Abstract: Disease-causing pathogens are among the most intriguing forces shaping human evolution, as they have a tremendous impact on our genome and themselves evolve over time. A genome-wide survey of human variation identified two genes biologically linked to Lassa fever as among the strongest signals of natural selection in West Africans1. Lassa fever is a severe hemorrhagic disease endemic in West Africa, and our findings suggest it is an ancient selective force driving the emergence of genetic resistance. While poorly understood, Lassa fever has arguably the greatest potential impact of all infectious diseases of humans, because of its unique status as both an immediate public health crisis and a category A potential bioterrorist agent. With the aim to pursue the intriguing signal of natural selection linked to Lassa fever, we first set out to address critical gaps in knowledge, capacity, and diagnostics. We established a basic diagnostic and research lab in Irrua, Nigeria, where yearly outbreaks of Lassa fever occur with population exposure of ~30%. Preliminary data suggests our initial measures have significantly reduced fatality from an estimated 65% to 20% among Lassa fever cases. We now aim to design a robust, field-deployable diagnostic, based on genomesequencing of diverse strains, to rapidly detect and distinguish Lassa virus strains. This work addresses immediate public health needs and sets the foundations for research into the genetic factors in both virus and human that underlie resistance to Lassa fever found among many West Africans. The ultimate goal of our work is to identify natural mechanisms of defense and illuminate the evolutionary adaptations that have allowed humans to withstand some of our most complex and challenging selective agents. Moreover, these efforts will create new opportunities in Lassa virus research, including investigations of viral pathogenicity and evolution and development of novel vaccines. Public Health Relevance: Lassa hemorrhagic fever, a severe illness endemic in West Africa, is estimated to infect more than 300,000 individuals, hospitalize 100,000, and cause 20,000 or more deaths each year. These numbers are likely to be underestimates;few serological surveys have been conducted in the last 2 decades, most patients are never seen in the hospital due to poor access to medical facilities, and the initial symptoms are similar to other febrile illnesses and commonly misdiagnosed. Lassa is arguably one of the most neglected of the tropical diseases, given the number of people that it affects, its case fatality, its potential as a bioterrorist agent, and the unaddressed need for better diagnostics, field studies, and therapies. I have established a basic diagnostic and research lab at the Specialist Teaching Hospital in Irrua, Nigeria, a rural area where yearly outbreaks of Lassa fever occur and where estimated population exposure is 30%. Our initial measures there have already begun to reduce fatality among hospital cases of Lassa fever, from an estimated 65% to 20%. The diagnostic we are using, however, still misses many cases of the disease and is not readily available in the clinical setting. The RT-PCR assay is based on a single Lassa strain isolated 2 decades ago in Sierra Leone, and has an estimated sensitivity of 50%. Genetic divergence between Lassa virus strains likely underlies this poor performance, with the greatest diversity of virus strains in Nigeria.
We aim to design a robust, field-deployable diagnostic test that will rapidly diagnose Lassa virus, by sequencing 100 isolates of Lassa virus. This work will address urgent public health needs in this widespread and devastating disease. Moreover we will use these foundations to investigate host genetic factors underlying resistance to Lassa fever seen among many West Africans, and potentially uncover natural mechanisms of disease resistance. Finally these efforts will create new opportunities in Lassa virus research, including investigations of viral pathogenicity and evolution and development of novel vaccines.

National Institute of Health (NIH)
Office of The Director, National Institutes of Health (OD)
NIH Director’s New Innovator Awards (DP2)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1-NDIA-O (02))
Program Officer
Basavappa, Ravi
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
Schools of Arts and Sciences
United States
Zip Code
Tewhey, Ryan; Kotliar, Dylan; Park, Daniel S et al. (2016) Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay. Cell 165:1519-1529
Morgan, Josh Lyskowski; Berger, Daniel Raimund; Wetzel, Arthur Willis et al. (2016) The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus. Cell 165:192-206
Matranga, Christian B; Gladden-Young, Adrianne; Qu, James et al. (2016) Unbiased Deep Sequencing of RNA Viruses from Clinical Samples. J Vis Exp :
Kamberov, Yana G; Karlsson, Elinor K; Kamberova, Gerda L et al. (2015) A genetic basis of variation in eccrine sweat gland and hair follicle density. Proc Natl Acad Sci U S A 112:9932-7
Andersen, Kristian G; Shapiro, B Jesse; Matranga, Christian B et al. (2015) Clinical Sequencing Uncovers Origins and Evolution of Lassa Virus. Cell 162:738-50
Kasthuri, Narayanan; Hayworth, Kenneth Jeffrey; Berger, Daniel Raimund et al. (2015) Saturated Reconstruction of a Volume of Neocortex. Cell 162:648-61
1000 Genomes Project Consortium; Auton, Adam; Brooks, Lisa D et al. (2015) A global reference for human genetic variation. Nature 526:68-74
Stremlau, Matthew H; Andersen, Kristian G; Folarin, Onikepe A et al. (2015) Discovery of novel rhabdoviruses in the blood of healthy individuals from West Africa. PLoS Negl Trop Dis 9:e0003631
Matranga, Christian B; Andersen, Kristian G; Winnicki, Sarah et al. (2014) Enhanced methods for unbiased deep sequencing of Lassa and Ebola RNA viruses from clinical and biological samples. Genome Biol 15:519
Shlyakhter, Ilya; Sabeti, Pardis C; Schaffner, Stephen F (2014) Cosi2: an efficient simulator of exact and approximate coalescent with selection. Bioinformatics 30:3427-9

Showing the most recent 10 out of 19 publications