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Delta Smelt Genome Assembly & Sex Marker Identification

Delta Smelt Genome Assembly & Sex Marker Identification

Background and Significance of Study

For my Ph.D. research, I intend to create a reference quality genome assembly for Hypomesus transpacificus (delta smelt). A high-quality reference genome is a useful tool in many areas of modern biology. It will allow for fine scale resolution of population genetic analyses and may help to elucidate genetic and genomic sex-determining regions in delta smelt, which are currently unknown. The genome will be leveraged in several upcoming research projects, including the adaptation of thermal tolerance, the examination of domestication selection on hatchery populations, understanding the genetic basis of life history migration phenotypes in the wild population, and various epigenetic studies. More broadly, our chromosome-scale scaffolded delta smelt genome will be the first Osmerid sequenced and assembled beyond 15x coverage, and the first and only Hypomesus reference-quality genome freely available. Our work will add to the collaborative efforts of documenting the world’s diversity.


In order to construct a reference genome, we will be using a combination of linked reads, long reads and hi-c chromatin conformation capture with the goal of assembling chromosome-scale scaffolds. This work is done in collaboration with the UC Davis DNA Technologies Core on campus.


Additionally, the GVL will be using the reference genome to identify sex markers in delta smelt. Currently, wild fish can only be sexed using the expression of gametes in ripe adult fish. Because identification relies on the physiological status of an individual fish, only a limited number of wild delta smelt can be sexually identified without dissection. Knowledge of the genetic underpinnings of sex determination in fishes assists with the management of captive populations, basic knowledge of life history characteristics of the species, ecological surveys, and management regarding population metrics, species modeling, demographic inference, and sex-based survival.


This work was done by former graduate student Shannon E. K. Joslin

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