Assessing population structure and migration patterns of wild boar (Sus scrofa) in Japan

Geographical wildlife patterns reflect historical range expansion and connectivity and provide insights into wildlife population management. In our large-scale phylogeographic population analysis of wild boars (Sus scrofa leucomystax) in Japan, we identified 15 clusters using 29 microsatellite markers, each structured within a range of approximately 200 km. This suggests that evolution was essentially driven by isolation by distance, and that the range of gene flow was limited. One cluster contained subpopulations located approximately 900 km apart, indicating the occurrence of past anthropogenic introductions. Moreover, we estimated effective migration to visualize the geographic genetic population diversity. This analysis identified six potential barriers, one of which involved large plains and mountainous areas in the Kanto region of eastern Japan. This barrier likely persisted in the two eastern clusters for an extended period, restricting migration to the neighboring areas. Overall, our study sheds light on the demographic history of wild boar in Japan, provides evidence of past anthropogenic introductions from distant areas, and highlights the importance of geographic barriers in shaping genetic diversity and population dynamics. This knowledge will be beneficial for forming informed wildlife management strategies toward the conservation of genetic integrity and ecological balance of wild boar populations in Japan.

Genetic basis of growth, spring phenology, and susceptibility to biotic stressors in maritime pine

Forest ecosystems are increasingly challenged by extreme events, for example, drought, storms, pest attacks, and fungal pathogen outbreaks, causing severe ecological and economic losses. Understanding the genetic basis of adaptive traits in tree species is of key importance to preserve forest ecosystems, as genetic variation in a trait (i.e., heritability) determines its potential for human-mediated or evolutionary change. Maritime pine (Pinus pinaster Aiton), a conifer widely distributed in southwestern Europe and northwestern Africa, grows under contrasted environmental conditions promoting local adaptation. Genetic variation at adaptive phenotypes, including height, spring phenology, and susceptibility to two fungal pathogens (Diplodia sapinea and Armillaria ostoyae) and an insect pest (Thaumetopoea pityocampa), was assessed in a range-wide clonal common garden of maritime pine. Broad-sense heritability was significant for height (0.219), spring phenology (0.165-0.310), and pathogen susceptibility (necrosis length caused by D. sapinea, 0.152; and by A. ostoyae, 0.021, measured on inoculated, excised branches under controlled conditions), but not for pine processionary moth incidence in the common garden. The correlations of trait variation among populations revealed contrasting trends for pathogen susceptibility to D. sapinea and A. ostoyae with respect to height. Taller trees showed longer necrosis length caused by D. sapinea while shorter trees were more affected by A. ostoyae. Moreover, maritime pine populations from areas with high summer temperatures and frequent droughts were less susceptible to D. sapinea but more susceptible to A. ostoyae. Finally, an association study using 4227 genome-wide SNPs revealed several loci significantly associated with each trait (range of 3-26), including a possibly disease-induced translation initiation factor, eIF-5, associated with needle discoloration caused by D. sapinea. This study provides important insights to develop genetic conservation and breeding strategies integrating species responses to biotic stressors.

Local effects drive heterozygosity-fitness correlations in an outcrossing long-lived tree

Heterozygosity-fitness correlations (HFCs) have been used to understand the complex interactions between inbreeding, genetic diversity and evolution. Although frequently reported for decades, evidence for HFCs was often based on underpowered studies or inappropriate methods, and hence their underlying mechanisms are still under debate. Here, we used 6100 genome-wide single nucleotide polymorphisms (SNPs) to test for general and local effect HFCs in maritime pine (Pinus pinaster Ait.), an iconic Mediterranean forest tree. Survival was used as a fitness proxy, and HFCs were assessed at a four-site common garden under contrasting environmental conditions (total of 16 288 trees). We found no significant correlations between genome-wide heterozygosity and fitness at any location, despite variation in inbreeding explaining a substantial proportion of the total variance for survival. However, four SNPs (including two non-synonymous mutations) were involved in significant associations with survival, in particular in the common gardens with higher environmental stress, as shown by a novel heterozygosity-fitness association test at the species-wide level. Fitness effects of SNPs involved in significant HFCs were stable across maritime pine gene pools naturally growing in distinct environments. These results led us to dismiss the general effect hypothesis and suggested a significant role of heterozygosity in specific candidate genes for increasing fitness in maritime pine. Our study highlights the importance of considering the species evolutionary and demographic history and different spatial scales and testing environments when assessing and interpreting HFCs.

Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways

Background

Reproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks.

Results

Comparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes.

Conclusions

Our comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length).

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users.

