Manuel A. Rivas

Graduate Research Prize Winner 2016

In 2008, After completing my undergraduate degree in Mathematics at MIT, I began a staff scientist position at the Broad Institute under the guidance of Mark Daly and David Altshuler focused on making sense of data generated from emerging sequencing technologies. During my tenure at the Broad I learned that I was passionate about developing statistical techniques and computational tools and using them to analyze data and making impactful scientific discoveries. At that time I had a strong desire to continue research in human genetics and to obtain a graduate degree in the field. Fortunately, I was given an opportunity at Oxford University to fulfill my dreams. Here, I joined the labs of Prof. Peter Donnelly and Prof. Mark McCarthy to study the medical relevance and functional consequences of protein truncating variants.

The main motivation for focusing on protein truncating variants (PTVs) during my DPhil studies was the possibility of Identifying PTVs that conferred protection to disease (beneficial). Identification of protective PTVs have strong translational promise as they provide examples of genes where chemical inhibition may likely be safe and effective (e.g. PCSK9). However, even in this simple setting (studying protein truncating variants) many challenges arise. My research focused on tackling these challenges, proposing alternate study designs, developing statistical methods for assessing association, and developing approaches for interpreting the transcriptional consequences of these DNA sequence variants.

During my graduate studies at Oxford I also developed software for the analysis and interpretation of genomic sequencing data, including:

• MAMBA - (main developer) a program for analyzing DNA and RNA sequencing data, enabling tissue-specific isoform variant annotation, characterizing power for alternate study designs, and incorporation of statistical methods for cross-phenotype and cross-disorder rare variant association studies from population biobank sample collections; and
• PLINK/SEQ - (core developer) a toolset for working with human genetic variation data.

After graduating I returned to the Broad where I led an effort to analyze massive amounts of exome, RNA, and microbiome sequencing data sets to identify the genetic and environmental factors contributing to Inflammatory Bowel Disease (IBD) predisposition. We launched projects in the following areas: 1) IBD in unique populations; 2) very early-onset IBD; 3) adverse response to IBD medications; and 4) genetics to stratify clinical phenotypes.

I recently joined the Department of Biomedical Data Science at Stanford University as a tenure-track Assistant Professor. I will be leading a group focused on developing statistical methods and computational tools to analyze massive human genetic datasets to address fundamental questions in medicine and biology. Our current research concentrates on four themes: 1) generating effective therapeutic hypotheses from human genetic data; 2) developing technologies for integrated learning healthcare systems; 3) inferring the global distribution of common and rare disease predisposition genes; and 4) developing statistical learning models and optimization algorithms.

Selected publications of research conducted at Oxford University

Fuchsberger, C., Flannick, J., Teslovich, T.M., Mahajan, A., Agarwala, V., Gaulton, K.J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D.J., Rivas, M.A., et al. The genetic architecture of type 2 diabetes. Nature 2016.

Rivas, M.A. ,et al. Effect of predicted protein-truncating genetic variants on the human transcrip-tome. Science 2015

Pirinen, M., ..., Rivas, M.A. Assessing allele specific expression across multiple tissues from RNA-seq read data. Bioinformatics 2015

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 2015.

Clarke, G., Rivas M.A. and Morris, A. A flexible approach for the analysis of rare variants allowingfor a mixture of effects on either binary or quantitative traits. PLoS Genetics 2013