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\begin{document}

\title{Genetic architecture and heritability of early-life telomere length in a
wild passerine}



\author[1]{Michael Pepke}%
\author[2]{Thomas Kvalnes}%
\author[3]{Sarah Lundregan}%
\author[4]{Winnie Boner}%
\author[4]{Pat Monaghan}%
\author[2]{Bernt-Erik Sæther}%
\author[2]{Henrik Jensen}%
\author[2]{Thor Harald Ringsby}%
\affil[1]{Norges teknisk-naturvitenskapelige universitet}%
\affil[2]{Norwegian University of Science and Technology}%
\affil[3]{Norges Teknisk Naturvitenskapelige Universitet Institutt for biologi}%
\affil[4]{Glasgow University}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\selectlanguage{english}
\begin{abstract}
Early-life telomere length (TL) is associated with fitness in a range of
organisms. Little is known about the genetic basis of variation in TL in
wild animal populations, but to understand the evolutionary and
ecological significance of TL it is important to quantify the relative
importance of genetic and environmental variation in TL. In this study,
we measured TL in 2746 house sparrow nestlings sampled across 20 years
and used an animal model to show that there is a small heritable
component of early-life TL (h2=0.04), but with a strong component of
maternal inheritance. Variation in TL among individuals was mainly
driven by environmental (year) variance, but also brood and parental
effects. We did not find evidence for a negative genetic correlation
underlying the observed negative phenotypic correlation between TL and
structural body size. Thus, TL may evolve independently of body size and
the negative phenotypic correlation is likely to be caused by
non-genetic environmental effects. We further used genome-wide
association analysis to identify genomic regions associated with TL
variation. We identified several putative genes underlying TL variation;
these have been inferred to be involved in oxidative stress, cellular
growth, skeletal development, cell differentiation and tumorigenesis in
other species. Together, our results show that TL is a lowly heritable,
polygenic trait which is strongly affected by environmental conditions
in a free-living bird.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Telomeres are nucleoprotein structures that cap the ends of linear
chromosomes in most eukaryotes (Blackburn, 1991). Understanding the
causes of individual variation in telomere length (TL) is important
because this trait has been shown to predict variation in survival or
lifespan within and among species (Joeng, Song, Lee, \& Lee, 2004; Bize,
Criscuolo, Metcalfe, Nasir, \& Monaghan, 2009; Monaghan, 2010; Heidinger
et al., 2012; Tricola et al., 2018; Wilbourn et al., 2018; Pepke \&
Eisenberg, 2021) and individual fitness in wild animals (Eastwood et
al., 2019). Telomeres shorten through life in many organisms (Dantzer \&
Fletcher, 2015) due to cell division, oxidative stress, and other
factors (Jennings, Ozanne, \& Hales, 2000; Reichert \& Stier, 2017),
which can ultimately result in telomere dysfunction, genome instability,
and cell death (Nassour et al., 2019) and organismal senescence (Herbig,
Ferreira, Condel, Carey, \& Sedivy, 2006). Individual TLs may act as
biomarkers or sensors of exposure to intrinsic and extrinsic stressors
(Houben, Moonen, van Schooten, \& Hageman, 2008), and hence reflect
individual condition (Rollings et al., 2017), but the physiological
mechanisms underlying the ontogenetic variation in TL is not well known
(Monaghan, 2014; Erten \& Kokko, 2020). Several studies have
investigated the potential of telomere dynamics (i.e. individual
differences in TL and telomere loss rate) in mediating life-history
trade-offs both across (Dantzer \& Fletcher, 2015; Pepke \& Eisenberg,
2020) and within relatively long-lived species (Monaghan, 2010; Spurgin
et al., 2018). However, despite being an ecologically important trait in
many species (Wilbourn et al., 2018), knowledge about the genetic
architecture of TL or its adaptive potential in wild populations remains
scarce (Dugdale \& Richardson, 2018).

Quantifying the additive genetic variance of a trait is required to
understand mechanisms driving adaptive evolution, i.e. the response to
selection on a trait (Lande, 1979; Ellegren \& Sheldon, 2008; Kruuk,
Slate, \& Wilson, 2008). However, the magnitude of the heritability and
mode of inheritance of TL is not well-known in populations of wild
animals, and few general patterns have been described (Horn et al.,
2011; Dugdale \& Richardson, 2018; Bauch, Boonekamp, Korsten, Mulder, \&
Verhulst, 2019). Utilizing long-term pedigree data, individual variation
in early-life TL can be decomposed into various genetic and
environmental sources of variation through a type of mixed-effect model
(`animal model'), which takes all relationships from the pedigree into
account (Kruuk, 2004; Wilson et al., 2010). Estimates of TL
heritabilities from studies using animal models (reviewed in Dugdale \&
Richardson, 2018) have varied considerably across bird species from
\emph{h\textsuperscript{2}} =0 (\emph{n} =177, in wild white-throated
dippers, \emph{Cinclus cinclus} , Becker et al., 2015)
to\emph{h\textsuperscript{2}} =0.99 (\emph{n} =125, in captive zebra
finches, \emph{Taeniopygia guttata} , Atema et al., 2015). While most
studies are characterized by relatively small sample sizes, recent
long-term studies on Seychelles warblers (\emph{Acrocephalus
sechellensis} , \emph{n} =1317, \emph{h\textsuperscript{2}} =0.03-0.08,
Sparks et al., 2021) and common terns (\emph{Sterna hirundo} ,\emph{n}
=387, \emph{h\textsuperscript{2}} =0.46-0.63, Vedder et al., 2021) also
revealed contrasting estimates of TL heritabilities. Epidemiological
studies of humans have documented consistently high TL heritabilities,
ranging from \emph{h\textsuperscript{2}} =0.34-0.82 (Broer et al.,
2013). In humans, some studies reported strong paternal inheritance
(e.g. Njajou et al., 2007) or maternal inheritance (e.g. Broer et al.,
2013) or that there were no differences in parental mode of inheritance
(e.g. Eisenberg, 2014). In birds, several studies have documented
maternal effects on offspring telomere dynamics (Horn et al., 2011;
Asghar, Bensch, Tarka, Hansson, \& Hasselquist, 2015; Reichert et al.,
2015; Heidinger et al., 2016), or effects of parental age at conception
on offspring TL (Eisenberg \& Kuzawa, 2018). Reichert et al. (2015)
found a significant correlation between mother-offspring TL measured at
10 days of age in king penguins (\emph{Aptenodytes patagonicus} ), but
not when TL was measured at later ages (\textgreater{}70 days). This may
be because post-natal telomere loss rate is strongly influenced by
individual environmental circumstances (Wilbourn et al., 2018;
Chatelain, Drobniak, \& Szulkin, 2020) and does not always correlate
strongly with chronological age (Boonekamp, Simons, Hemerik, \&
Verhulst, 2013; Boonekamp, Mulder, Salomons, Dijkstra, \& Verhulst,
2014).

Telomeres shorten during growth and a negative phenotypic correlation
between TL and body size has been documented within several species
(Monaghan \& Ozanne, 2018). This may indicate that there is a negative
genetic correlation between TL and size, which could act as an
evolutionary constraint on the response of TL to selection on body size
and contribute to the trade-off between growth and lifespan (Metcalfe \&
Monaghan, 2003; Roff \& Fairbairn, 2012). Thus, quantifying the genetic
correlation between TL and size enables us to determine whether TL can
evolve independently of body size. Pepke et al. (2021, \emph{submitted}
) showed that artificial directional selection on body size affected TL
in the opposite direction. However, it is not known if there is a
genetic correlation between the two traits, in which case selection
acting on TL will affect body size. It is also possible that the
negative phenotypic correlation between TL and size has no genetic basis
but is shaped by environmental (co)variances (Hadfield, 2008; Kruuk et
al., 2008).

