Genome to Phenome: Improving Animal Health, Production, and Well-Being – A New USDA Blueprint for Animal Genome Research 2018–2027

Abstract

In 2008, a consortium led by the Agricultural Research Service (ARS) and the National Institute for Food and Agriculture (NIFA) published the “Blueprint for USDA Efforts in Agricultural Animal Genomics 2008–2017,” which served as a guiding document for research and funding in animal genomics. In the decade that followed, many of the goals set forth in the blueprint were accomplished. However, several other goals require further research. In addition, new topics not covered in the original blueprint, which are the result of emerging technologies, require exploration. To develop a new, updated blueprint, ARS and NIFA, along with scientists in the animal genomics field, convened a workshop titled “Genome to Phenome: A USDA Blueprint for Improving Animal Production” in November 2017, and these discussions were used to develop new goals for the next decade. Like the previous blueprint, these goals are grouped into the broad categories “Science to Practice,” “Discovery Science,” and “Infrastructure.” New goals for characterizing the microbiome, enhancing the use of gene editing and other biotechnologies, and preserving genetic diversity are included in the new blueprint, along with updated goals within many genome research topics described in the previous blueprint. The updated blueprint that follows describes the vision, current state of the art, the research needed to advance the field, expected deliverables, and partnerships needed for each animal genomics research topic. Accomplishment of the goals described in the blueprint will significantly increase the ability to meet the demands for animal products by an increasing world population within the next decade.

Precise Genome Editing in Poultry and Its Application to Industries

“…Muscle mass significantly increased in MSTN knockout chicken and quail, and fat composition was reduced in G0S2 -knockout chicken [ 52 , 53 , 66 ]. These days, genome sequencing technologies have been rapidly developed and applied to poultry breeding to find genetic markers that influence productivity [ 73 ]. These genetic markers can be edited simultaneously using the CRISPR/Cas9 system.…”

A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock

“…Development of new algorithms, analytical tools, and approaches utilizing these digital measurements will require phenotypic data that is accessible, and uniform in structure and format from the start of data collection. But, to meet the challenge and opportunity to understand and make use of phenotypic data in the twenty-first century, we need to think beyond phenotypes, or even the combination of genotype and phenotype alone (Rexroad et al, 2019).…”

“…There have been advances, for example, researchers can now install feed bins that can register an animal ID on its collar as it comes up to feed, and weights are taken automatically every few seconds and sent automatically to a computer for analyses to monitor feed intake. Other sampling techniques remain largely unchanged, such as the blood draw to measure a vast number of phenotypes seen in blood analyses, which may as an example, inform animal health and productivity in the field of metabolomics (Rexroad et al, 2019).…”

KITSUNE: A Tool for Identifying Empirically Optimal K-mer Length for Alignment-Free Phylogenomic Analysis

“…Genome sequences have been used widely for species identification with high accuracy and have been useful to many research areas in the biotechnological (Costessi et al, 2018), environmental (Vandenkoornhuyse et al, 2010), evolutionary (Bruger and Marx, 2018;Sands, 2019), and clinical sciences (Balloux et al, 2018). With the rapid technological development of genome sequencing, more and more organisms have been sequenced across all kingdoms (Galagan et al, 2005;Land et al, 2015;Houldcroft et al, 2017;Chen et al, 2019;Rexroad et al, 2019). The enormous amount of data generated by sequencing has made it challenging to compare sequences with alignment-based approaches such as BLAST (Altschul et al, 1990).…”