An Integrative Statistical Framework for Assessing and Minimizing Errors in Ancient, Non-invasive and Forensic Genetic Studies

用于评估和最小化古代、非侵入性和法医遗传学研究中的错误的综合统计框架

• 批准号: 0089756
• 负责人: Lisette Waits
• 金额: $ 16.6万
• 依托单位: Regents of the University of Idaho
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-01 至 2004-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0089756&HistoricalAwards=false
• 关键词: Integrative; Statistical; Framework; Assessing; Minimizing

Molecular advances have made it possible to study wildlife populations by extracting and analyzing DNA from shed material such as hair or scat and from museum specimens. The familiar forensic practice of DNA fingerprinting can be used on samples of hair or scat to 'capture,' count and track individuals without ever handling or observing them. Unfortunately, these materials contain low quantities of DNA that introduce sporadic errors and inaccuracies into genetic data. Errors also occur when multiple individuals share the same DNA fingerprint or when one clump of hair, in fact, contains hair from two individuals. In this project, a set of statistical methods will be developed to assess the probability that a given sample contains each of the different possible errors. This leads to an efficient strategy for finding and removing errors by focusing further data acquisition on the most unreliable samples. Finally, the statistical framework will be packaged into a computer program and be made available to all researchers via the internet.Forensic, non-invasive and historical genetic information on wild populations can be used to address questions that are impractical or impossible to address by non-genetic methods. This research project will make a major contribution to accessing this type of information because it directly addresses two issues of paramount importance, accuracy and efficiency. But just as with forensic DNA evidence in a court of law, this information must be reliable for the conclusions to be accurate. Because collecting genetic information is also expensive, its acquisition must be efficient.

分子生物学的进步使得研究野生动物种群成为可能，方法是从毛发或粪便等脱落物和博物馆标本中提取和分析DNA。 DNA指纹识别的熟悉法医实践可以用于头发或粪便样本，以“捕获”，计数和跟踪个人，而无需处理或观察他们。 不幸的是，这些材料含有少量的DNA，这会给遗传数据带来零星的错误和不准确。 当多个个体共享相同的DNA指纹时，或者当一束头发实际上包含来自两个个体的头发时，也会发生错误。 在这个项目中，将开发一套统计方法来评估给定样本包含每个不同的可能错误的概率。 这导致了一种有效的策略，通过将进一步的数据采集集中在最不可靠的样本上来发现和消除错误。 最后，统计框架将被打包成一个计算机程序，并通过互联网提供给所有研究人员。有关野生种群的法医、非侵入性和历史遗传信息可用于解决非遗传方法不切实际或不可能解决的问题。 这一研究项目将对获取这类信息作出重大贡献，因为它直接解决了两个至关重要的问题，即准确性和效率。 但就像法庭上的法医DNA证据一样，这些信息必须可靠，结论才能准确。 由于收集遗传信息也很昂贵，因此必须有效地获取这些信息。