TITLE: Analysis of genetic relatedness and paternity assignment in wild Guinea baboons (Papio papio) based on microsatellites
AUTHORS: Federica Dal Pesco, Franziska Trede, Dietmar Zinner, Fischer Julia

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block"><div class = "justify" style = "text-align:justify"><span>This protocol provides a standardized methodology for the analyses of genetic relatedness and paternity assignment in wild Guinea baboons (</span><span style = "font-style:italic;">Papio papio</span><span>) based on 24 microsatellite markers. It describes all methodological steps from sample collection and storage, DNA extraction, microsatellite amplification, and genotyping to a detailed explanation of the analyses regarding loci descriptive statistic, dyadic relatedness, and parentage analysis. This protocol was developed as a standardized method utilized by the Cognitive Ethology Laboratory (German Primate Center) to analyse fecal samples from the wild Guinea baboon population at the CRP Simenti field site, PNNK, Senegal.</span></div><style>
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?. Sample collection and storage:
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							We collected samples for genetic analysis non-invasively conducting fecal sampling of all identified individuals belonging to our study groups. Immediately after defecation approximately 5 g of fecal material was collected from the surface of the fecal bolus and placed inside a sterile tube containing 90% ethanol. Fecal samples were stored following a 2-step preservation protocol, with an initial phase of conservation in 90% ethanol followed by desiccation and storage with silica beads (Roeder et al., 2004). Samples were shipped to the German Primate Center in Germany every year and subsequently stored at −20 °C.
?. DNA extraction, microsatellite amplification, and genotyping:
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							We extracted DNA from fecal samples using the First-DNA all tissue kit (Genial®). We characterized genetic variation in Guinea baboons by assessing the individual allele variation on 24 polymorphic autosomal microsatellite markers (see Table 1). This microsatellite panel was developed based on microsatellite markers used in humans and reported to also amplify in baboons (Roeder et al., 2009; Rogers et al., 2000), which have been successfully multiplexed in several studies of Guinea baboons (Kopp et al., 2015; Ferreira da Silva et al., 2014, 2018) including our study population (Patzelt et al, 2014). Note that the current panel (described in Table 1) was modified compared to the initial panel used by Patzelt et al. (2014). The 24 markers were amplified in 5 different multiplex systems (mean number of loci per multiplex, 4.80 ± 1.10 SD) using the Multiplex PCR Kit (QIAGEN) and fluorescent-labelled primers in concentrations ranging from 0.05 to 0.5 µM. PCR cycling conditions included a hot start polymerase activation step at 95°C for 15min, followed by 40 cycles with denaturation at 94°C for 30s, primer annealing at 57°C for 40s, elongation at 72°C for 40s, and a single final elongation step at 72°C for 30min. Detailed information regarding loci identities, primer sequences and concentrations, and fluorescent dyes are reported in table 1. PCR products were separated and detected through capillary gel electrophoresis on an ABI 3130xL Genetic Analyzer (16 capillary sequencer, Applied Biosystems®, USA). Microsatellite allele sizes were evaluated in comparison with the GeneScanTM -400HD size standard using Gene Mapper 5 (Applied Biosystems®). Allele calling for each locus was repeated until 5 completely consistent calls were achieved. To avoid contamination, extraction, polymerase chain reaction (PCR) and fragment length analysis were performed in separate rooms and monitored with negative controls.
