In this week's Nature Communications, a team led by scientists from the Max Planck Institute for the Science of Human History presents a genetic analysis of ancient Anatolians, providing clues as to whether their farming practices — some of the earliest known outside of the Fertile Crescent — developed locally or were introduced by migrants. The researchers analyzed genome-wide data from a 15,000-year-old Anatolian hunter-gatherer, as well as from seven Anatolian and Levantine early farmers, to create a genetic record of early agriculture in the region. They find that the Neolithic Anatolian populations derived a large portion of their ancestry from the hunter-gatherers, supporting the theory that farming arose independently in the region. They also discover genetic links with early Iranian/Caucasian, Levantine, and southern European populations. Altogether the findings point to a "limited role of human migration in the emergence of agriculture in central Anatolia," they write. GenomeWeb has more on this, here.
And in Nature Genetics, an international research team analyzes the genomes of two allotetraploid cotton species — Gossypium hirsutum, which has a high fiber yield and stress tolerance, and Gossypium barbadense, which is less hearty and productive but grows superior-quality fibers — to better understand the global genetic and molecular bases for the divergence of the two. Whole-genome comparative analyses show that species-specific alterations in gene expression, structural variations, and expanded gene families were responsible for speciation and the evolutionary history of the cotton species. The findings, the researchers write, "not only should enable breeders to improve fiber quality and resilience to ever-changing environmental conditions but also can be translated to other crops for better understanding of their domestication history and use in improvement."
Also in Nature Genetics, scientists from the UK and the US describe a method — called linked-read analysis, or LiRA — that can effectively identify mutations in single-cell DNA sequencing data, overcoming difficulties teasing apart amplification artifacts from biologically derived somatic mutations. LiRA, the investigators say, can accurately identify somatic single-nucleotide variants from amplification artifacts by using read-level phasing with nearby germline heterozygous polymorphisms, "thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells."