Biomedical subjects

Jeffrey D Jensen

Publications and source records attributed to Jeffrey D Jensen.

4 recordsLinked to original sources

PubMed2026

Inferring the demographic history of Chinese and Indian rhesus macaque (Macaca mulatta) populations from PacBio HiFi long-read sequencing data.

The rhesus macaque (Macaca mulatta) is one of the most widely used animal models in biomedical research, both as it resembles humans in key biological aspects and as it is characterized by a broad geographic range. Most of the individuals housed in U.S. research colonies have been sampled from either China or India, though notably the source population of these animals has significantly shifted over time. Given the substantial genetic and immunological differences between these populations, a deeper understanding of the underlying population structure is critically important for biomedical interpretation. Despite this, the demographic histories of these two populations remain poorly resolved. Here, we present an analysis of whole-genome, PacBio HiFi long-read sequencing data from ten unrelated individuals of each population, applying four related model- and non-model based demographic inference approaches, in order to reconstruct their ancestral history. We evaluated the fit of the subsequently estimated models against the empirical data, and incorporated underlying uncertainty in the mutation rates used for scaling. We inferred a well-fitting population history characterized by substantial structure between Chinese and Indian populations, with a split time ∼140,000 generations ago from an ancestral population of ∼65,000 individuals. We additionally inferred the subsequent history of size change within, and gene flow between, these populations, reaching the current estimated sizes of ∼220,000 individuals in the Chinese population and ∼14,000 individuals in the Indian population. The robust baseline demographic model established in this study will serve as a valuable resource for future research on this species, including for improved fine-scale recombination mapping, selection inference, and association studies.

Cercopithecidae↗

PubMed2006

Controlling the false-positive rate in multilocus genome scans for selection.

Rapid typing of genetic variation at many regions of the genome is an efficient way to survey variability in natural populations in an effort to identify segments of the genome that have experienced recent natural selection. Following such a genome scan, individual regions may be chosen for further sequencing and a more detailed analysis of patterns of variability, often to perform a parametric test for selection and to estimate the strength of a recent selective sweep. We show here that not accounting for the ascertainment of loci in such analyses leads to false inference of natural selection when the true model is selective neutrality, because the procedure of choosing unusual loci (in comparison to the rest of the genome-scan data) selects regions of the genome with genealogies similar to those expected under models of recent directional selection. We describe a simple and efficient correction for this ascertainment bias, which restores the false-positive rate to near-nominal levels. For the parameters considered here, we find that obtaining a test with the expected distribution of P-values depends on accurately accounting both for ascertainment of regions and for demography. Finally, we use simulations to explore the utility of relying on outlier loci to detect recent selective sweeps. We find that measures of diversity and of population differentiation are more effective than summaries of the site-frequency spectrum and that sequencing larger regions (2.5 kbp) in genome-scan studies leads to more power to detect recent selective sweeps.

Demography↗

PubMed2006

Phylogenetic incongruence in the Drosophila melanogaster species group.

Drosophila melanogaster and its close relatives are used extensively in comparative biology. Despite the importance of phylogenetic information for such studies, relationships between some melanogaster species group members are unclear due to conflicting phylogenetic signals at different loci. In this study, we use twelve nuclear loci (eleven coding and one non-coding) to assess the degree of phylogenetic incongruence in this model system. We focus on two nodes: (1) the node joining the Drosophila erecta-Drosophila orena, Drosophila melanogaster-Drosophila simulans, and Drosophila yakuba-Drosophila teissieri lineages, and (2) the node joining the lineages leading to the melanogaster, takahashii, and eugracilis subgroups. We find limited evidence for incongruence at the first node; our data, as well as those of several previous studies, strongly support monophyly of a clade consisting of D. erecta-D. orena and D. yakuba-D. teissieri. By contrast, using likelihood based tests of congruence, we find robust evidence for topological incongruence at the second node. Different loci support different relationships among the melanogaster, takahashii, and eugracilis subgroups, and the observed incongruence is not easily attributable to homoplasy, non-equilibrium base composition, or positive selection on a subset of loci. We argue that lineage sorting in the common ancestor of these three subgroups is the most plausible explanation for our observations. Such lineage sorting may lead to biased estimation of tree topology and evolutionary rates, and may confound inferences of positive selection.

Animals↗

PubMed2005

Distinguishing between selective sweeps and demography using DNA polymorphism data.

In 2002 Kim and Stephan proposed a promising composite-likelihood method for localizing and estimating the fitness advantage of a recently fixed beneficial mutation. Here, we demonstrate that their composite-likelihood-ratio (CLR) test comparing selective and neutral hypotheses is not robust to undetected population structure or a recent bottleneck, with some parameter combinations resulting in a false positive rate of nearly 90%. We also propose a goodness-of-fit test for discriminating rejections due to directional selection (true positive) from those due to population and demographic forces (false positives) and demonstrate that the new method has high sensitivity to differentiate the two classes of rejections.

Computer Simulation↗