Biology subjects

Gamal El-Dien, O.

Publications and source records attributed to Gamal El-Dien, O..

2 recordsLinked to original sources

bioRxiv2022

The western redcedar genome reveals low genetic diversity in a self-compatible conifer

We assembled the 9.8 Gbp genome of western redcedar (WRC, Thuja plicata), an ecologically and economically important conifer species of the Cupressaceae. The genome assembly, derived from a uniquely inbred tree produced through five generations of self-fertilization (selfing), was determined to be 86% complete by BUSCO analysis - one of the most complete genome assemblies for a conifer. Population genomic analysis revealed WRC to be one of the most genetically depauperate wild plant species, with an effective population size of approximately 300 and no significant genetic differentiation across its geographic range. Nucleotide diversity, {pi}, is low for a continuous tree species, with many loci exhibiting zero diversity, and the ratio of {pi} at zero-to four-fold degenerate sites is relatively high ([~] 0.33), suggestive of weak purifying selection. Using an array of genetic lines derived from up to five generations of selfing, we explored the relationship between genetic diversity and mating system. While overall heterozygosity was found to decline faster than expected during selfing, heterozygosity persisted at many loci, and nearly 100 loci were found to deviate from expectations of genetic drift, suggestive of associative overdominance. Non-reference alleles at such loci often harbor deleterious mutations and are rare in natural populations, implying that balanced polymorphisms are maintained by linkage to dominant beneficial alleles. This may account for how WRC remains responsive to natural and artificial selection, despite low genetic diversity.

plant biology↗

bioRxiv2019

Inter- and Intra-Generation Genomic Predictions for Douglas-fir Growth in Unobserved Environments

Conifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previous studies have shown much reduced prediction accuracies (PA) of breeding values in unobserved environments, which may impede its adoption. The impact of explicit environmental heterogeneity modeling including genotype-by-environment (GxE) interaction effects using environmental covariates (EC) in a reaction-norm genomic prediction model was tested using single-step GBLUP (ssGBLUP). A three-generation coastal Douglas-fir experimental population with 14 genetic trials (n = 13,615) permitted estimation of intra- and inter-generation PA in unobserved environments using 66,969 SNPs derived from exome capture. Intra- and inter-generation PAs ranged from 0.447-0.640 and 0.317-0.538, respectively. The inclusion of ECs in the prediction models explained up to 23% of the phenotypic variation for the fully specified model and resulted in the best model fit. Modeling GxE effects in the training population increased PA up to 6% and 13% over the base model for inter- and intra-generations, respectively. GS-PA can be substantially improved using ECs to explain environmental heterogeneity and GxE effects. The ssGBLUP methodology allows historical genetic trials containing non-genotyped samples to contribute in genomic prediction, and, thus, effectively boosting training population size which is a critical step. Further pheno- and enviro-typing developments may improve GS-PA.

genetics↗