TL is a complex phenotypic trait (Aviv, 2012; Hansen et al., 2016)
expect to be polygenic, i.e. affected by small effects of many genes
(Hill, 2010; Dugdale \& Richardson, 2018). Accordingly, numerous
genome-wide association studies (GWAS), which tests associations of
single-nucleotide polymorphisms (SNPs) with specific traits, have
identified several loci correlated with TL in humans that map to genes
involved in telomere and telomerase maintenance, DNA damage repair,
cancer biology, and several nucleotide metabolism pathways (e.g.
Vasa-Nicotera et al., 2005; Andrew et al., 2006; Codd et al., 2010; Levy
et al., 2010; Mirabello et al., 2010; Jones et al., 2012; Mangino et
al., 2012; Soerensen et al., 2012; Codd et al., 2013; Deelen et al.,
2013; Liu et al., 2014; Mangino et al., 2015; Ojha et al., 2016; Delgado
et al., 2018; Zeiger et al., 2018; Coutts et al., 2019; Nersisyan et
al., 2019; Li et al., 2020). None of the GWA studies in humans
specifically tested the marker associations of early-life TL, which pose
a challenge to the interpretation of the results, as TL shortens through
life in humans (Blackburn, Epel, \& Lin, 2015) and genes may have
different impacts at various life stages. Furthermore, large sample
sizes and dense sampling of genetic loci is needed to ensure high power
in GWA studies (Mackay, Stone, \& Ayroles, 2009) and resolve any
pleiotropic effects (Prescott et al., 2011). The genes influencing TL in
humans that were identified through GWAS only explain a small proportion
of the inter-individual variation in TL (\textless{}2 \%, Aviv, 2012;
Codd et al., 2013; Fyhrquist, Saijonmaa, \& Strandberg, 2013). One GWAS
on TL of a non-human species (dairy cattle, \emph{Bos taurus} ) was
recently performed (Ilska-Warner et al., 2019) supporting the polygenic
nature of early-life TL. However, domesticated species in captivity may
display TL dynamics that are not representative for natural populations
(Eisenberg, 2011; Pepke \& Eisenberg, 2021). There is a paucity of GWAS
on TL performed in natural populations.

In this study, we aim to provide novel insights into the genetic
architecture of TL and the evolutionary mechanisms by which natural
selection can alter telomere ecology using data from a passerine bird.
We sampled TL of most individuals (\emph{n} =2746) born within 20
cohorts in two natural insular populations of wild house sparrows
(\emph{Passer domesticus} ) at about the same age (11 days), in addition
to individuals at the same age in two insular populations that underwent
artificial selection on body size for 4 consecutive years (\emph{n}
=569, Kvalnes et al., 2017; Pepke et al., 2021, \emph{submitted} ).
First, we estimate the phenotypic correlations between TL and tarsus
length (as a proxy for body size, Araya-Ajoy et al., 2019) in house
sparrow nestlings. Second, we test for effects of parental age on
offspring TL. Third, we estimate heritability, environmental variances,
and parental effects on early-life TL, and test for genetic correlations
between TL, body size, and body condition in the natural populations
(primary analyses). We then use similar analyses in the artificially
selected populations to validate our results from the primary analyses.
Finally, we use high-density genome-wide Single Nucleotide Polymorphism
(SNP) genotype data (Lundregan et al., 2018) in a GWAS to identify
genetic regions and potential candidate genes underlying variation in
early-life TL within wild house sparrows (up to\emph{n} =383).

\textbf{MATERIALS AND METHODS}

\textbf{Study populations and data collection}

The study was performed in four insular house sparrow populations off
the coast of northern Norway (Fig. S1.1 in Appendix S1). The study
periods differed between the populations with data from Hestmann\selectlanguage{ngerman}øy
(66°33'N, 12°50`E) in the years 1994-2013, Træna (Husøy island, 66°30'N,
12°05`E) in the years 2004-2013, and Leka (65°06'N, 11°38'E) and Vega
(65°40'N, 11°55'E) both in the years 2002-2006. Hestmannøy and Træna
were unmanipulated natural populations and are included in the primary
analyses. The populations of Leka and Vega underwent artificial size
selection (see Kvalnes et al., 2017; Pepke et al., 2021,\emph{submitted}
) and were analyzed separately in a set of secondary analyses as
replications of the primary analyses. All four islands are characterized
by heathland, mountains, and sparse forest. The sparrows live closely
associated with humans and within the study area they are found mainly
on dairy farms (Hestmannøy, Vega and Leka), where they have access to
food and shelter all year, or in gardens and residential areas (Træna),
where they may be more exposed to weather conditions (Araya-Ajoy et al.,
2019). Natural nests inside barns or artificial nest boxes were visited
at least every 9\textsuperscript{th} day during the breeding season
(May-August) to sample fledglings (5-14 days old, with a median of 11
days). All individuals were ringed using a unique combination of a metal
ring and three plastic color rings. Fledged juvenile sparrows and
unmarked adults were captured using mist nets during the summer and
autumn (September-October). These procedures ensured that approximately
90\% of all adult birds were marked on all islands during the study
period (Jensen, Steinsland, Ringsby, \& Sæther, 2008; Kvalnes et al.,
2017). For most fledglings, we measured tarsometatarsus (tarsus) length
using digital slide calipers to nearest 0.01 mm and body mass to nearest
0.1 g with a Pesola spring balance (see details in Appendix S1). For 234
nestlings, no nestling morphological measurements were available.
Following Schulte-Hostedde, Zinner, Millar, and Hickling (2005) nestling
body condition was calculated as the residuals of a linear regression of
mass on tarsus length (both log\textsubscript{10}-transformed). To avoid
collinearity in models where both nestling age and tarsus length were
included as covariates, we age-corrected tarsus length by using the
residuals from a regression of tarsus length on age and age squared (to
account for the diminishing increase in tarsus length with age). A blood
sample (25 \selectlanguage{greek}μ\selectlanguage{english}L) was collected from all individuals, which was stored in
96\% ethanol at room temperature in the field and subsequently at -20\selectlanguage{ngerman}°C
in the laboratory until DNA extraction.

\textbf{Molecular sexing and pedigree construction}

DNA extraction is described in Appendix S1. Sex of most fledglings
(\emph{n} =2641) was determined using amplification of the CHD-gene
located on the avian sex chromosomes as described in Griffiths, Double,
Orr, and Dawson (1998). 21 individuals were sexed exclusively based on
their phenotype as adults and 84 nestlings could not be sexed. We used
individual genotypes on 13 polymorphic microsatellite markers scored
using the GeneMapper 4.0 software (Applied Biosystems) to assign
parentage in CERVUS 3.0 (Kalinowski, Taper, \& Marshall, 2007), as
detailed in Rønning et al. (2016). Briefly, for each nestling, CERVUS
calculates a LOD\selectlanguage{english}-score (log\selectlanguage{english}-likelihood ratio) for all putative parents,
which is compared to the critical values generated by the simulated
parentage analyses, resulting in a 95\% parentage assignment confidence.
Nestlings within the same clutch were assumed to have the same mother.
Nestlings with missing (unassigned) parents were assigned dummy parents,
assuming that nestlings within the same clutch were full siblings and
thus had the same (unassigned) parents. The dummy parents were included
in the pedigree as founders. We calculated individual inbreeding
coefficients (\emph{F} ) based on the microsatellite pedigree using the
R package `pedigree' (Coster, 2012). Pedigrees were ordered using the R
package `MasterBayes' (Hadfield, Richardson, \& Burke, 2006) and pruned
to only contain informative individuals. The pruned pedigrees included
4118 individuals (3093 maternities and 3130 paternities) in the natural
populations, and 1057 individuals in artificially selected populations.
Maximum pedigree depth was 13 generations, the number of equivalent
complete generations (the sum of the proportion of known ancestors
across all generations, Wellmann, 2021) was 1.510, and mean pairwise
relatedness was 0.003.

\textbf{Telomere length measurements}

Relative erythrocyte telomere lengths (TL) of 2746 nestlings from
Hestmannoy and Traena were successfully measured using the real-time
quantitative polymerase chain reaction (qPCR) amplification method by
Cawthon (2002) with modifications by Criscuolo et al. (2009). Primer
sequences, PCR assay setup and thermal profiles followed Pepke et al.
(2021, \emph{submitted} ) and are detailed in Appendix S1. Briefly, this
method measures the ratio of telomere sequence relative to the amount of
a non-variable gene (GAPDH) and a reference sample. The reference sample
consisted of pooled DNA from 6 individuals, which was also included as a
2-fold serial dilution (40-2.5 ng/well) on all plates to produce a
standard curve, in addition to a non-target control sample (all in
triplicates). Samples were randomized and run on 2x127 96-well plates
(telomere and GAPDH assays, respectively). The qPCR data was analyzed
using the qBASE software (Hellemans, Mortier, De Paepe, Speleman, \&
Vandesompele, 2007), which computes relative TL as the ratio (T/S) of
the telomere repeat copy number (T) to a single copy gene number (S)
similar to Cawthon (2002). In qBASE the T/S ratio is calculated as
calibrated normalized relative quantities (CNRQ) that control for
differences in amplification efficiency between plates and for inter-run
variation by including three inter-run calibrators from the standard
curve. All individual plate efficiencies were within 100+-10\% (mean
telomere assay efficiency was 97.5+-3.9\%, and 97.6+-4.2\% for GAPDH
assays). The average of the reference sample cycle thresholds (Ct)
across all plates were 10.54+-0.03 S.D. and 21.53+-0.02 S.D. for telomere
and GAPDH assays, respectively. Thus, while reproducibility of TL
measurements within the reference sample of the same DNA sample extract
is high, we performed DNA re-extraction of the same blood samples for 25
individuals to test TL consistency across DNA extractions (Appendix S1).
The re-extractions were run on different plates and the TL estimates of
these samples remained highly correlated (R\textsuperscript{2}=0.75,
Fig. S1.2). For these individuals, the average of the TL measurements
was used in subsequent analyses. All reactions for the primary analyses
(from the populations on Hestmannoy and Traena) were performed by the
same person (MLP). MLP and WB generated the secondary dataset (\emph{n}
=569 on 2x21 plates, from the populations on Leka and Vega) as described
in Pepke et al. (2021, \emph{submitted} ). The primary and secondary
datasets used different reference samples and are therefore not combined
in the analyses.

