Genomics and Applied Biology 2024, Vol.15, No.6, 307-319 http://bioscipublisher.com/index.php/gab 315 7.1 Limitations of marker density and sample size One of the primary challenges in genomic selection is achieving sufficient marker density to capture the genetic variation in yam. Marker density refers to the number of genetic markers (e.g., SNPs) used across the genome to predict breeding values accurately. Higher marker density generally improves prediction accuracy, but genotyping at high density can be cost-prohibitive, particularly for programs with limited resources. Low-density markers may miss essential genetic information, reducing the accuracy of genomic estimated breeding values (GEBVs) (Norman et al., 2018). Sample size is another critical factor that influences the accuracy of genomic predictions. Smaller sample sizes limit the statistical power of GS models, leading to lower predictive accuracy. In GS, the training population must be representative of the breeding population to ensure that GEBVs are reliable when applied to new genotypes. Studies in crops like wheat have shown that increasing the sample size in training populations significantly enhances prediction accuracy (Grenier et al., 2015). To address these limitations, one strategy is to use a moderate-density SNP panel, where markers are strategically spaced to capture as much genetic variation as possible with fewer markers. Alternatively, combining low-density genotyping with imputation techniques can fill in gaps and approximate high-density data cost-effectively (Lillehammer et al., 2013). Increasing the training population size is another solution, especially by pooling data from multiple breeding programs, which can improve prediction accuracy by diversifying the genetic representation. 7.2 Complexity of environment and gene interactions Genotype-by-environment (GxE) interactions are particularly complex in yam breeding due to the crop’s cultivation across diverse environments. GxE interactions occur when different genotypes respond variably to environmental conditions, affecting the stability of trait predictions across locations and climates. In genomic selection, failing to account for GxE interactions can lead to biased predictions, as traits expressed in one environment may not translate consistently to another (Reyna et al., 2021). To manage GxE complexity, models that incorporate environment-specific terms and allow for marker-by-environment interactions have been developed. For instance, mixed models that include GxE interaction terms improve prediction accuracy in multiple-environment trials. Studies in other crops, such as barley, show that these models capture spatial and environmental variability, allowing for more reliable trait predictions across diverse growing conditions (Oakey et al., 2016). Incorporating environmental covariates, like temperature and rainfall data, can also improve the precision of predictions by directly modeling environmental effects on trait expression. Furthermore, stratifying the breeding program to target specific environments or selecting genotypes within environment-specific sub-populations can reduce the impact of GxE interactions. This approach allows for the development of varieties tailored to particular environmental conditions, improving the robustness and applicability of genomic predictions. 7.3 Strategies to improve prediction accuracy (e.g., increasing data scale and marker diversity) Improving the accuracy of GS predictions in yam breeding requires strategies that enhance both the scale and diversity of the data used in training models. One approach is to increase the training population size by combining data from multiple breeding programs or by incorporating historical phenotype and genotype data. Studies indicate that expanding the dataset improves model robustness and predictive power, especially when the genetic diversity within the training population reflects that of the target breeding population (Arruda et al., 2015). Marker diversity is another crucial factor. High-density SNP arrays are traditionally used, but alternative marker types, such as microsatellites or multi-allelic markers, may capture additional genetic variation. Employing a mix of markers or using haplotype-based approaches can capture a broader range of genetic diversity, improving the power of GS models to detect quantitative trait loci (QTL) linked to important traits (Solberg et al., 2008).
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