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Collier, N. W.

Publications and source records attributed to Collier, N. W..

2 recordsLinked to original sources

Causal inference clarifies the roles of background selection and mutation rate variation in shaping human genetic diversity

Decades of theoretical and empirical work have struggled to reconcile competing views on how evolution shapes patterns of genetic variation. We now understand the genome as a mosaic molded by both neutral and selective forces, but quantifying their relative contributions remains an open challenge. A major obstacle has been the tendency to analyze each evolutionary process in isolation. But different processes may leave similar signatures on genetic variation, making it challenging to draw conclusions from correlations alone. To address this gap, we make predictions of the landscape of diversity based on background selection and mutation rate variation. We then develop structural equation models describing how mutation, recombination and selection jointly shape the genomic landscape of diversity. This approach offers a more realistic representation of biological interactions and enables rigorous evaluation of hypothesized causal structures, marking both conceptual and practical improvements over previous studies. Analyses of human data reveal large variation in the explanatory power of candidate models across chromosomes. We find that previous studies likely overestimated the predictive accuracy of background selection; although it emerges as the overall main driver of diversity, in some chromosomes mutation rate variation has a comparable impact. We also show that recombination increases diversity more strongly through its influence on background selection than through its mutagenic effect, resolving a longstanding debate. This work demonstrates that modeling variation inherent in genome biology substantially improves our ability to explain human genetic diversity. At the same time, evidence for unmodeled covariance between mutation rates and density of constrained sites reinvigorates an ongoing discussion about the evolution of the mutation landscape, although additional work is needed to determine its origins.

evolutionary biology↗

Inferring hominin history with recurrent gene flow from single unphased genomes and a two-locus statistic

The emerging picture of hominin evolution is one of complexity, population structure, and gene flow, which recent genomic inference approaches have begun to resolve. Among these are methods based on two-locus statistics, which summarize information contained in genealogical correlations between linked loci. Although the inclusion of ancient samples could provide increased power to distinguish between competing models, these methods typically rely on large samples from present-day populations, and it remains challenging to apply them to ancient DNA (aDNA), which is sparsely sampled, unphased, and time-stratified. Here we develop an inference framework based on a set of multi-population two-locus statistics that are applicable to aDNA because they can be estimated from single unphased diploid genomes. We connect these statistics to an existing system of two-locus summaries and use them to model divergence and gene flow among populations represented by seven ancient hominin individuals and one contemporary human. We infer a demographic model with two episodes of gene flow from early anatomically modern humans (AMH) to Neanderthals and an introgression from an unsampled hominin lineage to Denisovan ancestors, broadly consistent with previous work. We also learn parameters of ancient Eurasian AMH population structure, reinforcing previous findings that early European farmers traced a large fraction of their ancestry to a lineage which split early from other non-African AMH and received little or no introgression from Neanderthals. Using both simulation and empirical data, we show that accurately estimating parameters associated with multiple gene flow episodes requires their joint inference due to their correlated effects on diversity.

evolutionary biology↗