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• W3157358435 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). Variation in TL among individuals was mainly driven by environmental (annual) variance, but also brood and parental effects. Parent-offspring regressions showed a large maternal inheritance component in TL ( = 0.44), but no paternal inheritance. 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 nongenetic 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 has a low heritability and is a polygenic trait strongly affected by environmental conditions in a free-living bird. Telomerlængden (TL) i det tidlige liv er associeret med overlevelses- og formeringsevner i mange organismer. Endnu vides kun lidt om det genetiske fundament for TL i bestande af vilde dyr, men for at kunne forstå den evolutionære og økologiske betydning af TL er det vigtigt at kvantificere den relative indflydelse af genetisk og miljømæssig variation i TL. I dette studie har vi målt TL i 2746 gråspurveunger over en periode på 20 år, og vi har brugt kvantitative genetiske modeller til at vise, at der er en lille arvelig komponent i TL i det tidlige liv (h2 = 0.04). Variationen i TL mellem individer var primært drevet af miljømæssig (årlig) varians, men også af kuld- og forældreeffekter. Forældre-afkom regressionsanalyse viste en stor maternel arvelig komponent i TL (h2maternel = 0.44), men ikke nogen paternel arvelighed. Vi fandt ikke bevis for nogen negativ genetisk korrelation, som kunne ligge bag den observerede negative fænotypiske korrelation mellem TL og strukturel kropsstørrelse. Dermed kan TL udvikle sig uafhængigt af kropsstørrelse og den negative fænotypiske korrelation er sandsynligvis forårsaget af ikke-genetiske miljøeffekter. Desuden brugte vi associationsanalyser på tværs af genomet (GWAS) til at identificere områder af genomet, som var associeret med variation i TL. Vi identificerede adskillige mulige gener, som kan ligge bag variationen i TL. Disse gener er involveret i oxidativt stress, cellulær vækst, skeletudvikling, celledifferentiering og svulstdannelse i andre arter. Tilsammen viser vores resultater, at TL har lav arvelighed og er et polygenet træk, som er stærkt påvirket af miljøforhold hos en fritlevende fugl. 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, particularly in birds (Bize et al., 2009; Froy et al., 2021; Heidinger et al., 2012; Joeng et al., 2004; Monaghan, 2010; Pepke & Eisenberg, 2021; Tricola et al., 2018; Wilbourn et al., 2018) and individual fitness in wild animals (Eastwood et al., 2019, but see Wood & Young, 2019). Telomeres shorten through life in many organisms (Dantzer & Fletcher, 2015; Remot et al., 2021) due to cell division, oxidative stress, and other factors (Jennings et al., 2000; Reichert & Stier, 2017). This can result in telomere dysfunction, genome instability, cell death (Nassour et al., 2019), and organismal senescence (Herbig et al., 2006). Individual TL or telomere loss may act as biomarkers or sensors of exposure to intrinsic and extrinsic stressors (Bateson, 2016; Houben et al., 2008), and hence reflect individual condition (Rollings et al., 2017), but the physiological mechanisms underlying the ontogenetic variation in TL are not well understood (Erten & Kokko, 2020; Monaghan, 2014). 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 and 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, that is, the response to selection on a trait (Ellegren & Sheldon, 2008; Kruuk et al., 2008; Lande, 1979). 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 (Bauch et al., 2019; Dugdale & Richardson, 2018; Horn et al., 2011). 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 wild bird populations, from h2 = 0 (n = 177, in white-throated dippers, Cinclus cinclus, Becker et al., 2015) to h2 = 0.74 (n = 715, in western jackdaws, Coloeus monedula, Bauch et al., 2021). While most studies are characterized by relatively small sample sizes, recent long-term studies on Seychelles warblers (Acrocephalus sechellensis, n = 1317, h2 = 0.03–0.08, Sparks et al., 2021) and common terns (Sterna hirundo, n = 387, h2 = 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 h2 = 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 (Asghar et al., 2015; Heidinger et al., 2016; Horn et al., 2011; Reichert et al., 2015), or effects of parental age at conception on offspring TL (Eisenberg & Kuzawa, 2018; Marasco et al., 2019; Noguera José et al., 2018). Reichert et al. (2015) found a significant correlation between mother-offspring TL measured at 10 days of age in king penguins (Aptenodytes patagonicus), but not when TL was measured at later ages (>70 days). This may be because post-natal telomere loss rate is strongly influenced by individual environmental circumstances (Chatelain et al., 2020; Wilbourn et al., 2018) and does not always correlate strongly with chronological age (Boonekamp et al., 2013, 2014). Faster growth in early life is associated with reduced longevity (Metcalfe & Monaghan, 2003) and TL may be involved in mediating the trade-off between growth rate