Determining the genomic elements underlying adaptive evolution in a species is essential for connecting genetic variation to phenotypes and fitness, but current statistical methods overlook the confounding effect population histories have on the identification and localization of adaptive mutations. The field of genomics urgently needs methods that (i) model the complex interaction between various modes of selection and population histories; (ii) accurately identify and localize mutations, genes, and pathways underlying adaptive traits for further experimental validation; and (iii) efficiently analyze large scale datasets. Without such methods, the role of adaptation in human molecular evolution cannot be determined. The long-term goal of the researchers is to develop state-of-the-art methods for the detailed inference of evolutionary parameters and disease pathways from next-generation sequencing datasets. The objective of this application is to characterize the genomic elements underlying adaptive evolution in the human genome, through the development and application of a suite of novel statistical and computational methods.
The aims of the proposal are to: 1) identify adaptive mutations in diverse human populations using novel, probabilistically interpretable frameworks; 2) develop a frame-work for joint inference of selection and population history from whole-genome sequences; and 3) characterize gene subnetworks underlying human adaptive evolution by developing and applying new tests for polygenic adaption to human genomic data. The methods developed will be applicable to existing and emerging genome- wide polymorphism and next-generation sequencing datasets for humans and a range of other organisms. The contribution of the proposed research will be significant because it will shed light on the mutations that allowed human ancestors to survive in the face of novel environments, diets, and pathogens; humans will face similar environmental pressures in the future, and the proposed research will determine genetic pathways that are critical to human survival in a hostile world. The proposed research is innovative in many distinct ways. First, these new methods will be able to test for multiple modes of selection, moving beyond classifying sites as simply neutral or adaptive. Second, the methods developed here will control for dependencies among statistics measuring selection, enabling new understanding of which combinations of genomic signatures are most informative for the detection of different modes of selection. Third, the proposed research will expand the focus of population-genomic studies of adaptation beyond monogenic adaptation to polygenic adaptation. The out- comes of this research will have an important positive impact: giving new insight into the interaction between selection and dynamic population histories in generating human genetic diversity, while determining how adaptation shapes the human phenotype and advancing our understanding of the biology of the human genome.

Public Health Relevance

Adaptation to shifting environments and diets has been critical to the survival of the human species since modern humans emerged from Africa ~70,000 years ago. The proposed research is relevant to public health because the new methods developed here will be applied to large-scale sequencing datasets to identify mutations that allowed human ancestors to survive in the face of novel environments, diets, and pathogens; these results will also be used to characterize genetic pathways critical to human survival in a hostile world. Thus, the proposed research is relevant to the part of NIH's mission pertaining to developing fundamental knowledge that will enhance health.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Janes, Daniel E
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Brown University
Schools of Medicine
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Sugden, Lauren Alpert; Atkinson, Elizabeth G; Fischer, Annie P et al. (2018) Localization of adaptive variants in human genomes using averaged one-dependence estimation. Nat Commun 9:703
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