Deciphering the genomic architecture of the stickleback brain with a novel multilocus gene-mapping approach

Heterogeneous genomic architecture of skeletal armour traits in sticklebacks

Whether populations adapt to similar selection pressures using the same underlying genetic variants depends on population history and the distribution of standing genetic variation at the metapopulation level. Studies of sticklebacks provide a case in point: when colonizing and adapting to freshwater habitats, three-spined sticklebacks (Gasterosteus aculeatus) with high gene flow tend to fix the same adaptive alleles in the same major loci, whereas nine-spined sticklebacks (Pungitius pungitius) with limited gene flow tend to utilize a more heterogeneous set of loci. In accordance with this, we report results of quantitative trait locus (QTL) analyses using a backcross design showing that lateral plate number variation in the western European nine-spined sticklebacks mapped to 3 moderate-effect QTL, contrary to the major-effect QTL in three-spined sticklebacks and different from the 4 QTL previously identified in the eastern European nine-spined sticklebacks. Furthermore, several QTL were identified associated with variation in lateral plate size, and 3 moderate-effect QTL with body size. Together, these findings indicate more heterogenous and polygenic genetic underpinnings of skeletal armour variation in nine-spined than three-spined sticklebacks, indicating limited genetic parallelism underlying armour trait evolution in the family Gasterostidae.

Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which, in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogeneous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.

Genetic Mapping and Characterization of Verticillium Wilt Resistance in a Recombinant Inbred Population of Upland Cotton

Verticillium wilt (VW) is an important and widespread disease of cotton and once established is long-lived and difficult to manage. In Australia, the non-defoliating pathotype of Verticillium dahliae is the most common, and extremely virulent. Breeding cotton varieties with increased VW resistance is the most economical and effective method of controlling this disease and is greatly aided by understanding the genetics of resistance. This study aimed to investigate VW resistance in 240 F7 recombinant inbred lines (RIL) derived from a cross between MCU-5, which has good resistance, and Siokra 1-4, which is susceptible. Using a controlled environment bioassay, we found that resistance based on plant survival or shoot biomass was complex but with major contributions from chromosomes D03 and D09, with genomic prediction analysis estimating a prediction accuracy of 0.73 based on survival scores compared to 0.36 for shoot biomass. Transcriptome analysis of MCU-5 and Siokra 1-4 roots uninfected or infected with V. dahliae revealed that the two cultivars displayed very different root transcriptomes and responded differently to V. dahliae infection. Ninety-nine differentially expressed genes were located in the two mapped resistance regions and so are potential candidates for further identifying the genes responsible for VW resistance.

Genetic population structure constrains local adaptation in sticklebacks

Repeated and independent adaptation to specific environmental conditions from standing genetic variation is common. However, if genetic variation is limited, the evolution of similar locally adapted traits may be restricted to genetically different and potentially less optimal solutions or prevented from happening altogether. Using a quantitative trait locus (QTL) mapping approach, we identified the genomic regions responsible for the repeated pelvic reduction (PR) in three crosses between nine-spined stickleback populations expressing full and reduced pelvic structures. In one cross, PR mapped to linkage group 7 (LG7) containing the gene Pitx1, known to control pelvic reduction also in the three-spined stickleback. In the two other crosses, PR was polygenic and attributed to 10 novel QTL, of which 90% were unique to specific crosses. When screening the genomes from 27 different populations for deletions in the Pitx1 regulatory element, these were only found in the population in which PR mapped to LG7, even though the morphological data indicated large-effect QTL for PR in several other populations as well. Consistent with the available theory and simulations parameterized on empirical data, we hypothesize that the observed variability in genetic architecture of PR is due to heterogeneity in the spatial distribution of standing genetic variation caused by >2× stronger population structuring among freshwater populations and >10× stronger genetic isolation by distance in the sea in nine-spined sticklebacks as compared to three-spined sticklebacks.

Genome-Wide Association Study Dissects the Genetic Architecture of Maize Husk Tightness

The husk is a leafy outer tissue that encloses a maize ear. Previously, we identified the optimum husk structure by measuring the husk length, husk layer number, husk thickness and husk width. Husk tightness (HTI) is a combined trait based on the above four husk measurements. Unveiling the genetic basis of HTI will aid in guiding the genetic improvement of maize for mechanical harvesting and for protecting the ear from pest damage and pathogen infection. Here, we used a maize associate population of 508 inbred lines with tropical, subtropical and temperate backgrounds to analyze the genetic architecture of HTI. Evaluating the phenotypic diversity in three different environments showed that HTI exhibited broad natural variations and a moderate heritability level of 0.41. A diversity analysis indicated that the inbred lines having a temperate background were more loosely related than those having a tropical or subtropical background. HTI showed significant negative correlations with husk thickness and width, which indicates that thicker and wider husks wrapped the ear tighter than thinner and slimmer husks. Combining husk traits with ∼1.25 million single nucleotide polymorphisms in a genome-wide association study revealed 27 variants that were significantly associated with HTI above the threshold of P < 7.26 × 10^-6. We found 27 candidate genes for HTI that may participate in (1) husk senescence involving lipid peroxidation (GRMZM2G017616) and programmed cell death (GRMZM2G168898 and GRMZM2G035045); (2) husk morphogenesis involving cell division (GRMZM5G869246) and cell wall architecture (GRMZM2G319798); and (3) cell signal transduction involving protein phosphorylation (GRMZM2G149277 and GRMZM2G004207) and the ABSISIC ACID INSENSITIVE3/VIVIPAROUS1 transcription factor (GRMZM2G088427). These results provide useful information for understanding the genetic basis of husk development. Further studies of identified candidate genes will help elucidate the molecular pathways that regulate HTI in maize.

