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Jeffrey P. Spence

Researcher at University of California, Berkeley

Publications -  38
Citations -  2094

Jeffrey P. Spence is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Biology & Population. The author has an hindex of 12, co-authored 22 publications receiving 1371 citations. Previous affiliations of Jeffrey P. Spence include Stanford University.

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Journal ArticleDOI

The Simons Genome Diversity Project: 300 genomes from 142 diverse populations

Swapan Mallick, +104 more
- 13 Oct 2016 - 
TL;DR: It is demonstrated that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.
Journal ArticleDOI

Early human dispersals within the Americas.

J. Víctor Moreno-Mayar, +60 more
- 07 Dec 2018 - 
TL;DR: Analysis of the oldest genomes suggests that there was an early split within Beringian populations, giving rise to the Northern and Southern lineages, and that the early population spread widely and rapidly suggests that their access to large portions of the hemisphere was essentially unrestricted, yet there are genomic and archaeological hints of an earlier human presence.
Journal ArticleDOI

Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations.

TL;DR: Differences in the recombination landscape across the genome and between populations are driven by variation in the gene that encodes the DNA binding protein PRDM9, and a demography-aware method is developed and applied to 26 diverse human populations, inferring population-specific recombination maps.
Journal ArticleDOI

Model-based detection and analysis of introgressed Neanderthal ancestry in modern humans.

TL;DR: Evidence that selection against Neanderthal ancestry was due to higher genetic load in Neanderthals resulting from small effective population size, rather than widespread Dobzhansky–Müller incompatibilities (DMIs) that could contribute to reproductive isolation is found.
Proceedings Article

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks.

TL;DR: This article developed an exchangeable neural network that performs summary statistic-free, likelihood-free inference for recombination hotspot testing, which can be applied in a black-box fashion across a variety of simulation-based tasks.