P0958
Developing Foundational Genomic Resources For High Throughput Phenotyping Of Pistachio

William J. Palmer , Genome Center, University of California Davis, Davis, CA
Ewelina Jacygrad , Genome Center, University of California Davis, Davis, CA
John E. Preece , Nat'l Clonal Germplasm Repository, Davis, CA
Deborah Golino , Plant Pathology, University of California Davis, Davis, CA
Richard Michelmore , Genome Center, University of California Davis, Davis, CA
Over 99% of the $1.6 billion American pistachio industry occurs in the Central Valley of California. The environment here is highly consistent, yet growers observe considerable variation in tree size, form, and yield - sometimes necessitating costly removal and replanting of stunted individuals. In the absence of disease or environmental factors, it seems likely that this variation is due to genetic segregation.

The majority of Central Valley orchards consist of vegetative clones of Pistacia vera. However, these are typically grafted onto an interspecific F1 rootstock, UCB-1. The UCB-1 rootstock parents, clones of individual P. atlantica and P. integerrima trees, are thought to be outbreeding and heterozygous. We expect that this diversity is driving rootstock (and overall) performance. Development of diagnostic markers for rootstock vigour would therefore be highly beneficial to the industry - allowing removal of seedlings with probable poor performance prior to planting.

Genomic resources are currently limited for this valuable crop. We are therefore developing foundational resources to enable the development of predictive markers for commercially relevant traits. We have generated draft genome assemblies for P. vera, atlantica, and integerrima - together with an F1 linkage map generated from a mapping population of over nine hundred UCB-1 rootstock seedlings. To generate these data, we leveraged short read and long read technologies including PacBio RSII and 10x Chromium, as well as GBS and Skim-Seq sequencing strategies. Additionally, the UCB-1 mapping population has been phenotyped for numerous traits over several years. This will enable QTL analysis in the near future.