III: Medium: Collaborative Research: Scalable Kinship Inference in Wild Populations Across Years and Generations

点击翻译按钮获取中文摘要

Scalable kinship inference in wild populations across years and generationsA cornerstone of research in molecular ecology is the reconstruction of family groups (kinship analysis).Understanding how individuals in free-living populations are related to each other provides the bestopportunity to study many important biological processes, ranging from sexual selection to patternsof dispersal and recruitment. Recent advances in molecular DNA technologies and computationalmethods have made these studies possible. However, many conceptual and computational challengesremain and need to be addressed in order to advance these studies. To date, existing research workon kinship analysis has primarily focused on computational methods that address a single relationship, such as parentage assignment or reconstruction of full sib groups. Inclusion of multiple objectives, such as half-sib reconstruction with minimum parentage assignment, or hierarchy over multiple generations, makes formulation of the underlying computational problem extremely challenging, and simple extensions of previous methods do not address in a practical, scalable, and robust manner the problem of kinship reconstruction for data sets that include multiple generations of species or involve multiple optimization functions.The goal of the proposed research is to design robust, parsimonious, and versatile computationalapproaches for inferring multi-generation kinship relationships in wild populations from multiallelicmarkers. Parsimony assumption is fundamental to these approaches as it requires no prior knowledge,assumptions about sampling methodology, or existence of models, which is the case for most free-livingpopulations. The diverse tasks of this project include formulating computational kinship inferenceproblems based on existing biological studies, analyzing computational complexity of and providingsolutions to the resulting combinatorial optimization problems, and designing robust, scalable andefficient high performance implementations. The resulting computational methods will be evaluatedon datasets collected from existing biological studies and will be deployed to the biological communitythrough the Kinalyzer web-based service, currently actively used for sibship inference only.The research proposed in this project will greatly impact diverse application areas including funda-mental research in combinatorial optimization and data mining, and within biology, areas as diverse asbehavioral ecology, evolutionary genetics, conservation, forensics, and epidemiology. The multidisci-plinary nature of the project and the research team will enhance curriculum design of related areas andintroduce new cross-disciplinary courses. This cohesive, multidisciplinary project will provide trainingopportunities in biology, operation research, algorithms analysis, bioinformatics and high performancecomputing, within a single application framework. The project will leverage the diverse scientific ex-pertise and extensive mentoring experience of the team to foster a true interdisciplinary collaborationand to provide a thriving environment for a new generation of interdisciplinary scientists.