NSF Award
2222929
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.<br/><br/>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.<br/><br/>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.
