Category: Statistics

Improving environmental conditions favoured higher local population density, which favoured domestication

agricultural-origins

New paper (behind paywall) Hindcasting global population densities reveals forces enabling the origin of agriculture, by Kavanagh et al., Nature Human Behaviour (2018)

Abstract (emphasis mine):

The development and spread of agriculture changed fundamental characteristics of human societies1,2,3. However, the degree to which environmental and social conditions enabled the origins of agriculture remains contested4,5,6. We test three hypothesized links between the environment, population density and the origins of plant and animal domestication, a prerequisite for agriculture: (1) domestication arose as environmental conditions improved and population densities increased7 (surplus hypothesis); (2) populations needed domestication to

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Mitochondrial DNA unsuitable to test for IBD, and undersampling genomes show biased time and rate estimates

Two interesting papers questioning previous methods have been published.

Open access Mitochondrial DNA is unsuitable to test for isolation by distance, by Teske et al. Scientific Reports (2018) 8:8448.

Abstract (emphasis mine):

Tests for isolation by distance (IBD) are the most commonly used method of assessing spatial genetic structure. Many studies have exclusively used mitochondrial DNA (mtDNA) sequences to test for IBD, but this marker is often in conflict with multilocus markers. Here, we report a review of the literature on IBD, with the aims of determining (a) whether significant IBD is primarily a result of lumping spatially discrete

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Contrastive principal component analysis (cPCA) to explore patterns specific to a dataset

Interesting open access paper Exploring patterns enriched in a dataset with contrastive principal component analysis, by Abid, Zhang, Bagaria & Zou, Nature Communications (2018) 9:2134.

Abstract (emphasis mine):

Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis

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