Genetic interactions improve models of quantitative traits

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Agronomic Traits, Fresh Food Processing Characteristics and Sensory Quality of 26 Mung Bean (Vigna radiata L.) Cultivars (Fabaceae) in China

In recent years, with the expansion of mung bean (Vigna radiata L.) planting areas and the increase of consumer demand, it has become imperative to screen high-quality mung bean cultivars. In this study, the agronomic traits, fresh bean characteristics, and sensory evaluation of boiled beans were analyzed for 26 mung bean cultivars. The results showed that the variation coefficient and genetic diversity index of six agronomic traits of mung bean ranged from 9.04% to 44.98%, 1.68 to 1.96, respectively, with abundant genetic variation, and the highest was the grain yield. Mung bean cultivars with higher grain yield had more advantage in the number of branches, number of pods per plant, and 100-seed weight. The fresh bean traits were relatively stable, with an average coefficient variation of 8.48%. The trait with the highest genetic diversity index was the number of seeds per pod (2.03). The cultivar with the highest total sensory evaluation score of boiled beans was Zhanglv 3 (75.67), which had more advantages in taste and color. Through the comprehensive evaluation of grey relational analysis, the cultivars suitable for fresh food processing were Zhonglv 3 (0.960), Jilv 11 (0.942), Zhonglv 1 (0.915), CES-78 (0.899) and Kelv 2 (0.896). Generally, the high-quality cultivars with higher yield and fresh food processing characteristics were CES-78, Kelv 2, Zhonglv 16, and Zhonglv 2. This study provided a preference for the breeding of fresh mung bean cultivars, development of new products and improvement of mung bean resource utilization.

Revisiting genome-wide association studies from statistical modelling to machine learning

Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of diseases, to develop new drugs, to advance precision medicine and to boost breeding. However, the potential of GWAS is still underexploited due to methodological limitations. Many challenges have emerged, including detecting epistasis and single-nucleotide polymorphisms (SNPs) with small effects and distinguishing causal variants from other SNPs associated through linkage disequilibrium. These issues have motivated advancements in GWAS analyses in two contrasting cultures-statistical modelling and machine learning. In this review, we systematically present the basic concepts and the benefits and limitations in both methods. We further discuss recent efforts to mitigate their weaknesses. Additionally, we summarize the state-of-the-art tools for detecting the missed signals, ultrarare mutations and gene-gene interactions and for prioritizing SNPs. Our work can offer both theoretical and practical guidelines for performing GWAS analyses and for developing further new robust methods to fully exploit the potential of GWAS.