\textbf{Statistical analyses}

\emph{The correlation between tarsus length and telomere length}

We first tested the phenotypic correlation between TL and tarsus length
(as a proxy for body size) within 2462 house sparrow nestlings from
Hestmannoy and Traena. TL (response variable) was
log\textsubscript{10}-transformed and linear mixed-effects models (LMMs)
were fitted with a Gaussian error distribution (R package `\emph{lme4}
', Bates, Machler, Bolker, \& Walker, 2015). Sex differences in TL are
known for house sparrows (Pepke et al., 2021,\emph{submitted} ). Thus,
models included sex, (continuous) fledgling age at sampling, hatch day
(ordinal date mean centered across years), and island identity as fixed
effects. We fitted random intercepts for brood identity and year to
account for the non-independence of nestlings from the same brood and
year. Because our study populations are known to be affected by
inbreeding depression (Niskanen et al., 2020), we included the
inbreeding coefficient (\emph{F} ) as a fixed effect (Reid \& Keller,
2010). We then compared models with and without (age-standardized)
tarsus length using Akaike's information criterion corrected for small
sample sizes (\emph{AICc} , Akaike, 1973; Hurvich \& Tsai, 1989), and
Akaike weights (\emph{w} ) and evidence ratios (\emph{ER} ) to determine
the relative fit of models given the data (Burnham \& Anderson, 2002).
Models were validated visually by diagnostic plots and model parameters
are from models refitted with restricted maximum likelihood (REML).
Estimates and 95\% confidence intervals (CI) are reported.

\emph{Parental age effects on offspring telomere length}

We tested whether maternal age at conception (MAC {[}mean 1.8+-1.1 S.D.
years, range 1-7 years{]}, \emph{n} =373 mothers with \emph{n} =1967
offspring) or paternal age at conception (PAC {[}mean 2.1+-1.2 S.D.
years, range 1-8 years{]}, \emph{n} =388 fathers with \emph{n} =1927
offspring) predicted TL in offspring from Hestmannoy and Traena. We
applied within-subject centering (van de Pol \& Wright, 2009) to
separate within-parental age effects (e.g. senescence) from
between-parental age effects (e.g. selective disappearance), by
including both the mean parental age at conception and the deviation
from the mean parental age for each parent as fixed effects in two LMMs
(for fathers and mothers, respectively) explaining variation in
offspring TL (log\textsubscript{10}-transformed). Both models included
island identity and sampling age as fixed effects, and random intercepts
for year and either maternal identity or paternal identity.

\emph{Heritabilities and genetic correlation of telomere length, tarsus
length, and body condition}

We used a multivariate Bayesian animal model (Kruuk, 2004; Hadfield,
2019) fitted with Markov chain Monte Carlo (MCMC) to estimate
heritability and genetic correlations of early-life TL, age-standardized
tarsus length and body condition in the two natural island populations
(Hestmannoy and Traena, \emph{n} =2662) and the two manipulated island
populations (Leka \& Vega, \emph{n} =569) that underwent artificial size
selection. TL was log\textsubscript{10}-transformed and all traits were
fitted with a Gaussian error distribution using the R package `MCMCglmm'
(Hadfield, 2010). Models included sex, fledgling age at sampling, island
identity, and inbreeding coefficient (\emph{F} ) as fixed effects
(Wilson, 2008), which were fitted such that different regression slopes
were estimated for each trait (Hadfield, 2019). To estimate variance
components, random intercepts were included for individual identity
(`animal', \emph{V\textsubscript{A}} ), brood identity
(\emph{V\textsubscript{B}} ) nested under mother identity, father
(\emph{V\textsubscript{F}} ) and mother identity
(\emph{V\textsubscript{M}} ), and birth year (cohort
effects,\emph{V\textsubscript{Y}} ). Parental effects include those
influences on offspring TL that are repeatable across the lifetime of
the mother or father (Kruuk \& Hadfield, 2007), while brood identity
accounts for other common environmental effects (McAdam, Garant, \&
Wilson, 2014). House sparrows are multi-brooded laying up to 3 clutches
in a season and may breed in multiple years, with an average of 3.6+-1.3
S.D. fledglings per brood in this study. They are socially monogamous,
but extra-pair paternity occurs at rates of 14-18 \% in wild populations
(Ockendon, Griffith, \& Burke, 2009; Hsu, Schroeder, Winney, Burke, \&
Nakagawa, 2014). Using genetic pedigrees, extra-pair paternity can be
seen as natural cross-fostering experiments that improve statistical
power to separate genetic and environmental variance components (Kruuk
\& Hadfield, 2007). Random effects were specified with 3x3 covariance
matrices to estimate the variances and covariances between the effects
for each trait.

We also ran univariate models of TL, tarsus length and body condition
including the same fixed and random effects as in the multivariate model
(Appendix S2). For comparison with previous studies (e.g. Asghar et al.,
2015), we tested whether maternal TL and/or paternal TL predicted
offspring TL using two LMMs (parent-offspring regressions, Appendix S2).
Furthermore, we included maternal (\emph{V\textsubscript{DAM}} ) and
paternal (\emph{V\textsubscript{SIRE}} ) genetic effects (e.g. Wolf \&
Wade, 2016) in a multivariate animal model to quantify these effects
while accounting for the environmental variances specified above
(Appendix S2). To test for sex-specific heritabilities (e.g. Jensen et
al., 2003; Olsson et al., 2011), we ran a bivariate animal model of TL
in females and males as two different phenotypic traits with a genetic
correlation between them (Appendix S2).

We used inverse-Wishart priors for random effects and residual variances
in the multivariate model (V=I\textsubscript{3} and nu=3, Hadfield,
2019). We re-ran analyses with other relevant priors (parameter
expanded) to verify that results were not too sensitive to the choice of
prior. The MCMC chain was run for 2,000,000 iterations, sampling every
500 iterations after a burn-in of 5\% (100,000 iterations). Mixing and
stationarity of the MCMC chain was checked visually and using
Heidelberger and Welch's convergence test (Heidelberger \& Welch, 1983)
implemented in the `coda' package (Plummer, Best, Cowles, \& Vines,
2006). All autocorrelation values were \textless{}0.1 and effective
sample sizes were \textgreater{}3,000. The narrow-sense heritability was
calculated as the posterior mode of the proportion of phenotypic
variance explained by additive genetic variance (Wilson et al.,
2010):\(h^{2}=\frac{V_{A}}{(V_{A}+{V_{B}+V_{F}+V_{M}+V_{R}+\ V}_{Y})}\), where \emph{V\textsubscript{R}} is the
residual variance. Estimates are provided as their posterior mode with
95\% highest posterior density intervals (HPD). All analyses were
performed in R version 3.6.3 (R Core Team, 2020).

\emph{SNP genotype data and association analyses}

Nestlings that survived to adulthood (recruited) on Hestmannoy and Traena
were genotyped on a high-density 200K SNP array (detailed in Lundregan
et al., 2018) with median distances between SNPs shorter than 5,000 bp.
SNPs were originally identified from whole-genome re-sequencing of 33
individual house sparrows which were mapped to the house sparrow
reference genome (Elgvin et al., 2017). DNA was extracted as described
in Hagen et al. (2013), separately from telomere analyses. Data
preparation and quality checks were performed using the `GenABEL'
package (GenABEL project developers, 2013). We removed SNPs or
individuals for which there was more than 5\% missing data, the minor
allele frequency (MAF) was less than 1\%, or pairwise identity-by-state
(IBS) was more than 95\%. After quality control, the genomic
relationship matrix (GRM) was computed based on 180,650 {[}180,666{]}
autosomal markers in 373 {[}383{]} individuals (142 {[}145{]} males and
137 {[}142{]} females from Hestmannoy and 47 {[}48{]} males and 47
{[}48{]} females from Traena) with numbers in brackets showing sample
sizes when individuals with missing tarsus length measurements are
included. We then performed two GWA analyses by fitting LMMs for the
variation in TL using the package `RepeatABEL' (Ronnegard et al., 2016):
The first model included age-standardized tarsus length as a covariate,
and the second model did not. Both models included sex, age, hatch day
(mean centered), \emph{F} , and island identity as fixed effects, and
brood identity, year, and the GRM fitted as random effects. Finally, we
determined if SNPs significantly associated with TL were within 100 kb
of any gene within the annotated house sparrow genome, because this is
the distance that linkage disequilibrium decays to background levels in
this species (Elgvin et al., 2017; Hagen et al., 2020).

