Global shifts in climate and precipitation patterns are altering the diversity and structure of forests. The ability for species to adapt is especially difficult for long lived foundation species with unknown genetic and trait diversity. We harnessed genomic, physiological, and climate data to determine adaptation constraints. We used denovo assembly and 6.5 million genomic variants with drought related traits from 432 individuals sourced from across the complete range of the foundation tree species Corymbia calophylla. We found genomic variants determining traits predominantly in gene regulatory regions. The ability for populations to adapt was limited by within population genetic diversity associated with traits, and epistatic interactions within traits and pleiotropic interactions among traits. Nevertheless, we could accurately predict adaptive traits using genomic and climate data to guide reforestation. Our study indicated that some populations may contain variation sufficient for the species to adapt to the effects of drought, while other populations will need increased variability from those sources.

/pdf/genomic-constraints-to-drought-adaptation-1mp8omjnnc.pdf

Genomic constraints to drought adaptation

Summary Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy‐based phenotypic trade‐offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non‐pleiotropy‐induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large‐scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability. 

Epistasis and pleiotropy‐induced variation for plant breeding

Genetic analysis of keel bone fractures in laying hens housed in a quasi-commercial aviary

Drought adaptation is critical to many tree species persisting under climate change, however our knowledge of the genetic basis for trees to adapt to drought is limited. This knowledge gap impedes our fundamental understanding of drought response and application to forest production and conservation. To improve our understanding of the genomic determinants, architecture, and trait constraints, we assembled a reference genome and detected ~ 6.5 M variants in 432 phenotyped individuals for the foundational tree Corymbia calophylla. 

Genomic determinants, architecture, and constraints in drought-related traits in Corymbia calophylla

To identify genetic pathways for addiction, we analyzed intravenous self-administration of cocaine or saline in a panel of 84 inbred and recombinant inbred mouse strains over 10 days. We integrated the behavior data with RNA-Seq data from the medial frontal cortex and nucleus accumbens from 41 strains. The self-administration of cocaine and saline showed distinct genetic bases. We maximized power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci were identified in the genome-wide association study (GWAS). A transcriptome-wide association study (TWAS) highlighted the Trpv2 ion channel as a key locus for cocaine self-administration from the GWAS. In addition, 17 genes supplementary to the GWAS were identified including Arhgef26, Slc18b1 and Slco5a1. We found numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, a known cannabinoid receptor, for the response to cocaine as well as identifying further relevant loci.

Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in inbred and recombinant inbred mice

Epistasis, or gene-gene interaction, contributes substantially to trait variation in organisms ranging from yeast to humans, and modeling epistasis directly is critical to understanding the genotype-phenotype map. However, inference of genetic interactions is challenging compared to inference of individual allele effects due to low statistical power. Furthermore, genetic interactions can appear inconsistent across different quantitative traits, presenting a challenge for the interpretation of detected interactions. Here we present a method called the Combined Analysis of Pleiotropy and Epistasis (CAPE) that combines information across multiple quantitative traits to infer directed epistatic interactions. By combining information across multiple traits, CAPE not only increases power to detect genetic interactions but also interprets these interactions across traits to identify a single interaction that is consistent across all observed data. This method generates informative, interpretable interaction networks that explain how variants interact with each other to influence groups of related traits. This method could potentially be used to link genetic variants to gene expression, physiological endophenotypes, and higher-level disease traits.

The Combined Analysis of Pleiotropy and Epistasis (CAPE)

It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

/pdf/effects-of-kinship-correction-on-inflation-of-genetic-2gwxayt2a5.pdf

Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations.

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The Combined Analysis of Pleiotropy and Epistasis (CAPE)

Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations.