DOI: 10.5061/dryad.08kprr57s
title: Early life telomeres are influenced by environments acting at multiple temporal and spatial scales

description: An individual’s telomere length early in life may reflect or contribute to
 key life history processes sensitive to environmental variation. Yet, the
 relative importance of genetic and environmental factors in shaping early
 life telomere length is not well understood as it requires samples
 collected from multiple generations with known developmental histories. We
 used a confirmed pedigree and conducted an animal model analysis of
 telomere lengths obtained from nestling house sparrows
 (Passer domesticus) sampled over a span of 22 years. We found
 significant additive genetic variation for early life telomere length, but
 it comprised a relatively small proportion (9%) of the total biological
 variation. Three sources of environmental variation were important: among
 cohorts, among breeding attempts within years and families, and among
 nestmates. The magnitude of variation among breeding attempts and among
 nestmates also differed by cohort, suggesting that interactive effects of
 environmental factors across time or spatial scales were important, yet we
 were unable to identify the specific causes of these interactions. The
 mean amount of precipitation during the breeding season positively
 predicted telomere length, but neither weather during a given breeding
 attempt nor date in the breeding season contributed to an offspring’s
 telomere length. At the residual level, level of individual nestlings,
 offspring sex, size, and mass at 10 days of age also did not predict
 telomere length. Environmental effects appear especially important in
 shaping early life telomere length in some species, and an array of
 complex more focus on how environmental factors that interactions across
 scales may help to explain some of the variation observed among studies.
description: Study population: This study made use of a library of blood
 samples collected from 1993 to 2014 from a population of house sparrows
 located at the University of Kentucky’s North Farm Agricultural Research
 Station. Westneat et al. (2002, 2009) and Heidinger et al. (2021) provide
 details about this population and the standard methods used in the field
 to mark individuals, monitor their reproduction and the collection of and
 storage of blood samples. Briefly, all nestlings were given uniquely
 numbered metal bands at 10 days of age, and both independent juveniles and
 adults were given unique combinations of colored-plastic bands during
 trapping sessions throughout the year. The present data consists of the
 families of breeding adults who had hatched in focal nest boxes, were
 banded as nestlings, and had a blood sample taken when they were 10 days
 old. While we had samples from any nesting attempt of these focal birds
 that reached 10 days of age in one of our boxes, we often targeted a
 subset of these. If the focal adult produced only one brood, we analyzed
 the DNA of all offspring. If they had multiple broods but only within a
 single season, we analyzed the brood with the most offspring. If they had
 broods across more than one season, we sampled the largest brood in each
 season. Some additional broods were analyzed for other reasons and these
 were also included in the analysis. We identified breeding partners of
 target individuals and if a blood sample was available, we measured the
 telomere length from that sample. Most such partners were captured as
 adults, so their exact age was unknown, but we knew relative age from the
 date of capture. This sampling regime resulted in 1,591 individuals with
 telomere lengths of which 1,500 had been sampled at 10 days of
 age. Telomere measures: Some of the telomere measures
 used in this analysis came from the dataset used in Heidinger et al.
 (2021). Subjects in that analysis had all been sampled at 10 days of age
 and resampled at least once, but many did not breed. The present analysis
 included breeders only and also added individuals sampled at 10 days of
 age that were not recaptured and resampled. The birds used in the
 Heidinger et al. (2021) dataset were randomly assigned to assays that were
 carried out by A. Kucera whereas the remaining samples were randomly
 assigned to assays and processed by R. Young. All assays used the same
 reference sample (described below). Telomeres were
 measured in whole blood samples, a highly replicative tissue that can be
 non-destructively sampled and is well suited for telomere analyses,
 especially in birds which have nucleated red blood cells. We extracted DNA
 using Qiagen DNeasy Blood and Tissue DNA Extraction Spin Column Kits and
 following the manufacturer’ instructions. DNA concentration was quantified
 using a Nanodrop 8000 Spectrophotometer (Thermo Scientific, Waltham, MA,
 USA). DNA quality was assured using electrophoresis on a 2% agarose
 gel.<br><br><br>We used real-time quantitative
