DOI: 10.5068/d1b69n
title: Clock-linked genes underlie seasonal migratory timing in a diurnal raptor

description: Seasonal migration is a dynamic natural phenomenon that allows organisms
 to exploit favorable habitats across the annual cycle. While the
 morphological, physiological, and behavioral changes associated with
 migratory behavior are well characterized, the genetic basis of migration
 and its link to endogenous biological timekeeping pathways is poorly
 understood. Historically, genome-wide research has focused on genes of
 large effect, whereas many genes of small effect may work together to
 regulate complex traits like migratory behavior. Here, we explicitly relax
 stringent outlier detection thresholds and, as a result, discover how
 multiple biological timekeeping genes are important to migratory timing in
 an iconic raptor species, the American Kestrel (Falco sparverius). To
 validate the role of candidate loci in migratory timing, we genotyped
 Kestrels captured across autumn migration and found significant
 associations between migratory timing and genetic variation in metabolic
 and light input pathway genes that modulate biological clocks (TOP1,
 PHLPP1, CPNE4, and PEAK1). Further, we demonstrate that migrating
 individuals originated from a single panmictic source population,
 suggesting the existence of distinct early and late migratory genotypes
 (i.e. chronotypes). Overall, our results provide empirical support for the
 existence of a within population-level polymorphism in genes underlying
 migratory timing in a diurnally migrating raptor.
description: We designed Fluidigm SNPtype assays and used them to screen
 additional breeding and migrating American Kestrels that were independent
 of the RAD-seq analyses above.  Specifically, we used the R package
 <i>snps2assays </i>(Anderson 2015) to evaluate the efficacy of
 designing assays for candidate loci. We considered the assays designable
 if GC content was less than 0.65, there were no insertions or deletions
 (indels) within 30bp of the target variant, and there were no additional
 variants within 20bp of the targeted variable site. We filtered out assays
 with primers that mapped to multiple locations in the genome (<i>bwa
 mem</i>:<i> </i>Li and Durbin 2009), resulting in assays
 for nine loci in nine candidate genes. We used the resulting Fluidigm
 assays to genotype the nine candidate migration genes in 738 breeding
 American Kestrels from 83 sites and 165 migrating American Kestrels from a
 single migration station in Boise, Idaho collected in a three-month
 time-series spanning fall migration over two years. We
 then used a multi-gene and single gene framework to determine whether
 migratory timing was significantly associated with allele frequency shifts
 in the nine candidate migration genes. To determine how the nine candidate
 genes covary with each other, we conducted an ordinal principal component
 analysis (PCA) using the R software package <i>gifi (</i>Mair
 and De Leeuw 2019) . We used a linear regression to evaluate whether
 migration timing (day of year when a fall migrant was captured) was
 associated with genetic variation as measured by PC1 and PC2, and included
 a covariate of sex to account for the potential influence of differential
 migration between sexes on migration timing. To investigate single gene
 effects, we fit linear regression models of each allele frequency of the
 top 4 candidate genes, <i>i.e.</i> those that loaded strongly
 on PC1, <i>TOP1</i>, <i>PEAK1</i>,
 <i>PHLPP1</i> and <i>CPNE4</i>, to migration
 timing as defined by the midpoint day of each week during the autumn
 migration period and using the <i>lm</i> model in the R
 software package <i>stats</i> v 3.6.2 (R Core Team 2019). The
 nonlinear decline in allele frequency over time prompted the fitting of a
 curved regression model, and we tested whether this linear regression
 polynomial model provided a better fit using a likelihood ratio test in
 the R package <i>lmtest</i> v 0.9-37 (Zeileis
 and Hothorn 2002). To test whether seasonal allele
 frequency trends result from different populations migrating through the
 migration station at different times or distinct migratory chronotypes, we
 examined the association between PC1 and latitude as well as allele
 frequency in our 4 top ranked loci and latitude of kestrels breeding
 across the west. Further, we genotyped 151 of the 165 migrating birds from
 Boise, Idaho (all samples for which we had high quality DNA remaining)
 with population-specific SNP-type assays used in Ruegg <i>et
 al</i>. (2021), and assigned these birds to the breeding population
 of origin using <i>rubias</i> (Anderson and Moran
 2018).   Anderson, E. C.
 <i>snps2assays: Prepare SNP assay orders from ddRAD or RAD
 loci</i>. (2015) Anderson, E. C. & Moran, B.
 <i>rubias: Bayesian Inference from the Conditional Genetic Stock
 Identification Model</i>. (2018) Li, H. &
 Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler
 transform. <i>Bioinformatics</i> <b>25</b>,
 1754–1760 (2009). Mair, P. & De Leeuw, J.
 <i>Gifi: Multivariate analysis with optimal scaling</i>.
 (2019). R Core Team.
 <i>R: A language and environment for statistical
 computing</i>. (R Foundation for Statistical   Computing,
 2019). Ruegg, K. C.
 <i>et al.</i> The American Kestrel genoscape (<i>Falco
 sparverius</i>): Implications for monitoring, management, and
 subspecies boundaries. <i>Auk</i> (2021) Zeileis, A. & Hothorn, T.
 Diagnostic checking in regression relationships. R News 2(3), 7-10.
 <i>R News</i> <b>2</b>, 7–10 (2002).