Advancing Statistical Methodologies for Composite Phenotype Analysis in Genome-Wide Association Studies

Abstract

Listen

Translate article

Genome wide association studies (GWAs) have revolutionized our understanding of the genetic basis of complex traits by linking specific genetic variants to phenotypes. However, the analysis of composite phenotypes derived from multiple interrelated variables presents unique challenges, particularly in maintaining statistical power and interpretability. Conventional approaches often analyze each trait independently at a single time point, potentially neglecting the underlying correlations and developmental dynamics across different growth stages, which could significantly enhance the detection power of genetic associations. This study presents a novel statistical approach that combines phenotypic data from different plant developmental stages using a compressed linear mixed model (CLMM) to efficiently link the genotypes to phenotypes. The CLMM offers computational efficiency by leveraging dimensionality reduction and data compression techniques, making it suitable for analyzing large-scale GWAs datasets. This capability is crucial given the rapidly growing volume of genomic data. The data used in this study was obtained from Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)-Gatersleben, Germany, database. It includes maize phenomic data and 50K SNPs data for 262 maize inbred lines. The modelling was done in R-statistical software following the guidelines of the Gapit tool. Plant growth was assessed at three time points: 11, 26 and 42 days after sowing (DAS). The models were compared using Akaike Information Criterion (AIC) and Information Criterion (BIC). The results showed that the model based incorporating plant volume, plant height, and plant surface area provided a better fit to the data compared to the models based on plant volume and plant surface area or plant height and plant volume. This is evidenced by lower AIC value of 1967.630 and BIC value of 1999.870 for the model incorporating three phenotypic traits (plant volume, Height and Surface area), compared to the AIC of 2008.560 and BIC of 2040.795 and AIC of 2312.930 for the model based on two phenotypic traits (plant volume and surface area) and BIC of 2351.321 for the for the model based on two phenotypic traits (plant height and volume). In the GWAs analysis, the results showed that the model incorporating three phenotypic traits (plan volume, height and area) detected the highest number of SNPs, with a total of 22 SNPs identified, compared to 11 SNPs detected using the model based on two phenotypic traits (plant surface area and volume) and 9 SNPs for the model based on two phenotypic traits (plant height and volume) across all growth stages considered. These findings suggest that combining traits to generate composite phenotypes in GWAS across different growth time points provides a robust framework that enhances the detection of genetic associations while preserving the biological relevance of the relationships between traits. This approach has significant implications for future GWAS, particularly in the study of complex traits, where understanding the interplay of multiple phenotypic variables is crucial for unraveling the genetic basis of complex traits.