 polymerase chain reaction (qPCR) on an Mx3000P (Stratagene, San Diego, CA,
 USA) to measure relative telomere length. The methods followed those of
 Criscuolo et al. (2009) adapted for house sparrows (Young et al. 2022). To
 measure relative telomere lengths (T/S ratios) for each sample, we ran
 separate qPCR reactions for telomeres and the single copy control gene,
 Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), on different plates. The
 suitability of GAPDH as a control gene in this species has previously been
 verified using a melt curve analysis (Young et al. 2022).  All the samples
 were run in duplicate and randomly distributed across
 plates.<br><br>Each telomere and GAPDH reaction contained a
 total volume of 25 µl comprised of 20 ng of DNA and either telomere
 ((forward tel1b (5’-CGGTTTGTTTGGGTTTGGGTTTGGGTTTGGGTTTGGGTT-3’) and
 reverse tel2b (5’- GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACCCT-3’)) or GAPDH
 primers (GAPDH - forward (5’-AACCAGCCAAGTACGATGACAT-3’) and reverse GAPDH
 (5’-CCATCAGCAGCAGCCTTCA-3’)) at 200 nM concentration mixed with 12.5 ul of
 perfecta SYBRE green supermix Low Rox (Quantabio). The thermal profiles
 were 10 min at 95 °C, followed by 27 cycles of 15 s at 95 °C, 30 s at 58
 °C, and 30 s at 72 °C, finishing with 1 min at 95 °C, 30 s at 58 °C, and
 30 s at 95 °C for the telomere reactions and 10 min at 95 °C, followed by
 40 cycles of 30 s at 95 °C and 30 s at 60 °C, finishing with 1 min at 95
 °C, 30 s at 55 °C, and 30 s at 95 °C.<br><br>We calculated
 relative telomere lengths (T/S ratio) according to the following formula:
 2<sup>ΔΔCt</sup>, where ΔΔCt =
 (Ct <sup>telomere</sup> - Ct <sup>GAPDH</sup>)
 reference sample – (Ct <sup>telomere </sup>-
 Ct <sup>GAPDH</sup>) focal sample (Stratagene 2007), where the
 Ct value is the number of PCR cycles necessary to accumulate a sufficient
 fluorescent signal to cross a threshold. Individuals with longer telomeres
 cross this threshold more quickly than individuals with shorter telomeres,
 relative to the same house sparrow reference sample used on all plates.
 This same reference sample was also used to create a 5-point standard
 curve (40, 20, 10, 5, 2.5 ng) to ensure that all samples fell within the
 bounds of the standard curve and to calculate reaction efficiencies. In
 total we ran 53 plates and in all cases the efficiencies were between the
 recommended 85-115 % (mean ± SEM: telomere: 96.5 ± 0.85% and GAPDH 100 ±
 0.99%). This assay produces highly repeatable T/S ratios (ICC: 0.86-0.88)
 when samples are run in random well locations across plates (Heidinger et
 al. 2021, Young et al. 2022).<br><br>Parentage confirmation
 and pedigree construction: We confirmed maternity and paternity using
 genotypes obtained from a multiplex PCR of 5 highly polymorphic
 microsatellite loci adapted from well-established protocols (Stewart et
 al. 2006, Dawson et al. 2012). Generally, parents and offspring were
 organized by family, amplified on the same plate, and products analyzed in
 the same round on an ABI 3730. If adults had multiple broods across
 several years or switched mates, they may have been run on a different
 plate than some of their offspring. In some cases, subjects were analyzed
 2-3 times on different plates and in all cases, their allele scores were
 within 2 base pairs of each other. Adults were excluded as parents if 2 or
 more loci diverged by more than 1 base pair. In some cases, adults were
 excluded from being the parent of a whole brood. This sometimes led to the
 identification of alternative adults that did match, and if these were not
 among our target individuals, that brood was excluded from further
 analysis. Only offspring that were genetically related to a target adult
 were included in the dataset. If the focal adult was a male and the
 offspring was extra-pair, it was excluded from our analysis. If the focal
 adult was a female, then any of her offspring sired by an extra-pair male
 were included in the analysis, but we did not identify the true sire in
 these cases. The parentage information allowed us to create a pedigree of
 individuals with known telomere lengths and their relatedness to other
 individuals.<br><br>The resulting pedigree of 1,629
 individuals had 1,591 telomere measures (both nestlings and adults), 1,500
 of which were from individuals sampled at 10 days of age. This included
 211 sires and 228 mothers, no cases of inbreeding, and 7 tiers (Table S1,
 Figure S1) with one lineage having 6 generations. The data set covered 428
 nesting attempts with at least one nestling sampled. Most mothers had only
 one nesting attempt (N = 152) but two were represented by offspring from
 10 attempts. Mothers were typically part of only one pair (N = 215),
 meaning that they only had one male partner, but 44 had 2 or more partners
 with one having 6. Since extra-pair offspring were not assigned to a sire,
 these numbers reflect social pairings that produced within-pair offspring.