\textbf{RESULTS}

\emph{The correlation betweentarsus length and telomere length}

The model that included tarsus length was the highest ranked model for
explaining variation in TL (\emph{w} \textsubscript{1}=0.78,\emph{ER}
\textsubscript{1}=\emph{w\textsubscript{1}/w\textsubscript{2}} =3.55,
model without tarsus length: \selectlanguage{english}\emph{[?]\textsubscript{2}AICc} =2.5). Thus,
there was a negative association between tarsus length and TL
(\selectlanguage{greek}\emph{β\textsubscript{tarsus length}}\selectlanguage{english} =-0.004\selectlanguage{ngerman}±0.002, CI={[}-0.007,
-0.000{]}, \emph{n} =2462, Fig. 1 and Table 1), such that larger
nestlings generally had slightly shorter early-life telomeres.

\emph{Parental age effects on offspring telomere length}

There was no evidence for associations between offspring TL and MAC
(\selectlanguage{greek}\emph{β\textsubscript{[?]MAC}} =0.004\selectlanguage{ngerman}±0.004, CI={[}-0.005,
0.013{]},\selectlanguage{greek}\emph{β\textsubscript{mean MAC}}\selectlanguage{english} =0.002\selectlanguage{ngerman}±0.005, CI={[}-0.008,
0.012{]}, Fig. S2.1a,c) or PAC (\selectlanguage{greek}\emph{β\textsubscript{[?]PAC}}
=0.000\selectlanguage{ngerman}±0.003, CI={[}-0.005, 0.006{]}, \selectlanguage{greek}\emph{β\textsubscript{mean PAC}}\selectlanguage{english}
=0.002\selectlanguage{ngerman}±0.005, CI={[}-0.007, 0.012{]}, Fig. S2.1b,d).

\emph{Heritabilities and genetic correlations of telomere length, tarsus
length, and body condition}

We found non-zero additive genetic variances (\emph{V\textsubscript{A}}
) for TL (\emph{V\textsubscript{A}} =0.009, HPD={[}0.008, 0.010{]}),
tarsus length (\emph{V\textsubscript{A}} =0.190, HPD={[}0.116, 0.315{]})
and body condition (\emph{V\textsubscript{A}} =0.006, HPD={[}0.005,
0.006{]}) in the natural populations (Table 2, Fig. 3). The main
component contributing to variance in TL was between-year differences
(\emph{V\textsubscript{Y}} , explaining 70\% of the variance), while
maternal (\emph{V\textsubscript{M}} , 8\%), paternal
(\emph{V\textsubscript{F}} , 7\%), and brood variances
(\emph{V\textsubscript{B}} , 6\%) also explained considerable
proportions of the total phenotypic variance (Fig. 3). Combined, these
environmental effects captured 91\% of the phenotypic variance in TL.
For tarsus length and condition, the main variance components were among
different broods (38\%) and among years (77\%), respectively (Table 2,
Fig. 3). The heritabilities were \emph{h\textsuperscript{2}} =0.042 for
TL (HPD={[}0.024, 0.064{]}), \emph{h\textsuperscript{2}} =0.076
(HPD={[}0.045, 0.123{]}) for tarsus length,
and\emph{h\textsuperscript{2}} =0.028 (HPD={[}0.016, 0.0.043{]}) for
body condition. The heritability estimates were of the same magnitude in
the univariate animal models (Table S2.1). There was no evidence for a
genetic correlation between TL and tarsus length
(\emph{r\textsubscript{A}} =-0.017, HPD={[}-0.120, 0.091{]}) or between
TL and condition (\emph{r\textsubscript{A}} =-0.010, HPD={[}-0.079,
0.060{]}).

Parent-offspring regressions showed a large maternal inheritance
component in TL (\emph{h\textsuperscript{2}\textsubscript{maternal}}
=0.45±0.16, CI={[}0.136, 0.758{]}), but no paternal inheritance (Fig.
S2.2). Including parental genetic effects in a multivariate animal model
confirmed slightly higher maternal
(\emph{h\textsuperscript{2}\textsubscript{maternal}} =0.078,
HPD={[}0.042, 0.104{]}) than paternal heritability of TL
(\emph{h\textsuperscript{2}\textsubscript{paternal}} =0.070,
HPD={[}0.038, 0.094{]}, Table S2.2). We found no evidence of differences
in sex-specific heritabilities of TL (Table S2.3).

In the analyses of the artificially selected populations (Leka and Vega,
Table S2.4) we found comparable heritability estimates for TL
(\emph{h\textsuperscript{2}} =0.035, HPD={[}0.008, 0.084{]}) and body
condition (\emph{h\textsuperscript{2}} =0.019, HPD={[}0.004, 0.050{]}),
and a slightly higher estimate for tarsus length
(\emph{h\textsuperscript{2}} =0.114, HPD={[}0.045, 0.247{]}). Similarly,
there was no evidence for genetic correlations between TL and tarsus
(\emph{r\textsubscript{A}} =-0.027, HPD={[}-0.224, 0.181{]}) or between
TL and body condition (\emph{r\textsubscript{A}} =-0.006, HPD={[}-0.130,
0.134{]}, Table S2.4).

\emph{GWA analyses}

Six SNPs showed evidence for an association with early-life TL (Table 3,
Fig. 4), with a Bonferroni corrected threshold (nominal P\textless{}0.05
and P\selectlanguage{english}[?]2.77x10\textsuperscript{-7} at the genome-wide P-value threshold)
and a genomic inflation factor \selectlanguage{greek}λ\selectlanguage{english}=1.0476\selectlanguage{ngerman}±0.0002 (Fig. S2.3). Seven SNPs
that showed weak evidence for an association with TL (nominal
0.05\textless{}P\textless{}0.10) are also shown in Table 3. Using the
annotated house sparrow genome, a total of sixteen genes on four
chromosomes were found to be located within proximity (±100 kb) of four
of the six top SNPs (Table 4). Among five of the seven SNPs with weak
evidence for an association with TL we identified 10 genes within ±100
kb on three chromosomes (Table S2.5).

SNPa429690 is located on chromosome 2 within the Aquaporin-1 (AQP1)
gene, which encodes the AQP1 water channel membrane protein. The AQP1
protein is abundant in erythrocytes (where TL is measured) and important
in regulating body water transport and balance (Nielsen et al., 2002),
but also in a range of other physiological functions including cell
migration, wound healing, fat metabolism and oxidative stress (Saadoun,
Papadopoulos, Hara-Chikuma, \& Verkman, 2005; Verkman, Anderson, \&
Papadopoulos, 2014). The same SNP is located 39 kb from the growth
hormone-releasing hormone receptor (GHRHR), which controls body growth
(Mullis, 2005), and has been associated with telomerase activity (Banks
et al., 2010), lifespan (Soerensen et al., 2012) and the progression of
several types of cancer (Chu et al., 2016; Schally et al., 2018;
Villanova et al., 2019). Humans with over-expression of growth hormones
and consequently insulin-like growth factor 1 (IGF-1) have shorter
telomeres (Aulinas et al., 2013; Deelen et al., 2013; Matsumoto et al.,
2015; Monaghan \& Ozanne, 2018). SNPa17235 was close (11 kb) to FRMD4B
(FERM domain-containing protein 4B), which is involved in epithelial
cell polarity that is important in tissue morphogenesis (Ikenouchi \&
Umeda, 2010). This SNP was also near other genes related to cell
proliferation (UBA3 and TMF1), skeletal muscle organization (LMOD3) and
oxidative stress (ARL6IP5, see Table 4). SNPa108592 was in the vicinity
(43-84 kb) of several genes on chromosome 15 linked to cell
proliferation, ubiquitination and immune response (Table 4). SNPa450086
was 76 kb from OXR1 (oxidation resistance protein 1) that regulates
expression of several antioxidant enzymes (Volkert, Elliott, \& Housman,
2000).