and lifespan (Salmón et al., 2021; Young, 2018). Accordingly, a negative phenotypic correlation between TL and body size or growth rate has been documented within several species (Monaghan & Ozanne, 2018, but see Boonekamp et al., 2021). Telomeres are known to shorten during growth (Ringsby et al., 2015), but a negative phenotypic correlation may also indicate the existence of a negative genetic correlation (Roff, 1995; Roff & Fairbairn, 2012). Froy et al. (2021) reported a modest negative genetic correlation (rA = –0.2) between bodyweight and TL in feral Soay sheep (Ovis aries). Furthermore, we have previously shown that artificial directional selection on body size in wild house sparrows (Passer domesticus) affected TL in the opposite direction (Pepke et al., 2021). This suggests that there is a genetic correlation between the two traits. Thus, quantifying the genetic correlation between TL and body size enables us to determine whether the two traits can evolve independently of each other or if the pattern of selection on both traits is needed for predicting evolutionary responses (Kruuk et al., 2008). Telomere length is a complex phenotypic trait (Aviv, 2012; Hansen et al., 2016) expected to be polygenic, that is, affected by small effects of many genes (Dugdale & Richardson, 2018; Hill, 2010). Accordingly, numerous genome-wide association studies (GWAS), which tests correlative 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., Andrew et al., 2006; Codd et al., 2010, 2013; Coutts et al., 2019; Deelen et al., 2013; Delgado et al., 2018; Jones et al., 2012; Levy et al., 2010; Li et al., 2020; Liu et al., 2014; Mangino et al., 2012, 2015; Mirabello et al., 2010; Nersisyan et al., 2019; Ojha et al., 2016; Soerensen et al., 2012; Vasa-Nicotera et al., 2005; Zeiger et al., 2018). 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 et al., 2015) and genes may have different impacts at various life stages (Weng et al., 2016). Furthermore, large sample sizes and dense sampling of genetic loci is needed to ensure high power in GWA studies (Mackay et al., 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 interindividual variation in TL (<2%, Aviv, 2012; Codd et al., 2013; Fyhrquist et al., 2013). One GWAS on TL of a nonhuman species (dairy cattle, 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 of natural populations (Eisenberg, 2011; Pepke & Eisenberg, 2021). There are to the best of our knowledge no previous GWAS on TL performed in natural populations. In this study, we aimed to provide novel insights into the genetic architecture of TL and the evolutionary mechanisms by which natural selection can alter telomere dynamics using data from a passerine bird. We obtained a single measure of TL in individuals (n = 2746) born within 20 cohorts in two natural insular populations of wild house sparrows at a similar age (c. 10 days), in addition to individuals at the same age in two insular populations that underwent artificial selection on body size for four consecutive years (n = 569, Kvalnes et al., 2017; Pepke et al., 2021). First, we estimated the phenotypic correlations between TL and tarsus length (as a proxy for body size, Araya-Ajoy et al., 2019; Senar & Pascual, 1997) in house sparrow nestlings. Second, we tested for effects of parental age on offspring TL. Third, we estimated heritability, environmental variances, and parental effects on early-life TL, and test for genetic correlations between TL, tarsus length, and body condition in the natural populations (primary analyses). Nestling body condition (body mass corrected for structural body size, Schulte-Hostedde et al., 2005) is included here to account for the component of body size that is not explained by tarsus length, which could be due to variation in the mass of other tissues or fat reserves (Peig & Green, 2010). We then used similar analyses in the artificially selected populations to validate our results from the primary analyses. Finally, we used 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 n = 383). The study was performed in four insular house sparrow populations off the coast of northern Norway (Figure S1.1 in Appendix S1). The study periods differed between the populations with data from Hestmannøy (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) and were analysed 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 ninth day during the breeding season (May–August) to sample fledglings (5–14 days old, with a median of 10 days). All individuals were ringed using a unique combination of a metal ring and three plastic colour rings. Fledged juvenile sparrows and unmarked adults were captured using mist nets from May to October. These procedures ensured that approximately 90% of all adult birds were marked on all islands during the study period (Jensen et al., 2008; Kvalnes et al., 2017). 