Ani A. Elias – författare

Genomic selection (GS) is a promising tool in the field of breeding especially in the era where genomic data is becoming cheaper. The potential of this tool has not been realized due to its limited adaptation in various crops. Marker Assisted Selection (MAS) has been the method of choice for plant breeders while using the genomic information in the breeding pipeline. MAS, however, fails to capture vital minor gene effects while focusing only on the major genes, which is not ideal for breeding advancement especially for quantitative traits such as yield. The main aim of statistical methodologies coming under the umbrella of GS on using the whole genome information is to predict potential candidates for breeding advancement while optimizing the use of resources such as land, manpower, and most importantly time. Lack of proper understanding of the methods and their applications is one of the reasons why breeders shy away from this tool. The book is meant for biologists, especially breeders, and provides a comprehensive idea of the statistical methodologies used in GS, guidance on the choice of models, and design of datasets. The book also encourages the readers to adopt GS by demonstrating the current scenarios of these models in some of the important crops among oilseeds, vegetables, legumes, tuber crops, and cereals. For ease of implementation of GS, the book also provides hands-on scripts on GS data design and modeling in a popular open-source statistical program. Additionally, prospective in GS model development and thereby enhancement in crop improvement programs is discussed.

Genomic selection (GS) is a promising tool in the field of breeding especially in the era where genomic data is becoming cheaper. The potential of this tool has not been realized due to its limited adaptation in various crops. Marker Assisted Selection (MAS) has been the method of choice for plant breeders while using the genomic information in the breeding pipeline. MAS, however, fails to capture vital minor gene effects while focusing only on the major genes, which is not ideal for breeding advancement especially for quantitative traits such as yield. The main aim of statistical methodologies coming under the umbrella of GS on using the whole genome information is to predict potential candidates for breeding advancement while optimizing the use of resources such as land, manpower, and most importantly time. Lack of proper understanding of the methods and their applications is one of the reasons why breeders shy away from this tool. The book is meant for biologists, especially breeders, and provides a comprehensive idea of the statistical methodologies used in GS, guidance on the choice of models, and design of datasets. The book also encourages the readers to adopt GS by demonstrating the current scenarios of these models in some of the important crops among oilseeds, vegetables, legumes, tuber crops, and cereals. For ease of implementation of GS, the book also provides hands-on scripts on GS data design and modeling in a popular open-source statistical program. Additionally, prospective in GS model development and thereby enhancement in crop improvement programs is discussed.

This book offers foundational knowledge to advance genomic approaches in forest tree improvement and genetic resource conservation. The tropical tree breeding sector has fallen behind in genomic breeding not due to a lack of resources but rather a limited understanding and an underdeveloped genomic breeding pipeline. While marker-assisted selection (MAS) has been the preferred method for incorporating genomic data into breeding programs, it primarily targets major genes, overlooking minor gene effects. This limitation makes MAS less effective for enhancing quantitative traits such as heartwood content.

The primary goal of statistical methodologies using whole-genome information is to predict promising candidates for breeding advancement/commercialization while efficiently managing resources such as land, labor, and, most critically, time. Given the long rotation period of forest tree crops, the ability to identify, select, and modify genotypes with high heritability for economically valuable traits within a shorter timeframe marks a significant advancement in breeding. This book provides comprehensive guidelines on leveraging genomic data, including pathogenomics, to breed resilient, future-ready trees and manage populations effectively. To reinforce these guidelines, the book presents case studies on species such as Tectona grandis, Santalum album, Casuarina, Shorea, Artocarpus, tropical and sub-tropical pines, and tropical fruit trees. Additionally, the book explores broader applications of genetic data, including timber tracing and the conservation of germplasm while minimizing genetic redundancy.

This book will be a valuable resource for tropical tree breeders and researchers, equipping them with the methods and tools needed to adopt advanced genomic breeding. Additionally, students and scholars will benefit from the comprehensive information it provides, enhancing their understanding of modern breeding techniques.