Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.
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http://dx.doi.org/10.1111/ppl.14349 | DOI Listing |
Brain Commun
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Department of Pharmacology and Therapeutics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, CanadaR3E 0T6.
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January 2025
Department of Biology, Brigham Young University, Provo, UT, 84061, USA; Simmons Center for Cancer Research, Brigham Young University, Provo, UT 84602, USA. Electronic address:
Using rare cancer predisposition alleles derived from The Cancer Genome Atlas (TCGA) and high cancer prevalence (14% of participants) in All of Us (version 6), we assessed the impact of these rare alleles on cancer occurrence in six broad groups of genetic similarity provided by All of Us: African/African American (AFR), Admixed American/Latino (AMR), East Asian (EAS), European (EUR), Middle Eastern (MID), or South Asian (SAS). We observed that germline susceptibility to cancer consistently replicates in EUR-like participants but less so in other participants. We found that All of Us participants from the EUR (p = 1.
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Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results.
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January 2025
Department of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Shangcheng District, Hangzhou, 310000, Zhejiang Province, China.
Primary aldosteronism (PA), characterized by autonomous aldosterone overproduction, is a major cause of secondary hypertension with significant cardiovascular complications. Current treatments mainly focus on symptom management rather than addressing underlying mechanisms. This study aims to discover novel therapeutic targets for PA using integrated bioinformatics and experimental validation approaches.
View Article and Find Full Text PDFTheor Appl Genet
January 2025
Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.
Phenomic selection based on parental spectra can be used to predict GCA and SCA in a sparse factorial design. Prediction approaches such as genomic selection can be game changers in hybrid breeding. They allow predicting the genetic values of hybrids without the need for their physical production.
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