MCA: Estimating quantitative genetic parameters via SNP based relatedness

MCA：通过基于 SNP 的相关性估计定量遗传参数

• 批准号: 2222929
• 负责人: Ned Dochtermann
• 金额: $ 25.02万
• 依托单位: North Dakota State University Fargo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222929&HistoricalAwards=false
• 关键词: MCA; Estimating; quantitative; genetic; parameters

Over the last twenty years our understanding of genetics has greatly increased due to the development of genomic tools and approaches. However, because behaviors are typically influenced by the combined effects of large numbers of genes, our understanding of much of behavior still requires classic approaches to estimating the genetic contributions to the behaviors we see animals express. Unfortunately, these classic approaches are largely restricted to work done with lab populations of animals or populations that have been extensively monitored for many generations and, frequently, many decades. Here we will evaluate the ability of alternative approaches to estimate genetic contributions by comparing methods built on modern genetic approaches to known values. We will also develop approaches to incorporate estimation error into analyses. Combined this will allow behavioral researchers to better understand the genetic influences on behavior and the evolutionary consequences of these influences.Understanding the evolutionary consequences, of behavioral variation and covariation—i.e. animal personality and behavioral syndromes—requires estimation of genetic variances and covariances in natural populations. Unfortunately, estimating these parameters is rarely feasible because relatedness among individuals is typically unknown in natural populations. Consequently, our quantitative genetic understanding of behavioral (co)variation is primarily based on laboratory studies or field studies conducted at different hierarchical levels (e.g. among-individual variation rather than additive genetic variation). An alternative to classic quantitative genetic analyses is to estimate the relevant parameters based on SNP based genomic relatedness values. This approach harnesses the power of sequencing advances to determine relatedness among individuals and then use these relatedness values in subsequent analyses. This allows questions about the genetic architecture connecting behaviors to be asked in natural populations. Unfortunately, this approach has rarely been used and its limitations are poorly understood. In this project we will assess the ability of SNP-based estimation of relatedness to properly estimate known heritabilities and genetic covariances. Simultaneously, we will develop methods to incorporate relatedness estimation error from SNPs into quantitative genetic analyses. Combined, this project will foster the development of necessary quantitative genetic tools and facilitate the bridging of genomic and quantitative genetic methodologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的二十年里，由于基因组工具和方法的发展，我们对遗传学的理解大大增加了。然而，由于行为通常受到大量基因的综合影响，我们对许多行为的理解仍然需要经典的方法来估计我们看到的动物表达的行为的遗传贡献。不幸的是，这些经典的方法在很大程度上仅限于对实验室动物或种群所做的工作，这些动物或种群已经被广泛监测了许多代，经常是几十年。在这里，我们将通过将建立在现代遗传方法基础上的方法与已知值进行比较，来评估替代方法估计遗传贡献的能力。我们还将开发将估计误差纳入分析的方法。这将使行为研究人员能够更好地了解遗传对行为的影响以及这些影响的进化后果。要理解行为变异和协变--即动物个性和行为综合征--的进化后果，需要估计自然种群中的遗传方差和协方差。不幸的是，估计这些参数很少是可行的，因为在自然种群中，个体之间的关联性通常是未知的。因此，我们对行为(共同)变异的定量遗传理解主要是基于在不同等级水平上进行的实验室研究或实地研究(例如，个体间变异而不是累加遗传变异)。经典定量遗传分析的另一种选择是根据基于SNP的基因组关联值来估计相关参数。这种方法利用测序进展的力量来确定个体之间的关联性，然后在随后的分析中使用这些关联性值。这使得人们可以在自然种群中提出关于连接行为的遗传结构的问题。不幸的是，这种方法很少被使用，其局限性也鲜为人知。在这个项目中，我们将评估基于SNP的关联性估计正确估计已知遗传力和遗传协方差的能力。同时，我们将开发将SNPs的关联度估计误差纳入定量遗传分析的方法。结合起来，该项目将促进必要的数量遗传工具的开发，并促进基因组和数量遗传方法论之间的联系。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。