Preprint on filtering genetic variants in cancer

By | September 13, 2017

Our recent work with Erik Knudsen on variant calling in tumors is now on bioRxiv. The paper introduces a new approach for analyzing genetic variants called from next-gen sequencing of tumors. A key challenge in such sequencing is spurious variants calls, particularly when sequencing tumors that have been xenografted into mice. We introduce and characterize a simple BLAST-based algorithm for removing spurious calls caused by mouse contamination or paralogs in the genome. This algorithm is as effective as much more computationally or bioinformatically intense approaches. Our testing also revealed biases that may be introduced by commonly used variant callers, an important caution for the community. Congratulations Brian!

Paper on genetic adaptation in Siberians published

By | September 13, 2017

Our collaborative paper with Michael Hammer on polygenic adaptation in Siberians has been published in MBE. In the paper, we develop a comprehensive model of the join demographic history of Europeans, East Asians, and Siberians. We then use that model as a null model to scan for gene sets enriched in signatures of population-specific adaptation. Three of our hit gene sets are related to diet, particularly fat metabolism, consistent with adaptation to a fat-rich animal diet. Congratulations Benson! (Also thank you to co-author Ludmila Osipova for the accompanying photo.)

Tenured

By | May 1, 2017

Ryan is please to announce that he has been promoted to Associate Professor with tenure.

He gives his heartfelt thanks all his current and former students and postdocs. They truly accomplish the groups’ research, and mentoring them is the best part of his job. He also thanks all his colleagues: within MCB, the University of Arizona, and across the globe. He is thrilled to be part of the vibrant scientific community.

Preprint on demographic history inference

By | February 15, 2017

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.