Among the SNPs with weak evidence for an association with TL, SNPa34968
was close (11 kb) to the ZBED1 (zinc finger BED domain-containing
protein 1) gene that is involved in cell proliferation and DNA
replication (Ohshima, Takahashi, \& Hirose, 2003; Hansen, Traynor,
Ditzel, \& Gjerstorff, 2018), and may also regulate telomere length
in\emph{Drosophila} flies (Silva-Sousa, Varela, \& Casacuberta, 2013).
Expression of the SCN4A gene (68 kb from SNPa491204) has previously been
correlated with TL in human stem cells (Wang et al., 2017). SNPa491204
was also near (49 kb) the growth hormone gene GH (which is linked to TL
as described above, see also Pauliny, Devlin, Johnsson, \& Blomqvist,
2015) and WNT9B (40 kb) of the Wnt/\selectlanguage{greek}β-\selectlanguage{english}catenin signaling pathway, which is
modulated by telomerase (Park et al., 2009). SNPi16410 was closest to
SHCBP1 (70 kb) and CDCA4 (76 kb), which are both involved in cell
proliferation and probably apoptosis (Wang et al., 2008; Asano et al.,
2014; Xu, Wu, Li, Huang, \& Zhu, 2018; Zou et al., 2019). SHCBP1 is
upregulated by growth factor stimulation (Schmandt, Liu, \& McGlade,
1999). CDCA4 is likely involved in the regulation of hematopoietic stem
cells from where erythrocytes (reflecting TL) are derived (Abdullah,
Jing, Spassov, Nachtman, \& Jurecic, 2001).

Searching beyond the \selectlanguage{ngerman}±100 kb limits, the top marker, SNPa223513, was
found closest (106 kb) to the SAMD5 (sterile alpha motif
domain-containing protein 5) gene, which function is unknown, but may
play a role in tumorigenesis (Sa, Lee, Hong, Kong, \& Nam, 2017), cancer
cell proliferation (Matsuo et al., 2014) or tumor suppression in the
cytoplasm (Yagai et al., 2017). SNPa108592 is 263 kb from LRRC43
(leucine-rich repeat-containing protein 43) that belongs to a class of
poorly known proteins often associated with innate immunity (Ng \&
Xavier, 2011). Members of the LRRC superfamily have previously been
associated with TL variation in humans (Codd et al., 2010). The same SNP
is 363 kb from ZCCHC8 (zinc finger CCHC domain-containing protein 8)
that is required for telomerase functioning (Gable et al., 2019).

When not controlling for the effect of tarsus length on TL, the same six
top SNPs were identified as in the analysis above including tarsus
length (Table S2.6). In addition, SNPa208275 was associated with TL and
found 47 kb from FGFR2 encoding a tyrosine-protein kinase that is a
receptor for fibroblast growth factors~that regulates several aspects of
cell proliferation and bone morphogenesis (Table S2.7, Katoh, 2009).

\textbf{DISCUSSION}

The evolutionary response to selection on telomere length depends on the
additive genetic variance of TL and the strength and sign of any genetic
correlations with other traits under selection. Dugdale and Richardson
(2018) criticized past quantitative genetic studies of TL on the main
grounds that 1) they applied basic regression analyses that did not
consider environmental effects impacting TL and as a consequence of
that, additive genetic effects may have been overestimated in previous
studies; 2) TL changes with age, complicating the fact that parents and
offspring are often sampled at different ages; and 3) sample sizes were
too small to provide enough power to separate genetic and environmental
effects using animal models. Here, we have accommodated this critique by
1) using mixed-effect animal models to partition genetic and
environmental effects; 2) measuring early-life TL in both offspring and
parents at the same time point in life (as around 11 days old
fledglings); and 3) collect TL data from more than 3300 individuals
across 4 populations, which represent a considerably larger sample size
than those of previous wild animal studies.

We found that around 4\% of the variation in early-life TL in house
sparrows at the end of the nestling growth period was determined by
additive genetic variation. The relatively small additive genetic
variance and large year variance in early-life TL appears to be in
accordance with the effects of relative growth and weather conditions on
TL in similar sparrow populations (Pepke et al., 2021,\emph{submitted}
). Similarly small but significant heritabilities of TL have been
reported using animal models for e.g. nestling collared flycatchers,
\emph{Ficedula albicollis}(\emph{h\textsuperscript{2}} =0.09, Voillemot
et al., 2012), Seychelles warblers (\emph{h\textsuperscript{2}}
=0.03-0.08, Sparks et al., 2021) and adult greater mouse-eared bats,
\emph{Myotis myotis}(\emph{h\textsuperscript{2}} =0.01-0.06, Foley et
al., 2020), in which TL correlates with several weather variables. These
studies also documented considerable year effects on TL (Foley et al.,
2020; Sparks et al., 2021) similar to studies finding no heritability of
TL in white\selectlanguage{english}-throated dippers (Becker et al., 2015) and European badgers
(\emph{Meles meles} , van Lieshout et al., 2021). In comparison, studies
based on parent-offspring regression have often found higher TL
heritabilities in e.g. king penguins (\emph{h\textsuperscript{2}} =0.2,
Reichert et al., 2015), jackdaws (\emph{Coloeus monedula}
,\emph{h\textsuperscript{2}} =0.72, Bauch et al., 2019), and sand
lizards (\emph{Lacerta agilis} , \emph{h\textsuperscript{2}} =0.5-1.2,
Olsson et al., 2011). The heritability of TL in house sparrows is
comparable to that of many life-history traits and considerably lower
than many morphological traits (e.g. Mousseau \& Roff, 1987; Visscher,
Hill, \& Wray, 2008), which may suggest that TL is under strong
selection in the wild (Voillemot et al., 2012) or that there are
considerable non-additive genetic or environmental influences on
early-life TL. Curiously, Pepke et al. (2021, \emph{submitted} )
reported indications of weak non-linear or negative associations between
TL and various measures of fitness (survival and reproductive success)
in house sparrows, suggesting that the environmentally pliant TL
dynamics of these relatively fast-lived birds may be very different from
several other bird species (reviewed in Wilbourn et al., 2018). In other
species, positive associations between early-life TL and survival have
been documented (Wilbourn et al., 2018), which may translate into an
increased lifetime reproductive success (Eastwood et al., 2019; Sudyka,
2019; Bichet et al., 2020).

A considerable proportion of the phenotypic variance in TL could be
attributed to brood and parental effects (Fig. 3). However, we did not
find evidence that parental effects were transmitted through a parental
age at conception effect (Fig. S2.1). Paternal age effects, which has
been observed in several other species (Eisenberg \& Kuzawa, 2018), may
not manifest in these house sparrows because the mean age at
reproduction was low (around 2 years). Parent-offspring regressions
(Fig. S2.2) and parental genetic effects models (Table S2.2) suggested a
stronger component of maternal heritability of TL, which is similar to
the inheritance pattern found in several bird species (Horn et al.,
2011; Asghar et al., 2015; Becker et al., 2015; Reichert et al., 2015)
and some studies on humans (Broer et al., 2013). Maternal effects on
offspring TL are expected to be strongest in early-life (Wolf, Brodie
Iii, Cheverud, Moore, \& Wade, 1998) and could act through e.g.
yolk-deposited components in the egg (Criscuolo, Torres, Zahn, \&
Williams, 2020; Stier et al., 2020a) or post-laying through maternal
care behavior (e.g., incubation and feeding rate, Stier, Metcalfe, \&
Monaghan, 2020b; Viblanc et al., 2020). Since TL is a heritable trait, a
positive maternal effect on offspring TL may be expected to increase the
expected rate of adaptive evolution of TL (Wolf et al., 1998; Rasanen \&
Kruuk, 2007). Parental and environmental effects documented in other
studies (Monaghan \& Metcalfe, 2019) suggest that some of the variation
in TL may be inherited through epigenetic carry-over effects (Bauch et
al., 2019; Eisenberg, 2019) that are not resolved by comparing
early-life TLs. Thus, such effects may be more important in shaping
nestling TL loss, rather than early-life TL (Heidinger et al., 2016).
However, TL maintenance are at present not well known within house
sparrows (Vangorder-Braid et al., 2021).

There was evidence for additive genetic variance in the tarsus length of
sparrow nestlings, but the heritability estimate
(\emph{h\textsuperscript{2}} =0.076, Table 2) was considerably smaller
than those of adult house sparrows in a larger sample of populations in
the same area (Jensen et al., 2008; Araya-Ajoy et al., 2019) and other
avian species (Merila \& Sheldon, 2001). However, there was a large
brood effect on nestling tarsus length suggesting common environmental
effects within broods (e.g. Potti \& Merino, 1994). For instance,
variation in clutch size, seasonal differences in food availability,
weather conditions (Ringsby, Saether, Tufto, Jensen, \& Solberg, 2002),
and provisioning rates by parents (Ringsby, Berge, Saether, \& Jensen,
2009) may induce intra-clutch competition and variation in the degree to
which nestlings are able to achieve their adult tarsus lengths at
fledging (Naef-Daenzer \& Keller, 1999; Metcalfe \& Monaghan, 2001).
Furthermore, measurement error is probably higher for the incompletely
ossified nestling tarsi, which are covered by a soft fleshy skin tissue
that contributes to the measured length.

