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1. QTL for the Kinematic Traits That Define the Arabidopsis Root Elongation Zone and Their Relationship to Gravitropism

   https://doi.org/10.3390/plants13091189  

   Henry, Ashley R. ; Miller, Nathan D. ; Spalding, Edgar P. ( May 2024 , Plants)

   Cell expansion in a discrete region called the elongation zone drives root elongation. Analyzing time lapse images can quantify the expansion in kinematic terms as if it were fluid flow. We used horizontal microscopes to collect images from which custom software extracted the length of the elongation zone, the peak relative elemental growth rate (REGR) within it, the axial position of the REGR peak, and the root elongation rate. Automation enabled these kinematic traits to be measured in 1575 Arabidopsis seedlings representing 162 recombinant inbred lines (RILs) derived from a cross of Cvi and Ler ecotypes. We mapped ten quantitative trait loci (QTL), affecting the four kinematic traits. Three QTL affected two or more traits in these vertically oriented seedlings. We compared this genetic architecture with that previously determined for gravitropism using the same RIL population. The major QTL peaks for the kinematic traits did not overlap with the gravitropism QTL. Furthermore, no single kinematic trait correlated with quantitative descriptors of the gravitropism response curve across this population. In addition to mapping QTL for growth zone traits, this study showed that the size and shape of the elongation zone may vary widely without affecting the differential growth induced by gravity.

    

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2. A comparative approach for selecting orthologous candidate genes underlying signal in genome-wide association studies across multiple species

   Whitt, L ; Mahood, E H ; Ziegler, G ; Luebbert, C ; Gillman, J D ; Norton, G J ; Price, A H ; Salt, DE ; Dilkes, B ; Baxter, I ( October 2023 , bioRxiv)

   Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, genomic regions can contain hundreds to thousands of genes, and progressing from genomic regions to candidate genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, a mere 5% of loci are associated with a known ionomic gene - indicating that many causal genes are still unknown. To select candidates for the remaining 95% of loci, we developed a method to identify conserved genes underlying GWAS loci in multiple species. For 19 ionomic traits, we identified 14,336 candidates across Arabidopsis, soybean, rice, maize, and sorghum. We calculated the likelihood of candidates with random permutations of the data and determined that most of the top 10% of candidates were orthologous genes linked to GWAS loci across all five species. The candidate list also includes orthologous genes with previously established ionomic functions in Arabidopsis and rice. Our methods highlight the conserved nature of ionomic genetic regulators and enable the identification of previously unknown ionomic genes. 

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3. Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

   https://doi.org/doi.org/10.1038/s41467-021-25893-w  

   Cheng, CY ; Li, Y ; Varala, K ; Bubert, J ; Huang, J ; Kim, GJ ; Halim, J ; Arp, J ; Shih, HJS ; Levinson, G ; et al ( September 2021 , Nature communications)

   null (Ed.)

   Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine. 

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4. Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

   https://doi.org/10.1038/s41467-021-25893-w  

   Cheng, Chia-Yi ; Li, Ying ; Varala, Kranthi ; Bubert, Jessica ; Huang, Ji ; Kim, Grace J. ; Halim, Justin ; Arp, Jennifer ; Shih, Hung-Jui S. ; Levinson, Grace ; et al ( September 2021 , Nature Communications)

   Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.

    

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5. Convergent selection of a WD40 protein that enhances grain yield in maize and rice

   https://doi.org/10.1126/science.abg7985  

   Chen, Wenkang ; Chen, Lu ; Zhang, Xuan ; Yang, Ning ; Guo, Jianghua ; Wang, Min ; Ji, Shenghui ; Zhao, Xiangyu ; Yin, Pengfei ; Cai, Lichun ; et al ( March 2022 , Science)

   INTRODUCTION During the independent process of cereal evolution, many trait shifts appear to have been under convergent selection to meet the specific needs of humans. Identification of convergently selected genes across cereals could help to clarify the evolution of crop species and to accelerate breeding programs. In the past several decades, researchers have debated whether convergent phenotypic selection in distinct lineages is driven by conserved molecular changes or by diverse molecular pathways. Two of the most economically important crops, maize and rice, display some conserved phenotypic shifts—including loss of seed dispersal, decreased seed dormancy, and increased grain number during evolution—even though they experienced independent selection. Hence, maize and rice can serve as an excellent system for understanding the extent of convergent selection among cereals. RATIONALE Despite the identification of a few convergently selected genes, our understanding of the extent of molecular convergence on a genome-wide scale between maize and rice is very limited. To learn how often selection acts on orthologous genes, we investigated the functions and molecular evolution of the grain yield quantitative trait locus KRN2 in maize and its rice ortholog OsKRN2 . We also identified convergently selected genes on a genome-wide scale in maize and rice, using two large datasets. RESULTS We identified a selected gene, KRN2 ( kernel row number2 ), that differs between domesticated maize and its wild ancestor, teosinte. This gene underlies a major quantitative trait locus for kernel row number in maize. Selection in the noncoding upstream regions resulted in a reduction of KRN2 expression and an increased grain number through an increase in kernel rows. The rice ortholog, OsKRN2 , also underwent selection and negatively regulates grain number via control of secondary panicle branches. These orthologs encode WD40 proteins and function synergistically with a gene of unknown function, DUF1644, which suggests that a conserved protein interaction controls grain number in maize and rice. Field tests show that knockout of KRN2 in maize or OsKRN2 in rice increased grain yield by ~10% and ~8%, respectively, with no apparent trade-off in other agronomic traits. This suggests potential applications of KRN2 and its orthologs for crop improvement. On a genome-wide scale, we identified a set of 490 orthologous genes that underwent convergent selection during maize and rice evolution, including KRN2/OsKRN2 . We found that the convergently selected orthologous genes appear to be significantly enriched in two specific pathways in both maize and rice: starch and sucrose metabolism, and biosynthesis of cofactors. A deep analysis of convergently selected genes in the starch metabolic pathway indicates that the degree of genetic convergence via convergent selection is related to the conservation and complexity of the gene network for a given selection. CONCLUSION Our findings show that common phenotypic shifts during maize and rice evolution acting on conserved genes are driven at least in part by convergent selection, which in maize and rice likely occurred both during and after domestication. We provide evolutionary and functional evidence on the convergent selection of KRN2/OsKRN2 for grain number between maize and rice. We further found that a complete loss-of-function allele of KRN2/OsKRN2 increased grain yield without an apparent negative impact on other agronomic traits. Exploring the role of KRN2/OsKRN2 and other convergently selected genes across the cereals could provide new opportunities to enhance the production of other global crops. Shared selected orthologous genes in maize and rice for convergent phenotypic shifts during domestication and improvement. By comparing 3163 selected genes in maize and 18,755 selected genes in rice, we identified 490 orthologous gene pairs, including KRN2 and its rice ortholog OsKRN2 , as having been convergently selected. Knockout of KRN2 in maize or OsKRN2 in rice increased grain yield by increasing kernel rows and secondary panicle branches, respectively. 

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