The vast majority of participants in genetic studies are of European descent, limiting the generalizability of genetic technologies across globally diverse populations. While the fundamental biology discovered by genetic studies at specific sites in the genome are mostly shared across humanity, Eurocentric study biases mean we miss low-hanging fruit in other populations and limit disease risk prediction accuracy from polygenic scores in globally diverse populations. We currently predict heritable complex diseases risk several-fold more accurately in European ancestry populations than in genetically diverged populations. As genomic studies increase in scale, they explain more heritable variation and hold greater potential for clinical translation, but their implementation today would exacerbate health disparities. To ameliorate these issues while accelerating genomic discoveries, we are increasing the scale and diversity of genomic studies (e.g., Pan-UK Biobank Project, Global Biobank Meta-analysis Initiative, and other studies). (Learn more)
Psychiatric disorders such as schizophrenia and bipolar disorder are highly heritable and account for a large proportion of the global burden of disease. Genetics therefore provides major opportunities in psychiatry, both for understanding disease etiology and as biomarkers. However, genetic findings in psychiatric genetic studies have largely come from European ancestry cohorts. Humans originated in Africa, so African populations are the most genetically diverse globally yet are vastly underrepresented in medical genetics research. To this end, we are developing public genomics resources of diverse African genomes related to the Genome Aggregation Database (gnomAD). We are also conducting population and statistical genetic analyses in the Neuropsychiatric Genetics in African Populations (NeuroGAP) study, which is enrolling and sequencing the genomes of >35,000 participants in a case/control study spanning Ethiopia, Kenya, South Africa, and Uganda. As part of an umbrella project, we also help coordinate the data analysis team in the Population Underrepresented in Mental illness Association Studies (PUMAS) project, which is building a sample bank and will be sequencing the genomes of 120,000 people to medium coverage across cases and controls across African and Latin American populations. In a parallel research capacity building effort, we also actively contribute to the Global Initiative for Neuropsychiatric Genetics Education in Research (GINGER) Program. (Learn more)
We are interested in using genetic data to look back through time to learn about how demographic history, including migration, admixture, and population size changes, shaped human history. Understanding human evolutionary history has implications for study design, interpreting and accounting for population stratification, learning about the genetic architecture of complex traits, and gaining insights into forces of natural selection. We are especially interested in understanding the genetic basis of traits that have been evolutionarily important throughout human history, such as skin pigmentation and height, and how they changed throughout the human diaspora. To this end, we have focused in several geographic areas, including on very recent admixture in the Americas, recent history for the bottlenecked Finnish population, and with a particular emphasis on deeper and more complex history in sub-Saharan Africa. (Learn more)
As a population and statistical geneticist, my research examines the role of human history in shaping global genetic and phenotypic diversity. Given vast Eurocentric study biases, my group quantifies the transferability of knowledge gained from large-scale genetic studies across globally diverse populations. I am particularly interested in ensuring that the translation of genetic technologies via polygenic risk does not exacerbate health disparities induced by these study biases. Towards this end, we are developing statistical methods, community resources, and research capacity for multi-ancestry studies.
I have a background in Medical Microbiology having completed my undergraduate and MSc degrees in this field. I slightly shifted fields to pursue a PhD in Human Genetics at the University of Cape Town (UCT). My PhD thesis investigated the genetic risk factors associated with schizophrenia in the South African Xhosa population. I am an alumni member of the Global Initiative of Neuropsychiatric Genetics Education and Research (GINGER) programme at Harvard T.H. Chan School of Public Health, and currently pursuing a Postdoctoral fellowship on the Neuropsychiatric Genetics in African Populations (NeuroGAP) project. When I am not doing research, I tutor mathematics to high school learners for a non-profit organization called Fun Learning for Youth (FLY). I also enjoy running on road and trail, hiking and visiting food markets in my downtime.
Ying is a postdoctoral research fellow in Dr. Alicia Martin’s lab. She did her PhD in statistical/quantitative genetics at the University of Queensland. During her PhD, she worked on the application of genomic prediction methods in diverse ancestry populations and development of such methods to improve transferability of polygenic scores in trans-ancestry predictions. She has a broad interest in research areas related to multi-ancestry studies and population genetics.
Kristin is a PhD student in the Biological and Biomedical Sciences (BBS) program at Harvard Medical School, co-advised by Alicia Martin and Mark Daly. She is broadly interested in developing approaches to study disease risk prediction in diverse populations, and in particular, leveraging biobank data to model genetic and non-genetic contributors to human disease. She grew up in Princeton, New Jersey, and completed her undergraduate degree in Integrative Biology at Harvard. Outside of the lab, she enjoys attending dance workshops, playing cello, and hunting for a New Jersey-quality bagel in Boston (spoiler: there are none).
Zan is an Computational Associate at the Broad Institute. They are interested in creating community tools which contribute to overall global health equity. Under the mentorship of Alicia Martin and Elizabeth Atkinson, they are working on running analyses and developing tools for datasets with diverse ancestry populations such as NeuroGAP-Psychosis, HGDP, and the 1000 Genomes Project. Zan graduated from Rensselaer Polytechnic Institute in 2019 with a BS in Bioinformatics and Molecular Biology. In their free time they enjoy painting, hiking, knitting, and playing games of all kinds.
Lindo is an Associate Computational Biologist in the Martin and Neale Labs at the Broad Institute. He is interested in the development of tools/software to analyse and visualise genomic data. Lindo completed his Honours degree in Bioinformatics in 2019 and his project was on assessing the sensitivity and accuracy of various variant callers in African populations. He also likes ice cream and sneaker shopping.
Mary is an Associate Computational Biologist at the Broad Institute. She is originally from Addis Ababa, Ethiopia. She completed her undergraduate study in Biology with a concentration in Mathematical Biology and Bioinformatics (Pre-Medical track) at Clark University in 2019 and graduated from Boston University in 2020 with an MS in Bioinformatics. She is currently working on running QC pipelines for the HGDP+1000 Genomes Project under the supervision of Alicia Martin. Additionally, she has been working with Tarjinder Singh, a postdoctoral fellow in the Analytic and Translational Genetics Unit, to develop a pipeline for calculating genome-wide sequencing coverage of Schizophrenia data. She plans to continue working on projects that allow her to apply her classroom knowledge/experiences while acquiring new ones. She is interested in the application of bioinformatics in medicine and hopes to eventually go back and work in her home country. Outside of work, Mary enjoys spending time with friends and her Bible study small groups, loves exploring new food places, and finds joy in making people laugh.
The Atkinson lab is a close collaborator of ours, working on improving genomics for underserved groups via improvements to statistical and population genetics. Their work is centered around neuropsychiatric traits with particular focus on admixed populations. Learn more about the Atkinson lab.
Our open-source scalable pipeline for conducting QC, PCA, phasing, and imputation in the cloud is under development!
Github repo here.
Analytic and Translational Genetics Unit
Massachusetts General Hospital
Richard B. Simches Research Center
185 Cambridge Street, CPZN-6818
Boston, MA 02114