Collaborative Research: SEI: Computational Methods for Kinship Reconstruction

合作研究：SEI：亲属关系重建的计算方法

• 批准号: 0612044
• 负责人: Tanya Berger-Wolf
• 金额: $ 60.82万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0612044&HistoricalAwards=false
• 关键词: Collaborative; Research; SEI; Computational; Methods

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.

从自然种群中收集基因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、适应性特征的遗传性、亲缘选择和扩散模式。从新兴的基因型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，只有当统计和计算方法与我们对生物体进行采样并获得其基因型的能力保持同步时，才有可能充分利用基因型数据。基因型信息的力量和潜力往往取决于我们重建个体间系谱关系的能力。目前的亲属关系（低阶谱系）重建的计算方法主要是在人类种群的背景下开发的。自然种群对遗传研究提出了独特的计算和科学挑战：数据收集通常限于人口统计学亚组，如青少年;研究中的人口测试数据很少;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。几乎所有目前可用的亲属关系重建方法都是统计的，因此对噪声和不完整的数据敏感，并且需要关于各种参数分布的先验知识，这在自然群体中是难以满足的条件。该研究的目的是开发一种强大的计算方法，用于从微卫星遗传数据重建亲属关系。所提出的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来找到那些最吝啬的。 由此产生的家庭重建方法需要采样的最小数量的世代，使用很少的假设数据的结构，并依赖于很少的先验知识的抽样人口。该项目的各种任务包括生物建模，算法设计和实现，优化集成和实验验证，其中许多可能超出遗传学的范围。该研究团队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各种专业知识，为重建家庭关系开发准确有效的方法。 拟议的跨学科研究将对不同的研究群体产生更广泛的影响。 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。所提出的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，人类谱系学，法医学和流行病学，任何时候都必须从遗传数据中推断出家族关系。拟议项目产生的研究成果和软件将在计算界和生物界传播，并通过跨学科培训活动得到加强。 组成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。