MCA: Estimating quantitative genetic parameters via SNP based relatedness

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。然而，由于行为通常受到大量基因的综合作用的影响，我们对大部分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局限于对实验室动物种群或已被广泛监测了许多代甚至几十年的种群进行的研究。在这里，我们将评估替代方法的能力，通过比较现代遗传学方法的已知值来估计遗传贡献。我们还将开发将估计误差纳入分析的方法。结合这将使行为研究人员更好地了解遗传对行为的影响和这些影响的进化后果。了解行为变异和协变的进化后果，即动物的个性和行为综合征，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，因为在自然群体中，个体之间的相关性通常是未知的。因此，我们对行为（共）变异的定量遗传理解主要基于在不同层次水平上进行的实验室研究或田间研究（例如，非个体变异而不是加性遗传变异）。经典定量遗传分析的替代方案是基于基于SNP的基因组相关性值来估计相关参数。这种方法利用测序进展的力量来确定个体之间的相关性，然后在随后的分析中使用这些相关性值。这就允许在自然种群中提出有关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，其局限性知之甚少。在这个项目中，我们将评估基于SNP的相关性估计正确估计已知遗传力和遗传协方差的能力。同时，我们将开发方法，将相关性估计误差从单核苷酸多态性到定量遗传分析。结合起来，这个项目将促进必要的定量遗传工具的发展，并促进基因组和定量遗传方法的桥梁。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.