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 Appendix S1). Morphological measurements were taken by different fieldworkers. All fieldworkers were carefully trained to consistently use the same measurement technique of THR or, in some cases, another experienced fieldworker (Kvalnes et al., 2017). For 234 out of 2746 nestlings, no nestling morphological measurements were made. Following Schulte-Hostedde et al. (2005) nestling body condition was calculated as the residuals of a linear regression of mass on tarsus length (both log10-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). One blood sample (25 μl) was collected from each fledgling, which was stored in 96% ethanol at room temperature in the field and subsequently at –20°C in the laboratory until DNA extraction. DNA extraction is described in Appendix S1. Sex of most fledglings (n = 2641) was determined using amplification of the CHD-gene located on the avian sex chromosomes as described in Griffiths et al. (1998). 21 individuals were sexed exclusively based on their phenotype as adults and 84 nestlings could not be sexed. The pedigree construction is detailed in previous studies (Billing et al., 2012; Jensen et al., 2003, 2008; Rønning et al., 2016). Briefly, 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 et al., 2007). Nestlings within the same clutch were assumed to have the same mother. Nestlings with missing parents (unassigned: n = 662 with missing mother and n = 700 with missing father) 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 (F) based on the microsatellite pedigree using the R package pedigree (Coster, 2012). Pedigrees were ordered using the R package MasterBayes (Hadfield et al., 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. Relative erythrocyte telomere lengths (TL) of 2746 nestlings from Hestmannøy and Træna (sample sizes are detailed in Table S1.1 in Appendix S1) 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) and are detailed in Appendix S1. Briefly, this method measures the ratio of telomere sequence relative to the amount of a nonvariable gene (GAPDH) and a reference sample. The reference sample consisted of pooled DNA from six individuals, which was also included as a two-fold serial dilution (40–2.5 ng/well) on all plates to produce a standard curve, in addition to a nontarget control sample (all in triplicates). Samples were randomized and run on 2 × 125 96-well plates (telomere and GAPDH assays, respectively). The qPCR data was analysed using the qbase software (Hellemans et al., 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 SD and 21.53 ± 0.02 SD 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 (R2 = 0.75, Figure S1.3 in Appendix S1). For these individuals, the average of the TL measurements was used in subsequent analyses. All reactions for the primary analyses (from the populations on Hestmannøy and Træna) were performed by the same person (MLP). MLP and WB generated the secondary data set (n = 569 on 2 × 21 plates, from the populations on Leka and Vega) as described in Pepke et al. (2021). The primary and secondary data sets used different reference samples and are therefore not combined in the analyses. We first tested the phenotypic correlation between TL and tarsus length (as a proxy for body size) within 2462 house sparrow nestlings from Hestmannøy and Træna. TL (response variable) was log10-transformed and linear mixed-effects models (LMMs) were fitted with a Gaussian error distribution (R package lme4, Bates et al., 2015). Sex differences in TL are known for house sparrows (Pepke et al., 2021). Thus, models included sex (continuous) fledgling age at sampling, hatch day (numbered day of year mean centred across years), and island identity as fixed effects. We fitted random intercepts for brood identity, year, and qPCR plate identity to account for the non-independence of nestlings from the same brood, year and plate. Because our study populations are known to be affected by inbreeding depression (Niskanen et al., 2020), we included the inbreeding coefficient (F, continuous) 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 (AICc, Akaike, 1973; Hurvich & Tsai, 1989), and Akaike weights (w) and evidence ratios (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. We tested whether maternal age at conception (MAC [mean 1.8 ± 1.1 SD years, range 1–7 years], n = 373 mothers with n = 1967 offspring) or paternal age at conception (PAC [mean 2.1±1.2 SD years, range 1–8 years], n = 388 fathers with n = 1927 offspring) predicted TL in offspring from Hestmannøy and Træna. We applied within-subject centring (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 (log10-transformed). Both models included island identity and sampling age as fixed effects, and random intercepts for year, qPCR plate identity, and either maternal identity or paternal identity. We used a multivariate Bayesian animal model (Hadfield, 2019; Kruuk, 2004) 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 (Hestmannøy and Træna, n = 2662) and the two manipulated island populations (Leka & Vega, n = 569) that underwent artificial size selection. TL was log10-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 (associated only with TL and condition), island identity, and inbreeding coefficient (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 linked to the pedigree (“animal”, VA), brood identity (VB) nested under mother identity, father (VF) and mother identity (VM), and birth year (cohort effects, VY). 