Azam-Mandana Yazdani, PhD

Pronouns

She/Her/Hers

Rank

Research Fellow

Institution

BWH

BWH-MGH Title

Research Scientist

Department

Anesthesia

Authors

Azam Yazdani, Jochen D. Muehlschlegel

Systematic analysis of large-scale multi-omics augmented by genetics to infer causal pathways underlying the progression to cardiovascular diseases

WMSS 2022

While going to elementary school, my family was divided due to the 20th-Century-longest-conventional-warfare. Now, I work at the interface of important areas: causality, statistics, computer-science, and biology. It is difficult to be trained in theory and “break into” the translational biomedical enterprise. Very few scholars can conduct research on causality while causality is the very first step in disease risk prediction. My multi-omics-systematic-integration algorithm received the innovation award at the University of Pennsylvania. My novel findings are validated clinically and promoted efficacious treatment. My life shows that women are strong and capable to overcome difficulties in life and science.

Background

Due to recent technological advances, big data acquisition of molecular entities has been realized, and one of the biggest challenges for elucidating disease mechanisms is to understand the topology and dynamics of relationships between entities. Therefore, advanced analytic methods are desperately needed to systematically integrate these data and bridge data analysis to the mechanistic understanding of diseases.

Methods

We applied state-of-the-art integrative systems biology methods to combine multidimensional OMICs data to reveal underlying biological networks established in the principles of Mendelian Randomization.

Results

Using machine learning algorithms, we explored the networks and evaluated the conjoint contributions of genetic variants across the genome, and variation in the circulating transcriptomics and metabolomics to interindividual variation in cardiovascular disease traits and endpoints.

Identification of biological networks facilitated understanding of OMICs system and identifying targets for “intervention” and “prediction”, elucidating gene networks, pathways, and disease modules impacting health and cardiovascular disease.

Conclusions

What we learn from the systematic analysis of OMICs is critical for the development and application of new drugs that target risk factors of diseases. It provides a complete context to interpret the findings and therefore, provides insights into complex processes and advances the understanding of the molecular etiology of diseases.