WhichTF is dominant in your open chromatin data?

Tue, 30 Aug 2022 00:00:00 +0000

We develop an ontology-guided approach to ranking tissue-/cell-type-specific transcription factors (TFs) from chromatin accessibility data.

A cross-population atlas of genetic associations for 220 human phenotypes

Thu, 30 Sep 2021 00:00:00 +0000

Using a set of GWAS summary statistics of diseases characterized from both European (UK Biobank and FinnGen) and East Asian (Biobank Japan) populations, we dissected latent DeGAs components of multi-ethnic association summary statistics.

Polygenic risk modeling with latent trait-related genetic components

Mon, 08 Feb 2021 00:00:00 +0000

Polygenic risk score (PRS) has been proposed for disease risk prediction with potential clinical relevance for some traits, but its personalized interpretation is generally difficult, especially when there exist disease subtypes driven by different genetic components.

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

Tue, 31 Dec 2019 00:00:00 +0000

While many pleiotropic genetic loci have been identified, how they contribute to phenotypes across traits and diseases is unclear. We developed DeGAs to address this issue.

When analyzing the genetics of complex traits, extreme polygenicity and pervasive pleiotropy are challenges in the interpretation and translational application of genetic findings.

To address this challenge, we propose to introduce latent components of genetic associations.

In DeGAs (Decomposition of Genetic Associations), we identify latent components of genetic association by applying truncated singular-value decomposition (TSVD) on a matrix consisting of genome-wide association summary statistics computed for thousands of phenotypes. Using those components and our quantitative scores, we represent the genetics of a disease as a mixture of different components. This provides a more interpretable view of disease genetics.

When applied to 2000+ phenotypes in UK Biobank, we found that a related set of phenotypes and variants are captured in DeGAs latent space. For example, standing and sitting heights are in the same direction, even though we applied DeGAs to association summary statistics.

When we look at the top two DeGAs components for body mass index (BMI), the top one (PC2) is mainly driven by fat-related traits, whereas the second most important one (PC1) is mainly driven by fat-free traits, providing an enhanced interpretation of the genetics of BMI.

To prioritize genes for experiments, we applied DeGAs to a subset of the dataset consisting of protein-truncating variants and identified PDE3B and GPR151 as the top two candidates for obesity. Our siRNA knockdown of Gpr151 showed a dramatic decrease in lipids in adipocytes!

Some extensions of DeGAs

In the Rivas lab, we have several projects that extend the work presented in DeGAs.

: We propose dPRS, a method to enhance the interpretability of polygenic risk score (PRS) using DeGAs latent components.
: In DeGAs, we took the summary statistics from univariate association scan across genetic variants and phenotypes. We propose a method to directly fit multi-response sparse regression models.

We provide a resource for the research community. We developed as a part of , whose video tutorial is shown above.

The datasets used in the study are available from figshare.

Y. Tanigawa, and M. A. Rivas, Decomposed matrices used for the analysis described in ‘Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology’. (2019).

SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs

Thu, 01 Nov 2018 00:00:00 +0000

We propose SNPs2ChIP, a method to infer biological functions of non-coding variants through unsupervised statistical learning methods applied to publicly-available epigenetic datasets.

Latent Components | Tanigawa Lab

WhichTF is dominant in your open chromatin data?

A cross-population atlas of genetic associations for 220 human phenotypes

Polygenic risk modeling with latent trait-related genetic components

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs