Detecting Evolution of Amino-Acid Fitness in Vertebrate Genomes

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The genomes of hundreds of vertebrate species have now been sequenced to near-completion, and nearly 10,000 more are slated to be determined over the next several years (“Genome10k”). A major motivation for this work is to increase the power of comparative analysis to illuminate how gene and genome function evolves (and how it has evolved). Although dense taxonomic sampling across the major lineages of vertebrate biodiversity is expected to substantially increase the ability to make statistical inferences about the genetic changes that “mattered” during evolution, substantial computational and analytic barriers to progress exist. Objectives The primary objective of this project is to exploit computational and modeling innovations to reliably detect coordinated changes in amino-acid fitness (“fitness shifts”) across multiple positions in the protein-coding regions of up to hundreds to thousands of vertebrate genomes. Such shifts are an expected outcome of changes in the functional requirements of a protein by directional selection, and thus may imply functional divergence or adaptation. To characterize the limits of reliable detection, detailed calculations of statistical information under alternative experimental designs (numbers of species, divergence levels, etc.) will be performed to determine how well comparative data can distinguish fitness shifts from other phenomena (e.g., reductions in population size). Fast Markov Chain Monte Carlo methods of inferring fitness shifts in large comparative datasets will be developed and evaluated. Scientific approach We recently developed several general approaches for rapid, Bayesian analysis of large phylogenomic datasets, which can help eliminate computational bottlenecks and in some cases reduce data analysis times from months to minutes. In this project, we will integrate these techniques, along with unpublished improvements, with algorithms that exploit the massive parallelism of inexpensive, many-core coprocessors of emerging importance in scientific computing. We will use these approaches to implement models of discrete spatial and temporal heterogeneity in selective constraints and population size, and will examine their performance on a large set of vertebrate single-copy genes. Throughout, scalability of computations and reductions in time-complexity (even at the expense of demonstrably mild approximations) will be prioritized to maximize utility in large datasets. Asymptotic power analysis methods that we have recently begun developing (unpublished) will be elaborated and used to characterize the impact of experimental design on power and to evaluate the limits of inference. Expected significance With increasingly large numbers of vertebrate genomes now available, tremendous opportunities are emerging to advance knowledge of the genetic basis for fundamental evolutionary processes including functional divergence and adaptation. Statistical methods for detecting functional divergence are among the most widely used ways that functional inferences are made from genomic data. Although it is widely appreciated that such approaches are oversimplified and flawed in important ways, they are tolerated because of their computational convenience. By focusing here on algorithms for fast and scalable inference, it is hoped that recent progress in molecular evolution can be “scaled up” to enable more principled approaches in comparative genomics. Through the development of a quantitative framework for experimental design, more reasoned methods for selecting which and how many species are needed to address a particular question will emerge. Thus, the tools developed here will facilitate advances in both the rational design and execution of large-scale comparative genomic studies.