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This Week in Genome Research: Dec 5, 2018

Researchers from the University of Toronto and elsewhere present a physical and genetic map for the pot plant Cannabis sativa, producing a cross between the hemp variety Finola and a previously sequenced strain Purple Kush. The team did single-molecule real-time sequencing on male Finola and female Purple Kush plants, along with short-read sequencing on 99 hybrids produced by crossing them. Along with a look at C. sativa chromosome structure, the map provided a look at cannabinoid biosynthesis gene linkage, or lack thereof, while highlighting retrotransposon-rich regions containing genes that determine drug or hemp chemotypes. Moreover, the authors say, the map "should facilitate vastly improved genetic analysis, including [quantitative trait locus] mapping, which will accelerate crop improvement efforts."

A McGill University-led team takes a look at the mutations prompted by environmental stressors in the Daphnia pulex water flea model. The researchers did genome sequencing on 60 D. pulex representatives grown in minimal selection environments that allowed mutation accumulation more than 100 generations. Their results revealed a rise in deletions and duplications in water fleas exposed to metals, particularly copper and nickel, which appeared to enhance the rates of mutation caused by DNA strand breaks and non-homologous recombination. "Our findings suggest that environmental stress, in particular multiple stressors, can have profound effects on large-scale mutation rates and mutational load of multicellular organisms," they write.

Researchers in Qatar and the US evaluate population-based, family-based, and combined approaches for imputing genotypes and increasing variant density in 20 large simulated family pedigrees of European or African ancestry. When it came to rare variant imputation, for example, the team saw advantages with family-based imputation, while population-based imputation methods appear to perform better for finding many common variants. "Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools in the same family data," the authors write, "and provide guidelines for future studies."