The use of shared haplotype length information for pedigree reconstruction in asexually propagated outbreeding crops, demonstrated for apple and sweet cherry

Pedigree information is of fundamental importance in breeding programs and related genetics efforts.However,many individuals have unknown pedigrees.While methods to identify and confirm direct parent–offspring relationships are routine,those for other types of close relationships have yet to be effectively and widely implemented with plants,due to complications such as asexual propagation and extensive inbreeding.The objective of this study was to develop and demonstrate methods that support complex pedigree reconstruction via the total length of identical by state haplotypes(referred to in this study as“summed potential lengths of shared haplotypes”,SPLoSH).A custom Python script,HapShared,was developed to generate SPLoSH data in apple and sweet cherry.HapShared was used to establish empirical distributions of SPLoSH data for known relationships in these crops.These distributions were then used to estimate previously unknown relationships.Case studies in each crop demonstrated various pedigree reconstruction scenarios using SPLoSH data.For cherry,a full-sib relationship was deduced for‘Emperor Francis,and‘Schmidt’,a half-sib relationship for‘Van’and‘Windsor’,and the paternal grandparents of‘Stella’were confirmed.For apple,29 cultivars were found to share an unknown parent,the pedigree of the unknown parent of‘Cox’s Pomona’was reconstructed,and‘Fameuse’was deduced to be a likely grandparent of‘McIntosh’.Key genetic resources that enabled this empirical study were large genome-wide SNP array datasets,integrated genetic maps,and previously identified pedigree relationships.Crops with similar resources are also expected to benefit from using HapShared for empowering pedigree reconstruction.

EASE-DGGS:a hybrid discrete global grid system for Earth sciences

Although we live in an era of unprecedented quantities and access to data,deriving actionable information from raw data is a hard problem.Earth observation systems(EOS)have experienced rapid growth and uptake in recent decades,and the rate at which we obtain remotely sensed images is increasing.While significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster,less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative analysis.Discrete global grid systems(DGGS)have been proposed as one potential solution that addresses the challenge of geospatial data integration and interoperability.Here,we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data sets.We describe the extensions as well as present implementation as an application programming interface(API),which forms part of the University of Minnesota’s GEMS(Genetic x Environment x Management x Socioeconomic)Informatics Center’s API portfolio.