Research

TOPMed Circos plot showing summary statistics and rare variant sharing for first ~40K unrelated samples.

Our group has been involved in many large-scale sequencing and multi-omics initiatives. These include the NHLBI Exome Sequencing Project (ESP) (Tennessen et al. (2012), Fu et al. (2013), O’Connor et al. (2013), O’Connor et al. (2014)); the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) Project (Mathias et al. (2016), Kessler et al. (2016), Johnston et al. (2017), Daya et al. (2019)); and most recently the Trans-Omics for Precision Medicine (TOPMed) Project (Taliun et al. (2019), Kessler et al. (2019), Harris et al. (2019)).

In all of these projects, we explore what rare variants tell us about fine-scale population structure and in turn what this tells us about genetic epidemiology approaches. We have also started projects exploring how deep learning (DL) of multi-omics medicine and testing hypotheses of how rapid molecular phenotypes evolve.

Identity-by-descent network of Peruvians. Animation goes from high to low amount of sharing.

Using these large-scale genomic data we have also explored questions of population history, especially underrepresented populations. We led the analysis and sequencing efforts of the Peruvian Genome Project (Harris et al. (2018), Harris et al. (2019)). This project is one of the first efforts to sequence Native Americans from South America and provides a great opportunity to study the peopling of this region. We found that modern Mestizo populations descend from cosmopolitan Native American groups prior to their admixture with Europeans.

We have also applied similar methods to understand the population history of Samoans (Harris et al. 2019) and are now combining these efforts with other global initiatives to develop models of the population history of the Pacific Rim.

Correlation of African ancestry with number of pathogenic variants as defined by ClinVar's monthly updates.

Many genomic resources have focused on European individuals at the detriment of non-European populations. We have made efforts to quantify this bias in clinical genetic databases (Kessler et al. (2016), Kessler & O’Connor (2017)) and cancer cell line resources (Kessler et al. (2019)).

We are now working with colleagues at the Department of Defense, Gynecological Cancer Center of Excellence, to incorporate detailed population genetic models into analyses of cancer outcomes for diverse non-European populations.

Estimated Effective Migration Surfaces (EEMS) of P. falciparum in South East Asia showing regions of low (orange/brown) and high (blue) migration.

Many of the methods we use and develop in human populations can have an immediate impact on the understanding of other groups of biomedical relevance. We have started researching how population history and migration patterns of the Malaria parasites Plasmodium falciparum and P. vivax can inform public health efforts to eliminate these disease-causing bugs (Shetty et al. 2019). We will again utilize rare variants and measures of fine-scale population structure to better understand the dynamics of these species and hopefully lead to their containment if not eradication.