Individuals with shorter tarsi (a proxy for structural size, Araya-Ajoy
et al., 2019) were found to have longer telomeres, although the effect
of tarsus length on TL was small and there was considerable variation in
TL for a given size (Fig. 1). This confirms previous observations of a
prevailing negative correlation between body size and TL within house
sparrows (Ringsby et al., 2015; Pepke et al., 2021, \emph{submitted} )
and other species (Monaghan \& Ozanne, 2018). We did not find evidence
for a significant negative genetic correlation between TL and tarsus
length (Table 2). Instead, the negative phenotypic association between
TL and tarsus length may be induced by common environmental effects that
affects both traits in opposite directions. The lack of a genetic
correlation between TL, tarsus length or body condition could also be
attributed to selection acting simultaneously on some correlated,
unmeasured trait (Merila, Sheldon, \& Kruuk, 2001). Both with and
without controlling for the effect of tarsus length on TL, our GWAS on
TL identified several genes involved in skeletal development, cellular
growth and differentiation that may regulate body growth or size (e.g.
GHRHR, Tmem120b, LMOD3, GH, POU1F1, SHCBP1, and FGFR2, Table 4, S2.5,
and S2.7), which could, however, suggest some genetic basis of the
negative correlation between TL and size. For instance, several growth
factors were downregulated in telomerase deficient mouse bone marrow
stromal stem cells (Saeed \& Iqtedar, 2015) suggesting that short
telomeres or telomere loss could also be a constraint on proliferation
potential. Thus, because several of the genes that may regulate TL
during early development appear to also be involved in cell
proliferation or morphogenesis, such genes may have co-evolved.

None of the genes highlighted in our analysis have previously been
linked to TL in GWA studies (reviewed in the introduction). However,
the\emph{Drosophila} orthologue of ZBED1 (dDREF) has been linked to
telomere maintenance in \emph{Drosophila} flies (Tue et al., 2017), but
telomere biology in this taxon is very different from most other
eukaryotes and does not involve telomerase (Casacuberta, 2017). Yet, the
dDREF/ZBED1 is important for cell proliferation in both\emph{Drosophila}
(Matsukage, Hirose, Yoo, \& Yamaguchi, 2008), bats (\emph{Rhinolophus
ferrumequinum} , Xiao et al., 2016) and human cancer cells (Jiang et
al., 2018, but see Hansen et al., 2018). Several of the identified
candidate genes (ZBED1, AQP1, SHCBP1, CDCA4, ARL6IP5, UBA3, RNF34, RHOF,
ANAPC5, and FGFR2) are involved in cell proliferation and apoptosis
during which TL and telomerase activity invariably play an important
role (Greider, 1998; Masutomi et al., 2003). The RHOF gene product
functions cooperatively with CDC42 and Rac to organize the actin
cytoskeleton (Ellis \& Mellor, 2000). While the latter complex
participates in the control of telomerase activity in human cancer cells
(Yeh, Pan, \& Wang, 2005), any direct link between RHOF and TL remains
unexplored. CDC42 is activated by FGD4 (Chen et al., 2004), which was
found within a major locus affecting TL in humans (Vasa-Nicotera et al.,
2005). SNPa108592 was found near several genes involved in cell
proliferation, differentiation, immune response, and ubiquitination
(Table 4). Ubiquitination regulates several shelterin components and
telomerase activity (Peuscher \& Jacobs, 2012; Yalcin, Selenz, \&
Jacobs, 2017). The closest gene, ORAI1 (43 kb), the keeper of the gates
of calcium ions (Homer, 1924), is crucial for lymphocyte activation and
immune response (Feske et al., 2006). Although not linked to ORAI1
mutations, calcium ion levels can modulate telomerase activity (reviewed
in Farfariello, Iamshanova, Germain, Fliniaux, \& Prevarskaya, 2015).

We identified a particularly interesting gene associated with TL, AQP1.
The AQP1 channel not only conducts water across cell membranes, but also
hydrogen peroxide, a major reactive oxygen species (ROS, Tamma et al.,
2018), and nitric oxide (Herrera, Hong Nancy, \& Garvin Jeffrey, 2006),
which is an important regulator of oxidative stress (Pierini \& Bryan,
2015) and a weak oxidant itself (Radi, 2018). Furthermore, increased
availability of nitric oxide may activate telomerase and thereby prevent
replicative senescence (in endothelial cells, Vasa, Breitschopf, Zeiher
Andreas, \& Dimmeler, 2000). Enhanced oxidative stress associated with
endothelial cell senescence may also be mediated by AQP1-regulated
nitric oxide flow (Tamma et al., 2018; Chen et al., 2020). In AQP1
knocked-out erythrocytes (where TL was measured) cell lifespan was
shortened (Mathai et al., 1996) and angiogenesis is inhibited in AQP1
knocked-out chicken embryos (Camerino et al., 2006) and mice (Saadoun et
al., 2005). Telomeres are particularly sensitive to ROS and shorten due
to oxidative stress during growth (von Zglinicki, 2002; Reichert \&
Stier, 2017). For instance, Kim, Noguera, Morales, and Velando (2011)
found a negative genetic correlation between growth and resistance to
oxidative stress in yellow-legged gull (\emph{Larus michahellis} )
chicks, which could be mediated by TL (see also Smith, Nager, \&
Costantini, 2016). Another candidate gene, OXR1, 76 kb from SNPa450086,
has a well-described antioxidant function (Volkert et al., 2000; Oliver
et al., 2011) and is upregulated in senescent human cells (Zhang et al.,
2018). Knockdown of OXR1 increases ROS production and ultimately induces
apoptosis (Oliver et al., 2011; Zhang et al., 2018), which could be due
to telomere crisis.

Over-expression of AQP1 has been associated with several types of cancer
(Verkman, Hara-Chikuma, \& Papadopoulos, 2008), suppression of apoptosis
(Yamazato et al., 2018) and may play an important role in tumor biology
(Saadoun et al., 2005; Tomita et al., 2017). Other candidate genes
including GHRHR, SAMD5, SHCBP1 (Tao et al., 2013), GH (Boguszewski \&
Boguszewski, 2019), and OXR1 (Yang et al., 2015) are also involved in
tumorigenesis. Cancer prevalence is not well-studied in wildlife
(Pesavento, Agnew, Keel, \& Woolard, 2018), but tumors have been
documented in house sparrows (Moller, Erritzoe, \& Soler, 2017). Long
telomeres or increased telomerase activity may increase the risk of
acquiring an oncogenic mutation before cell proliferation ceases due to
telomere crisis (Aviv, Anderson, \& Shay, 2017; Pepke \& Eisenberg,
2021). However, long telomeres also increase immune function required to
combat cancers (Helby, Nordestgaard, Benfield, \& Bojesen, 2017) and
short telomeres can result in chromosomal instability leading to some
types of cancer (Ma et al., 2011; Aviv et al., 2017). This TL paradox is
not yet resolved (Eisenberg \& Kuzawa, 2018). However, genes affecting
both TL and cancer risk (Tacutu, Budovsky, Yanai, \& Fraifeld, 2011;
Jones et al., 2012) could underlie the antagonistic pleiotropy of
trade-offs between long telomeres in early-life (with potential benefits
to growth, reproduction, and other oxidative stress inducing processes)
and later-life cancer mortality (Tian et al., 2018). For instance,
Vedder et al. (2021) found a significant genetic correlation between TL
and lifespan in wild common terns. Cancer is often viewed as a
senescence-related pathology (Lemaitre et al., 2020). However, the
absence of cancer in early-life should not lead us to conclude that a
somatic and potentially fitness-related cost is not paid to maintain
that status (Thomas et al., 2018).

We have shown that TL is a heritable, polygenic trait with considerable
environmental variation and a maternal inheritance component in a wild
passerine. It is, however, important that future studies attempt to
confirm the candidate genes identified here as associated with TL in
other wild populations. Even though the additive genetic component was
small, selection on variation in TL may produce evolutionary change in
TL over time in wild populations. The large component of variation in
early-life TL caused by annual environmental stochasticity suggests that
this will generate heterogeneity in TL among cohorts. Although we did
not find a negative genetic correlation underlying the negative
phenotypic correlation between TL and body size, we may hypothesize that
selection for larger nestling size, which may enhance survival until
recruitment (Ringsby, Saether, \& Solberg, 1998), will be associated with
selection for shorter early-life TL due to non-genetic mechanisms, which
can ultimately influence lifespan or reproductive success.

\textbf{ACKNOWLEDGEMENT}

We thank all field workers who collected data for this study and the
island inhabitants that made it possible, laboratory technician Randi
Rosbak (NTNU, Trondheim) for extracting DNA and microsatellite
genotyping, Darryl McLennan (University of Glasgow) for help with
telomere data analyses, Yimen Araya-Ajoy (NTNU, Trondheim) for helpful
comments on an earlier version of this manuscript, and
``\emph{Forsknings- og undervisningsfondet i Trondheim} '' for financial
support (to MLP). This work was funded by the Research Council of Norway
(274930) and through its Centres of Excellence scheme (223257). The
study was carried out in accordance with permits from the Norwegian
Animal Research Authority (FOTS id 11904) and the Ringing Centre at
Stavanger Museum, Norway. The authors declare no conflicts of interest.