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							Table 1 - List of 24 microsatellite loci used in this study: multiplex-PCR, loci identities, primer sequences, fluorescence dye, and primer concentration
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							  ABCDE1Multiplex PCRLocus IDPrimer 5’-3’Fluoresc.Primer&nbsp;Conc. (uM)2M1D6S264FAGC TGA CTT TAC GCT GTT CFam0.05357°CD6S264RTTT TCC ATG CCC TTC TAT CAFam0.054&nbsp;D7S503FATG ACT TGG AGT AAT GGG&nbsp;&nbsp;AGTamra0.155&nbsp;D7S503RGTC CCT GAA AAC CTT TAA TCATamra0.156&nbsp;D12S375FTTG TTG AGG GTC TTT CTC CAFam0.097&nbsp;D12S375RTCT TCT TAT TTG GAA AAG TAA CFam0.098&nbsp;D3S1766FACC ACA TGA GCC AAT TCT GTTamra0.059&nbsp;D3S1766RACC CAA TTA TGG TGT TGT TACTamra0.0510M2D14S306FTCA GCT ACA TCC AAA TTA GGTTamra0.051157°CD14S306RTGA CAA AGA AAC TAA AAT GTC CTamra0.0512&nbsp;D1S533FTAT CCC CCC CAA AAA TAT ATAFam0.0513&nbsp;D1S533RTTG CTA ACC AAA ATA ACA ATG GGFam0.0514&nbsp;D2S1329FTTG TAG AAC CCT CTC AAA TATTamra0.515&nbsp;D2S1329RGAA ACT TCC ACC CTG GGT TTamra0.516&nbsp;D2S1326FAGA CAG TCA AGA ATA ACT GCHex0.0517&nbsp;D2S1326RCTG TGA CCC AAA AGC CGAHex0.0518M3D10S611FTAT ACA GGA AAC TGT GTA GTGTamra0.21957°CD10S611RCTA TAT TTA TGT GTG TGG ATGTamra0.220&nbsp;D8S1106FTTG TTT ACC CCT GCA CCA CHex0.221&nbsp;D8S1106RTTC TCA GAA TTG CTC ATA GTGHex0.222&nbsp;D17S791FATG TTC TCC AGT TAT TCC CCTamra0.523&nbsp;D17S791RGCT GGT CCT TTG GAA GAG TTamra0.524&nbsp;D6S501FGCT GGA AAC TGA TAA GGG CHex0.225&nbsp;D6S501RGCC ACC CTG GCT AAG TTAHex0.226&nbsp;D17S1290FGAC AAC AGA GCA AGA CTG TFam0.2527&nbsp;D17S1290RAGA AGC AGT TAA ATG GCC AAAFam0.2528&nbsp;D6S311FATG TCC TCA TTT GTG TTG TGTamra0.329&nbsp;D6S311RGAT TCA GAG CCC AGG AAG ATamra0.330M4D5S1457FTAG GTT CTT GGC ATG TCT GTTamra0.23157°CD5S1457RTGC TTG GCA TAC TTC AGG GTamra0.232&nbsp;D8S505FCTA AAG TGA ACC CAA ACC TAAFam0.1533&nbsp;D8S505RAGT GCT AAG TCC CAG ACC AFam0.1534&nbsp;D10S1432FCAG TGG ACA CCA AAC ACA ATTamra0.435&nbsp;D10S1432RTAG GTT ATC TAA ATA GTG GAT TTTamra0.436&nbsp;D5S820FATT GCA TGG CAA CTC TTC TCFam0.337&nbsp;D5S820RGTT CTT CAG AGA AAC AGA ACFam0.338&nbsp;D3S1768FGGT TGC TGC CAA AGA TTA GAHex0.1539&nbsp;D3S1768RCAC TGT AAT TTG CTG TTG GATHex0.1540&nbsp;D7S2204FTCA TGA CAA AAC AGA AAA TAA GTFam0.441&nbsp;D7S2204RAGT AAA TGG AAT TGC TTG TTA CFam0.442M5D1S207FCAC TTC TCC TTG AAT CGC TTHex0.14357°CD1S207RGCA AGT CCT GTT CCA AGT CHex0.144&nbsp;D4S243FTCA GTC TCT CTT TCT CCT TGFam0.1545&nbsp;D4S243RTAG GAG CCT GAG GTC CTGFam0.1546&nbsp;D1S548FGAA CTC ATT GGC AAA AGG AAHex0.1547&nbsp;D1S548RGCC TCT TTG TTG CAG TGA TTHex0.1548&nbsp;D21S1442FCTC CTC CCC ACT GCA GATFam0.549&nbsp;D21S1442RTCT CCA GAA TCA CAT GAG CFam0.5 
