The functional genes underlying phenotypic variation and their interactions represent“genetic mysteries”.Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to ag...The functional genes underlying phenotypic variation and their interactions represent“genetic mysteries”.Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences.Due to advances in highthroughput multi-omics technologies,we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research.In this article,we provide a brief overview of current strategies to explore genetic mysteries.We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources.Next,we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries.We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning,which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits,and also provide a detailed procedure for systematic dissection of genetic mysteries,Finally,we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.展开更多
Deciphering the genetic mechanisms underlying agronomic traits is of great importance for crop improvement. Most of these traits are controlled by multiple quantitative trait loci (QTLs), and identifying the underlyin...Deciphering the genetic mechanisms underlying agronomic traits is of great importance for crop improvement. Most of these traits are controlled by multiple quantitative trait loci (QTLs), and identifying the underlying genes by conventional QTL fine-mapping is time-consuming and labor-intensive. Here, we devised a new method, named quantitative trait gene sequencing (QTG-seq), to accelerate QTL fine-mapping. QTGseq combines QTL partitioning to convert a quantitative trait into a near-qualitative trait, sequencing of bulked segregant pools from a large segregating population, and the use of a robust new algorithm for identifying candidate genes. Using QTG-seq, we fine-mapped a plant-height QTL in maize (Zea mays L.), qPH7, to a 300-kb genomic interval and verified that a gene encoding an NF-YC transcription factor was the functional gene. Functional analysis suggested that qPH7-encoding protein might influence plant height by interacting with a CO-like protein and an AP2 domain-containing protein. Selection footprint ana卜 ysis indicated that qPH7 was subject to strong selection during maize improvement. In summary, QTG-seq provides an efficient method for QTL fine-mapping in the era of “big data".展开更多
Long non-coding RNAs(lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize(Zea mays L.) refer...Long non-coding RNAs(lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize(Zea mays L.) reference genome provides an essential genomic resource, genome-wide annotations of maize lncRNAs have not been updated. Here, we report on a large transcriptomic dataset collected from 749 RNA sequencing experiments across different tissues and stages of the maize reference inbred B73 line and 60 from its wild relative teosinte. We identified 18,165 high-confidence lncRNAs in maize, of which 6,873 are conserved between maize and teosinte. We uncovered distinct genomic characteristics of conserved lncRNAs,non-conserved lncRNAs, and protein-coding transcripts.Intriguingly, Shannon entropy analysis showed that conserved lncRNAs are likely to be expressed similarly to protein-coding transcripts. Co-expression network analysis revealed significant variation in the degree of co-expression. Furthermore, selection analysis indicated that conserved lncRNAs are more likely than non-conserved lncRNAs to be located in regions subject to recent selection, indicating evolutionary differentiation. Our results provide the latest genomewide annotation and analysis of maize lncRNAs and uncover potential functional divergence between proteincoding, conserved lncRNA, and non-conserved lncRNA genes, demonstrating the high complexity of the maize transcriptome.展开更多
Dear Editor,Eukaryotic life is a complex system(Trewavas,2006).Networks offer a reasonable means of describing complex life systems.Networks constructed for animals and plants have significantly furthered our understa...Dear Editor,Eukaryotic life is a complex system(Trewavas,2006).Networks offer a reasonable means of describing complex life systems.Networks constructed for animals and plants have significantly furthered our understanding of complex life systems and functional genomics(Consortium,2011;Walley et al.,2016;Altmann et al.,2020;Luck et al.,2020;McWhite et al.,2020;Zander et al.,2020).However,integrative networks spanning different layers of genetic information are lacking.展开更多
基金This research was supported by the National Natural Science Foundation of China(92035302,31922068)the Fundamental Research Funds for the Central Universities(ZK201908)+2 种基金the Fundamental Research Funds for the Central Universities(2662020ZKPY017)the Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(2015R016)the China Postdoctoral Science Foundation(2019M662666).
