Our primary research interests focus on the identification of genes and genetic loci contributing to the risk of obesity and related metabolic traits.
We have been involved in gene - discovery since 2005, when ‘genome - wide association’ was introduced and have since actively contributed to many consortia that use this approach to identify genetic loci for a large number of metabolic traits.
By identifying genes that influence the risk of obesity and related metabolic traits, we aim to provide new insights in the mechanisms that regulate body weight and risk of metabolic disease. Eventually, such biological insights might results in a better tailored treatment or prevention.
Increasingly, our gene - discovery work also focuses on the identification of low - frequency variants through the implementation exome - chip genotyping and sequencing projects, not only in individuals of white European descent, but also in those of African and Hispanic decent.
We have established consortia to study the genetics of body fat percentage, circulating leptin levels, physical activity and heart rate and continue to invited studies for participation to study common and low-frequency variants.
We are actively involved in the GIANT (Genetic Investigation of ANthropometric Traits) Consortium, lead by Prof. Joel Hirschhorn, with whom we have discovered more than 90 common variants associated with body mass index (BMI; assesses overall body sizes), over 40 common variants for waist-to-hip ratio (WHR; assesses body fat distribution), and hundreds for height. The GIANT Consortium is currently inviting studies to participate in their ExomeChip efforts.
Even though many complex diseases have a substantial heritability, changes in our environment have fueled the increasing prevalence of conditions such as obesity, type 2 diabetes, hypertension, hyperlipidemia, and various cancers over the past three decades. Yet, not everyone responds in a similar way to the same environment; response to the environment depends at least in part on a person’s genetic susceptibility.
We aim to examine how a lifestyle influences people’s genetic susceptibility to obesity and related diseases. We study these interactions between genes and lifestyle through following up on established diseases-associated genetic loci, as well as through screening the whole genome. Lifestyle factors studies include physical activity, smoking and diet.
In recent studies, we have shown that physical activity can reduce one’s genetic susceptibility to obesity, assessed based on 12 BMI-associated loci, by up to 30%.
Differences in disease burden between ancestrally diverse populations are a major cause of health disparities in the United States, generally affecting racial and ethnic minorities the most. While lifestyle, cultural values, health care access and socioeconomic status are undeniably important contributors to the disproportionate disease burden, it has been suggested that genetic variation between ancestrally diverse populations contributes to differences in disease susceptibility.
Through our participation in the The PAGE (Population Architecture using Genomics and Epidemiology) II Consortium, we aim to examine disease-associated loci across ancestries and assess their generalizability or ancestry-specificity. By taking advantage of the differential genetic architecture, we aim to fine-map loci to the causal genetic variant.
We use data from the Mount Sinai BioMe Biobank and focus on cardiometabolic diseases, including type 2 diabetes, cardiovascular disease, chronic kidney disease, obesity, blood lipids, amongst others. The BioMe Biobank, a broadly-consented electronic medical records (EMR) linked population resource, represents the ancestral diversity of the local Upper Manhattan communities, with 25% of African Ancestry, 30% of European Ancestry, 36% of Hispanic/Latino Ancestry, and 9% of other ancestry.
By understanding these genetic factors that contribute to population’s disease susceptibility, health disparities can be reduced by tailoring prevention and personalizing treatment for specific subgroups.