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ABCDE1Multiplex PCRLocus IDPrimer 5’-3’Fluoresc.Primer&nbsp;Conc. (uM)2M1D6S264FAGC TGA CTT TAC GCT GTT CFam0.05357°CD6S264RTTT TCC ATG CCC TTC TAT CAFam0.054&nbsp;D7S503FATG ACT TGG AGT AAT GGG&nbsp;&nbsp;AGTamra0.155&nbsp;D7S503RGTC CCT GAA AAC CTT TAA TCATamra0.156&nbsp;D12S375FTTG TTG AGG GTC TTT CTC CAFam0.097&nbsp;D12S375RTCT TCT TAT TTG GAA AAG TAA CFam0.098&nbsp;D3S1766FACC ACA TGA GCC AAT TCT GTTamra0.059&nbsp;D3S1766RACC CAA TTA TGG TGT TGT TACTamra0.0510M2D14S306FTCA GCT ACA TCC AAA TTA GGTTamra0.051157°CD14S306RTGA CAA AGA AAC TAA AAT GTC CTamra0.0512&nbsp;D1S533FTAT CCC CCC CAA AAA TAT ATAFam0.0513&nbsp;D1S533RTTG CTA ACC AAA ATA ACA ATG GGFam0.0514&nbsp;D2S1329FTTG TAG AAC CCT CTC AAA TATTamra0.515&nbsp;D2S1329RGAA ACT TCC ACC CTG GGT TTamra0.516&nbsp;D2S1326FAGA CAG TCA AGA ATA ACT GCHex0.0517&nbsp;D2S1326RCTG TGA CCC AAA AGC CGAHex0.0518M3D10S611FTAT ACA GGA AAC TGT GTA GTGTamra0.21957°CD10S611RCTA TAT TTA TGT GTG TGG ATGTamra0.220&nbsp;D8S1106FTTG TTT ACC CCT GCA CCA CHex0.221&nbsp;D8S1106RTTC TCA GAA TTG CTC ATA GTGHex0.222&nbsp;D17S791FATG TTC TCC AGT TAT TCC CCTamra0.523&nbsp;D17S791RGCT GGT CCT TTG GAA GAG TTamra0.524&nbsp;D6S501FGCT GGA AAC TGA TAA GGG CHex0.225&nbsp;D6S501RGCC ACC CTG GCT AAG TTAHex0.226&nbsp;D17S1290FGAC AAC AGA GCA AGA CTG TFam0.2527&nbsp;D17S1290RAGA AGC AGT TAA ATG GCC AAAFam0.2528&nbsp;D6S311FATG TCC TCA TTT GTG TTG TGTamra0.329&nbsp;D6S311RGAT TCA GAG CCC AGG AAG ATamra0.330M4D5S1457FTAG GTT CTT GGC ATG TCT GTTamra0.23157°CD5S1457RTGC TTG GCA TAC TTC AGG GTamra0.232&nbsp;D8S505FCTA AAG TGA ACC CAA ACC TAAFam0.1533&nbsp;D8S505RAGT GCT AAG TCC CAG ACC AFam0.1534&nbsp;D10S1432FCAG TGG ACA CCA AAC ACA ATTamra0.435&nbsp;D10S1432RTAG GTT ATC TAA ATA GTG GAT TTTamra0.436&nbsp;D5S820FATT GCA TGG CAA CTC TTC TCFam0.337&nbsp;D5S820RGTT CTT CAG AGA AAC AGA ACFam0.338&nbsp;D3S1768FGGT TGC TGC CAA AGA TTA GAHex0.1539&nbsp;D3S1768RCAC TGT AAT TTG CTG TTG GATHex0.1540&nbsp;D7S2204FTCA TGA CAA AAC AGA AAA TAA GTFam0.441&nbsp;D7S2204RAGT AAA TGG AAT TGC TTG TTA CFam0.442M5D1S207FCAC TTC TCC TTG AAT CGC TTHex0.14357°CD1S207RGCA AGT CCT GTT CCA AGT CHex0.144&nbsp;D4S243FTCA GTC TCT CTT TCT CCT TGFam0.1545&nbsp;D4S243RTAG GAG CCT GAG GTC CTGFam0.1546&nbsp;D1S548FGAA CTC ATT GGC AAA AGG AAHex0.1547&nbsp;D1S548RGCC TCT TTG TTG CAG TGA TTHex0.1548&nbsp;D21S1442FCTC CTC CCC ACT GCA GATFam0.549&nbsp;D21S1442RTCT CCA GAA TCA CAT GAG CFam0.5
?. Genetic markers descriptive statistic:
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							We calculated descriptive statistics for all 24 markers, estimated FIS, expected and observed heterozygosity, and tested for Hardy-Weinberg equilibrium (HWE) for all loci using the R package PopGenReport (Adamack & Gruber, 2014). We tested for the presence of null alleles using MICRO-CHECKER (Van Oosterhout et al., 2004). All loci that showed signs of null alleles and/or significant deviations from HWE were excluded from following analyses.
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?. Genetic relatedness analysis:
