Animal Molecular Breeding 2024, Vol.14, No.1, 36-44 http://animalscipublisher.com/index.php/amb 40 2.3 Technological advances in GWAS and data analysis methods Technological advances in GWAS have greatly advanced its application in poultry genetic research. In particular, the development of high-density SNP microarrays and next-generation sequencing technologies has enabled researchers to explore the genome at a more detailed level and discover more genetic variants associated with traits. The application of these technologies has not only improved the resolution of GWAS, but also enabled the exploration of rare variants that were previously undetectable, providing new opportunities to understand the genetic basis of complex traits. Along with technological advances, data analysis methods have also been revolutionized in GWAS research. To meet the challenges of large-scale data analysis, researchers have developed a variety of complex statistical models and computational tools, such as multi-locus mixed linear models and machine learning algorithms (Han et al., 2023). These methods not only improve the accuracy and efficiency of analysis, but also can solve the problems of complex genetic background and traits controlled by multiple genes. With these advanced analytical tools, GWAS can more accurately identify genetic factors associated with traits and facilitate the development of poultry genetic improvement and breeding strategies. 3 Case Study of GWAS Research on Poultry Egg-Laying Performance 3.1 Study design and sample selection The research team of Liu et al.(2019) conducted a GWAS in 2019 to understand the genetic architecture of egg-laying performance by measuring the age at first egg (AFE) and the number of eggs laid per week (Egg number, EN) at different stages in 1078 Rhode Island Reds. This study identified important SNPs associated with egg-laying traits, providing promising genes and SNP markers for marker-based breeding selection (Table 1). Table 1 Descriptive statistics for age at first egg and egg number in different stage (Liu et al, 2019) Traits Nnumber of samples Mean Standard deviation Coefficient of variance (%) Pedigree-based heritability (standard error) Age at first egg 1063 137.37 5.7 4.15 0.51 (0.09) From egg laying to 23 weeks 1063 24.15 5.48 22.67 0.53 (0.08) From 23 to 37 weeks 1063 95.16 2.16 2.27 0.16 (0.06) From 37 to 50 weeks 1063 84.58 6.07 7.17 0.24 (0.07) From 50 to 61 weeks 1060 64.71 11.17 17.27 0.23 (0.07) From 61 to 80 weeks 1004 105.57 27.33 25.89 0.14 (0.06) Total number of eggs from spawning to 80 weeks 1063 368.13 47.84 13.00 0.09 (0.05) Lien et al. (2020) performed (Quantitative trait locus, QTL) QTL localization and GWAS to identify genetic markers and chromosomal regions associated with egg laying in a tropical climate in a cross between Taiwanese Country Hens and Rhode Island Red Laying hens. The study identified 11 QTL and 102 SNP effects associated with egg-laying traits. When conducting a genome-wide association analysis (GWAS) study of poultry egg-laying performance, careful study design is key to ensuring the success of the study. The design phase includes defining the study objectives, selecting appropriate poultry breeds, determining sample size, and choosing appropriate analytical methods. A good study design maximizes the detection of genetic markers associated with egg-laying performance while reducing the rate of false-positive findings. Sample selection is critical to the success of a GWAS. Researchers need to select poultry populations with sufficient genetic diversity to capture genetic variation that affects egg-laying performance. Selecting individuals that exhibit extreme differences in egg-laying performance improves the efficiency of the study, helps enhance the genetic contrast of the study, and makes the detection of relevant genetic markers more sensitive.
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