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\textbf{DATA ACCESSIBILITY}

All data will be made available on Dryad or another open access channel
upon acceptance of the manuscript. SNP genotype data is available on
Dryad (https://doi.org/10.5061/dryad.hp758sn).

\textbf{AUTHOR CONTRIBUTIONS}

MLP measured telomeres, analyzed the data, and wrote the manuscript with
comments from all authors. WB and PM advised telomere measurements. TK,
HJ, THR, and SL advised statistical analyses. B-ES, THR, and HJ
established the study system. THR, HJ, and TK contributed to the
fieldwork.

\textbf{TABLES AND FIGURES}

\textbf{Figure 1:} The negative association between age-corrected tarsus
length and telomere length (log\textsubscript{10}-transformed) in 2462
house sparrow nestlings with a regression line from a LMM shown in Table
1. The 95\% confidence interval (grey) reflects only the fixed effects.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Table 1:} Estimates, standard errors (SE), lower and upper 95\%
confidence intervals (CI) from a LMM of variation in telomere length
(TL). The model included random intercepts for brood identity and year.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Response variable: log\textsubscript{10}(TL) & \emph{Estimate} & SE &
Lower CI & Upper CI\tabularnewline
\midrule
\endhead
intercept & -0.012 & 0.020 & -0.051 & 0.026\tabularnewline
\emph{tarsus length} & \emph{-0.004} & \emph{0.002} & \emph{-0.007} &
\emph{-0.000}\tabularnewline
sex {[}female{]} & -0.005 & 0.004 & -0.013 & 0.003\tabularnewline
island identity {[}Hestmann\selectlanguage{ngerman}øy{]} & -0.002 & 0.007 & -0.013 &
0.016\tabularnewline
age & -0.002 & 0.002 & -0.005 & 0.001\tabularnewline
\emph{inbreeding coefficient (F)} & \emph{-0.194} & \emph{0.096} &
\emph{-0.383} & \emph{-0.005}\tabularnewline
hatch day & -0.000 & 0.000 & -0.000 & 0.000\tabularnewline
\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2}\textsubscript{brood ID} (\emph{n}=948) & 0.004 & &
0.003 & 0.004\tabularnewline
\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2}\textsubscript{year} (\emph{n}=20) & 0.002 & & 0.001
& 0.004\tabularnewline
Marginal R\textsuperscript{2} / Conditional R\textsuperscript{2}: 0.006
/ 0.408 & Marginal R\textsuperscript{2} / Conditional
R\textsuperscript{2}: 0.006 / 0.408 & Marginal R\textsuperscript{2} /
Conditional R\textsuperscript{2}: 0.006 / 0.408 & Marginal
R\textsuperscript{2} / Conditional R\textsuperscript{2}: 0.006 / 0.408 &
Marginal R\textsuperscript{2} / Conditional R\textsuperscript{2}: 0.006
/ 0.408\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2:} Mean posterior distribution estimates of a
multivariate animal model of the co-variation of early-life telomere
length, tarsus length, and body condition (\emph{n} =2662) with fixed
effects, variance components, and lower and upper 95\% highest posterior
density intervals (HPD). Abbreviations refer to:
heritability\emph{h\textsuperscript{2}} , additive genetic
variance\emph{V\textsubscript{A}} , brood variance
\emph{V\textsubscript{B}} , maternal variance \emph{V\textsubscript{M}}
, paternal variance\emph{V\textsubscript{F}} , year variance
\emph{V\textsubscript{Y}} , residual variance \emph{V\textsubscript{R}}
, and with identical subscripts for the co-variances (\emph{Cov} )
including the additive genetic correlation \emph{r\textsubscript{A}} .\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
Variable & log\textsubscript{10}(telomere length) &
log\textsubscript{10}(telomere length) & log\textsubscript{10}(telomere
length) & tarsus length & tarsus length & tarsus length & tarsus length
& tarsus length & body condition & body condition & body
condition\tabularnewline
\midrule
\endhead
Fixed effects & \textbf{Esti-mate} & \textbf{HPD} & \textbf{HPD} &
\textbf{Esti-mate} & \textbf{Esti-mate} & \textbf{HPD} & \textbf{HPD} &
\textbf{HPD} & & \textbf{HPD} & \textbf{HPD}\tabularnewline
& & \textbf{Lower} & \textbf{Upper} & & & \textbf{Lower} &
\textbf{Lower} & \textbf{Upper} & \textbf{Esti-mate} & \textbf{Lower} &
\textbf{Upper}\tabularnewline
intercept & 0.0214 & -0.1799 & 0.2003 & 0.2115 & 0.2115 & -0.3083 &
-0.3083 & 0.8021 & 0.0205 & -0.1723 & 0.2063\tabularnewline
sex {[}female{]} & -0.0036 & -0.0137 & 0.0070 & -0.0587 & -0.0587 &
-0.1523 & -0.1523 & 0.0305 & 0.0009 & -0.0075 & 0.0094\tabularnewline
island identity {[}Hestmann\selectlanguage{ngerman}øy{]} & 0.0078 & -0.0455 & 0.0470 & 0.0024 &
0.0024 & -0.2657 & -0.2657 & 0.2427 & -0.0025 & -0.0435 &
0.0379\tabularnewline
inbreeding coefficient (\emph{F}) & -0.2434 & -0.5632 & 0.1491 & -0.9219
& -0.9219 & -3.4966 & -3.4966 & 1.6858 & -0.0207 & -0.3587 &
0.2694\tabularnewline
age & -0.0049 & -0.0096 & -0.0008 & -0.0260 & -0.0260 & -0.0658 &
-0.0658 & 0.0215 & -0.0014 & -0.0061 & 0.0042\tabularnewline
Variance components & & & & & & & & & & &\tabularnewline
\emph{h\textsuperscript{2}} & 0.0424 & 0.0242 & 0.0641 & 0.0764 & 0.0764
& 0.0450 & 0.0450 & 0.1234 & 0.0278 & 0.0155 & 0.0429\tabularnewline
\emph{V\textsubscript{A}} & 0.0092 & 0.0084 & 0.0103 & 0.1896 & 0.1896 &
0.1161 & 0.1161 & 0.3151 & 0.0057 & 0.0052 & 0.0062\tabularnewline
\emph{V\textsubscript{B}} & 0.0117 & 0.0104 & 0.0135 & 0.9273 & 0.9273 &
0.7949 & 0.7949 & 1.0869 & 0.0095 & 0.0083 & 0.0108\tabularnewline
\emph{V\textsubscript{M}} & 0.0144 & 0.0126 & 0.0170 & 0.1479 & 0.1479 &
0.0986 & 0.0986 & 0.2339 & 0.0127 & 0.0110 & 0.0147\tabularnewline
\emph{V\textsubscript{F}} & 0.0135 & 0.0117 & 0.0155 & 0.1407 & 0.1407 &
0.0895 & 0.0895 & 0.2059 & 0.0106 & 0.0093 & 0.0123\tabularnewline
\emph{V\textsubscript{Y}} & 0.1321 & 0.0747 & 0.2877 & 0.2199 & 0.2199 &
0.1184 & 0.1184 & 0.4585 & 0.1457 & 0.0759 & 0.2823\tabularnewline
\emph{V\textsubscript{R}} & 0.0073 & 0.0066 & 0.0079 & 0.8165 & 0.8165 &
0.7331 & 0.7331 & 0.9040 & 0.0043 & 0.0039 & 0.0047\tabularnewline
Co-variances between TL and tarsus & & & & & & & \textbf{Co-variances
between TL and condition} & \textbf{Co-variances between TL and
condition} & & &\tabularnewline
\emph{r\textsubscript{A}} & -0.0170 & -0.1204 & 0.0914 & & & &
\emph{r\textsubscript{A}} & \emph{r\textsubscript{A}} & -0.0098 &
-0.0789 & 0.0599\tabularnewline
\emph{Cov\textsubscript{A}} & -0.0006 & -0.0056 & 0.0038 & & & &
\emph{Cov\textsubscript{A}} & \emph{Cov\textsubscript{A}} & 0.0000 &
-0.0005 & 0.0005\tabularnewline
\emph{Cov\textsubscript{B}} & -0.0049 & -0.0155 & 0.0068 & & & &
\emph{Cov\textsubscript{B}} & \emph{Cov\textsubscript{B}} & 0.0001 &
-0.0010 & 0.0010\tabularnewline
\emph{Cov\textsubscript{M}} & -0.0012 & -0.0073 & 0.0056 & & & &
\emph{Cov\textsubscript{M}} & \emph{Cov\textsubscript{M}} & 0.0000 &
-0.0014 & 0.0015\tabularnewline
\emph{Cov\textsubscript{F}} & 0.0005 & -0.0057 & 0.0054 & & & &
\emph{Cov\textsubscript{F}} & \emph{Cov\textsubscript{F}} & 0.0000 &
-0.0013 & 0.0011\tabularnewline
\emph{Cov\textsubscript{Y}} & 0.0004 & -0.1041 & 0.1035 & & & &
\emph{Cov\textsubscript{Y}} & \emph{Cov\textsubscript{Y}} & 0.0047 &
-0.0807 & 0.0828\tabularnewline
\emph{Cov\textsubscript{R}} & -0.0043 & -0.0099 & 0.0007 & & & &
\emph{Cov\textsubscript{R}} & \emph{Cov\textsubscript{R}} & -0.0001 &
-0.0004 & 0.0003\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 3:} Variance components for TL, tarsus length and body
condition visualized as relative proportions of the total phenotypic
variance. Abbreviations refer to: additive genetic
variance\emph{V\textsubscript{A}} , brood variance
\emph{V\textsubscript{B}} , maternal variance \emph{V\textsubscript{M}}
, paternal variance\emph{V\textsubscript{F}} , year variance
\emph{V\textsubscript{Y}} , and residual variance
\emph{V\textsubscript{R}} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 4:} Manhattan plot showing genomic location plotted
against -log\textsubscript{10}(P-value) of the GWA analysis results for
early-life telomere length in house sparrows (\emph{n} =373). The dotted
line indicates the genome-wide significance threshold (corresponding
to\emph{p} \textless{}0.05 divided by the number of tests \emph{n}
=180,650 SNPs) used to determine the top SNPs listed in Table 3.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Table 3:} Single nucleotide polymorphisms (SNPs) with evidence
(italics, above the dashed line) or weak evidence for an association
with early-life telomere length in house sparrows (\emph{n} =373).
Chromosome number, SNP position, reference allele A1, effect allele A2,
estimated effect size (\selectlanguage{greek}\emph{β} \selectlanguage{english}) with standard error (SE), p-value, and
Bonferroni adjusted p-value are shown.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
SNP\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Chro-mosome\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Position\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
A1\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
A2\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
\selectlanguage{greek}\emph{β}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
SE\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
p-value\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