 We determined the sex of all nestlings using PCR of extracted DNA,
 following procedures described in Westneat et al.
 (2002).<br><br>Statistical Analysis Goal 1:
 Variance partitioning. Our initial goal was to partition the variance in
 telomere lengths among sources given the structure of the data. The
 initial animal model included all adults with a telomere measure,
 regardless of when they were sampled. We repeated the analysis restricting
 the set of adult telomere measures to those that were sampled at 10 days
 of age. These models were fitted with a set of random effects and no fixed
 effects. We included the relatedness matrix as a key term for assessing
 covariances among individuals by relatedness, the assay identity as a
 measure of lab artifacts, year (cohort) to assess effects of year-to-year
 environmental variation (note: prior analyses showed no effect of latency
 between sampling and analysis on telomere length, Heidinger et al. 2021),
 breeding attempt identity to assess all environmental effects common to a
 breeding attempt (e.g., date in the season, weather conditions during
 embryo and nestling development, size of the brood), nesting location
 (labeled as “barn” since nest boxes were clustered on the sides of farm
 buildings) to capture local environmental effects common to offspring
 reared in the same location, and pair identity to capture consistent joint
 influences of the male and female associated with breeding attempts. We
 did not include maternal identity alone, as males can provide extensive
 care during incubation and chick rearing, and most adult females in the
 dataset were members of only one pair and had only one breeding attempt,
 so the combination of pairD, motherID, and attemptID (or attemptID,
 motherID and sireID) would have over-specified the model.<br>We
 reanalyzed the initial model to include only data from individuals sampled
 at 10 days of age. Variance partitioning models were fitted twice in the R
 computing environment (R Core Team 2019), first
 using <em>brms </em>(Burkner 2017) with 2 chains, default
 priors, 10,000 iterations with a burnin of 1000 and a sampling every 5
 iterations. We used this to assess how well the model performed and get
 initial insight into the magnitude of variance components. The models took
 a long time to run in<em> brms</em>, so once we confirmed that
 models were fitting well (no divergent chains and rhats less than 1.1), we
 reran models in the program MCMCglmm (Hadfield 2010) using weakly
 informative priors (V = 1, nu = 0.002, Lemoine 2019), 1.1 x
 10<sup>5</sup> iterations, a burnin of 10,000 and sampling
 every 50 iterations. We then analyzed the data in more
 detail to assess three possible influences on genetic variance in
 telomeres. First, we asked if parent age at the time of fertilization
 influenced telomere length (sex-specific parent age entered as two fixed
 effects).  Second, we asked if the genetic variance in telomere length was
 a function of parent age via separate analysis of sire-specific (1-9) or
 mother-specific (1-6) age as a linear random slope in the “animal’ random
 effect (Class et al. 2019. In these models we dropped any random effects
 that could not be distinguished from 0 and replaced pair identity with
 either mother or sire identity depending on which sex was in the random
 slope term. We allowed the model to estimate covariances between the
 animal intercept and slope. These models were fitted
 in <em>brms</em> with a linear slope, cauchy priors (0,2),
 10000 iterations with a burnin of 1000, and a sample every 5
 iterations. Finally, we assessed the role of offspring
 sex, both as a fixed effect to repeat a test for any sex differences in
 telomere length (Heidinger et al. 2021, Le Pepke 2021) and to assess
 sex-specific heritability. The former was tested by adding a fixed effect
 of offspring sex to the models described below that explored specific
 environmental factors. For sex-specific heritability, we fitted a
 bivariate model with the telomere length in male and female nestlings as
 two responses to investigate differences in heritability (Olsson et al.
 2011, Chick et al. 2022) and estimate genetic correlations between the
 sexes. We also included assay identity, year, and attempt identity as
 random effects and modelled the covariance between male and female for
 each of these as well. Models were fitted
 in<em> brms </em>with each of three prior types for
 variance/covariances: default, V = diag(2), nu = 1.002, and V =
 diag(2)*(0.002/1.002), nu = 1.002. Results were affected only slightly.