Quantitative Trait Loci (QTL) Mapping

Quantitative trait loci (QTL) are genetic regions that influence phenotypic variation of a complex trait, often through genetic interactions with each other and the environment. These are commonly identified through a statistical genetic analysis known as QTL mapping. Here, I present a step-by-step, practical approach to QTL mapping along with a sample data file. I focus on methods commonly used and discoveries that have been made in fishes, and utilize a multiple QTL mapping (MQM) approach in the free software package R/qtl.

Yes, correct context is indeed the key: An answer to Haave-Audet et al. 2019

We published a study recently testing the link between brain size and behavioural plasticity using brain size selected guppy (Poecilia reticulata) lines (2019, Journal of Evolutionary Biology, 32, 218-226). Only large-brained fish showed habituation to a new, but actually harmless environment perceived as risky, by increasing movement activity over the 20-day observation period. We concluded that "Our results suggest that brain size likely explains some of the variation in behavioural plasticity found at the intraspecific level". In a commentary published in the same journal, Haave-Audet et al. challenged the main message of our study, stating that (a) relative brain size is not a suitable proxy for cognitive ability and (b) habituation measured by us is likely not adaptive and costly. In our response, we first show that a decade's work has proven repeatedly that relative brain size is indeed positively linked to cognitive performance in our model system. Second, we discuss how switching from stressed to unstressed behaviour in stressful situations without real risk is likely adaptive. Finally, we point out that the main cost of behavioural plasticity in our case is the development and maintenance of the neural system needed for information processing, and not the expression of plasticity. We hope that our discussion with Haave-Audet et al. helps clarifying some central issues in this emerging research field.

Robust inference of genetic architecture in mapping studies

The genetic architecture of a trait usually refers to the number and magnitude of loci that explain phenotypic variation. A description of genetic architecture can help us to understand how genetic variation is maintained, how traits have evolved and how phenotypes might respond to selection. However, linkage mapping and association studies can suffer from problems of bias, especially when conducted in natural populations where the opportunity to perform studies with very large sample sizes can be limited. In this issue of Molecular Ecology, Li and colleagues perform an association study of brain traits in ninespine sticklebacks Pungitius pungitius. They use a sophisticated approach that models all of the genotyped markers simultaneously; conventional approaches fit each marker individually. Although the single-marker and multi-marker approaches find similar regions of the genome that explain phenotypic variation, the overall conclusions about trait architecture are somewhat different, depending on the approach used. Single-marker methods identify regions that explain quite large proportions of genetic variation, whereas the multi-marker approach suggests the traits are far more polygenic. Simulations suggest the multi-marker approach is robust. This study highlights how molecular quantitative genetics in wild populations can be used to address hypothesis-driven questions, without making unrealistic assumptions about effect sizes of individual quantitative trait loci.

Analysis of phenotypic- and Estimated Breeding Values (EBV) to dissect the genetic architecture of complex traits in a Scots pine three-generation pedigree design

In forest tree breeding, family-based Quantitative Trait Loci (QTL) studies are valuable as methods to dissect the complexity of a trait and as a source of candidate genes. In the field of conifer research, our study contributes to the evaluation of phenotypic and predicted breeding values for the identification of QTL linked to complex traits in a three-generation pedigree population in Scots pine (Pinus sylvestris L.). A total of 11 470 open pollinated F₂-progeny trees established at three different locations, were measured for growth and adaptive traits. Breeding values were predicted for their 360 mothers, originating from a single cross of two grand-parents. A multilevel LASSO association analysis was conducted to detect QTL using genotypes of the mothers with the corresponding phenotypes and Estimated Breeding Values (EBV). Different levels of genotype-by-environment (G × E) effects among sites at different years, were detected for survival and height. Moderate-to-low narrow sense heritabilities and EBV accuracies were found for all traits and all sites. We identified 18 AFLPs and 12 SNPs to be associated with QTL for one or more traits. 62 QTL were significant with percentages of variance explained ranging from 1.7 to 18.9%. In those cases where the same marker was associated to a phenotypic or an ebvQTL, the ebvQTL always explained higher proportion of the variance, maybe due to the more accurate nature of Estimated Breeding Values (EBV). Two SNP-QTL showed pleiotropic effects for traits related with hardiness, seed, cone and flower production. Furthermore, we detected several QTL with significant effects across multiple ages, which could be considered as strong candidate loci for early selection. The lack of reproducibility of some QTL detected across sites may be due to environmental heterogeneity reflected by the genotype- and QTL-by-environment effects.

Cerebellum size is positively correlated with geographic distribution range in anurans

The ‘cognitive buffer’ hypothesis predicts that the costs of relatively large brains are compensated for later in life by the increased benefits of large brains providing a higher chance of survival under changing environments through flexible behaviors in the animal kingdom. Thus, animals that live in a larger range (with a higher probability of environmental variation) are expected to have larger brains than those that live in a restricted geographic range. Here, to test the prediction of the ‘cognitive buffer’ hypothesis that larger brains should be expected to occur in species living in geographic ranges of larger size, we analyzed the relationship between the size of the geographic range and brain size and the size of various brain regions among 42 species of anurans using phylogenetic comparative methods. The results show that there is no correlation between relative brain size and size of the species’ geographic range when correcting for phylogenetic effects and body size. Our findings suggest that the effects of the cognitive buffer and the energetic constraints on brains result in non-significant variation in overall brain size. However, the geographic range is positively correlated with cerebellum size, but not with optic tecta, suggesting that species distributed in a wider geographic range do not exhibit larger optic tecta which would provide behavioral flexibility to allow for an early escape from potential predators and discovery of new food resources in unpredictable environments.