adjusted p-value\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SNPa223513}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{3}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{46984591}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{T}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{C}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.5864}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0857}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{8.00E-12}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{1.44E-06}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SNPa17235}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{12}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{14959355}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{G}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{A}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.3051}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0485}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{3.19E-10}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{5.76E-05}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SNPa500415}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{30}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{133629}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{C}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{T}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.2866}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0477}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{1.80E-09}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0003}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SNPa429690}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{2}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{145079103}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{G}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{A}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.3619}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0640}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{1.56E-08}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0028}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SNPa108592}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{15}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{11173875}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{G}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{T}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.3426}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0637}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{7.65E-08}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0138}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{SNPa450086}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{2}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{17261563}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{G}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{T}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.3515}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0656}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{8.40E-08}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{0.0152}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
SNPa34968\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
78883614\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
C\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.2237\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0436\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.92E-07\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0527\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
SNPa491204\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1191908\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
C\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.1386\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0271\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.10E-07\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0561\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
SNPa392732\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
13674493\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
A\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
G\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.5023\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0984\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.29E-07\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0594\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
SNPa374949\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
33502667\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
C\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.2189\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0431\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.81E-07\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0688\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
SNPa374964\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
33523052\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
G\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
A\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.2189\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0431\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.81E-07\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0688\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
SNPi16410\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
53016672\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
G\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
A\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.2206\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0438\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
4.58E-07\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0828\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
SNPa8679\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5482366\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
C\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.2643\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0525\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
4.85E-07\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0876\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4:} Genes found within \selectlanguage{ngerman}±100 kb of SNPs in Table 3 with
evidence for an association with early-life telomere length house
sparrows. Chromosome number, distance (in bp) between SNP and gene,
general molecular or biological function or relevance to telomere
biology are indicated with references. The list is sorted first by SNP
p-value and then by gene distance.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Chr. & Gene & SNP & Distance & Function & Reference\tabularnewline
\midrule
\endhead
12 & FRMD4B: FERM domain-containing protein 4B (\emph{Homo sapiens}) &
SNPa17235 & 11287 & Epithelial cell polarity, scaffolding protein &
Ikenouchi \& Umeda, 2010\tabularnewline
12 & LMOD3: Leiomodin-3 (\emph{Homo sapiens}) & SNPa17235 & 34383 &
Skeletal muscle filaments organization & Yuen et al.,
2014\tabularnewline
12 & ARL6IP5: PRA1 family protein 3 (\emph{Gallus gallus}) & SNPa17235 &
42339 & Regulates taurine and glutamate transport, apoptosis, oxidative
stress & Akiduki \& Ikemoto, 2008; Zhou, Ye, Zhao, Li, \& Zhou,
2008\tabularnewline
12 & UBA3: NEDD8-activating enzyme E1 catalytic subunit (\emph{Homo
sapiens}) & SNPa17235 & 54117 & Cell proliferation, protein neddylation
& Osaka et al., 1998; Gong \& Yeh, 1999\tabularnewline
12 & TMF1: TATA element modulatory factor (\emph{Homo sapiens}) &
SNPa17235 & 67507 & Cell growth, immune response, androgen receptor
coactivator & Garcia et al., 1992; Perry et al., 2004\tabularnewline
12 & EOGT: EGF domain-specific O-linked N-acetylglucosamine transferase
(\emph{Gallus gallus}) & SNPa17235 & 86629 & Cell metabolism,
developmental signaling & M\selectlanguage{ngerman}üller, Jenny, \& Stanley, 2013\tabularnewline
2 & AQP1: Aquaporin-1 (\emph{Sus scrofa}) & SNPa429690 & 0 & Water
transport, oxidative stress, cell migration, wound healing, fat
metabolism, apoptosis & Saadoun et al., 2005; Monzani, Bazzotti, Perego,
\& La Porta, 2009; Verkman et al., 2014; Tomita et al.,
2017\tabularnewline
2 & GHRHR: Growth hormone-releasing hormone receptor (\emph{Homo
sapiens}) & SNPa429690 & 38572 & Regulation of growth hormone & Mullis,
2005; Soerensen et al., 2012; Villanova et al., 2019\tabularnewline
15 & ORAI1: Calcium release-activated calcium channel protein 1
(\emph{Gallus gallus}) & SNPa108592 & 42546 & Immune response, calcium
transport & Feske et al., 2006\tabularnewline
15 & morn3: MORN repeat-containing protein 3 (\emph{Xenopus laevis}) &
SNPa108592 & 53962 & Spermatogenesis in \emph{Mus musculus} otherwise
uncharacterized & Zhang et al., 2015\tabularnewline
15 & Kdm2b: Lysine-specific demethylase 2B (\emph{Mus musculus}) &
SNPa108592 & 61359 & Ubiquitination, hematopoietic cell differentiation
& Vargas-Ayala et al., 2019\tabularnewline
15 & RNF34: E3 ubiquitin-protein ligase RNF34 (\emph{Bos taurus}) &
SNPa108592 & 71094 & Ubiquitination, apoptosis, immune response & Sasaki
et al., 2002; Konishi, Sasaki, Watanabe, Kitayama, \& Nagawa, 2005;
Zhang et al., 2014\tabularnewline
15 & Tmem120b: Transmembrane protein 120B (\emph{Mus musculus}) &
SNPa108592 & 71684 & Fat cell differentiation, obesity & Byerly et al.,
2010; Batrakou, de las Heras, Czapiewski, Mouras, \& Schirmer,
2015\tabularnewline
15 & RHOF: Rho-related GTP-binding protein RhoF (\emph{Homo sapiens}) &
SNPa108592 & 82475 & Cell proliferation, migration and polarity,
cytoskeleton organization & Ellis \& Mellor, 2000\tabularnewline
15 & ANAPC5: Anaphase-promoting complex subunit 5 (\emph{Gallus gallus})
& SNPa108592 & 83811 & Cell proliferation, ubiquitination & Jin,
Williamson, Banerjee, Philipp, \& Rape, 2008\tabularnewline
2 & OXR1: Oxidation resistance protein 1 (\emph{Homo sapiens}) &
SNPa450086 & 75676 & Oxidative stress protection & Volkert et al., 2000;
Zhang et al., 2018\tabularnewline
\bottomrule
\end{longtable}

\textbf{SUPPORTING INFORMATION}

Additional Supporting Information may be found in the online version of
this article.

Appendix S1: Notes on methods.

Appendix S2: Notes on results.

\selectlanguage{english}
\FloatBarrier
\end{document}