文摘The functional genes underlying phenotypic variation and their interactions represent“genetic mysteries”.Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences.Due to advances in highthroughput multi-omics technologies,we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research.In this article,we provide a brief overview of current strategies to explore genetic mysteries.We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources.Next,we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries.We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning,which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits,and also provide a detailed procedure for systematic dissection of genetic mysteries,Finally,we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.
基金the National Key Research and Development Program of China (2016YFD0100404)the National Basic Research Program of China (2014CB138200)+4 种基金the National Natural Science Foundation of China (91735305,1571268)the Fundamental Research Funds of the Central Non-profit Scientific Institution (Y2018LM04)the Xinjiang Key R&D Program (2018B01006-3)and the Huazhong Agricultural University Scientific & Technological Self-innovation Foundation (2662016PY096014RC020).This research was also partly supported by the open funds of the National Key Laboratory of Crop Genetic Improvement.
文摘Deciphering the genetic mechanisms underlying agronomic traits is of great importance for crop improvement. Most of these traits are controlled by multiple quantitative trait loci (QTLs), and identifying the underlying genes by conventional QTL fine-mapping is time-consuming and labor-intensive. Here, we devised a new method, named quantitative trait gene sequencing (QTG-seq), to accelerate QTL fine-mapping. QTGseq combines QTL partitioning to convert a quantitative trait into a near-qualitative trait, sequencing of bulked segregant pools from a large segregating population, and the use of a robust new algorithm for identifying candidate genes. Using QTG-seq, we fine-mapped a plant-height QTL in maize (Zea mays L.), qPH7, to a 300-kb genomic interval and verified that a gene encoding an NF-YC transcription factor was the functional gene. Functional analysis suggested that qPH7-encoding protein might influence plant height by interacting with a CO-like protein and an AP2 domain-containing protein. Selection footprint ana卜 ysis indicated that qPH7 was subject to strong selection during maize improvement. In summary, QTG-seq provides an efficient method for QTL fine-mapping in the era of “big data".
基金supported by the National Key Research and Development Program of China(2016YFD0100404)the Huazhong Agricultural University Scientific & Technological Self-innovation Foundation(2662016-PY096)
文摘Long non-coding RNAs(lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize(Zea mays L.) reference genome provides an essential genomic resource, genome-wide annotations of maize lncRNAs have not been updated. Here, we report on a large transcriptomic dataset collected from 749 RNA sequencing experiments across different tissues and stages of the maize reference inbred B73 line and 60 from its wild relative teosinte. We identified 18,165 high-confidence lncRNAs in maize, of which 6,873 are conserved between maize and teosinte. We uncovered distinct genomic characteristics of conserved lncRNAs,non-conserved lncRNAs, and protein-coding transcripts.Intriguingly, Shannon entropy analysis showed that conserved lncRNAs are likely to be expressed similarly to protein-coding transcripts. Co-expression network analysis revealed significant variation in the degree of co-expression. Furthermore, selection analysis indicated that conserved lncRNAs are more likely than non-conserved lncRNAs to be located in regions subject to recent selection, indicating evolutionary differentiation. Our results provide the latest genomewide annotation and analysis of maize lncRNAs and uncover potential functional divergence between proteincoding, conserved lncRNA, and non-conserved lncRNA genes, demonstrating the high complexity of the maize transcriptome.
基金supported by the National Natural Science Foundation of China(32272158,32270712,92035302)Outstanding Youth Team Project of Center Universities(2662023PY007).
文摘Dear Editor,Eukaryotic life is a complex system(Trewavas,2006).Networks offer a reasonable means of describing complex life systems.Networks constructed for animals and plants have significantly furthered our understanding of complex life systems and functional genomics(Consortium,2011;Walley et al.,2016;Altmann et al.,2020;Luck et al.,2020;McWhite et al.,2020;Zander et al.,2020).However,integrative networks spanning different layers of genetic information are lacking.