 All models had 10,000 iterations, burnin of 1000, and a sampling of every
 5. Goal 2: Influence of environmental effects. We
 examined types of environmental effects in more detail using a sequenced
 approach. Step one was to assess the magnitude of variance explained by
 the “environmental” variance components in the initial animal model
 described above. Our plan was to then explore specific environmental
 variables in more detail by adding fixed effects that varied most at the
 requisite level. For example, we expected from Le Pepke et al (2022b) that
 cohort (year) might explain some variation. If so, then yearly differences
 in weather might be relevant, so we gathered year-specific mean
 temperature and precipitation in either the “spring” months (February and
 March) preceding each breeding season or “summer” months through the
 period of breeding (April – August). Similarly, any among-attempt
 variation might be influenced by date in the season in which attempts were
 started, brood size at hatch, or the specific mean precipitation that
 occurred during the 25 days after the first egg was laid in that breeding
 attempt. Because temperature for a specific nesting attempt is highly
 correlated with date, and previous analyses of both variables revealed
 that date was a better variable to include for some traits (Westneat et
 al. 2009), we left attempt-specific temperature out of the model. Because
 these models focused on environmental sources of variance, we omitted the
 pedigree and included year and attempt identity as random effects. Each of
 these investigations was modelled in<em> brms </em>using
 default priors, 10,000 iterations, a burnin of 1000, and a sample every 5
 iterations. We also explored the potential for
 interactive effects among environmental factors in two ways. To gather
 general evidence of interactions, we paired down the mixed model to just
 the informative and relevant random effects (omitting PairID, Barn, and
 the pedigree). We then split either the breeding attempt random term or
 the residual via grouping by a higher order random effect. For example, we
 calculated among-breeding attempt variance and
 among-nestling-within-attempt (residual) variance in telomere for each
 year. We also assessed if among- or within-attempt variance differed among
 parental ages by converting parental age to a random effect (lumping older
 ages into a 5+ category) and splitting among attempt and residual variance
 by age, with each parent tested in separate models. These models were
 fitted in a frequentist framework in SAS 9.4 (SAS 2015) <em>Proc
 Mixed</em> given the ease with which SAS coding allows this (see
 supplementary material). The magnitude of improved fit was assessed using
 a likelihood ratio test against the base model without group-specific
 variances. We also tested a set of two-way interactions
 as fixed effects that we reasoned were likely to explain variance at the
 appropriate level. For instance, if variation among attempts was
 important, then date by brood size seemed a likely influence given prior
 work showing that date affects clutch size (Westneat et al. 2009) and
 nestling growth (Mock et al. 2009) and brood size influences telomeres in
 a North Dakota population (Young et al. 2022). Similarly, at the residual
 level, brood size or parent age by the sex of the nestling might be
 important. We constructed these models after running the initial random
 effect models.  Because these proposed fixed effects differed dramatically
 in measurement scale, we standardized ((x-mean)/SD) all values of all
 variables and analyzed them in a mixed model with a reduced set of random
 effects (omitting Barn, PairID, and the pedigree) in
 <em>brms</em> with default priors, 10,000 iterations, a burnin
 of 1000, and sampling every 5 iterations. Goal 3:
 Phenotypic and genetic covariance between telomeres and body size. Two
 opposing results already published encouraged us to investigate links
 between individual offspring telomere length and their condition at the
 time of measurement. Young et al. (2022) found that relative offspring
 size positively predicted day 10 telomere length but Le Pepke et al.
 (2022a) found that telomere length was negatively correlated with
 offspring size with age controlled, suggesting that higher growth led to
 shorter telomeres. We used two bivariate analyses to explore these
 relationships in our data set. One of the bivariate equations had telomere
 length as the response, included assay identity as a random effect and the
 important random effects from the analyses described above (year and
 attempt identity) along with the relatedness matrix. The other equation
 had either nestling tarsus length, a standard measure of size, or nestling
 mass as the response. These equations both included nestling age as a
 fixed effect since there was some variation in the age at which nestlings
 were measured, and year, attempt identity, and the relatedness matrix as
 random effects. We set the models to extract covariances for each random
 effect including the relatedness matrix to assess environmental and
 genetic correlations and the residual covariance. These models were fitted
 using<em> brms</em> using default priors, 10,000 iterations, a
 burnin of 1000 and sampling every 5. As a check, we reran these models
 with Cholesky priors (lkj(2) as recommended for correlations among random
 effects in the set prior notes for <em>brms</em>. We found no
 substantive differences in results.  
