Page 10 - MDC Abstract Book & Guide

Serena Sanna IRGB-CNR Institute
Serena Sanna is a statistical geneticist currently working at the IRGB-CNR Institute in Italy. She obtained a master degree in Mathematics at the University of Cagliari (Italy) and a PhD in Genetics at the University of Groningen (Netherlands). Her research focuses on implementing and applying statistical methods to large-scale population data to identify genetic factors that influence human phenotypes. Coupling cutting-edge technologies and methods with a special population, the Sardinians, she identified key molecular mechanisms underlying hundreds of complex traits. She recently applied methods from the field of statistical genetics to identify causal relationships between the gut microbiome and metabolic traits.
Presentation Title
Causal Relationships among the Gut Microbiome, Short-chain Fatty Acids and Metabolic Diseases
Abstract
Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity. However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available, then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using a state-of-the-art statistical genetics method called Mendelian Randomisation (MR), we provided evidence of a causal effect of specific gut microbiome components on metabolic traits. During my presentation, I will explain the rational and framework of this method and the results we obtained. Finally, I will highlight the power and limitations for the use of MR as a means to elucidate causal relationships from microbiome-wide association findings in the future.
Jean M Macklaim DNA Genotek
Jean Macklaim is a Bioinformatics Scientist at DNA Genotek, where she is developing and implementing bioinformatics and analytics approaches to sequence data. These R&D efforts are focused on deriving consistent, stable, and meaningful results from metagenomic, metatranscriptomic, and other omics data from a variety of human sample types including stool, oral, and urogenital. She completed her PhD at Western University, Canada with a research focus on computational biology approaches for understanding microbiome function and composition. Her postdoctoral work contributed to developing compositional data analysis tools and methods for differential analysis in metatranscriptomic and high-throughput sequencing data for a number of human health, agriculture, in vitro, and environmental applications.
Presentation Title
Compositional Data Analysis Approaches to Improve Microbiome Studies - From Collection to Conclusions