Link genotype to phenotype (G2P) is one of the fundamental questions in biology. Decades of explorations have revealed a polygenic nature of complex traits, and many of the underlying alleles exhibit high dimensional interactions with each other and surrounding environments. However, these complex interactions among alleles (GxG) and with surrounding environments (GxE) are frequently overlooked in genetics analyses. Such interactions are crucial to uncovering the molecular mechanisms, improving genomic prediction, and increasing knowledge of how species respond to climate changes. Beyond commemorating Gregor Medel's 200th and Barbara McClintock's 120th birthday and reaffirming their legacies, we believe the research on diverse model organisms, plants and animals is essential not only because of their values within specific field but would also continue to advance our understanding of fundamental questions in quantitative and evolutionary genetics.
Understanding statistical epistasis (or GxG) and genotype by environmental interaction (GxE) shaping trait variation remain challenging. Recent advances in sequencing technologies have allowed us to accurately characterize genomic variations (e. g., TEs, SVs) that may play critical roles in genetic interactions and environmental responses. Integrating these increasingly available genomics data (i.e., transcriptome across multiple tissues, epigenetic modifications, host-associated microbiomes) provides a promising new direction for elucidating interactions between genotype and phenotype. Meanwhile, novel statistical models and machine learning methods are increasing our abilities to capture those non-linear effects and integrate omics datasets in dissecting underlying genetic architecture and perform genomic selection, particularly in response to rising climate risks.
Scope and theme: This topic would contain new advances and perspectives in the context of capturing interactions of gene-by-gene (GxG) and gene-by-environment (GxE), and how the integration of these interactions would promote the genetic improvement facing the changing climate in both plants and animals. We especially welcome contributions in the form of research articles, reviews, mini reviews and, perspectives, from both experimental and computational studies that cover, but are not limited to, following themes:
? Application of cutting-edge analytical techniques or development of statistical or machine learning models
? Benefit of new genomic or molecular technologies (e.g. long-read sequencing, gene editing)
? Multi-omics integration and interpretation
? New or improved study designs
? Role of GxG and GxE in genomic prediction
? Significant case study on genetic interactions affecting those phenotypes potentially contribute to future climate changes
Link genotype to phenotype (G2P) is one of the fundamental questions in biology. Decades of explorations have revealed a polygenic nature of complex traits, and many of the underlying alleles exhibit high dimensional interactions with each other and surrounding environments. However, these complex interactions among alleles (GxG) and with surrounding environments (GxE) are frequently overlooked in genetics analyses. Such interactions are crucial to uncovering the molecular mechanisms, improving genomic prediction, and increasing knowledge of how species respond to climate changes. Beyond commemorating Gregor Medel's 200th and Barbara McClintock's 120th birthday and reaffirming their legacies, we believe the research on diverse model organisms, plants and animals is essential not only because of their values within specific field but would also continue to advance our understanding of fundamental questions in quantitative and evolutionary genetics.
Understanding statistical epistasis (or GxG) and genotype by environmental interaction (GxE) shaping trait variation remain challenging. Recent advances in sequencing technologies have allowed us to accurately characterize genomic variations (e. g., TEs, SVs) that may play critical roles in genetic interactions and environmental responses. Integrating these increasingly available genomics data (i.e., transcriptome across multiple tissues, epigenetic modifications, host-associated microbiomes) provides a promising new direction for elucidating interactions between genotype and phenotype. Meanwhile, novel statistical models and machine learning methods are increasing our abilities to capture those non-linear effects and integrate omics datasets in dissecting underlying genetic architecture and perform genomic selection, particularly in response to rising climate risks.
Scope and theme: This topic would contain new advances and perspectives in the context of capturing interactions of gene-by-gene (GxG) and gene-by-environment (GxE), and how the integration of these interactions would promote the genetic improvement facing the changing climate in both plants and animals. We especially welcome contributions in the form of research articles, reviews, mini reviews and, perspectives, from both experimental and computational studies that cover, but are not limited to, following themes:
? Application of cutting-edge analytical techniques or development of statistical or machine learning models
? Benefit of new genomic or molecular technologies (e.g. long-read sequencing, gene editing)
? Multi-omics integration and interpretation
? New or improved study designs
? Role of GxG and GxE in genomic prediction
? Significant case study on genetic interactions affecting those phenotypes potentially contribute to future climate changes