description: # 'Early life telomeres are influenced by environments acting at
 multiple temporal and spatial scales
 [https://doi.org/10.5061/dryad.08kprr57s](https://doi.org/10.5061/dryad.08kprr57s) Data used in the analyses came from four data files, processed in R. The first is a file ("pedigree.csv") containing basic information on parent-offspring relationships that was converted into a pedigree in R. The other three files contained information on nestlings or weather; these were merged in R for the main analyses. ## Description of the data and file structure Pedigree file (“Pedigree.csv”) The pedigree file consisted of 1629 individuals who were a mix of nestlings sampled at 10 days of age (1591) and their parents if known. Variables were 1.      **SampleID**: Unique number given to individual, usually representing their blood sample number. 2.      **Dad**: SampleID of the male at the nest, confirmed as the genetic father or observed there. 3.      **Mom**: SampleID of the female at the nest, confirmed as genetic mother or observed there. Missing data is expected for birds in the pedigree with no known parent of one or both sexes. These are indicated by blank cells. Telomere file (“Telomere.csv”) This file contained the core data used in the animal model analysis. It consisted of 1591 entries of individuals for which we had telomere measures. Variables were: **SampleID**: Unique number given to individual, usually representing their blood sample number. **NestID**: Unique number assigned to each breeding attempt over the history of the study **Barn**: Location of nest site, usually on a single physical structure, with one level referring to solitary nest sites not clustered on a structure. **FED**: First egg date of the focal nest attempt in days with 1 Jan = 1 (Julian date). **Clutch**: Number of eggs laid in focal attempt **Hatch**: Number of hatchlings in focal attempt, treated as starting brood size **Band**: Number of nestlings that were banded (typically at age10 days) **Identity**: Code for male (M) or female (F) if adult or for nestling (N). **Sex**: M for male and F for female **Dad**: ID for the sire **Mom**: ID for the mother **PairID**: A concatenated identity indicating he combination of the two parents **Sample10**: Code for if the focal bird was sampled at day 10 (Y) or not (N). **DadAge**: Count of the breeding season for the male at the breeding attempt **MomAge**: Count of the breeding season for the female at the breeding attempt **Year**: Year focal individual was hatched **Assay**: Unique name for each assay, consisting of the plate with the samples and the reference plate. **TSratio**: Measure of telomere length **TSratio10**: Measure of telomere length on Day 10. **TSratioMale**: Measure of telomere length for male nestlings **TSratioFem**: Measure of telomere length for female nestlings Several variables will have missing data for some rows--these are indicated by blank cells. Nestling measures file (“Nmeasures.csv”) Data on the mass and tarsus length of nestlings at the time their blood was sampled. Variables: **SampleID**: The unique number given to the blood sample from an individual **Date**: Date of sampling in month/day/year format **JDate**: Date of sampling in Julian days, where 1 Jan of the focal year = 1. **Nage**: Age of nestling at time of sampling **Tarsus**: Length of metatarsus in millimeters. **Weight**: Mass of nestling in grams. **AvTempC**: Daily mean temperature in Celsius over the previous 25 days covering he active period of the nesting attempt. **AvPrecipCM**: Daily mean precipitation over the previous 25 days covering he active period of the nesting attempt. Some variables can have missing values; these are indicated by blank cells. Weather summaries file (“weather.csv”) Summary of weather data for the years of the study. Variables: **Year**: The calendar year of data collection, from 1993-2014 **SumTemp**: Daily mean temperature over the period April 1-August 31 in degrees Fahrenheit. **SumTC**: Daily mean temperature over the period April 1-August 31 converted to Celsius **SumTMC**: Variable SumTC