. 1. We developed LAMPLINK, in which LAMP is integrated into PLINK, widely used GWAS analysis software. 2. We developed HWY, in which we utilized General Purpose GPU to accelerate GWAS statistical calculations with permutation test procedure for comprehensive calculation of the statistics of pairs of SNPs efficiently. 3. We collaborated with the two largest cohorts in Japan, Biobank Japan and Tohoku Medical Megabank, and we applied LAMP to the datasets. 4. We extend LAMP to handle survival curves used in long-term cohort. It also applicable to somatic mutation analysis in cancer cells. Integrating these tools allows us to understand the keys of missing heritability.
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| Academic Significance and Societal Importance of the Research Achievements |
遺伝性疾患の原因因子解析において、通常は単一変異と疾患の関係が調査されるが、本研究で開発したツール群を利用することで、複数の変異が同時に起こった場合に発症してしまう疾患を統計的有意性を持って特定することが可能となった。これにより、今までに比べてより多くの疾患が遺伝的に関連していることを見つけることができる可能性があるだけでなく、投薬等において、今までは副作用の起こる人と起こらない人がいる要因がわからない場合においても、遺伝的変異を用いて説明できる可能性がある。今まではあまり考えられてこなかった「組み合わせ」因子を考える重要性を本研究で示し、ツール群を整えた。
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Report
(5 results)
Research Products
(10 results)