Discovering candidate genes that regulate resin canal number in Pinus taeda stems by integrating genetic analysis across environments, ages, and populations

Genetically improving constitutive resin canal development in Pinus stems may enhance the capacity to synthesize terpenes for bark beetle resistance, chemical feedstocks, and biofuels. To discover genes that potentially regulate axial resin canal number (RCN), single nucleotide polymorphisms (SNPs) in 4027 genes were tested for association with RCN in two growth rings and three environments in a complex pedigree of 520 Pinus taeda individuals (CCLONES). The map locations of associated genes were compared with RCN quantitative trait loci (QTLs) in a (P. taeda × Pinus elliottii) × P. elliottii pseudo-backcross of 345 full-sibs (BC1). Resin canal number was heritable (h(2) ˜ 0.12-0.21) and positively genetically correlated with xylem growth (rg ˜ 0.32-0.72) and oleoresin flow (rg ˜ 0.15-0.51). Sixteen well-supported candidate regulators of RCN were discovered in CCLONES, including genes associated across sites and ages, unidirectionally associated with oleoresin flow and xylem growth, and mapped to RCN QTLs in BC1. Breeding is predicted to increase RCN 11% in one generation and could be accelerated with genomic selection at accuracies of 0.45-0.52 across environments. There is significant genetic variation for RCN in loblolly pine, which can be exploited in breeding for elevated terpene content.

Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA

In genome-wide association studies, the primary task is to detect biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) that have nontrivial associations with a disease phenotype and some other important clinical/environmental factors. However, the extremely large number of SNPs comparing to the sample size inhibits application of classical methods such as the multiple logistic regression. Currently the most commonly used approach is still to analyze one SNP at a time. In this paper, we propose to consider the genotypes of the SNPs simultaneously via a logistic analysis of variance (ANOVA) model, which expresses the logit transformed mean of SNP genotypes as the summation of the SNP effects, effects of the disease phenotype and/or other clinical variables, and the interaction effects. We use a reduced-rank representation of the interaction-effect matrix for dimensionality reduction, and employ the L₁-penalty in a penalized likelihood framework to filter out the SNPs that have no associations. We develop a Majorization-Minimization algorithm for computational implementation. In addition, we propose a modified BIC criterion to select the penalty parameters and determine the rank number. The proposed method is applied to a Multiple Sclerosis data set and simulated data sets and shows promise in biomarker detection.

In situ genetic association for serotiny, a fire-related trait, in Mediterranean maritime pine (Pinus pinaster)

Wildfire is a major ecological driver of plant evolution. Understanding the genetic basis of plant adaptation to wildfire is crucial, because impending climate change will involve fire regime changes worldwide. We studied the molecular genetic basis of serotiny, a fire-related trait, in Mediterranean maritime pine using association genetics. A single nucleotide polymorphism (SNP) set was used to identify genotype : phenotype associations in situ in an unstructured natural population of maritime pine (eastern Iberian Peninsula) under a mixed-effects model framework. RR-BLUP was used to build predictive models for serotiny in this region. Model prediction power outside the focal region was tested using independent range-wide serotiny data. Seventeen SNPs were potentially associated with serotiny, explaining approximately 29% of the trait phenotypic variation in the eastern Iberian Peninsula. Similar prediction power was found for nearby geographical regions from the same maternal lineage, but not for other genetic lineages. Association genetics for ecologically relevant traits evaluated in situ is an attractive approach for forest trees provided that traits are under strong genetic control and populations are unstructured, with large phenotypic variability. This will help to extend the research focus to ecological keystone non-model species in their natural environments, where polymorphisms acquired their adaptive value.

Association genetics of oleoresin flow in loblolly pine: discovering genes and predicting phenotype for improved resistance to bark beetles and bioenergy potential

Rapidly enhancing oleoresin production in conifer stems through genomic selection and genetic engineering may increase resistance to bark beetles and terpenoid yield for liquid biofuels. We integrated association genetic and genomic prediction analyses of oleoresin flow (g 24 h(-1)) using 4854 single nucleotide polymorphisms (SNPs) in expressed genes within a pedigreed population of loblolly pine (Pinus taeda) that was clonally replicated at three sites in the southeastern United States. Additive genetic variation in oleoresin flow (h(2) ≈ 0.12-0.30) was strongly correlated between years in which precipitation varied (r(a) ≈ 0.95), while the genetic correlation between sites declined from 0.8 to 0.37 with increasing differences in soil and climate among sites. A total of 231 SNPs were significantly associated with oleoresin flow, of which 81% were specific to individual sites. SNPs in sequences similar to ethylene signaling proteins, ABC transporters, and diterpenoid hydroxylases were associated with oleoresin flow across sites. Despite this complex genetic architecture, we developed a genomic prediction model to accelerate breeding for enhanced oleoresin flow that is robust to environmental variation. Results imply that breeding could increase oleoresin flow 1.5- to 2.4-fold in one generation.