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Bellis, E. S.

Publications and source records attributed to Bellis, E. S..

4 recordsLinked to original sources

Genomic history and ecology of the geographic spread of rice

Rice (Oryza sativa) is one of the worlds most important food crops. We reconstruct the history of rice dispersal in Asia using whole-genome sequences of >1,400 landraces, coupled with geographic, environmental, archaeobotanical and paleoclimate data. We also identify extrinsic factors that impact genome diversity, with temperature a leading abiotic factor. Originating [~]9,000 years ago in the Yangtze Valley, rice diversified into temperate and tropical japonica during a global cooling event [~]4,200 years ago. Soon after, tropical rice reached Southeast Asia, where it rapidly diversified starting [~]2,500 yBP. The history of indica rice dispersal appears more complicated, moving into China [~]2,000 yBP. Reconstructing the dispersal history of rice and its climatic correlates may help identify genetic adaptation associated with the spread of a key domesticated species.\n\nOne sentence summaryWe reconstructed the ancient dispersal of rice in Asia and identified extrinsic factors that impact its genomic diversity.

evolutionary biology

Minor QTLs mining through the combination of GWAS and machine learning feature selection

IntroductionMinor QTLs mining has a very important role in genomic selection, pathway analysis and trait development in agricultural and biological research. Since most individual loci contribute little to complex trait variations, it remains a challenge for traditional statistical methods to identify minor QTLs with subtle phenotypic effects. Here we applied a new framework which combined the GWAS analysis and machine learning feature selection to explore new ways for the study of minor QTLs mining.\n\nResultsWe studied the soybean branching trait with the 2,137 accessions from soybean (Glycine max) diversity panel, which was sequenced by 50k SNP chips with 42,080 valid SNPs. First as a baseline study, we conducted the GWAS GAPIT analysis, and we found that only one SNP marker significantly associated with soybean branching was identified. We then combined the GWAS analysis and feature importance analysis with Random Forest score analysis and permutation analysis. Our analysis results showed that there are 36,077 features (SNPs) identified by Random Forest score analysis, and 2,098 features (SNPs) identified by permutation analysis. In total, there are 1,770 features (SNPs) confirmed by both of the Random Forest score analysis and the permutation analysis. Based on our analysis, 328 branching development related genes were identified. A further analysis on GO (gene ontology) term enrichment were applied on these 328 genes. And the gene location and gene expression of these identified genes were provided.\n\nConclusionsWe find that the combined analysis with GWAS and machine learning feature selection shows significant identification power for minor QTLs mining. The presented research results on minor QTLs mining will help understand the biological activities that lie between genotype and phenotype in terms of causal networks of interacting genes. This study will potentially contribute to effective genomic selection in plant breeding and help broaden the way of molecular breeding in plants.

bioinformatics

Minor QTLs mining through the combination of GWAS and machine learning feature selection

IntroductionMinor QTLs mining has a very important role in genomic selection, pathway analysis and trait development in agricultural and biological research. Since most individual loci contribute little to complex trait variations, it remains a challenge for traditional statistical methods to identify minor QTLs with subtle phenotypic effects. Here we applied a new framework which combined the GWAS analysis and machine learning feature selection to explore new ways for the study of minor QTLs mining.\n\nResultsWe studied the soybean branching trait with the 2,137 accessions from soybean (Glycine max) diversity panel, which was sequenced by 50k SNP chips with 42,080 valid SNPs. First as a baseline study, we conducted the GWAS GAPIT analysis, and we found that only one SNP marker significantly associated with soybean branching was identified. We then combined the GWAS analysis and feature importance analysis with Random Forest score analysis and permutation analysis. Our analysis results showed that there are 36,077 features (SNPs) identified by Random Forest score analysis, and 2,098 features (SNPs) identified by permutation analysis. In total, there are 1,770 features (SNPs) confirmed by both of the Random Forest score analysis and the permutation analysis. Based on our analysis, 328 branching development related genes were identified. A further analysis on GO (gene ontology) term enrichment were applied on these 328 genes. And the gene location and gene expression of these identified genes were provided.\n\nConclusionsWe find that the combined analysis with GWAS and machine learning feature selection shows significant identification power for minor QTLs mining. The presented research results on minor QTLs mining will help understand the biological activities that lie between genotype and phenotype in terms of causal networks of interacting genes. This study will potentially contribute to effective genomic selection in plant breeding and help broaden the way of molecular breeding in plants.

bioinformatics

Genomics of sorghum local adaptation to a parasitic plant

Host-parasite coevolution can maintain high levels of genetic diversity in traits involved in species interactions. In many systems, host traits exploited by parasites are constrained by use in other functions, leading to complex selective pressures across space and time. Here, we study genome-wide variation in the staple crop Sorghum bicolor (L.) Moench and its association with the parasitic weed Striga hermonthica (Delile) Benth., a major constraint to food security in Africa. We hypothesize that geographic selection mosaics across gradients of parasite occurrence maintain genetic diversity in sorghum landrace resistance. Suggesting a role in local adaptation to parasite pressure, multiple independent loss-of-function alleles at sorghum LOW GERMINATION STIMULANT 1 (LGS1) are broadly distributed among African landraces and geographically associated with S. hermonthica occurrence. However, low frequency of these alleles within S. hermonthica-prone regions and their absence elsewhere implicate potential tradeoffs restricting their fixation. LGS1 is thought to cause resistance by changing stereochemistry of strigolactones, hormones that control plant architecture and belowground signaling to mycorrhizae and are required to stimulate parasite germination. Consistent with tradeoffs, we find signatures of balancing selection surrounding LGS1 and other candidates from analysis of genome-wide associations with parasite distribution. Experiments with CRISPR-Cas9 edited sorghum further indicate the benefit of LGS1-mediated resistance strongly depends on parasite genotype and abiotic environment and comes at the cost of reduced photosystem gene expression. Our study demonstrates long-term maintenance of diversity in host resistance genes across smallholder agroecosystems, providing a valuable comparison to both industrial farming systems and natural communities. SIGNIFICANCE STATEMENTUnderstanding co-evolution in crop-parasite systems is critical to management of myriad pests and pathogens confronting modern agriculture. In contrast to wild plant communities, parasites in agricultural ecosystems are usually expected to gain the upper hand in co-evolutionary arms races due to limited genetic diversity of host crops in cultivation. Here, we develop a framework to characterize associations between genome variants in global landraces (traditional varieties) of the staple crop sorghum with the distribution of the devastating parasitic weed Striga hermonthica. We find long-term maintenance of diversity in genes related to parasite resistance, highlighting an important role of host adaptation for co-evolutionary dynamics in smallholder agroecosystems.

evolutionary biology