Harnessing genomics to fast-track genetic improvement in aquaculture

Forty years ago the advent of Sanger sequencing was revolutionary as it allowed complete genome sequences to be deciphered for the first time. A second revolution came when next-generation sequencing (NGS) technologies appeared, which made genome sequencing much cheaper and faster. However, NGS methods have several drawbacks and pitfalls, most notably their short reads. Recently, third-generation/long-read methods appeared, which can produce genome assemblies of unprecedented quality. Moreover, these technologies can directly detect epigenetic modifications on native DNA and allow whole-transcript sequencing without the need for assembly. This marks the third revolution in sequencing technology. Here we review and compare the various long-read methods. We discuss their applications and their respective strengths and weaknesses and provide future perspectives.

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Males and females share many traits that have a common genetic basis; however, selection on these traits often differs between the sexes, leading to sexual conflict. Under such sexual antagonism, theory predicts the evolution of genetic architectures that resolve this sexual conflict. Yet, despite intense theoretical and empirical interest, the specific loci underlying sexually antagonistic phenotypes have rarely been identified, limiting our understanding of how sexual conflict impacts genome evolution and the maintenance of genetic diversity. Here we identify a large effect locus controlling age at maturity in Atlantic salmon (Salmo salar), an important fitness trait in which selection favours earlier maturation in males than females, and show it is a clear example of sex-dependent dominance that reduces intralocus sexual conflict and maintains adaptive variation in wild populations. Using high-density single nucleotide polymorphism data across 57 wild populations and whole genome re-sequencing, we find that the vestigial-like family member 3 gene (VGLL3) exhibits sex-dependent dominance in salmon, promoting earlier and later maturation in males and females, respectively. VGLL3, an adiposity regulator associated with size and age at maturity in humans, explained 39% of phenotypic variation, an unexpectedly large proportion for what is usually considered a highly polygenic trait. Such large effects are predicted under balancing selection from either sexually antagonistic or spatially varying selection. Our results provide the first empirical example of dominance reversal allowing greater optimization of phenotypes within each sex, contributing to the resolution of sexual conflict in a major and widespread evolutionary trade-off between age and size at maturity. They also provide key empirical evidence for how variation in reproductive strategies can be maintained over large geographical scales. We anticipate these findings will have a substantial impact on population management in a range of harvested species where trends towards earlier maturation have been observed.