Biology subjects

Odeny, D. A.

Publications and source records attributed to Odeny, D. A..

4 recordsLinked to original sources

bioRxiv2024

Genomic prediction by modelling genotype by environment interaction for yield in groundnut

Multi-environment trials (METs) are routinely conducted in plant breeding to capture the genotype-by-environment (GxE) interaction effects. We assessed the potential of using genomic prediction (GP) in four groundnut traits (pod yield[PY], shelling percentage[SP], seed weight[SW] and seed weight of 100 seeds[SW100]) observed in four environments. Three prediction models (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) were evaluated under four cross-validation (CV) schemes that simulate realistic problems that breeders face at different stages of their programs. CV2 predicts lines tested in sparse multi-location trials; CV1 predicts newly developed lines; CV0 predicts tested lines in unobserved environments by leaving one environment out; CV00 predicts untested lines in unobserved environments. Under CV2, the average predictive ability (PA) range was 0.6-0.68 (PY), -0.26-0.05 (SP), 0.5-0.59 (SW) and 0.79-0.86 (SW100). For CV1, the PA range was -0.12-0.51 (PY), -0.08-0.35 (SP), -0.10-0.47 (SW) and -0.05-0.47 (SW100). In CV0, the PA range was 0.27-0.35 (PY), -0.18--0.17 (SP), 0.24-0.32 (SW) and 0.59-0.66 (SW100). In CV00, the PA range was 0.01-0.23 (PY), -0.03-0.07 (SP), 0.01-0.19 (SW) and -0.02-0.38 (SW100). In all the four CV schemes, including marker data improved the PA. Incorporating GxE in M3 model improved PA in CV2 and CV1 schemes and also reduced the residual and environment variances. The high PA in CV2 imply that sparse testing could be implemented. The low PA recorded in CV00 shows that it is difficult to predict untested lines in new environments.

genomics↗

bioRxiv2022

DeepVariant calling provides insights into race diversity and its implication for sorghum breeding

Due to evolutionary divergence, sorghum race populations exhibit vast genetic and morphological variations. A k-mer-based sorghum race sequence comparison identified the conserved k-mers of all sorghum race accessions and the race-specific genetic signatures identified the gene variability in 10,321 genes (PAVs). To understand the sorghum race structure, diversity and domestication, deep learning-based variant calling approach was employed in a set of genotypic data derived from a diverse panel of 272 sorghum accessions. The data resulted in 1.7 million high-quality genome-wide SNPs and identified selective signature (both positive and negative) regions through a genome-wide scan with different (iHS and XP-EHH) statistical methods. We discovered 2,370 genes associated with selection signatures including 179 selective sweep regions distributed over 10 chromosomes. Localization of these regions undergoing selective pressure with previously reported QTLs and genes revealed that the signatures of selection could be related to the domestication of important agronomic traits such as biomass and plant height. The developed k-mer signatures will be useful in the future to identify the sorghum race and SNP markers assist in plant breeding programs.

plant biology↗

bioRxiv2021

Genomic and phenotypic characterization of finger millet indicates a complex diversification history

Advances in sequencing technologies mean that insights into crop diversification aiding future breeding can now be explored in crops beyond major staples. For the first time, we use a genome assembly of finger millet, an allotetraploid orphan crop, to analyze DArTseq single nucleotide polymorphisms (SNPs) at the sub-genome level. A set of 8,778 SNPs and 13 agronomic traits characterizing a broad panel of 423 landrace accessions from Africa and Asia suggested the crop has undergone complex, context-specific diversification consistent with a long domestication history. Both Principal Component Analysis and Discriminant Analysis of Principal Components of SNPs indicated four groups of accessions that coincided with the principal geographic areas of finger millet cultivation. East Africa, the considered origin of the crop, appeared the least genetically diverse. A Principal Component Analysis of phenotypic data also indicated clear geographic differentiation, but different relationships among geographic areas than genomic data. Neighbour-joining trees of sub-genomes A and B showed different features which further supported the crops complex evolutionary history. Our genome-wide association study indicated only a small number of significant marker-trait associations. We applied then clustering to marker effects from a ridge regression model for each trait which revealed two clusters of different trait complexity, with days to flowering and threshing percentage among simple traits, and finger length and grain yield among more complex traits. Our study provides comprehensive new knowledge on the distribution of genomic and phenotypic variation in finger millet, supporting future breeding intra- and inter-regionally across its major cultivation range. Core ideasO_LI8,778 SNPs and 13 agronomic traits characterized a panel of 423 finger millet landraces. C_LIO_LI4 clusters of accessions coincided with major geographic areas of finger millet cultivation. C_LIO_LIA comparison of phenotypic and genomic data indicated a complex diversification history. C_LIO_LIThis was confirmed by the analysis of allotetraploid finger millets separate sub-genomes. C_LIO_LIComprehensive new knowledge for intra- and inter-regional breeding is provided. C_LI

genetics↗

bioRxiv2019

Draft genome sequence of the Solanum aethiopicum provides insights into disease resistance, drought tolerance and the evolution of the genome

BackgroundS. aethiopicum is a close relative to S. melongena and has been routinely used to improve disease resistance in S. melongena. However, these efforts have been greatly limited by the lack of a reference genome and the clear understanding of the genes involved during biotic and abiotic stress response. ResultsWe present here a draft genome assembly of S. aethiopicum of 1.02 Gb in size, which is predominantly occupied by repetitive sequences (76.2%), particularly long terminal repeat elements. We annotated 37,681 gene models including 34,905 protein-coding genes. We observed an expansion of resistance genes through two rounds of amplification of LTR-Rs, occurred around 1.25 and 3.5 million years ago, respectively. The expansion also occurred in gene families related to drought tolerance. A number of 14,995,740 SNPs are identified by re-sequencing 65 S. aethiopicum genotypes including "Gilo" and "Shum" accessions, 41,046 of which are closely linked to resistance genes. The domestication and demographic history analysis reveals selection of genes involved in drought tolerance in both "Gilo" and "Shum" groups. A pan-genome of S. aethiopicum with a total of 36,250 protein-coding genes was assembled, of which 1,345 genes are missing in the reference genome. ConclusionsOverall, the genome sequence of S. aethiopicum increases our understanding of the genomic mechanisms of its extraordinary disease resistance and drought tolerance. The SNPs identified are available for potential use by breeders. The information provided here will greatly accelerate the selection and breeding of the African eggplant as well as other crops within the Solanaceae family.

genomics↗