Leonardo Mariño-Ramirez, Ph.D.
Stadtman Tenure-Track Investigator
Laboratory of Genetic Epidemiology for Health Disparities
Dr. Leonardo Mariño-Ramirez staff profile | Lab members
Scientific Expertise
Biobanks, genetic ancestry, gene by environmental interactions, global genomics
Research and Programmatic Interests
Gene by Environmental Interactions
The research program aims to characterize how genetic and environmental risk factors interact to influence health disparities.
It is widely known that health outcomes are caused by genetics, the environment, and their interactions. Nevertheless, health disparities research often considers genetic and socioenvironmental determinants of health separately. The lab aims to consider the joint effects of genomics and place-based socioenvironmental determinants of health, with an emphasis on the role of gene-environment interactions.
The approach is to use genetic ancestry inference, together with population biobank data and causal inference, to decompose the genetic and socioenvironmental contributions to health disparities that disproportionately affect racial and ethnic minority groups in cosmopolitan countries of the global north.
Most of the work to date has been focused on the United Kingdom Biobank, and we have recently expanded our focus to include the NIH All of Us research project, which covers the U.S. population. It is focused on the study of complex, common diseases that have a high overall burden of morbidity and mortality along with disparate impacts among racial and ethnic groups, with a current focus on cardiometabolic disease and cancer.
Genetic Ancestry
Ancestry refers to the geographic origins of a person’s ancestors, and ancestry impacts health (disparities) in different ways. Ancestry can be defined socially, and it can be inferred genetically.
Socially defined ancestry (i.e. race and ethnicity) affects health via individuals’ social environment and their lived experiences, including socioeconomic deprivation, access to health care or lack thereof, and other factors.
Genetically inferred ancestry, as opposed to the social constructs of race and ethnicity, is a characteristic of the genome. Genetic ancestry affords several advantages for health disparities research: it can be inferred objectively and with precision, independently of the social dimensions of race and ethnicity, at different levels of evolutionary relatedness (continental and subcontinental), and at different levels of resolution (genome-wide or haplotype-specific) as a continuous variable.
A major aim of this lab is to use genetic ancestry inference, together with population biobank data, to decompose the genetic and socioenvironmental contributions to health disparities. However, current methods for genetic ancestry inference are slow and do not scale to biobank size datasets. The lab is currently developing fast and efficient genetic ancestry inference methods that scale to biobank size datasets to address this challenge.
Global Genomics
The “genomics research gap” refers to the issue in genomic studies where the majority of study participants have European ancestry. Genome-based medicine is at the heart of a revolution in medical care, and therefore enriching our genomics datasets has the potential to exacerbate health disparities. There is broad agreement on the need for genomic studies to be more representative of all populations so that people everywhere can benefit from improved health outcomes.
Three things are needed to close the genomics research gap:
- More data from global populations; admixed populations are understudied.
- Better research methods so that insights from current studies can be applied across populations (ancestries).
- Local capacity must be developed so that genomic approaches to health care can be implemented worldwide.
The lab is collaborating with colleagues from Colombia to develop CÓDIGO, Consorcio para la Diversidad Genómica, Ancestría y Salud en Colombia to address these pressing needs. The global aims of CÓDIGO are to enrich human genomic datasets in support of global precision medicine, with an emphasis on admixed genomes. The local aims are to support research on genomics, bioinformatics, and precision medicine in Colombia, building bridges between Colombia, Latin America, and the United States.
CÓDIGO is being developed as a comprehensive platform for analysis of Colombian genomes—which will provide data on sequence variants and clinical annotations—nation-wide and population-specific allele frequencies, and patterns of genetic ancestry and admixture.
Under the CÓDIGO model, individual investigators/laboratories share their genomic data with the CÓDIGO development team, with contributing investigators maintaining ownership and control of their data. Only summary statistics derived from the data are released to the public; no individual genomic data or meta data are released. Finally, all contributing investigators collaborate on and receive credit for the platform and any publications that arise from it. CÓDIGO has genomic variant data for close to 2,000 participants from 16 populations, with various proportions of African, European, and Native American ancestry.
CÓDIGO has genomic variant data for close to 2,000 participants from 16 populations, with various proportions of African, European, and Native American ancestry.