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							We used the individual genotypes to estimate dyadic relatedness. As relatedness estimators are influenced by the allele-frequency distributions and the true relationships of the individuals included in the dataset (Blouin, 2003; Pew et al., 2015), we used the R package “related” (Pew et al., 2015; also see Wang, 2011) to choose the best relatedness estimator for each specific dataset and project. This package provides calculations and comparisons of five non-likelihood (Queller and Goodnight, 1989; Li et al., 1993; Ritland, 1996; Lynch and Ritland, 1999; Wang, 2002) and two likelihood estimators (Milligan, 2003; Wang, 2007). Based on the characteristics of the molecular markers of our dataset, we simulated 100 pairs of individuals each of known relatedness categories (parent-offspring, full-sibling, half-sibling, and unrelated). We then compared the observed and expected relatedness values for each of the seven relatedness estimators using Spearman’s correlation and chose the estimator with the best performance and highest correlation coefficient. Depending on the type of estimator, the range of relatedness estimates and their interpretations varies. While the two maximum-likelihood estimators range from 0 to 1 (Wang 2007), the five non-likelihood estimators range from -1 to 1 and should be interpreted as a correlation as originally conceived by Wright (1921, 1922), where negative estimates indicate pairs that are less related than average (Wang, 2017).
?. Parentage analysis:
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							We assigned paternity using a likelihood approach with Cervus (Kalinowski et al., 2007). For all analyses we first ran a simulated parentage analysis with 10000 offspring followed by the actual parentage analysis. Confidence levels of parent assignments were set to 95% (“strict” criterion; while the “relaxed criterion” was 80%). The following parameters were used in all calculations: proportion of potential sires sampled 0.95, proportion of typed loci 1.00, and proportion of mistyped loci 0.01. All females belonging to the study groups were included in this analysis and all potential transfers of mothers to another primary male’s unit between time of conception and birth were examined. Identities of mothers were known from field observations, but we further checked all mother/offspring pairs with a maternity likelihood analysis (criteria for acceptance: identification as candidates with 0 mismatches). We ran a paternity analysis with a trio likelihood approach where the identity of the mother was known in order to determine the most likely father. For rare cases where the mother of the offspring was not known or not matching the field observations, the paternity analysis for this specific offspring was run not including mother identity. All adolescent (large juvenile and subadult) and adult males in our study were included as potential sires in this analysis. A male was considered to have sired an offspring when he was assigned as the most likely father, had 0 to maximum 1 mismatched alleles, and the confidence level for the assignments was more than 95%, according to the “strict” criterion.
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