Marta Guindo Martínez - Postdoctoral Fellowship 2021

Project summary:
The role of non-additive models in obesity and cardiovascular comorbidities.

In this proposed study, we aim to optimize the predictive accuracy of Polygenic Risk Scores (PRSs) by developing a PRS that includes the best fitting model of inheritance for each genetic variant. We subsequently aim to identify subsets of variants that cluster based on similar association signatures [1] to facilitate the biological interpretation and [2] to improve the prediction of people at risk of obesity and related cardiovascular disease (CVD) outcomes.

Project Title

Patient risk stratification for obesity to disentangle its link to cardiovascular disease

Background

Obesity is an important risk factor for CVD, and 40-70% of the variation in body mass index (BMI) is explained by genetic factors. PRSs are used to assess an individual’s genetic predisposition to obesity. However, PRSs are based on Genome-wide association studies (GWAS) results that, in general, assume an additive model of inheritance for all genetic variants (i.e., per variant, each additional risk allele increases risk proportionally), and ignore other possibly better-fitting models, such as the recessive model (i.e., per variant, two risk alleles are needed to increase disease risk).

Summary statistics from GWAS are increasingly used to examine whether there are subsets of variants that represent different biological mechanisms that can explain the heterogeneity among individuals with obesity with similar BMI but different CVD risk.

Applying better-fitting models of inheritance and subsequently identifying subsets of variants may help to elucidate some of the deeper biological pathways that remain uncovered when all variants are considered together.

Aim

The aim is to address current knowledge gaps in the field by determining the best fitting model of inheritance per genetic variant and identifying subsets of variants that cluster based on similar association signature, possibly pointing to distinct biological pathways.

Methods

  • Genome-wide association studies under the additive and the recessive models of inheritance.
  • Build a PRS based on the best fitting model per variant
  • Clustering analyses to identify subgroups of genetic variants
  • Test causality with CVD endpoints using Mendelian Randomization

Marta Guindo Martínez

  • MSc & PhD
  • Novo Nordisk Foundation Centre for Basic Metabolic Research
  • University of Copenhagen, Human Genomics and Metagenomics in Metabolism

Mentors:

Ruth J. F. Loos, PhD, Professor in Precision Health and Metabolism, Human Genomics and Metagenomics in Metabolism, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York

Partners:

  • Paul O'Reilly, PhD, Associate Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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