Population genetic inference based on the statistics of single loci, as summarized in the allele frequency spectrum, is known to be powerful for inferring demographic history. But substantial additional information is carried in two-locus statistics, which additionally capture patterns of linkage disequilibrium (LD). In this work, we developed a novel framework for demographic inference from two-locus statistics, characterized its power, and applied it to Drosophila melanogaster.
We first developed a novel solution of the two-locus diffusion equation, upon which we built a composite-likelihood inference framework. Applying this framework to simulated data, we found that two-locus statistics are substantially more powerful than single-locus statistics. In particular, the depth and duration of a bottleneck are strongly confounded when single-locus statistics are used in inference, but not when two-locus statistics are used. Moreover, two-locus statistics allow diversity-independent estimation of effective population size. We then applied our approach to a Zambia population of D. melanogaster. In this application, we show that two-locus statistics indeed result in much more precise inference of demographic parameters and much more accurate recapitulation of the observed LD decay. Interestingly, we also infer a substantially lower ancestral effective population size for D. melanogaster than previous works.
Critics of the large investment made into genome wide association studies have noted that few GWAS results have had direct clinical impact. Supporters note that GWAS can have major indirect impact by revealing novel genes involved in a disease and motivating follow-up research. But this indirect impact has never been measured.
In our manuscript, we quantify the impact of GWAS on biomedical research into individual genes, through scientific publications. We first show that GWAS have not changed the historical bias of biomedical research toward genes involved in Mendelian disease. We then show that GWAS hits do receive additional publications compared to control genes and model expectations, but this impact is diminishing. Our results thus demonstrate the need for reforms to ensure that promising GWAS hits are pursued.
There’s a nice story about our work with Michael Hammer on Pygmy evolutionary history in our local paper, the Arizona Daily Star.
Congratulations to Brian Mannakee on his paper that just came out in PLoS Genetics. In it, we use dynamical systems biology models to predict rates of protein domain evolution. The success of these predictions suggests that natural selection is acting to preserve network dynamics in the face of perturbations from mutations. Although the models are imperfect, we think this work shows the power of systems biology models for quantitatively predicting evolutionary processes.