Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes.

MotivationThere is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues.ResultsWe introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses.Availability and implementationSource code is at https://github.com/datduong/RECOV [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online

ゲノムワイド関連解析を用いた日本人女性の皮膚形質に関わる新規15遺伝子領域の同定

博士(医学) 乙第3020号(主論文の要旨、要約、審査結果の要旨、本文)，著者名:Chihiro ENDO・Todd A. JOHNSON・Ryoko MORINO・Kazuyuki NAKAZONO・Shigeo KAMITSUJI・Masanori AKITA・Maiko KAWAJIRI・Tatsuya YAMASAKI・Azusa KAMI・Yuria HOSHI・Asami TADA・Kenichi ISHIKAWA・Maaya HINE・Miki KOBAYASHI・Nami KURUME・Yuichiro TSUNEMI・Naoyuki KAMATANI・Makoto KAWASHIMA, タイトル:Genome-wide association study in Japanese females identifies fifteen novel skin-related trait associations，掲載誌:Scientific reports.(2045-2322)，巻・頁・年:8巻 1号 p.8974(2018)，著作権関連情報:© The Author(s) 2018，DOI:10.1038/s41598-018-27145-2.博士(医学)東京女子医科大

Context matters:the power of single-cell analyses in identifying context-dependent effects on gene expression in blood immune cells

The human immune system is a complex system that we still do not fully understand. No two humans react in the same way to attacks by bacteria, viruses or fungi. Factors such as genetics, the type of pathogen or previous exposure to the pathogen may explain this diversity in response. Single-cell RNA sequencing (scRNA-seq) is a new technique that enables us to study the gene expression of each cell individually, allowing us to study immune diversity in much greater detail. This increased resolution helps us discern how disease-associated genetic variants actually contribute to disease. In this thesis, I studied the relation between disease-associated genetic variants and gene expression levels in the context of different cell types and pathogen exposures in order to gain insight into the working mechanisms of these variants. For many variants we learnt in which cell types and under which pathogen exposures they affect gene expression, and we were even able to identify changes in gene co-expression, suggesting that disease-associated variants change how our genes interact with each other. With the single-cell field being so new, much of my work was showing the feasibility of using scRNA-seq to study the interplay between genetics and gene expression. To set up future research, we created guidelines for these analyses and established a consortium that brings together many major scientists in the field to enable large-scale studies across an even wider variety of contexts. This final work helps inform current and future large-scale scRNA-seq research