Statistical Methods and Algorithms for Population Genomic Inference

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Principal Investigator: Bruce  RANNALA
Organization: UNIVERSITY OF CALIFORNIA AT DAVIS
Fiscal Year: 2020
Award: $206,764
Funding agency: National Institute of General Medical Sciences

Project Summary/Abstract
The proposed supplement is in response to the “Notice of Special Interest (NOSI) regarding the Availability of
Urgent Competitive Revisions for Research on the 2019 Novel Coronavirus (2019-nCoV).” In particular, it is in
response to the stated NIGMS Interests: “Incorporation of data related to the 2019-nCoV into ongoing research
efforts to develop predictive models for the spread of coronaviruses and related infectious agents.” The proposed
research extends several aims of the existing grant NIH 5 R01 GM123306 to develop predictive models of the
spread of Coronavirus and link these models to genomic variation in hCoV19. Specifically, the proposed research
extends Aims 1 and 2 of the original proposal to develop new methods to infer admixture events and recombination
among lineages of hCoV19. Recombination is an important factor in the evolution of Coronaviruses and is linked
to changes of virulence and transmissibility and is therefore an important parameter of predictive models. The
proposed research also extends Aim 4 of the current award by: (1) implementing “tip-dating” using viral sampling
times to calibrate divergence-time estimates under a relaxed-molecular clock model, and; (2) implements realistic
epidemiological priors for gene trees. These extensions allow important epidemiological parameters, such as R0,
to be inferred from genomic sequence data while allowing for temporal changes in contacts between infected and
susceptible individuals and changes in sampling (intensity of genetic testing) over time across geographic areas.
Because CoV19 has a relatively low mutation rate and is under strong purifying selection, it is important to develop
integrative methods for analyzing the genetic data that incorporate information from other sources (travel histories,
testing regimes, social distancing measures, etc) and maximize the utility of the sequence data. Implementing such
an integrative approach is straightforward using the Bayesian framework proposed. The new priors we implement
will allow allow external sources of information to be incorporated.
 The parameters estimated in the preceding aims are essential for constructing predictive simulations of hCoV19.
The proposed research extends Aim 6 of the original proposal to develop new simulation methods for predicting
the progress of the pandemic from a molecular-genetics perspective. We will develop and implement these methods
in open-source software for jointly predicting both the spread of the COVID-19 pandemic through time and the
changes in the genomic variation of SARS-CoV-2 under different mitigation strategies. These simulations will
accommodate the selective constraints on the genome inferred from phylogenetic analyses of related Coronoaviruses.
Genetic variation is important both for understanding the potential power of different genetic sampling strategies for
analyzing the progress of the pandemic and for predicting the likelihood that adaptive changes may occur in CoV19
that could impact the severity of the illness and other biologically important features among infected individuals.
Predictive modeling of genomic variation in hCoV19 will be useful for designing sequencing strategies to track the
progress of the pandemic and for evaluating the reliability of estimates of epidemiological parameters obtained using
phylodynamic inference methods.

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