The goals of the Sabeti lab are to study the effect of natural selection on the human genome and the genomes of other organisms and uncover the traits that have emerged to shape these species, and to understand mechanisms of evolutionary adaptation in humans and pathogens. We are pursuing these goals through 3 research foci:
(1) Developing analytical methods to detect and investigate natural selection in the genome of humans and other species
(2) Pursuing signals of natural selection to identify their underlying functional trait and the mechanism of evolution (e.g hair, sweat, and teeth development, and resistance to Lassa fever virus)
(3) Examining signals of natural selection in pathogens to understand how they rapidly evolve, and studying their genetic diversity to guide long term intervention strategies.
Developing methods for detecting selection
In the era of genomics, we can now probe information buried in the millions of sequence variations that have occurred and persisted in the human genome, in search of signatures of genome evolution. We have developed computational methods, such as the LRH and XP-EHH tests, to detect genetic variants under positive selection. These methods identify variants that have recently emerged and spread through populations, relying on the breakdown of recombination as a clock for estimating the ages of alleles (Sabeti, Reich et al. 2002; Sabeti, Varilly et al. 2007). We have applied these methods to large datasets of human genetic variation finding many novel candidates for selection (The International Haplotype Map Consortium 2005; 2007). We are developing methods to further refine the signals from large candidate regions to localize the underlying selected polymorphism (Sabeti, Varilly et al. 2007). We have developed software to make detection of selection, by these and other methods, possible for the rapidly expanding empirical data on genetic variation in humans and other species (www.broad.mit.edu/mpg/sweep).
We are continuing to refine existing, and develop novel, methods and tools to detect and localize signals of selection in humans and other organisms. We are using approaches that take advantage of rapidly expanding datasets of genetic variation and larger population sampling, increasingly affordable full-genome sequencing, and new insights into the structure of genetic variation in the genome. We will apply our methods to look for instances of natural selection, using our own data and data collected for human in 2 international efforts: The International Haplotype Map Consortium (1000 individuals genotyped for 1 million polymorphisms) and the 1000 Genomes Consortium (full genome sequences from 1000 individuals).
Pursuing signals of natural selection
Through genome-wide studies, we have confirmed well-known cases of positive natural selection in humans, such as the sickle-cell and lactose tolerance traits (Sabeti, Schaffner et al. 2006) and identified many novel candidates, such as the genes Ecotodysplasin-Receptor (EDAR) and LARGE. For candidates, biological mechanism and therefore confirmation of selection is often limited (Sabeti, Varilly et al. 2007). We are investigating top candidate regions for selection. Beyond creating better techniques to localize signals of selection to single polymorphisms or genes, we are developing computational and visualization methods to identify DNA changes with functional molecular consequences. Truly understanding the role of adaptive evolution, however, will require case-by-case analysis of candidate loci to identify those with biological evidence for selection. We are currently focused here on deeper analysis of signals at EDAR and LARGE.
For the first, EDAR, we found a strong signal of selection at a Val370Ala substitution in an Asian study sample (Sabeti, Varilly et al. 2007). Mutations in EDAR, that lie near Val370Ala, cause hypohidrotic ectodermal dysplasia (HED) in humans and mice, characterized by defects in the development of hair, teeth, and exocrine glands (Botchkarev and Fessing 2005). There is evidence for selection at other genes in the EDA pathway in humans, as well as in stickleback fish (Colosimo, Hosemann et al. 2005), where the pathway regulates scale development, suggesting common mechanisms of evolution. With our collaborators at Harvard, we are examining EDAR and the EDA pathway through greater global population sampling, generating an EDAR-Ala370 transgenic mouse, biochemical studies, and an epidemiological survey.
For the second, LARGE, we found a strong signal of selection at the gene in a Nigerian study sample. The LARGE protein is critical for infection with Lassa fever virus, a severe illness that may infect an estimated 300,000 people or more each year with perhaps 20,000 or more deaths and nearly 100,000 people hospitalized. A serologic survey indicates ~21% of Nigerians have had previous exposure to the virus (WHO 2000). Depending on the region, 50-90% of individuals in West African populations infected with Lassa virus show mild, little, or no symptoms of the disease, while others suffer a complicated course that can lead to fever, encephalitis, deafness, and death (McCormick and Fisher-Hoch 2002). With collaborators at the University of Ibadan and The Specialist Teaching Hospital Irrua, in Nigeria, we will carry out studies of genetic susceptibility to Lassa hemorrhagic fever, investigate the genetic properties of resistance alleles, and develop genetic association methods that utilize signals of natural selection. To pursue this research, we will also develop technological capacity and field-deployable diagnostics, and investigate disease epidemiology.
Evolutionary adaptations between host and pathogen
Of all the forces shaping humans, perhaps the most intriguing are pathogens. They have had a tremendous impact on our evolution, and they themselves evolve over time. The same datasets and tools that revolutionized the study of genetic variation in humans will also make possible unprecedented studies in pathogens. Through studying their genetic diversity, we can examine how they have evolved to avoid our immune defenses and therapeutics, and track pathogen populations, towards guiding long term intervention strategies. Currently our main focus of this effort is Plasmodium falciparum malaria.
Genetic diversity is the key to malaria’s success as a human pathogen. Through its immense genetic diversity, the parasite is able to rapidly adapt and remain an agent of morbidity and mortality. Working with colleagues at the Harvard School of Public Health and the Broad Institute, we are characterizing genetic and phenotypic diversity in malaria as a first step towards understanding mechanisms of pathogenesis, immune evasion and drug resistance, and to developing surveillance tools to track parasite populations (Volkman, Sabeti et al. 2007). We have thus far identified ~98,000 polymorphisms in the malaria genome. We have developed whole-genome assay technology to detect natural selection in the malaria genome and to carry out association studies for clinical phenotypes. Our map of genetic diversity will particularly help rapidly map drug resistance, critical for establishing new population-based strategies for drug utilization. We are also developing field-deployable assays for diagnostics and surveillance. Our surveillance tools will help track transmission, investigate the evolutionary consequences of vaccine and drug intervention, and focus efforts on eliminating geographic disease epicenters or subpopulation reservoirs for the parasite.
Sabeti, P. C., D. E. Reich, et al. (2002). "Detecting recent positive selection in the human genome from haplotype structure." Nature 419(6909): 832-7.
The International Haplotype Map Consortium (2005). "A haplotype map of the human genome." Nature 437(7063): 1299-320.
Sabeti, P. C., S. F. Schaffner, et al. (2006). "Positive natural selection in the human lineage." Science 312(5780): 1614-20.
Volkman, S. K., P. C. Sabeti, et al. (2007). "A genome-wide map of diversity in Plasmodium falciparum." Nat Genet 39(1): 113-9.
The International Haplotype Map Consortium (2007). "A second generation human haplotype map of over 3.1 million SNPs." Nature 449(7164): 851-61.
Sabeti, P. C., P. Varilly, et al. (2007). "Genome-wide detection and characterization of positive selection in human populations." Nature 449(7164): 913-8.
Other publications cited
Botchkarev, V. A. and M. Y. Fessing (2005). "Edar signaling in the control of hair follicle development." J Investig Dermatol Symp Proc 10(3): 247-51.
Colosimo, P. F., K. E. Hosemann, et al. (2005). "Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles." Science 307(5717): 1928-33.
WHO (2000). "World Health Organization Lassa fever fact sheet No. 179. ." WHO Geneva.
McCormick, J. B. and S. P. Fisher-Hoch (2002). "Lassa fever." Curr Top Microbiol Immunol 262: 75-109.