mean centered **SumPrec**: Daily mean precipitation over the period April 1-August 31 in inches **SumPM**: Daily mean precipitation over the period April 1-August 31 in centimeters **SumPMC**: Variable SumPM mean-centered **SpringTemp**: Daily mean temperature over the period February 1-March 31 in degrees Fahrenheit. **SpringTC**: Daily mean temperature over the period February 1-March 31 in degrees Celsius. **SpringTMC**: Variable SpringTC mean-centered. **SpringPrec**: Daily mean precipitation over the period February 1-March 31 in inches. **SpringPM**: Daily mean precipitation over the period February 1-March 31 in centimeters. **SpringPMC**: Variable SpringPM mean-centered. ## Sharing/Access information Dryad is the main mechanism for sharing. ## Code/Software The analyses reported in the paper were conducted in the R computing environment and in SAS Proc Mixed (SAS 1994). Below is the code used to process the data. \#\# Read in files \`\`\`{r reading in pedigree data echo=FALSE} ped1 = read.table('Pedigree.csv', header=TRUE, sep=",", na.strings="NA") tel1 = read.table('Telomere.csv', header=TRUE, sep=",", na.strings="NA") n1 &lt;- read.table('Nmeasures.csv', header=TRUE, sep=",", na.strings="NA") w1 &lt;- read.table('Weathersummary.csv', header=TRUE, sep=",", na.strings="NA") \`\`\` ##Process data files \`\`\`{r merge, echo=FALSE} tel3 &lt;- merge(tel1, n1, by.x="SampleID", by.y = "SampleID", all.x = TRUE, all.y=FALSE) tel4 &lt;- merge(tel3, w1, by.x="Year", by.y = "Year", all.x = TRUE, all.y=FALSE) \`\`\` "tel4" is the primary dataframe for most analyses. ##Data modifications All data manipulations needed for analyses are listed below. \`\`\`{r modify, echo=FALSE} tel4$animal &lt;- tel4$SampleID tel4$MomAgecen &lt;- tel4$MomAge - 2 tel4$DadAgecen &lt;- tel4$DadAge - 2 tel4$NestID &lt;- as.factor(tel4$NestID) tel4$MomAgeF &lt;- as.factor(tel4$MomAge) tel4$DadAgeF &lt;- as.factor(tel4$DadAge) m1 &lt;- mean(tel4$Jdate, na.rm = TRUE) m2 &lt;- mean(tel4$AvTempC, na.rm = TRUE) m3 &lt;- mean(tel4$AvPrecipCM, na.rm = TRUE) tel4$JdateMC &lt;- tel4$Jdate-m1 tel4$AvTempMC &lt;- tel4$AvTempC-m2 tel4$AvPrecipMC &lt;- tel4$AvPrecipCM tel4$Nage10 &lt;- tel4$Nage-10 #Function for standardizing normFunc &lt;- function(x, na.rm=TRUE){(x-mean(x, na.rm=TRUE))/sd(x, na.rm=TRUE)} tel4$TSratioSTD &lt;- normFunc(tel4$TSratio) tel4$JdateSTD &lt;- normFunc(tel4$Jdate) tel4$HatchSTD &lt;- normFunc(tel4$Hatch) tel4$AvTempSTD &lt;- normFunc(tel4$AvTempC) tel4$AvPrecipSTD &lt;- normFunc(tel4$AvPrecipCM) tel4$SumTSTD &lt;- normFunc(tel4$SumTC) tel4$SumPSTD &lt;- normFunc(tel4$SumPrec) tel4$SpringTSTD &lt;- normFunc(tel4$SpringTC) tel4$SpringPSTD &lt;- normFunc(tel4$SpringPrec) tel4$MomAgeSTD &lt;- normFunc(tel4$MomAge) tel4$DadAgeSTD &lt;- normFunc(tel4$DadAge) tel4$YearF &lt;- as.factor(tel4$Year) \`\`\` ##Processing of pedigree #For brms Once duplicates were fixed, used nadiv to create relatedness matrix of pedigree. Since we will use brms for analysis, I will construct this using prepPed function and then the makeA matrix to explore the additive genetic variance of telomere length. Wolak, M. E. (2019). nadiv: an R package to create relatedness matrices for estimating non-additive genetic variances in animal models. Methods in Ecology and Evolution. \`\`\`{r "processing pedigree data" echo=FALSE} library(nadiv) #Creating pedigree matrix ("Amat") ped2 &lt;-prepPed(ped1, gender=NULL, check=TRUE) Amat &lt;- as.matrix(nadiv::makeA(ped2)) \`\`\` #For MCMCglmm Proper pedigree file, with names that MCMCglmm will recognize \`\`\`{r pedigree, echo=FALSE} library (pedigree) library (tidyverse) #Rename variables Ped &lt;- ped1 Ped &lt;- rename(Ped, animal = SampleID, sire = Dad, dam = Mom) Ped &lt;- Ped[1629:1,] (ord &lt;- orderPed(Ped)) Ped &lt;- Ped[order(ord),] \`\`\` Change data file to have same variable names \`\`\`{r base model, echo=FALSE} Tel &lt;- tel4 Tel &lt;-rename(Tel, animal = SampleID, sire = Dad, dam = Mom) \`\`\` Used "Tel" for MCMCglmm models Analyses are presented in the supplementary material for the MS. They used the R programs "brms" and "MCMCglmm" and SAS Proc Mixed.