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Data from: Can dominance genetic variance be ignored in evolutionary quantitative genetic analyses of wild populations?

Cite this dataset

Class, Barbara; Brommer, Jon (2020). Data from: Can dominance genetic variance be ignored in evolutionary quantitative genetic analyses of wild populations? [Dataset]. Dryad.


Accurately estimating genetic variance components is important for studying evolution in the wild. Empirical work on domesticated and wild outbred populations suggests that dominance genetic variance represents a substantial part of genetic variance, and theoretical work predicts that ignoring dominance can inflate estimates of additive genetic variance. Whether this issue is pervasive in natural systems is unknown, because we lack estimates of dominance variance in wild populations obtained in situ. Here, we estimate dominance and additive genetic variance, maternal variance, and other sources of non-genetic variance in 8 traits measured in over 9000 wild nestlings linked through a genetically resolved pedigree. We find that dominance variance, when estimable, does not statistically differ from zero and represents a modest amount (2-36%) of genetic variance. Simulations show that 1) inferences of all variance components for an average trait are unbiased; 2) the power to detect dominance variance is low; 3) ignoring dominance can mildly inflate additive genetic variance and heritability estimates but such inflation becomes substantial when maternal effects are also ignored. These findings hence suggest that dominance is a small source of phenotypic variance in the wild and highlight the importance of proper model construction for accurately estimating evolutionary potential.


Material and Methods

Data collection

Data was collected in a wild population of blue tits breeding in nest boxes in South-Western Finland (Tammisaari, 60°01′N, 23°31′E) and monitored yearly since 2003. Nest boxes were visited weekly in May, and daily starting from their expected hatching date until hatching was observed (D0). Two days after hatching (D2), nestlings were weighed (using a scale with 0.1g precision) and individually marked by clipping their nails. Between 2006 and 2010, reciprocal cross-fostering was performed between pairs of nests with similar hatching date and average mass (for more details see Brommer & Kluen 2012). Between D5 and D9, parents were caught in the box when feeding their young and identified based on unique alphanumeric codes on their metal ring or ringed if previously unringed. On D9, nestlings were weighed and ringed after their nail code, which provides information on their nest of origin, was read. On D16, nestlings were all transferred in a large paper bag and morphometric and behavioural measurements were taken following a fixed sequence (cf. Brommer & Kluen 2012). Firstly, each individual was held still on its back by an observer, who counted how many times it struggled during 10 seconds. Docility was calculated by multiplying the number of struggles per second by -1, such that higher docility values indicate a more docile animal. Directly following the docility assay, the time each bird took to take 30 breaths was recorded twice using a stopwatch. Breath rate (BR), which captures an individual’s stress response to handling (Carere et al. 2004), was calculated as 30 divided by the average of these two measures and expressed in number of breath/second. Morphometric measurements were then taken: First, the bird’s right tarsus and head-bill length were measured using a digital sliding caliper (0.1mm accuracy). Then, wing and tail length were measured using a ruler (1mm accuracy). A score was then given to each bird based on its behaviour (struggling, flapping wings) during morphometric measurements. This score, which is similarly measured in adults and called handling aggression (HA), ranges from 1 (for completely passive individuals) to 5 (for the individuals struggling continuously) and reflects the time it takes for each bird to calm down during handling. Finally, each nestling was weighed using a Pesola spring balance (0.1g accuracy) before being placed in a second large paper bag where it remained with its already measured siblings until the entire brood was processed and put back to its nest. In total, 8 traits (3 behavioural, 5 morphological) were measured in nestlings (Table 1) and analysed using quantitative genetic models.

Microsatellite genotyping

Blood was taken on all adults when caught and feathers were taken on nestlings on D9 for DNA extraction and genotyping. All laboratory work was carried out by the Center of Evolutionary Applications (University of Turku, Finland). DNA from feather samples was extracted using a silica fine and filter plate based method modified from Elphinstone et al (2003). DNA from blood samples was extracted with a method modified from Aljnabi & Martinez (1997). All samples were genotyped with 9 microsatellite markers using a multiplex PCR approach. PCR was carried out in one 8 µl reaction using QIAGEN Multiplex PCR Kit (Qiagen Inc. Valencia, CA, USA) with the annealing temperature of 57 °C and the primer concentration varying from 0.09 to 0.5 µM following the standard protocol (Table S1). To improve the microsatellite peak profiles, a GTTT-tail was added to the 5’ end of each reverse primer (Brownstein et al 1996). The sex of the offspring was determined by amplifying sex specific genes CHD1W and CHD1Z using P2 and P8 primers in an additional amplification reaction using the same standard protocol with annealing temperature at 55 °C (Griffiths 1998).

Amplifications were performed on Bio-Rad S1000 thermal cyclers and the size of the fragments was determined by capillary electrophoresis on an ABI PrismTM 3130xl genetic analysis instrument. To minimize fragment analysis costs, two samples were pooled for capillary electrophoresis. To enable pooling of samples, two alternative sets of fluorescent labels, i.e. all nine markers (+sexing marker) with both FAM/VIC and NED/PET labels were used. The peak profiles of the pooled samples could then be separated during scoring and visual inspection, using GeneMarker version 2.4.0 (SoftGenetics).

Population pedigree

Parentage assignment was done for each year separately (2007-2019), by combining genotype data and social pedigree and using the R package MasterBayes (Hadfield et al. 2006). After validating parent-offspring relationships between females and nestlings sampled on the same territory (mismatch tolerance=1), we assigned genetic fathers, among all males genotyped on the same year for each offspring, with a 95% probability threshold. These analyses revealed that between 2007 and 2019, extra-pair young represented 15% (standard deviation (sd)=3) of the young in the population and occurred in 45 % (sd=6) of broods. In nests that were not genotyped (before 2007, or when the mother or offspring was not genotyped), we assumed social parents to be the genetic parents of the offspring they reared. The resulting population pedigree hence combines social and genetic pedigrees and represents our best inference of the true pedigree in this population.

Phenotypic data is available for 9887 individuals and the pruned pedigree (which only includes informative individuals) holds record for 10946 individuals, 9890 maternities, 8620 paternities, 38507 full sibs, 82487 maternal sibs, 68748 paternal sibs, 43980 maternal half-sibs, 30241 paternal half-sibs, a mean family size of 12.4, a mean pairwise relatedness of 1.5*10-03 and a maximum pedigree depth of 9.


Academy of Finland, Award: 289456