# Multi-trait analysis informs genetic disease studies

Date:

I had a wonderful opportunity to give a virtual oral presentation at Informatics in Biology, Medicine, and Pharmacology conference, 2020. I talked about joint analysis of multiple traits in genetic disease studies using DeGAs and multi-PRS as example projects.

Update My talk received the Best Oral Presentation Award! Thank you for organizers and the folks attended to my talk.

2020年日本バイオインフォマティクス学会年会・第9回生命医薬情報学連合学会で発表の機会をいただきました。このページに，スライドを公開します。

(2020/9/3 追記) まことに光栄なことに，最優秀口頭発表賞に選ばれました。今後の励みとなり，とてもありがたいです。学会の組織委員のみなさま，運営のみなさま，また発表に参加してくださった聴衆のみなさまに感謝します。

## 発表要旨 (Abstract)

Population-based genotyped cohorts linked with dense phenotypic information provide opportunities for efficient identification of therapeutic targets with expanded knowledge of the genetic basis of disease. Analysis of such a cohort of hundreds of thousands of individuals with millions of genetic variants and thousands of phenotypic measurements, some of which may share the genetic basis, motivates the development of computationally efficient methods.

We developed methods for multi-trait analysis and demonstrated their utilities in genetic disease studies. In DeGAs (Tanigawa et al. 2019), we showed the utilities of latent components of genetic associations for interpreting the genetic basis of diseases and informing the downstream experiments. The functional roles of the identified genes were experimentally validated in adipocyte models. In multi-PRS and its applications to 35 biomarker traits (Sinnott-Armstrong et al. 2019), we showed the advantages of using the genetic basis influencing biomarker levels for the improved prediction of disease risks. Together, those results highlight the benefits of comprehensive phenotyping in populations and joint analysis of multiple traits.

## 文献リスト (Reference)

1. DeGAs: Y. Tanigawa*, J. Li*, et al., Nat Commun. 10, 1-14 (2019)
2. multi-PRS / Biomarkers: N. Sinnott-Armstrong*, Y. Tanigawa*, et al.,
3. Polygenic risk score with BASIL/snpnet: J. Qian, Y. Tanigawa, et al., bioRxiv, 630079 (2019)
4. Global Biobank Engine