New technologies for collecting genotypic data from natural populations open the possibilities of investigating many fundamental biological phenomena, including behavior, mating systems, heritabilities of adaptive traits, kin selection, and dispersal patterns. Mining the emerging genotype data for ecological and evolutionary information is one of the most challenging problems in modern biology. Yet full utilization of the genotypic data is only possible if statistical and computational approaches keep pace with our ability to sample organisms and obtain their genotypes. The power and potential of genotypic information often rests in our ability to reconstruct genealogical relationships among individuals. Current computational methods for kinship (lower order pedigree) reconstruction have been developed mainly in the context of human populations. Natural populations pose unique computational and scientific challenges for genetic research: data collection is often limited to a demographic subgroup, such as juveniles; test data for the population under study is rarely available; the number of used genetic markers is relatively small, and typical family sizes can be orders of magnitude larger than in humans. Almost all currently available kinship reconstruction methods are statistical and thus are sensitive to noisy and incomplete data and require a priori knowledge about various parameter distributions, a difficult condition to satisfy in natural populations. The goal of the proposed research is to develop a robust computational method for reconstructing kinship relationships from microsatellite genetic data. The proposed method uses the fundamental genetic laws of inheritance to limit the genetic configurations of possible kinship relationships and powerful optimization techniques to find among those the most parsimonious. The resulting familial reconstruction method requires sampling a minimal number of generations, uses few assumptions about the structure of the data, and relies on little prior knowledge about the sampled population. The diverse tasks of this project include biological modeling, algorithm design and implementation, optimization integration, and experimental validation, many of which may be of use beyond the scope of genetics. The research team will leverage diverse expertise of its members in molecular genetics, mathematical modeling, experimental and theoretical computer sciences to develop accurate and effective methods for familial relationships reconstruction. The proposed interdisciplinary research will have broader impacts on diverse research communities. Improved methods of analysis and inference of kinship relationships open the door to asking new biological questions. The combined advantages of the proposed approach would be applicable to and useful not only for population biology but to various areas of the life sciences, including conservation and management of endangered species, animal behavior, evolutionary genetics, human genealogy, forensics, and epidemiology, any time familial relationships must be inferred from genetic data. The research and software resulting from the proposed project will be disseminated both in computational and biological communities and enhanced by cross-disciplinary training activities. The diverse scientific tasks that comprised the proposed research are suitable for a wide range of students in biology and computer science and will serve to train a new generation of interdisciplinary scientists.
