Animal Molecular Breeding 2024, Vol.14, No.5, 297-306 http://animalscipublisher.com/index.php/amb 301 3.3 Recent advances in high-throughput genotyping and phenotyping Recent advances in high-throughput genotyping and phenotyping have revolutionized QTL mapping for egg production traits. Technologies like Restriction-site Associated DNA sequencing (RAD-seq) and genotyping-by-sequencing (GBS) have made it possible to rapidly genotype large populations at a fraction of the cost of traditional methods. These high-throughput methods allow for the identification of thousands of SNPs across the genome, leading to more precise and fine-scale mapping of QTLs. For example, a 2021 study used RAD-seq to identify nine QTLs associated with egg-related traits in Japanese quail, including egg weight, shell strength, and yolk size (Haqani et al., 2021). High-throughput phenotyping technologies have also become integral to modern QTL mapping efforts. These platforms, which include non-invasive imaging, robotics, and advanced sensor systems, allow for the rapid collection of detailed phenotypic data from large populations. For example, high-throughput imaging can track growth and egg production parameters across entire populations, significantly improving the accuracy and efficiency of phenotyping efforts. The integration of these technologies with genotyping has led to the identification of previously undetectable QTLs, particularly those associated with complex traits such as clutch size and egg quality (Jamann et al., 2015). The combination of high-throughput genotyping and phenotyping has enabled the development of high-density genetic maps that provide unprecedented resolution in QTL mapping. These maps facilitate the fine-mapping of QTLs and the identification of candidate genes with greater precision, which is essential for marker-assisted selection (MAS) in breeding programs. As a result, breeders can now apply MAS more effectively to improve traits such as egg number, egg weight, and shell quality in layer hens(Wang et al., 2020). 4 Case Study: QTL Mapping in Layer Hens 4.1 Study design and sample population In QTL (Quantitative Trait Loci) mapping studies for egg production traits in layer hens, researchers often employ crossbreeding strategies using breeds with different genetic backgrounds to create a diverse population. A notable example is the cross between Taiwanese L2 chickens and Rhode Island Red hens, which generates an F2 population suitable for studying adaptability to various climatic conditions and egg production traits. This study population consisted of 844 chickens, and their genotypes were analyzed using a 60K SNP (Single Nucleotide Polymorphism) chip, allowing researchers to investigate traits such as body weight, tibia length, and egg production rates (Lien et al., 2020). The research employed both genome-wide association studies (GWAS) and linkage-based QTL mapping to analyze the genomic data of these chickens. This combination allows the identification of key QTLs associated with traits such as eggshell strength, egg weight, and overall production performance. By combining high-density genotype data with phenotypic data, researchers could identify important genetic regions associated with the traits of interest, laying the groundwork for future functional genomic studies and marker-assisted selection (MAS) (Liu et al., 2019). To ensure accuracy in identifying QTLs, researchers maintained the F2 population in a controlled environment with consistent feeding and management practices. This minimized environmental influences on phenotypic variation, ensuring that the identified QTLs more accurately reflected genetic effects. This study design helped improve the precision of QTL mapping and provided valuable data for future breeding programs aimed at enhancing egg production traits (Stainton et al., 2015). 4.2 Identification of significant QTL for egg production Through genome-wide analysis and QTL mapping of the sample population, researchers identified several significant QTLs associated with egg production traits. For example, QTLs were found on GGA1, GGA6, and GGA24 chromosomes, controlling traits such as egg number, egg weight, and age at first egg (AFE). These QTLs demonstrated their impact across different stages of egg production, with GGA1 showing a significant
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