Oral Annual Conference of the Genetics Society of Australasia with the NZ Society for Biochemistry & Molecular Biology

Genotyping-By-Sequencing for diverse applications including population genetics (768)

Jeanne Jacobs 1 , Shannon Clarke 2 , Marty Faville 3 , Andrew Griffiths 3 , Mingshu Cao 3 , Rachel Tan 3 , Tracey Van Stijn 2 , Rayna Anderson 2 , Rachael Ashby 2 , Rudiger Brauning 2 , Alan McCulloch 2 , Ken Dodds 2 , John McEwan 2
  1. AgResearch, Christchurch, NZ, New Zealand
  2. Invermay Agricultural Centre, Mosgiel, New Zealand
  3. Grasslands Research Centre, Palmerston North, New Zealand

Genotyping-by-Sequencing (GBS) is a method used to develop rapid and cost-effective high-density genetic SNP marker data for diverse applications in biology. The NZ Genomics for Production and Security programme (MBIE C10X1306) has developed the infrastructure and skill base required to apply GBS for different purposes across a wide range of species. Thus far, we have optimised GBS in 50 different species encompassing plants, mammals, shellfish, fish, birds, and insects and have processed well over 100,000 samples. The methodology has been scaled from small sample sizes (< 100) for diversity studies up to many thousands in animal and plant breeding programmes where GBS underpins parentage determination and genomic selection. GBS is also being extended into population and conservation genetics studies. Continuous improvements in wet-lab methods have enabled increased quality and quantity of data generated, with high reproducibility. Furthermore, data analysis has been enhanced through improved bioinformatic pipelines, including a novel statistical method (KGD; Dodds et al., BMC Genomics (2015) 16:1047) designed specifically for utilising GBS data to develop genomic relationship matrices. KGD is particularly suited for the low depth sequencing frequently found with GBS allowing SNP markers with low coverage to be included rather than discarded. In addition KGD does not require imputation making it computationally favourable over other methods. Components of the data analysis pipeline are made available in a public Github repository (https://github.com/Agresearch). Many studies are in collaboration with NZ Crown Research Institutes, universities & research organisations, as well as commercial entities, both nationally and internationally.