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 et al., 2014). House sparrows are multibrooded laying up to three clutches in a season and may breed in multiple years, with an average of 3.6 ± 1.3 SD fledglings per brood in this study. Furthermore, to account for variance associated with measurement error we included qPCR plate identity (VO, associated only with TL, see e.g., Froy et al., 2021; Sparks et al., 2021). Random effects were generally specified with 3 × 3 covariance matrices to estimate the variances and covariances between the effects for each trait. We used inverse-Wishart priors for random effects and residual variances in the multivariate model (V = I3 and nu =3, Hadfield, 2019). We reran 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 burnin 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 et al., 2006). All autocorrelation values were <0.1 and effective sample sizes were >3000. The narrow-sense heritability was calculated as the posterior mode of the proportion of phenotypic variance (VP) explained by additive genetic variance (Wilson et al., 2010): , where VR is the residual variance. We also estimated heritabilities excluding VO from the total phenotypic variance since it does not represent biological variance (de Villemereuil et al., 2018). 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). 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). Parental heritabilities ( and ) can be estimated from parent-offspring regressions as the slope multiplied by two (one sex contributes half of the genes to their offspring). We used the R package pedantics (Morrissey & Wilson, 2010) to show that, based on parent-offspring regression, the pruned pedigree of the natural populations had ≥80% power to detect heritabilities ≥0.21 (see Figure S1.2 and Appendix S1). Furthermore, we estimated maternal (VDAM) and paternal (VSIRE) genetic effects (e.g., Wolf & Wade, 2016) in a multivariate animal model by fitting random intercepts for maternal and paternal identity linked to the pedigree to quantify these effects while accounting for the environmental variances specified above (Appendix S2). Maternal and paternal heritabilities were calculated as: and , respectively (Wilson et al., 2005). 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). Nestlings that survived to adulthood (recruited) on Hestmannøy and Træna were genotyped on a high-density 200K SNP array (detailed in Lundregan et al., 2018) with median distances between SNPs shorter than 5000 bp. SNPs were originally identified from whole-genome resequencing 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 Hestmannøy and 47 [48] males and 47 [48] females from Træna) 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 (Rönnegård 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 centred), F, and island identity as fixed effects, and brood identity, year, qPCR plate, and the GRM fitted as random effects. We estimated the proportion of phenotypic variance explained by each SNP as: , where p and q are the allele frequencies and β is the estimated allele substitution effect (Falconer & Mackay, 1996). 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). Gene ontology (GO) searches were performed using the gene ontology annotation (GOA) database (Binns et al., 2009; Huntley et al., 2015) to obtain an overview of biological processes and molecular functions known to be influenced by the genes. The model explaining variation in TL that included tarsus length was ranked higher than the model without tarsus length (∆AICc = 2.5, w1 = 0.78, ER1 = w1/w2 = 3.55). There was a negative association between tarsus length and TL (βtarsus length= –0.004 ± 0.002, CI = [–0.007, –0.000], n = 2462, Figure 1 and Table 1), such that larger nestlings generally had slightly shorter early-life telomeres. Thus, an increase in (age-corrected) tarsus length of 1 mm was associated with a decrease in TL of 0.8%. There was no evidence for associations between offspring TL and MAC (β∆MAC =0.001 ± 0.004, CI = [–0.007, 0.009], βmean MAC =0.001 0.005, CI = [–0.008, 0.010], Figures S2.1a,c in Appendix S2) or PAC (β∆PAC =0.005 0.003, CI = [–0.002, 0.011], βmean PAC= –0.001 ± 0.003, CI = [–0.008, 0.005], Figure S2.1b,d in Appendix S2). We found nonzero additive genetic variances (VA) for TL (VA = 0.009, HPD = [0.008, 0.010]), tarsus length (VA = 0.201, HPD = [0.111, 0.314]) and body condition (VA = 0.006, HPD = [0.005, 0.006]) in the natural populations (Table 2, Figure 2). The main component contributing to variance" @default.
• W3157358435 created "2021-05-10" @default.
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• W3157358435 date "2021-12-10" @default.
• W3157358435 modified "2023-10-06" @default.
• W3157358435 title "Genetic architecture and heritability of early‐life telomere length in a wild passerine" @default.
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