Collaborative Research: SEI: Computational Methods for Kinship Reconstruction

从自然种群中收集基因类型数据的新技术为研究许多基本的生物现象提供了可能性，包括行为、交配系统、适应性特征的遗传力、亲缘选择和扩散模式。从新兴的基因数据中挖掘生态和进化信息是现代生物学中最具挑战性的问题之一。然而，只有在统计和计算方法与我们采样生物体并获得其基因类型的能力保持同步的情况下，才有可能充分利用基因数据。基因类型信息的力量和潜力往往在于我们重建个体之间的谱系关系的能力。目前用于亲属关系(低阶谱系)重建的计算方法主要是在人类群体的背景下开发的。自然种群对遗传研究提出了独特的计算和科学挑战：数据收集往往限于人口统计亚群，如青少年；研究种群的测试数据很少；使用的遗传标记数量相对较少，典型的家庭规模可能比人类大几个数量级。几乎所有现有的亲属关系重建方法都是统计的，因此对噪声和不完全数据很敏感，并且需要关于各种参数分布的先验知识，这在自然种群中很难满足。这项研究的目标是开发一种稳健的计算方法，用于从微卫星遗传数据中重建亲属关系。该方法使用遗传的基本遗传规律来限制可能的亲属关系的遗传配置，并使用强大的优化技术在这些关系中找到最简约的。由此产生的家庭重建方法需要对最少的世代进行抽样，对数据结构使用很少的假设，并且几乎不依赖关于抽样人口的先验知识。这个项目的不同任务包括生物建模、算法设计和实现、优化集成和实验验证，其中许多可能超出了遗传学的范围。研究小组将利用其成员在分子遗传学、数学建模、实验和理论计算机科学方面的不同专业知识，为重建家庭关系开发准确和有效的方法。拟议的跨学科研究将对不同的研究界产生更广泛的影响。改进的亲属关系分析和推断方法为提出新的生物学问题打开了大门。拟议方法的综合优势不仅适用于种群生物学，而且适用于生命科学的各个领域，包括濒危物种的保护和管理、动物行为、进化遗传学、人类系谱、法医学和流行病学，任何时候必须从基因数据推断家庭关系。拟议项目产生的研究和软件将在计算界和生物界传播，并通过跨学科培训活动加以加强。拟议研究的各种科学任务适合生物学和计算机科学领域的广泛学生，并将有助于培养新一代跨学科科学家。

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.