Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive.

Computationally efficient demographic history inference from allele frequencies with supervised machine learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite likelihood optimization. However, dadi's optimization procedure can be computationally expensive.

dadi-cli: Automated and distributed population genetic model inference from allele frequency spectra.

Summary: dadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing.

Demes: a standard format for demographic models.

Understanding the demographic history of populations is a key goal in population genetics, and with improving methods and data, ever more complex models are being proposed and tested. Demographic models of current interest typically consist of a set of discrete populations, their sizes and growth rates, and continuous and pulse migrations between those populations over a number of epochs, which can require dozens of parameters to fully describe. There is currently no standard format to define such models, significantly hampering progress in the field.

The genomic origins of the world's first farmers.

The precise genetic origins of the first Neolithic farming populations in Europe and Southwest Asia, as well as the processes and the timing of their differentiation, remain largely unknown. Demogenomic modeling of high-quality ancient genomes reveals that the early farmers of Anatolia and Europe emerged from a multiphase mixing of a Southwest Asian population with a strongly bottlenecked western hunter-gatherer population after the last glacial maximum. Moreover, the ancestors of the first farmers of Europe and Anatolia went through a period of extreme genetic drift during their westward range expansion, contributing highly to their genetic distinctiveness.

Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations.

The effect of a mutation on fitness may differ between populations depending on environmental and genetic context, but little is known about the factors that underlie such differences. To quantify genome-wide correlations in mutation fitness effects, we developed a novel concept called a joint distribution of fitness effects (DFE) between populations. We then proposed a new statistic w to measure the DFE correlation between populations.

A community-maintained standard library of population genetic models.

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort.

The impact of genome-wide association studies on biomedical research publications.

The past decade has seen major investment in genome-wide association studies (GWAS). Among the many goals of GWAS, a major one is to identify and motivate research on novel genes involved in complex human disease. To assess whether this goal is being met, we quantified the effect of GWAS on the overall distribution of biomedical research publications and on the subsequent publication history of genes newly associated with complex disease.

Triallelic Population Genomics for Inferring Correlated Fitness Effects of Same Site Nonsynonymous Mutations.

The distribution of mutational effects on fitness is central to evolutionary genetics. Typical univariate distributions, however, cannot model the effects of multiple mutations at the same site, so we introduce a model in which mutations at the same site have correlated fitness effects. To infer the strength of that correlation, we developed a diffusion approximation to the triallelic frequency spectrum, which we applied to data from Drosophila melanogaster We found a moderate positive correlation between the fitness effects of nonsynonymous mutations at the same codon, suggesting that both mutation identity and location are important for determining fitness effects in proteins.

Testing whether metazoan tyrosine loss was driven by selection against promiscuous phosphorylation.

Protein tyrosine phosphorylation is a key regulatory modification in metazoans, and the corresponding kinase enzymes have diversified dramatically. This diversification is correlated with a genome-wide reduction in protein tyrosine content, and it was recently suggested that this reduction was driven by selection to avoid promiscuous phosphorylation that might be deleterious. We tested three predictions of this intriguing hypothesis.